


from __future__ import annotations

import os
import tempfile
import unittest

from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import (
           TFConvBertForMaskedLM,
           TFConvBertForMultipleChoice,
           TFConvBertForQuestionAnswering,
           TFConvBertForSequenceClassification,
           TFConvBertForTokenClassification,
           TFConvBertModel,
       )


class   a__	:

       def __init__(   self      :     Optional[Any]      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     int=1_3      ,							lowerCamelCase_      :     Optional[Any]=7      ,							lowerCamelCase_      :     Union[str, Any]=True      ,							lowerCamelCase_      :     int=True      ,							lowerCamelCase_      :     Union[str, Any]=True      ,							lowerCamelCase_      :     Optional[int]=True      ,							lowerCamelCase_      :     Optional[Any]=9_9      ,							lowerCamelCase_      :     List[Any]=3_2      ,							lowerCamelCase_      :     Optional[int]=2      ,							lowerCamelCase_      :     List[Any]=4      ,							lowerCamelCase_      :     List[Any]=3_7      ,							lowerCamelCase_      :     str="gelu"      ,							lowerCamelCase_      :     Union[str, Any]=0.1      ,							lowerCamelCase_      :     Optional[int]=0.1      ,							lowerCamelCase_      :     Any=5_1_2      ,							lowerCamelCase_      :     List[str]=1_6      ,							lowerCamelCase_      :     Optional[Any]=2      ,							lowerCamelCase_      :     str=0.0_2      ,							lowerCamelCase_      :     Any=3      ,							lowerCamelCase_      :     List[Any]=4      ,							lowerCamelCase_      :     List[Any]=None      ,							):
            a_    :     Dict										=    parent
            a_    :     str										=    1_3
            a_    :     int										=    7
            a_    :     str										=    True
            a_    :     Dict										=    True
            a_    :     str										=    True
            a_    :     Tuple										=    True
            a_    :     List[str]										=    9_9
            a_    :     Optional[Any]										=    3_8_4
            a_    :     Tuple										=    2
            a_    :     Union[str, Any]										=    4
            a_    :     Dict										=    3_7
            a_    :     Any										=    "gelu"
            a_    :     List[Any]										=    0.1
            a_    :     int										=    0.1
            a_    :     Tuple										=    5_1_2
            a_    :     List[Any]										=    1_6
            a_    :     int										=    2
            a_    :     Dict										=    0.0_2
            a_    :     Optional[Any]										=    3
            a_    :     List[Any]										=    4
            a_    :     Dict										=    1_2_8
            a_    :     Optional[Any]										=    2
            a_    :     Optional[int]										=    9
            a_    :     Optional[int]										=    1
            a_    :     Union[str, Any]										=    None

       def 						UpperCAmelCase(   self      :     int    ):
            a_    :     int										=    ids_tensor([self.batch_size, self.seq_length]      ,							self.vocab_size    )

            a_    :     str										=    None
            if self.use_input_mask:
                 a_    :     List[Any]										=    random_attention_mask([self.batch_size, self.seq_length]    )

            a_    :     Tuple										=    None
            if self.use_token_type_ids:
                 a_    :     Tuple										=    ids_tensor([self.batch_size, self.seq_length]      ,							self.type_vocab_size    )

            a_    :     Optional[int]										=    None
            a_    :     Optional[int]										=    None
            a_    :     List[Any]										=    None
            if self.use_labels:
                 a_    :     int										=    ids_tensor([self.batch_size]      ,							self.type_sequence_label_size    )
                 a_    :     Optional[Any]										=    ids_tensor([self.batch_size, self.seq_length]      ,							self.num_labels    )
                 a_    :     Optional[Any]										=    ids_tensor([self.batch_size]      ,							self.num_choices    )

            a_    :     Any										=    ConvBertConfig(
                vocab_size=self.vocab_size      ,							hidden_size=self.hidden_size      ,							num_hidden_layers=self.num_hidden_layers      ,							num_attention_heads=self.num_attention_heads      ,							intermediate_size=self.intermediate_size      ,							hidden_act=self.hidden_act      ,							hidden_dropout_prob=self.hidden_dropout_prob      ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,							max_position_embeddings=self.max_position_embeddings      ,							type_vocab_size=self.type_vocab_size      ,							initializer_range=self.initializer_range      ,							return_dict=A_      ,							)

            return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

       def 						UpperCAmelCase(   self      :     List[str]      ,							lowerCamelCase_      :     Optional[int]      ,							lowerCamelCase_      :     Optional[Any]      ,							lowerCamelCase_      :     Optional[int]      ,							lowerCamelCase_      :     List[str]      ,							lowerCamelCase_      :     List[Any]      ,							lowerCamelCase_      :     Tuple      ,							lowerCamelCase_      :     Optional[int]    ):
            a_    :     str										=    TFConvBertModel(config=A_    )
            a_    :     Optional[Any]										=    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

            a_    :     Optional[int]										=    [input_ids, input_mask]
            a_    :     Any										=    model(A_    )

            a_    :     int										=    model(A_    )

            self.parent.assertEqual(result.last_hidden_state.shape      ,							(self.batch_size, self.seq_length, self.hidden_size)    )

