'''simple docstring'''

import unittest

from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           DebertaVaForMaskedLM,
           DebertaVaForMultipleChoice,
           DebertaVaForQuestionAnswering,
           DebertaVaForSequenceClassification,
           DebertaVaForTokenClassification,
           DebertaVaModel,
       )
       from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST
class   SCREAMING_SNAKE_CASE			(						lowercase_):







  """simple docstring"""






  def __init__(   self		,						__A		,						__A=13		,						__A=7		,						__A=True		,						__A=True		,						__A=True		,						__A=True		,						__A=99		,						__A=32		,						__A=5		,						__A=4		,						__A=37		,						__A="gelu"		,						__A=0.1		,						__A=0.1		,						__A=512		,						__A=16		,						__A=2		,						__A=0.02		,						__A=False		,						__A=True		,						__A="None"		,						__A=3		,						__A=4		,						__A=None		,						) ->     List[str]:
        _lowerCAmelCase										=parent
        _lowerCAmelCase										=batch_size
        _lowerCAmelCase										=seq_length
        _lowerCAmelCase										=is_training
        _lowerCAmelCase										=use_input_mask
        _lowerCAmelCase										=use_token_type_ids
        _lowerCAmelCase										=use_labels
        _lowerCAmelCase										=vocab_size
        _lowerCAmelCase										=hidden_size
        _lowerCAmelCase										=num_hidden_layers
        _lowerCAmelCase										=num_attention_heads
        _lowerCAmelCase										=intermediate_size
        _lowerCAmelCase										=hidden_act
        _lowerCAmelCase										=hidden_dropout_prob
        _lowerCAmelCase										=attention_probs_dropout_prob
        _lowerCAmelCase										=max_position_embeddings
        _lowerCAmelCase										=type_vocab_size
        _lowerCAmelCase										=type_sequence_label_size
        _lowerCAmelCase										=initializer_range
        _lowerCAmelCase										=num_labels
        _lowerCAmelCase										=num_choices
        _lowerCAmelCase										=relative_attention
        _lowerCAmelCase										=position_biased_input
        _lowerCAmelCase										=pos_att_type
        _lowerCAmelCase										=scope






  def 		UpperCamelCase__       (   self				) ->     List[Any]:
        _lowerCAmelCase										=ids_tensor([self.batch_size, self.seq_length]		,						self.vocab_size				)

        _lowerCAmelCase										=None
        if self.use_input_mask:
              _lowerCAmelCase										=ids_tensor([self.batch_size, self.seq_length]		,						vocab_size=2				)

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

        _lowerCAmelCase										=None
        _lowerCAmelCase										=None
        _lowerCAmelCase										=None
        if self.use_labels:
              _lowerCAmelCase										=ids_tensor([self.batch_size]		,						self.type_sequence_label_size				)
              _lowerCAmelCase										=ids_tensor([self.batch_size, self.seq_length]		,						self.num_labels				)
              _lowerCAmelCase										=ids_tensor([self.batch_size]		,						self.num_choices				)

        _lowerCAmelCase										=self.get_config()

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






  def 		UpperCamelCase__       (   self				) ->     Optional[int]:
        return DebertaVaConfig(
            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		,						relative_attention=self.relative_attention		,						position_biased_input=self.position_biased_input		,						pos_att_type=self.pos_att_type		,						)






  def 		UpperCamelCase__       (   self		,						__A				) ->     str:
        self.parent.assertListEqual(list(result.loss.size()				)		,						[]				)






  def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A		,						__A		,						__A		,						__A		,						__A				) ->     List[str]:
        _lowerCAmelCase										=DebertaVaModel(config=__A				)
        model.to(__A				)
        model.eval()
        _lowerCAmelCase										=model(__A		,						attention_mask=__A		,						token_type_ids=__A				)[0]
        _lowerCAmelCase										=model(__A		,						token_type_ids=__A				)[0]
        _lowerCAmelCase										=model(__A				)[0]

        self.parent.assertListEqual(list(sequence_output.size()				)		,						[self.batch_size, self.seq_length, self.hidden_size]				)






  def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A		,						__A		,						__A		,						__A		,						__A				) ->     str:
        _lowerCAmelCase										=DebertaVaForMaskedLM(config=__A				)
        model.to(__A				)
        model.eval()
        _lowerCAmelCase										=model(__A		,						attention_mask=__A		,						token_type_ids=__A		,						labels=__A				)

        self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.seq_length, self.vocab_size)				)






