





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


UpperCamelCase__   : str					  = 'sshleifer/mar_enro_6_3_student'







class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[int]		):






						'''simple docstring'''







						super().setUp()

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





					@slow
					@require_torch_gpu
					def        SCREAMING_SNAKE_CASE							(						self    :  int		):






						'''simple docstring'''







						MarianMTModel.from_pretrained(UpperCAmelCase__		)





					@slow
					@require_torch_gpu
					def        SCREAMING_SNAKE_CASE							(						self    :  int		):






						'''simple docstring'''







						UpperCAmelCase_								=							{
						    '''$MAX_LEN''': 64,
						    '''$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
						UpperCAmelCase_								=							(self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split("""finetune.py"""		)[1].strip()
						UpperCAmelCase_								=							bash_script.replace("""\\\n""" ,					""""""		).strip().replace("""\"$@\"""" ,					""""""		)
						for k, v in env_vars_to_replace.items():
							UpperCAmelCase_								=							bash_script.replace(UpperCAmelCase__ ,					str(UpperCAmelCase__		)		)
						UpperCAmelCase_								=							self.get_auto_remove_tmp_dir()

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

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

						# Check metrics
						UpperCAmelCase_								=							load_json(model.metrics_save_path		)
						UpperCAmelCase_								=							metrics['''val'''][0]
						UpperCAmelCase_								=							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}"""] ,					UpperCAmelCase__		)

						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
						UpperCAmelCase_								=							os.listdir(UpperCAmelCase__		)
						UpperCAmelCase_								=							[x for x in contents if x.endswith(""".ckpt"""		)][0]
						UpperCAmelCase_								=							os.path.join(args.output_dir ,					UpperCAmelCase__		)
						UpperCAmelCase_								=							torch.load(UpperCAmelCase__ ,					map_location="""cpu"""		)
						UpperCAmelCase_								=							'''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:
							UpperCAmelCase_								=							{os.path.basename(UpperCAmelCase__		) 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   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					@timeout_decorator.timeout(6_00		)
					@slow
					@require_torch_gpu
					def        SCREAMING_SNAKE_CASE							(						self    :  str		):






						'''simple docstring'''







						UpperCAmelCase_								=							F"""{self.test_file_dir_str}/test_data/wmt_en_ro"""
						UpperCAmelCase_								=							{
						    '''--fp16_opt_level=O1''': '''''',
						    '''$MAX_LEN''': 1_28,
						    '''$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
						UpperCAmelCase_								=							(
						    (self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split("""distillation.py"""		)[1].strip()
						)
						UpperCAmelCase_								=							bash_script.replace("""\\\n""" ,					""""""		).strip().replace("""\"$@\"""" ,					""""""		)
						UpperCAmelCase_								=							bash_script.replace("""--fp16 """ ,					""" """		)

						for k, v in env_vars_to_replace.items():
							UpperCAmelCase_								=							bash_script.replace(UpperCAmelCase__ ,					str(UpperCAmelCase__		)		)
						UpperCAmelCase_								=							self.get_auto_remove_tmp_dir()
						UpperCAmelCase_								=							bash_script.replace("""--fp16""" ,					""""""		)
						UpperCAmelCase_								=							6
						UpperCAmelCase_								=							(
						    ['''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(UpperCAmelCase__ ,					"""argv""" ,					UpperCAmelCase__		):
							UpperCAmelCase_								=							argparse.ArgumentParser()
							UpperCAmelCase_								=							pl.Trainer.add_argparse_args(UpperCAmelCase__		)
							UpperCAmelCase_								=							SummarizationDistiller.add_model_specific_args(UpperCAmelCase__ ,					os.getcwd()		)
							UpperCAmelCase_								=							parser.parse_args()
							# assert args.gpus == gpus THIS BREAKS for multi_gpu

							UpperCAmelCase_								=							distill_main(UpperCAmelCase__		)

						# Check metrics
						UpperCAmelCase_								=							load_json(model.metrics_save_path		)
						UpperCAmelCase_								=							metrics['''val'''][0]
						UpperCAmelCase_								=							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}"""] ,					UpperCAmelCase__		)

						# check lightning ckpt can be loaded and has a reasonable statedict
						UpperCAmelCase_								=							os.listdir(UpperCAmelCase__		)
						UpperCAmelCase_								=							[x for x in contents if x.endswith(""".ckpt"""		)][0]
						UpperCAmelCase_								=							os.path.join(args.output_dir ,					UpperCAmelCase__		)
						UpperCAmelCase_								=							torch.load(UpperCAmelCase__ ,					map_location="""cpu"""		)
						UpperCAmelCase_								=							'''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:
							UpperCAmelCase_								=							{os.path.basename(UpperCAmelCase__		) 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









from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


UpperCamelCase__   : List[str]					  = logging.get_logger(__name__)







class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					def __init__(						self    :  Union[str, Any] ,					__lowercase    :  int ,					__lowercase    :  int ,					__lowercase    :  float ,					**__lowercase    :  Dict		):






						'''simple docstring'''







						UpperCAmelCase_								=     feature_size
						UpperCAmelCase_								=     sampling_rate
						UpperCAmelCase_								=     padding_value