       def 						UpperCAmelCase(   self      :     Optional[Any]      ,							lowerCamelCase_      :     List[str]      ,							lowerCamelCase_      :     Dict      ,							lowerCamelCase_      :     List[str]      ,							lowerCamelCase_      :     Any      ,							lowerCamelCase_      :     Optional[int]      ,							lowerCamelCase_      :     Optional[Any]      ,							lowerCamelCase_      :     List[Any]    ):
            a_    :     Tuple										=    TFConvBertForMaskedLM(config=A_    )
            a_    :     int										=    {
                "input_ids": input_ids,
                "attention_mask": input_mask,
                "token_type_ids": token_type_ids,
            }
            a_    :     Dict										=    model(A_    )
            self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.seq_length, self.vocab_size)    )

       def 						UpperCAmelCase(   self      :     Optional[Any]      ,							lowerCamelCase_      :     List[Any]      ,							lowerCamelCase_      :     str      ,							lowerCamelCase_      :     List[Any]      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     Optional[Any]      ,							lowerCamelCase_      :     Optional[int]    ):
            a_    :     Dict										=    self.num_labels
            a_    :     int										=    TFConvBertForSequenceClassification(config=A_    )
            a_    :     List[Any]										=    {
                "input_ids": input_ids,
                "attention_mask": input_mask,
                "token_type_ids": token_type_ids,
            }

            a_    :     Optional[Any]										=    model(A_    )
            self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.num_labels)    )

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     List[Any]      ,							lowerCamelCase_      :     Any      ,							lowerCamelCase_      :     Union[str, Any]      ,							lowerCamelCase_      :     Optional[int]      ,							lowerCamelCase_      :     Union[str, Any]      ,							lowerCamelCase_      :     Tuple      ,							lowerCamelCase_      :     Dict    ):
            a_    :     List[str]										=    self.num_choices
            a_    :     str										=    TFConvBertForMultipleChoice(config=A_    )
            a_    :     List[Any]										=    tf.tile(tf.expand_dims(A_      ,							1    )      ,							(1, self.num_choices, 1)    )
            a_    :     Dict										=    tf.tile(tf.expand_dims(A_      ,							1    )      ,							(1, self.num_choices, 1)    )
            a_    :     Any										=    tf.tile(tf.expand_dims(A_      ,							1    )      ,							(1, self.num_choices, 1)    )
            a_    :     List[str]										=    {
                "input_ids": multiple_choice_inputs_ids,
                "attention_mask": multiple_choice_input_mask,
                "token_type_ids": multiple_choice_token_type_ids,
            }
            a_    :     Optional[Any]										=    model(A_    )
            self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.num_choices)    )

       def 						UpperCAmelCase(   self      :     int      ,							lowerCamelCase_      :     List[Any]      ,							lowerCamelCase_      :     Dict      ,							lowerCamelCase_      :     Optional[Any]      ,							lowerCamelCase_      :     Any      ,							lowerCamelCase_      :     Optional[int]      ,							lowerCamelCase_      :     Dict      ,							lowerCamelCase_      :     int    ):
            a_    :     Dict										=    self.num_labels
            a_    :     str										=    TFConvBertForTokenClassification(config=A_    )
            a_    :     List[Any]										=    {
                "input_ids": input_ids,
                "attention_mask": input_mask,
                "token_type_ids": token_type_ids,
            }
            a_    :     str										=    model(A_    )
            self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.seq_length, self.num_labels)    )

       def 						UpperCAmelCase(   self      :     Union[str, Any]      ,							lowerCamelCase_      :     Tuple      ,							lowerCamelCase_      :     Union[str, Any]      ,							lowerCamelCase_      :     Tuple      ,							lowerCamelCase_      :     Tuple      ,							lowerCamelCase_      :     Any      ,							lowerCamelCase_      :     Dict      ,							lowerCamelCase_      :     Tuple    ):
            a_    :     List[str]										=    TFConvBertForQuestionAnswering(config=A_    )
            a_    :     Union[str, Any]										=    {
                "input_ids": input_ids,
                "attention_mask": input_mask,
                "token_type_ids": token_type_ids,
            }

            a_    :     Union[str, Any]										=    model(A_    )
            self.parent.assertEqual(result.start_logits.shape      ,							(self.batch_size, self.seq_length)    )
            self.parent.assertEqual(result.end_logits.shape      ,							(self.batch_size, self.seq_length)    )

       def 						UpperCAmelCase(   self      :     List[Any]    ):
            a_    :     Optional[int]										=    self.prepare_config_and_inputs()
            (
                a_					
            )    :     Optional[Any]										=    config_and_inputs
            a_    :     int										=    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
            return config, inputs_dict