  def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A		,						__A		,						__A		,						__A		,						__A				) ->     Optional[Any]:
        _lowerCAmelCase										=self.num_labels
        _lowerCAmelCase										=DebertaVaForSequenceClassification(__A				)
        model.to(__A				)
        model.eval()
        _lowerCAmelCase										=model(__A		,						attention_mask=__A		,						token_type_ids=__A		,						labels=__A				)
        self.parent.assertListEqual(list(result.logits.size()				)		,						[self.batch_size, self.num_labels]				)
        self.check_loss_output(__A				)






  def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A		,						__A		,						__A		,						__A		,						__A				) ->     List[str]:
        _lowerCAmelCase										=self.num_labels
        _lowerCAmelCase										=DebertaVaForTokenClassification(config=__A				)
        model.to(__A				)
        model.eval()
        _lowerCAmelCase										=model(__A		,						attention_mask=__A		,						token_type_ids=__A		,						labels=__A				)
        self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.seq_length, self.num_labels)				)






  def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A		,						__A		,						__A		,						__A		,						__A				) ->     List[Any]:
        _lowerCAmelCase										=DebertaVaForQuestionAnswering(config=__A				)
        model.to(__A				)
        model.eval()
        _lowerCAmelCase										=model(
            __A		,						attention_mask=__A		,						token_type_ids=__A		,						start_positions=__A		,						end_positions=__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		,						__A		,						__A		,						__A		,						__A		,						__A		,						__A		,						__A				) ->     Tuple:
        _lowerCAmelCase										=DebertaVaForMultipleChoice(config=__A				)
        model.to(__A				)
        model.eval()
        _lowerCAmelCase										=input_ids.unsqueeze(1				).expand(-1		,						self.num_choices		,						-1				).contiguous()
        _lowerCAmelCase										=token_type_ids.unsqueeze(1				).expand(-1		,						self.num_choices		,						-1				).contiguous()
        _lowerCAmelCase										=input_mask.unsqueeze(1				).expand(-1		,						self.num_choices		,						-1				).contiguous()
        _lowerCAmelCase										=model(
            __A		,						attention_mask=__A		,						token_type_ids=__A		,						labels=__A		,						)
        self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.num_choices)				)






  def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
        _lowerCAmelCase										=self.prepare_config_and_inputs()
        (
            (
            _lowerCAmelCase					
        )						,				(
            _lowerCAmelCase					
        )						,				(
            _lowerCAmelCase					
        )						,				(
            _lowerCAmelCase					
        )						,				(
            _lowerCAmelCase					
        )						,				(
            _lowerCAmelCase					
        )						,				(
            _lowerCAmelCase					
        )						,									
        )										=config_and_inputs
        _lowerCAmelCase										={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
        return config, inputs_dict

@require_torch
class   SCREAMING_SNAKE_CASE			(						lowercase_    ,			lowercase_    ,			unittest.TestCase):







  """simple docstring"""
  lowercase   :      List[Any]       =							(
      (
          DebertaVaModel,
          DebertaVaForMaskedLM,
          DebertaVaForSequenceClassification,
          DebertaVaForTokenClassification,
          DebertaVaForQuestionAnswering,
          DebertaVaForMultipleChoice,
      )
      if is_torch_available()
      else ()
  )
  lowercase   :      Union[str, Any]       =							(
      {
          '''feature-extraction''': DebertaVaModel,
          '''fill-mask''': DebertaVaForMaskedLM,
          '''question-answering''': DebertaVaForQuestionAnswering,
          '''text-classification''': DebertaVaForSequenceClassification,
          '''token-classification''': DebertaVaForTokenClassification,
          '''zero-shot''': DebertaVaForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )

  lowercase   :      Dict       =							True
  lowercase   :      List[Any]       =							False
  lowercase   :      Optional[int]       =							False
  lowercase   :      Dict       =							False
  lowercase   :      Optional[Any]       =							False






  def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
        _lowerCAmelCase										=DebertaVaModelTester(self				)
        _lowerCAmelCase										=ConfigTester(self		,						config_class=__A		,						hidden_size=37				)






  def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
        self.config_tester.run_common_tests()






  def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
        _lowerCAmelCase										=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_model(*__A				)






  def 		UpperCamelCase__       (   self				) ->     int:
        _lowerCAmelCase										=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_for_sequence_classification(*__A				)






  def 		UpperCamelCase__       (   self				) ->     Dict:
        _lowerCAmelCase										=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_for_masked_lm(*__A				)






  def 		UpperCamelCase__       (   self				) ->     int:
        _lowerCAmelCase										=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_for_question_answering(*__A				)






  def 		UpperCamelCase__       (   self				) ->     List[Any]:
        _lowerCAmelCase										=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_for_token_classification(*__A				)






  def 		UpperCamelCase__       (   self				) ->     Tuple:
        _lowerCAmelCase										=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_for_multiple_choice(*__A				)






  @slow
  def 		UpperCamelCase__       (   self				) ->     Dict:
        for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              _lowerCAmelCase										=DebertaVaModel.from_pretrained(__A				)
              self.assertIsNotNone(__A				)

@require_torch
@require_sentencepiece
@require_tokenizers
class   SCREAMING_SNAKE_CASE			(						unittest.TestCase):







  """simple docstring"""






  @unittest.skip(reason='Model not available yet'				)
  def 		UpperCamelCase__       (   self				) ->     int:
        pass






  @slow
  def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
        _lowerCAmelCase										=DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge'				)

        _lowerCAmelCase										=torch.tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]				)
        _lowerCAmelCase										=torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]				)
        with torch.no_grad():
              _lowerCAmelCase										=model(__A		,						attention_mask=__A				)[0]
        # compare the actual values for a slice.
        _lowerCAmelCase										=torch.tensor(
            [[[0.2_356, 0.1_948, 0.0_369], [-0.1_063, 0.3_586, -0.5_152], [-0.6_399, -0.0_259, -0.2_525]]]				)
        self.assertTrue(torch.allclose(output[:, 1:4, 1:4]		,						__A		,						atol=1E-4				)		,						F'''{output[:, 1:4, 1:4]}'''				)






'''simple docstring'''

import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


lowercase_     		= '''sshleifer/mar_enro_6_3_student'''
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""






		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								super().setUp()

								_lowerCAmelCase										=cached_path(
								    'https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'		,						extract_compressed_file=__A		,						)
								_lowerCAmelCase										=F'''{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'''






		@slow
		@require_torch_gpu
		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								MarianMTModel.from_pretrained(__A				)






		@slow
		@require_torch_gpu
		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								_lowerCAmelCase										={
								    '$MAX_LEN': 64,
								    '$BS': 64,
								    '$GAS': 1,
								    '$ENRO_DIR': self.data_dir,
								    'facebook/mbart-large-cc25': MARIAN_MODEL,
								    # "val_check_interval=0.25": "val_check_interval=1.0",
								    '--learning_rate=3e-5': '--learning_rate 3e-4',
								    '--num_train_epochs 6': '--num_train_epochs 1',
								}

								# Clean up bash script
								_lowerCAmelCase										=(self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py'				)[1].strip()
								_lowerCAmelCase										=bash_script.replace('\\\n'		,						''				).strip().replace('"$@"'		,						''				)
								for k, v in env_vars_to_replace.items():
														_lowerCAmelCase										=bash_script.replace(__A		,						str(__A				)				)
								_lowerCAmelCase										=self.get_auto_remove_tmp_dir()

								# bash_script = bash_script.replace("--fp16 ", "")
								_lowerCAmelCase										=F'''
            --output_dir {output_dir}
            --tokenizer_name Helsinki-NLP/opus-mt-en-ro
            --sortish_sampler
            --do_predict
            --gpus 1
            --freeze_encoder
            --n_train 40000
            --n_val 500
            --n_test 500
            --fp16_opt_level O1
            --num_sanity_val_steps 0
            --eval_beams 2
        '''.split()
								# XXX: args.gpus > 1 : handle multi_gpu in the future

								_lowerCAmelCase										=['finetune.py'] + bash_script.split() + args
								with patch.object(__A		,						'argv'		,						__A				):
														_lowerCAmelCase										=argparse.ArgumentParser()
														_lowerCAmelCase										=pl.Trainer.add_argparse_args(__A				)
														_lowerCAmelCase										=SummarizationModule.add_model_specific_args(__A		,						os.getcwd()				)
														_lowerCAmelCase										=parser.parse_args()
														_lowerCAmelCase										=main(__A				)

								# Check metrics
								_lowerCAmelCase										=load_json(model.metrics_save_path				)
								_lowerCAmelCase										=metrics['val'][0]
								_lowerCAmelCase										=metrics['val'][-1]
								self.assertEqual(len(metrics['val']				)		,						(args.max_epochs / args.val_check_interval)				)
								assert isinstance(last_step_stats[F'''val_avg_{model.val_metric}''']		,						__A				)

								self.assertGreater(last_step_stats['val_avg_gen_time']		,						0.01				)
								# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
								self.assertLessEqual(last_step_stats['val_avg_gen_time']		,						1.0				)

								# test learning requirements:

								# 1. BLEU improves over the course of training by more than 2 pts
								self.assertGreater(last_step_stats['val_avg_bleu'] - first_step_stats['val_avg_bleu']		,						2				)