@require_tf
class   a__	(						__snake_case						,       __snake_case						,       unittest.TestCase    ):
       lowerCamelCase__:     Dict							    =     (
           (
               TFConvBertModel,
               TFConvBertForMaskedLM,
               TFConvBertForQuestionAnswering,
               TFConvBertForSequenceClassification,
               TFConvBertForTokenClassification,
               TFConvBertForMultipleChoice,
           )
           if is_tf_available()
           else ()
       )
       lowerCamelCase__:     Optional[Any]							    =     (
           {
               'feature-extraction': TFConvBertModel,
               'fill-mask': TFConvBertForMaskedLM,
               'question-answering': TFConvBertForQuestionAnswering,
               'text-classification': TFConvBertForSequenceClassification,
               'token-classification': TFConvBertForTokenClassification,
               'zero-shot': TFConvBertForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       lowerCamelCase__:     Any							    =     False
       lowerCamelCase__:     int							    =     False
       lowerCamelCase__:     str							    =     False

       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
            a_    :     Dict										=    TFConvBertModelTester(self    )
            a_    :     Dict										=    ConfigTester(self      ,							config_class=A_      ,							hidden_size=3_7    )

       def 						UpperCAmelCase(   self      :     Optional[int]    ):
            self.config_tester.run_common_tests()

       def 						UpperCAmelCase(   self      :     Tuple    ):
            a_    :     Dict										=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*A_    )

       def 						UpperCAmelCase(   self      :     Tuple    ):
            a_    :     Dict										=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_lm(*A_    )

       def 						UpperCAmelCase(   self      :     Tuple    ):
            a_    :     List[str]										=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_multiple_choice(*A_    )

       def 						UpperCAmelCase(   self      :     Tuple    ):
            a_    :     List[str]										=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_question_answering(*A_    )

       def 						UpperCAmelCase(   self      :     Dict    ):
            a_    :     Tuple										=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_sequence_classification(*A_    )

       def 						UpperCAmelCase(   self      :     Any    ):
            a_    :     Optional[Any]										=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_token_classification(*A_    )

       @slow
       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
            a_    :     Dict										=    self.model_tester.prepare_config_and_inputs_for_common()
            a_    :     Optional[Any]										=    True
            a_    :     Any										=    True

            if hasattr(A_      ,							"""use_cache"""    ):
                 a_    :     List[str]										=    True

            a_    :     List[Any]										=    getattr(self.model_tester      ,							"""encoder_seq_length"""      ,							self.model_tester.seq_length    )
            a_    :     Any										=    getattr(self.model_tester      ,							"""key_length"""      ,							A_    )

            for model_class in self.all_model_classes:
                 a_    :     List[Any]										=    self._prepare_for_class(A_      ,							A_    )
                 a_    :     Dict										=    model_class(A_    )
                 a_    :     Optional[int]										=    len(model(A_    )    )

                 with tempfile.TemporaryDirectory() as tmpdirname:
                      model.save_pretrained(A_      ,							saved_model=A_    )
                      a_    :     Union[str, Any]										=    os.path.join(A_      ,							"""saved_model"""      ,							"""1"""    )
                      a_    :     Dict										=    tf.keras.models.load_model(A_    )
                      a_    :     str										=    model(A_    )

                      if self.is_encoder_decoder:
                           a_    :     Union[str, Any]										=    outputs["encoder_hidden_states"]
                           a_    :     Any										=    outputs["encoder_attentions"]
                      else:
                           a_    :     Any										=    outputs["hidden_states"]
                           a_    :     List[str]										=    outputs["attentions"]

                      self.assertEqual(len(A_    )      ,							A_    )

                      a_    :     int										=    getattr(
                          self.model_tester      ,							"""expected_num_hidden_layers"""      ,							self.model_tester.num_hidden_layers + 1    )

                      self.assertEqual(len(A_    )      ,							A_    )
                      self.assertListEqual(
                          list(output_hidden_states[0].shape[-2:]    )      ,							[self.model_tester.seq_length, self.model_tester.hidden_size]      ,							)

                      self.assertEqual(len(A_    )      ,							self.model_tester.num_hidden_layers    )
                      self.assertListEqual(
                          list(output_attentions[0].shape[-3:]    )      ,							[self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]      ,							)

       @slow
       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
            a_    :     Union[str, Any]										=    TFConvBertModel.from_pretrained("""YituTech/conv-bert-base"""    )
            self.assertIsNotNone(A_    )

       def 						UpperCAmelCase(   self      :     Dict    ):
            a_    :     Tuple										=    self.model_tester.prepare_config_and_inputs_for_common()
            a_    :     Dict										=    True
            a_    :     int										=    getattr(self.model_tester      ,							"""decoder_seq_length"""      ,							self.model_tester.seq_length    )
            a_    :     Optional[int]										=    getattr(self.model_tester      ,							"""encoder_seq_length"""      ,							self.model_tester.seq_length    )
            a_    :     Optional[int]										=    getattr(self.model_tester      ,							"""key_length"""      ,							A_    )
            a_    :     Optional[Any]										=    getattr(self.model_tester      ,							"""key_length"""      ,							A_    )

            def check_decoder_attentions_output(lowerCamelCase_      :     Union[str, Any]    ):
                 a_    :     Optional[Any]										=    len(A_    )
                 self.assertEqual(out_len % 2      ,							0    )
                 a_    :     Any										=    outputs.decoder_attentions
                 self.assertEqual(len(A_    )      ,							self.model_tester.num_hidden_layers    )
                 self.assertListEqual(
                     list(decoder_attentions[0].shape[-3:]    )      ,							[self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length]      ,							)