								# 2. BLEU finishes above 17
								self.assertGreater(last_step_stats['val_avg_bleu']		,						17				)

								# 3. test BLEU and val BLEU within ~1.1 pt.
								self.assertLess(abs(metrics['val'][-1]['val_avg_bleu'] - metrics['test'][-1]['test_avg_bleu']				)		,						1.1				)

								# check lightning ckpt can be loaded and has a reasonable statedict
								_lowerCAmelCase										=os.listdir(__A				)
								_lowerCAmelCase										=[x for x in contents if x.endswith('.ckpt'				)][0]
								_lowerCAmelCase										=os.path.join(args.output_dir		,						__A				)
								_lowerCAmelCase										=torch.load(__A		,						map_location='cpu'				)
								_lowerCAmelCase										='model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
								assert expected_key in ckpt["state_dict"]
								assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

								# TODO: turn on args.do_predict when PL bug fixed.
								if args.do_predict:
														_lowerCAmelCase										={os.path.basename(__A				) for p in contents}
														assert "test_generations.txt" in contents
														assert "test_results.txt" in contents
														# assert len(metrics["val"]) ==  desired_n_evals
														assert len(metrics['test']				) == 1

class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""






		@timeout_decorator.timeout(600				)
		@slow
		@require_torch_gpu
		def 		UpperCamelCase__       (   self				) ->     Tuple:
								_lowerCAmelCase										=F'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
								_lowerCAmelCase										={
								    '--fp16_opt_level=O1': '',
								    '$MAX_LEN': 128,
								    '$BS': 16,
								    '$GAS': 1,
								    '$ENRO_DIR': data_dir,
								    '$m': 'sshleifer/student_marian_en_ro_6_1',
								    'val_check_interval=0.25': 'val_check_interval=1.0',
								}

								# Clean up bash script
								_lowerCAmelCase										=(
								    (self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py'				)[1].strip()
								)
								_lowerCAmelCase										=bash_script.replace('\\\n'		,						''				).strip().replace('"$@"'		,						''				)
								_lowerCAmelCase										=bash_script.replace('--fp16 '		,						' '				)

								for k, v in env_vars_to_replace.items():
														_lowerCAmelCase										=bash_script.replace(__A		,						str(__A				)				)
								_lowerCAmelCase										=self.get_auto_remove_tmp_dir()
								_lowerCAmelCase										=bash_script.replace('--fp16'		,						''				)
								_lowerCAmelCase										=6
								_lowerCAmelCase										=(
								    ['distillation.py']
								    + bash_script.split()
								    + [
								        F'''--output_dir={output_dir}''',
								        '--gpus=1',
								        '--learning_rate=1e-3',
								        F'''--num_train_epochs={epochs}''',
								        '--warmup_steps=10',
								        '--val_check_interval=1.0',
								        '--do_predict',
								    ]
								)
								with patch.object(__A		,						'argv'		,						__A				):
														_lowerCAmelCase										=argparse.ArgumentParser()
														_lowerCAmelCase										=pl.Trainer.add_argparse_args(__A				)
														_lowerCAmelCase										=SummarizationDistiller.add_model_specific_args(__A		,						os.getcwd()				)
														_lowerCAmelCase										=parser.parse_args()
														# assert args.gpus == gpus THIS BREAKS for multi_gpu

														_lowerCAmelCase										=distill_main(__A				)

								# Check metrics
								_lowerCAmelCase										=load_json(model.metrics_save_path				)
								_lowerCAmelCase										=metrics['val'][0]
								_lowerCAmelCase										=metrics['val'][-1]
								assert len(metrics['val']				) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

								assert last_step_stats["val_avg_gen_time"] >= 0.01

								assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
								assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
								assert isinstance(last_step_stats[F'''val_avg_{model.val_metric}''']		,						__A				)

								# check lightning ckpt can be loaded and has a reasonable statedict
								_lowerCAmelCase										=os.listdir(__A				)
								_lowerCAmelCase										=[x for x in contents if x.endswith('.ckpt'				)][0]
								_lowerCAmelCase										=os.path.join(args.output_dir		,						__A				)
								_lowerCAmelCase										=torch.load(__A		,						map_location='cpu'				)
								_lowerCAmelCase										='model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
								assert expected_key in ckpt["state_dict"]
								assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

								# TODO: turn on args.do_predict when PL bug fixed.
								if args.do_predict:
														_lowerCAmelCase										={os.path.basename(__A				) for p in contents}
														assert "test_generations.txt" in contents
														assert "test_results.txt" in contents
														# assert len(metrics["val"]) ==  desired_n_evals
														assert len(metrics['test']				) == 1