            def check_encoder_attentions_output(lowerCamelCase_      :     Any    ):
                 a_    :     Dict										=    [
                     t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
                 ]
                 self.assertEqual(len(A_    )      ,							self.model_tester.num_hidden_layers    )
                 self.assertListEqual(
                     list(attentions[0].shape[-3:]    )      ,							[self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]      ,							)

            for model_class in self.all_model_classes:
                 a_    :     Union[str, Any]										=    True
                 a_    :     List[Any]										=    False
                 a_    :     Dict										=    model_class(A_    )
                 a_    :     Dict										=    model(self._prepare_for_class(A_      ,							A_    )    )
                 a_    :     List[str]										=    len(A_    )
                 self.assertEqual(config.output_hidden_states      ,							A_    )
                 check_encoder_attentions_output(A_    )

                 if self.is_encoder_decoder:
                      a_    :     int										=    model_class(A_    )
                      a_    :     Tuple										=    model(self._prepare_for_class(A_      ,							A_    )    )
                      self.assertEqual(config.output_hidden_states      ,							A_    )
                      check_decoder_attentions_output(A_    )

                 # Check that output attentions can also be changed via the config
                 del inputs_dict["output_attentions"]
                 a_    :     Tuple										=    True
                 a_    :     int										=    model_class(A_    )
                 a_    :     Dict										=    model(self._prepare_for_class(A_      ,							A_    )    )
                 self.assertEqual(config.output_hidden_states      ,							A_    )
                 check_encoder_attentions_output(A_    )

                 # Check attention is always last and order is fine
                 a_    :     Optional[int]										=    True
                 a_    :     List[str]										=    True
                 a_    :     Optional[int]										=    model_class(A_    )
                 a_    :     Optional[Any]										=    model(self._prepare_for_class(A_      ,							A_    )    )

                 self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)      ,							len(A_    )    )
                 self.assertEqual(model.config.output_hidden_states      ,							A_    )
                 check_encoder_attentions_output(A_    )


@require_tf
class   a__	(						unittest.TestCase    ):

       @slow
       def 						UpperCAmelCase(   self      :     int    ):
            a_    :     str										=    TFConvBertModel.from_pretrained("""YituTech/conv-bert-base"""    )
            a_    :     str										=    tf.constant([[0, 1, 2, 3, 4, 5]]    )
            a_    :     List[str]										=    model(A_    )[0]

            a_    :     int										=    [1, 6, 7_6_8]
            self.assertEqual(output.shape      ,							A_    )

            a_    :     List[str]										=    tf.constant(
                [
                    [
                        [-0.0_3_4_7_5_4_9_3, -0.4_6_8_6_0_3_4, -0.3_0_6_3_8_8_3_2],
                        [0.2_2_6_3_7_2_4_8, -0.2_6_9_8_8_6_4_6, -0.7_4_2_3_4_2_4],
                        [0.1_0_3_2_4_8_6_8, -0.4_5_0_1_3_5_0_8, -0.5_8_2_8_0_7_8_4],
                    ]
                ]    )
            tf.debugging.assert_near(output[:, :3, :3]      ,							A_      ,							atol=1E-4    )







import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


__lowerCamelCase        =     logging.getLogger(__name__)


class   a__	(						lowerCAmelCase_    ):
       lowerCamelCase__:     str							    =     """summarization"""
       lowerCamelCase__:     List[str]							    =     ["""loss"""]
       lowerCamelCase__:     List[str]							    =     ROUGE_KEYS
       lowerCamelCase__:     Union[str, Any]							    =     """rouge2"""

       def __init__(   self      :     Union[str, Any]      ,							lowerCamelCase_      :     Tuple      ,							**lowerCamelCase_      :     List[str]    ):
            if hparams.sortish_sampler and hparams.gpus > 1:
                 a_    :     Any										=    False
            elif hparams.max_tokens_per_batch is not None:
                 if hparams.gpus > 1:
                      raise NotImplementedError("""Dynamic Batch size does not work for multi-gpu training"""    )
                 if hparams.sortish_sampler:
                      raise ValueError("""--sortish_sampler and --max_tokens_per_batch may not be used simultaneously"""    )

            super().__init__(lowerCamelCase_      ,							num_labels=lowerCamelCase_      ,							mode=self.mode      ,							**lowerCamelCase_    )
            use_task_specific_params(self.model      ,							"""summarization"""    )
            save_git_info(self.hparams.output_dir    )
            a_    :     Any										=    Path(self.output_dir    ) / """metrics.json"""
            a_    :     Tuple										=    Path(self.output_dir    ) / """hparams.pkl"""
            pickle_save(self.hparams      ,							self.hparams_save_path    )
            a_    :     Dict										=    0
            a_    :     Optional[Any]										=    defaultdict(lowerCamelCase_    )
            a_    :     str										=    self.config.model_type
            a_    :     Union[str, Any]										=    self.config.tgt_vocab_size if self.model_type == """fsmt""" else self.config.vocab_size

            a_    :     dict										=    {
                "data_dir": self.hparams.data_dir,
                "max_source_length": self.hparams.max_source_length,
                "prefix": self.model.config.prefix or "",
            }
            a_    :     int										=    {
                """train""": self.hparams.n_train,
                """val""": self.hparams.n_val,
                """test""": self.hparams.n_test,
            }
            a_    :     List[str]										=    {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

            a_    :     Optional[Any]										=    {
                """train""": self.hparams.max_target_length,
                """val""": self.hparams.val_max_target_length,
                """test""": self.hparams.test_max_target_length,
            }
            assert self.target_lens["train"] <= self.target_lens["val"], F'''target_lens: {self.target_lens}'''
            assert self.target_lens["train"] <= self.target_lens["test"], F'''target_lens: {self.target_lens}'''
            if self.hparams.freeze_embeds:
                 freeze_embeds(self.model    )
            if self.hparams.freeze_encoder:
                 freeze_params(self.model.get_encoder()    )
                 assert_all_frozen(self.model.get_encoder()    )

            a_    :     int										=    get_git_info()["""repo_sha"""]
            a_    :     Union[str, Any]										=    hparams.num_workers
            a_    :     Tuple										=    None  # default to config
            if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer      ,							lowerCamelCase_    ):
                 a_    :     Dict										=    self.tokenizer.lang_code_to_id[hparams.tgt_lang]
                 a_    :     List[str]										=    self.decoder_start_token_id
            a_    :     Union[str, Any]										=    (
                SeqaSeqDataset if hasattr(self.tokenizer      ,							"""prepare_seq2seq_batch"""    ) else LegacySeqaSeqDataset
            )
            a_    :     Union[str, Any]										=    False
            a_    :     Any										=    self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
            if self.hparams.eval_max_gen_length is not None:
                 a_    :     Union[str, Any]										=    self.hparams.eval_max_gen_length
            else:
                 a_    :     Optional[Any]										=    self.model.config.max_length
            a_    :     Optional[int]										=    self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric

       def 						UpperCAmelCase(   self      :     List[Any]      ,							lowerCamelCase_      :     Dict[str, torch.Tensor]    ):
            a_    :     Optional[Any]										=    {
                k: self.tokenizer.batch_decode(v.tolist()    ) if """mask""" not in k else v.shape for k, v in batch.items()
            }
            save_json(lowerCamelCase_      ,							Path(self.output_dir    ) / """text_batch.json"""    )
            save_json({k: v.tolist() for k, v in batch.items()}      ,							Path(self.output_dir    ) / """tok_batch.json"""    )

            a_    :     List[Any]										=    True
            return readable_batch

       def 						UpperCAmelCase(   self      :     Union[str, Any]      ,							lowerCamelCase_      :     str      ,							**lowerCamelCase_      :     Tuple    ):
            return self.model(lowerCamelCase_      ,							**lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     List[int]    ):
            a_    :     str										=    self.tokenizer.batch_decode(
                lowerCamelCase_      ,							skip_special_tokens=lowerCamelCase_      ,							clean_up_tokenization_spaces=lowerCamelCase_    )
            return lmap(str.strip      ,							lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Optional[int]      ,							lowerCamelCase_      :     dict    ):
            a_    :     int										=    self.tokenizer.pad_token_id
            a_			,			a_    :     Optional[int]										=    batch["""input_ids"""], batch["""attention_mask"""]
            a_    :     Optional[Any]										=    batch["""labels"""]
            if isinstance(self.model      ,							lowerCamelCase_    ):
                 a_    :     List[Any]										=    self.model._shift_right(lowerCamelCase_    )
            else:
                 a_    :     Dict										=    shift_tokens_right(lowerCamelCase_      ,							lowerCamelCase_    )
            if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
                 a_    :     int										=    decoder_input_ids
                 self.save_readable_batch(lowerCamelCase_    )

            a_    :     str										=    self(lowerCamelCase_      ,							attention_mask=lowerCamelCase_      ,							decoder_input_ids=lowerCamelCase_      ,							use_cache=lowerCamelCase_    )
            a_    :     int										=    outputs["""logits"""]
            if self.hparams.label_smoothing == 0:
                 # Same behavior as modeling_bart.py, besides ignoring pad_token_id
                 a_    :     Any										=    nn.CrossEntropyLoss(ignore_index=lowerCamelCase_    )

                 assert lm_logits.shape[-1] == self.vocab_size
                 a_    :     Union[str, Any]										=    ce_loss_fct(lm_logits.view(-1      ,							lm_logits.shape[-1]    )      ,							tgt_ids.view(-1    )    )
            else:
                 a_    :     int										=    nn.functional.log_softmax(lowerCamelCase_      ,							dim=-1    )
                 a_			,			a_    :     str										=    label_smoothed_nll_loss(
                     lowerCamelCase_      ,							lowerCamelCase_      ,							self.hparams.label_smoothing      ,							ignore_index=lowerCamelCase_    )
            return (loss,)

       @property
       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
            return self.tokenizer.pad_token_id

       def 						UpperCAmelCase(   self      :     str      ,							lowerCamelCase_      :     Optional[Any]      ,							lowerCamelCase_      :     List[Any]    ):
            a_    :     Dict										=    self._step(lowerCamelCase_    )

            a_    :     Optional[int]										=    dict(zip(self.loss_names      ,							lowerCamelCase_    )    )
            # tokens per batch
            a_    :     str										=    batch["""input_ids"""].ne(self.pad    ).sum() + batch["""labels"""].ne(self.pad    ).sum()
            a_    :     Optional[int]										=    batch["""input_ids"""].shape[0]
            a_    :     int										=    batch["""input_ids"""].eq(self.pad    ).sum()
            a_    :     str										=    batch["""input_ids"""].eq(self.pad    ).float().mean()
            # TODO(SS): make a wandb summary metric for this
            return {"loss": loss_tensors[0], "log": logs}

       def 						UpperCAmelCase(   self      :     int      ,							lowerCamelCase_      :     List[Any]      ,							lowerCamelCase_      :     Tuple    ):
            return self._generative_step(lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Any      ,							lowerCamelCase_      :     List[str]      ,							lowerCamelCase_      :     Optional[int]="val"    ):
            self.step_count += 1
            a_    :     Optional[Any]										=    {k: torch.stack([x[k] for x in outputs]    ).mean() for k in self.loss_names}
            a_    :     Tuple										=    losses["""loss"""]
            a_    :     Optional[int]										=    {
                k: np.array([x[k] for x in outputs]    ).mean() for k in self.metric_names + ["""gen_time""", """gen_len"""]
            }
            a_    :     List[Any]										=    (
                generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
            )
            a_    :     torch.FloatTensor										=    torch.tensor(lowerCamelCase_    ).type_as(lowerCamelCase_    )
            generative_metrics.update({k: v.item() for k, v in losses.items()}    )
            losses.update(lowerCamelCase_    )
            a_    :     str										=    {F'''{prefix}_avg_{k}''': x for k, x in losses.items()}
            a_    :     Union[str, Any]										=    self.step_count
            self.metrics[prefix].append(lowerCamelCase_    )  # callback writes this to self.metrics_save_path
            a_    :     List[str]										=    flatten_list([x["""preds"""] for x in outputs]    )
            return {
                "log": all_metrics,
                "preds": preds,
                F'''{prefix}_loss''': loss,
                F'''{prefix}_{self.val_metric}''': metric_tensor,
            }

       def 						UpperCAmelCase(   self      :     Any      ,							lowerCamelCase_      :     Optional[int]      ,							lowerCamelCase_      :     Optional[Any]    ):
            return calculate_rouge(lowerCamelCase_      ,							lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     int      ,							lowerCamelCase_      :     dict    ):
            a_    :     Dict										=    time.time()

            # parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
            a_    :     Any										=    self.model.generate(
                batch["""input_ids"""]      ,							attention_mask=batch["""attention_mask"""]      ,							use_cache=lowerCamelCase_      ,							decoder_start_token_id=self.decoder_start_token_id      ,							num_beams=self.eval_beams      ,							max_length=self.eval_max_length      ,							)
            a_    :     Optional[int]										=    (time.time() - ta) / batch["""input_ids"""].shape[0]
            a_    :     List[str]										=    self.ids_to_clean_text(lowerCamelCase_    )
            a_    :     List[str]										=    self.ids_to_clean_text(batch["""labels"""]    )
            a_    :     Tuple										=    self._step(lowerCamelCase_    )
            a_    :     List[str]										=    dict(zip(self.loss_names      ,							lowerCamelCase_    )    )
            a_    :     Dict										=    self.calc_generative_metrics(lowerCamelCase_      ,							lowerCamelCase_    )
            a_    :     Optional[int]										=    np.mean(lmap(lowerCamelCase_      ,							lowerCamelCase_    )    )
            base_metrics.update(gen_time=lowerCamelCase_      ,							gen_len=lowerCamelCase_      ,							preds=lowerCamelCase_      ,							target=lowerCamelCase_      ,							**lowerCamelCase_    )
            return base_metrics

       def 						UpperCAmelCase(   self      :     int      ,							lowerCamelCase_      :     str      ,							lowerCamelCase_      :     Optional[int]    ):
            return self._generative_step(lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     str      ,							lowerCamelCase_      :     Any    ):
            return self.validation_epoch_end(lowerCamelCase_      ,							prefix="""test"""    )

       def 						UpperCAmelCase(   self      :     Any      ,							lowerCamelCase_      :     Any    ):
            a_    :     List[str]										=    self.n_obs[type_path]
            a_    :     Dict										=    self.target_lens[type_path]
            a_    :     Optional[Any]										=    self.dataset_class(
                self.tokenizer      ,							type_path=lowerCamelCase_      ,							n_obs=lowerCamelCase_      ,							max_target_length=lowerCamelCase_      ,							**self.dataset_kwargs      ,							)
            return dataset

       def 						UpperCAmelCase(   self      :     Union[str, Any]      ,							lowerCamelCase_      :     str      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     bool = False    ):
            a_    :     List[str]										=    self.get_dataset(lowerCamelCase_    )

            if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
                 a_    :     List[str]										=    dataset.make_sortish_sampler(lowerCamelCase_      ,							distributed=self.hparams.gpus > 1    )
                 return DataLoader(
                     lowerCamelCase_      ,							batch_size=lowerCamelCase_      ,							collate_fn=dataset.collate_fn      ,							shuffle=lowerCamelCase_      ,							num_workers=self.num_workers      ,							sampler=lowerCamelCase_      ,							)

            elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
                 a_    :     int										=    dataset.make_dynamic_sampler(
                     self.hparams.max_tokens_per_batch      ,							distributed=self.hparams.gpus > 1    )
                 return DataLoader(
                     lowerCamelCase_      ,							batch_sampler=lowerCamelCase_      ,							collate_fn=dataset.collate_fn      ,							num_workers=self.num_workers      ,							)
            else:
                 return DataLoader(
                     lowerCamelCase_      ,							batch_size=lowerCamelCase_      ,							collate_fn=dataset.collate_fn      ,							shuffle=lowerCamelCase_      ,							num_workers=self.num_workers      ,							sampler=lowerCamelCase_      ,							)

       def 						UpperCAmelCase(   self      :     Any    ):
            a_    :     int										=    self.get_dataloader("""train"""      ,							batch_size=self.hparams.train_batch_size      ,							shuffle=lowerCamelCase_    )
            return dataloader

       def 						UpperCAmelCase(   self      :     Dict    ):
            return self.get_dataloader("""val"""      ,							batch_size=self.hparams.eval_batch_size    )

       def 						UpperCAmelCase(   self      :     List[Any]    ):
            return self.get_dataloader("""test"""      ,							batch_size=self.hparams.eval_batch_size    )

       @staticmethod
       def 						UpperCAmelCase(   lowerCamelCase_      :     Dict      ,							lowerCamelCase_      :     Optional[Any]    ):
            BaseTransformer.add_model_specific_args(lowerCamelCase_      ,							lowerCamelCase_    )
            add_generic_args(lowerCamelCase_      ,							lowerCamelCase_    )
            parser.add_argument(
                """--max_source_length"""      ,							default=1_0_2_4      ,							type=lowerCamelCase_      ,							help=(
                    """The maximum total input sequence length after tokenization. Sequences longer """
                    """than this will be truncated, sequences shorter will be padded."""
                )      ,							)
            parser.add_argument(
                """--max_target_length"""      ,							default=5_6      ,							type=lowerCamelCase_      ,							help=(
                    """The maximum total input sequence length after tokenization. Sequences longer """
                    """than this will be truncated, sequences shorter will be padded."""
                )      ,							)
            parser.add_argument(
                """--val_max_target_length"""      ,							default=1_4_2      ,							type=lowerCamelCase_      ,							help=(
                    """The maximum total input sequence length after tokenization. Sequences longer """
                    """than this will be truncated, sequences shorter will be padded."""
                )      ,							)
            parser.add_argument(
                """--test_max_target_length"""      ,							default=1_4_2      ,							type=lowerCamelCase_      ,							help=(
                    """The maximum total input sequence length after tokenization. Sequences longer """
                    """than this will be truncated, sequences shorter will be padded."""
                )      ,							)
            parser.add_argument("""--freeze_encoder"""      ,							action="""store_true"""    )
            parser.add_argument("""--freeze_embeds"""      ,							action="""store_true"""    )
            parser.add_argument("""--sortish_sampler"""      ,							action="""store_true"""      ,							default=lowerCamelCase_    )
            parser.add_argument("""--overwrite_output_dir"""      ,							action="""store_true"""      ,							default=lowerCamelCase_    )
            parser.add_argument("""--max_tokens_per_batch"""      ,							type=lowerCamelCase_      ,							default=lowerCamelCase_    )
            parser.add_argument("""--logger_name"""      ,							type=lowerCamelCase_      ,							choices=["""default""", """wandb""", """wandb_shared"""]      ,							default="""default"""    )
            parser.add_argument("""--n_train"""      ,							type=lowerCamelCase_      ,							default=-1      ,							required=lowerCamelCase_      ,							help="""# examples. -1 means use all."""    )
            parser.add_argument("""--n_val"""      ,							type=lowerCamelCase_      ,							default=5_0_0      ,							required=lowerCamelCase_      ,							help="""# examples. -1 means use all."""    )
            parser.add_argument("""--n_test"""      ,							type=lowerCamelCase_      ,							default=-1      ,							required=lowerCamelCase_      ,							help="""# examples. -1 means use all."""    )
            parser.add_argument(
                """--task"""      ,							type=lowerCamelCase_      ,							default="""summarization"""      ,							required=lowerCamelCase_      ,							help="""# examples. -1 means use all."""    )
            parser.add_argument("""--label_smoothing"""      ,							type=lowerCamelCase_      ,							default=0.0      ,							required=lowerCamelCase_    )
            parser.add_argument("""--src_lang"""      ,							type=lowerCamelCase_      ,							default=""""""      ,							required=lowerCamelCase_    )
            parser.add_argument("""--tgt_lang"""      ,							type=lowerCamelCase_      ,							default=""""""      ,							required=lowerCamelCase_    )
            parser.add_argument("""--eval_beams"""      ,							type=lowerCamelCase_      ,							default=lowerCamelCase_      ,							required=lowerCamelCase_    )
            parser.add_argument(
                """--val_metric"""      ,							type=lowerCamelCase_      ,							default=lowerCamelCase_      ,							required=lowerCamelCase_      ,							choices=["""bleu""", """rouge2""", """loss""", None]    )
            parser.add_argument("""--eval_max_gen_length"""      ,							type=lowerCamelCase_      ,							default=lowerCamelCase_      ,							help="""never generate more than n tokens"""    )
            parser.add_argument("""--save_top_k"""      ,							type=lowerCamelCase_      ,							default=1      ,							required=lowerCamelCase_      ,							help="""How many checkpoints to save"""    )
            parser.add_argument(
                """--early_stopping_patience"""      ,							type=lowerCamelCase_      ,							default=-1      ,							required=lowerCamelCase_      ,							help=(
                    """-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"""
                    """ val_check_interval will effect it."""
                )      ,							)
            return parser


class   a__	(						lowerCAmelCase_    ):
       lowerCamelCase__:     List[Any]							    =     """translation"""
       lowerCamelCase__:     int							    =     ["""loss"""]
       lowerCamelCase__:     List[str]							    =     ["""bleu"""]
       lowerCamelCase__:     Optional[Any]							    =     """bleu"""

       def __init__(   self      :     Optional[Any]      ,							lowerCamelCase_      :     Optional[Any]      ,							**lowerCamelCase_      :     Tuple    ):
            super().__init__(lowerCamelCase_      ,							**lowerCamelCase_    )
            a_    :     Union[str, Any]										=    hparams.src_lang
            a_    :     Optional[int]										=    hparams.tgt_lang

       def 						UpperCAmelCase(   self      :     Optional[Any]      ,							lowerCamelCase_      :     Tuple      ,							lowerCamelCase_      :     int    ):
            return calculate_bleu(lowerCamelCase_      ,							lowerCamelCase_    )



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase=None ):
     Path(args.output_dir ).mkdir(exist_ok=__UpperCamelCase )
     check_output_dir(__UpperCamelCase  ,  expected_items=3 )

     if model is None:
          if "summarization" in args.task:
               a_    :     SummarizationModule										=    SummarizationModule(__UpperCamelCase )
          else:
               a_    :     SummarizationModule										=    TranslationModule(__UpperCamelCase )
     a_    :     Dict										=    Path(args.data_dir ).name
     if (
         args.logger_name == "default"
         or args.fast_dev_run
         or str(args.output_dir ).startswith("""/tmp""" )
         or str(args.output_dir ).startswith("""/var""" )
     ):
          a_    :     Union[str, Any]										=    True  # don't pollute wandb logs unnecessarily
     elif args.logger_name == "wandb":
          from pytorch_lightning.loggers import WandbLogger

          a_    :     Optional[int]										=    os.environ.get("""WANDB_PROJECT"""  ,  __UpperCamelCase )
          a_    :     str										=    WandbLogger(name=model.output_dir.name  ,  project=__UpperCamelCase )

     elif args.logger_name == "wandb_shared":
          from pytorch_lightning.loggers import WandbLogger

          a_    :     Optional[int]										=    WandbLogger(name=model.output_dir.name  ,  project=F'''hf_{dataset}''' )

     if args.early_stopping_patience >= 0:
          a_    :     Any										=    get_early_stopping_callback(model.val_metric  ,  args.early_stopping_patience )
     else:
          a_    :     str										=    False

     a_    :     Tuple										=    args.val_metric == """loss"""
     a_    :     pl.Trainer										=    generic_train(
         __UpperCamelCase  ,  __UpperCamelCase  ,  logging_callback=SeqaSeqLoggingCallback()  ,  checkpoint_callback=get_checkpoint_callback(
             args.output_dir  ,  model.val_metric  ,  args.save_top_k  ,  __UpperCamelCase )  ,  early_stopping_callback=__UpperCamelCase  ,  logger=__UpperCamelCase  ,  )
     pickle_save(model.hparams  ,  model.output_dir / """hparams.pkl""" )
     if not args.do_predict:
          return model

     a_    :     Union[str, Any]										=    """"""
     a_    :     int										=    sorted(glob.glob(os.path.join(args.output_dir  ,  """*.ckpt""" )  ,  recursive=__UpperCamelCase ) )
     if checkpoints:
          a_    :     Tuple										=    checkpoints[-1]
          a_    :     Tuple										=    checkpoints[-1]
     trainer.logger.log_hyperparams(model.hparams )

     # test() without a model tests using the best checkpoint automatically
     trainer.test()
     return model


if __name__ == "__main__":
       __lowerCamelCase        =     argparse.ArgumentParser()
       __lowerCamelCase        =     pl.Trainer.add_argparse_args(parser)
       __lowerCamelCase        =     SummarizationModule.add_model_specific_args(parser, os.getcwd())

       __lowerCamelCase        =     parser.parse_args()

       main(args)