						UpperCAmelCase_								=     kwargs.pop("""padding_side""" ,					"""right"""		)
						UpperCAmelCase_								=     kwargs.pop("""return_attention_mask""" ,					__lowercase		)

						super().__init__(**__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  List[str] ,					__lowercase    :  Union[
					        BatchFeature,
					        List[BatchFeature],
					        Dict[str, BatchFeature],
					        Dict[str, List[BatchFeature]],
					        List[Dict[str, BatchFeature]],
					    ] ,					__lowercase    :  Union[bool, str, PaddingStrategy] = True ,					__lowercase    :  Optional[int] = None ,					__lowercase    :  bool = False ,					__lowercase    :  Optional[int] = None ,					__lowercase    :  Optional[bool] = None ,					__lowercase    :  Optional[Union[str, TensorType]] = None ,					):






						'''simple docstring'''







						if isinstance(__lowercase ,					(list, tuple)		) and isinstance(processed_features[0] ,					(dict, BatchFeature)		):
							UpperCAmelCase_								=     {
							    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
							}

						# The model's main input name, usually `input_values`, has be passed for padding
						if self.model_input_names[0] not in processed_features:
							raise ValueError(
							    """You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"""
							    F""" to this method that includes {self.model_input_names[0]}, but you provided"""
							    F""" {list(processed_features.keys()		)}"""		)

						UpperCAmelCase_								=     processed_features[self.model_input_names[0]]
						UpperCAmelCase_								=     (
						    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
						)

						if len(__lowercase		) == 0:
							if return_attention_mask:
								UpperCAmelCase_								=     []
							return processed_features

						# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
						# and rebuild them afterwards if no return_tensors is specified
						# Note that we lose the specific device the tensor may be on for PyTorch

						UpperCAmelCase_								=     required_input[0]
						if isinstance(__lowercase ,					(list, tuple)		):
							# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
							UpperCAmelCase_								=     0
							while len(required_input[index]		) == 0:
								index += 1
							if index < len(__lowercase		):
								UpperCAmelCase_								=     required_input[index][0]

						if return_tensors is None:
							if is_tf_tensor(__lowercase		):
								UpperCAmelCase_								=     """tf"""
							elif is_torch_tensor(__lowercase		):
								UpperCAmelCase_								=     """pt"""
							elif isinstance(__lowercase ,					(int, float, list, tuple, np.ndarray)		):
								UpperCAmelCase_								=     """np"""
							else:
								raise ValueError(
								    F"""type of {first_element} unknown: {type(__lowercase		)}. """
								    """Should be one of a python, numpy, pytorch or tensorflow object."""		)

						for key, value in processed_features.items():
							if isinstance(value[0] ,					(int, float)		):
								UpperCAmelCase_								=     to_numpy(__lowercase		)
							else:
								UpperCAmelCase_								=     [to_numpy(__lowercase		) for v in value]

        # Convert padding_strategy in PaddingStrategy
						UpperCAmelCase_								=     self._get_padding_strategies(padding=__lowercase ,					max_length=__lowercase		)

						UpperCAmelCase_								=     processed_features[self.model_input_names[0]]

						UpperCAmelCase_								=     len(__lowercase		)
						if not all(len(__lowercase		) == batch_size for v in processed_features.values()		):
							raise ValueError("""Some items in the output dictionary have a different batch size than others."""		)

						UpperCAmelCase_								=     []
						for i in range(__lowercase		):
							UpperCAmelCase_								=     {k: v[i] for k, v in processed_features.items()}
							# truncation
							UpperCAmelCase_								=     self._truncate(
							    __lowercase ,					max_length=__lowercase ,					pad_to_multiple_of=__lowercase ,					truncation=__lowercase ,					)
							truncated_inputs.append(__lowercase		)

						if padding_strategy == PaddingStrategy.LONGEST:
							# make sure that `max_length` cannot be longer than the longest truncated length
							UpperCAmelCase_								=     max(len(input_slice[self.model_input_names[0]]		) for input_slice in truncated_inputs		)
							UpperCAmelCase_								=     PaddingStrategy.MAX_LENGTH

						UpperCAmelCase_								=     {}
						for i in range(__lowercase		):
							# padding
							UpperCAmelCase_								=     self._pad(
							    truncated_inputs[i] ,					max_length=__lowercase ,					padding_strategy=__lowercase ,					pad_to_multiple_of=__lowercase ,					return_attention_mask=__lowercase ,					)

							for key, value in outputs.items():
								if key not in batch_outputs:
									UpperCAmelCase_								=     []
								if value.dtype is np.dtype(np.floataa		):
									UpperCAmelCase_								=     value.astype(np.floataa		)
								batch_outputs[key].append(__lowercase		)

						return BatchFeature(__lowercase ,					tensor_type=__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  str ,					__lowercase    :  Union[Dict[str, np.ndarray], BatchFeature] ,					__lowercase    :  Optional[int] = None ,					__lowercase    :  PaddingStrategy = PaddingStrategy.DO_NOT_PAD ,					__lowercase    :  Optional[int] = None ,					__lowercase    :  Optional[bool] = None ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     processed_features[self.model_input_names[0]]

						if padding_strategy == PaddingStrategy.LONGEST:
							UpperCAmelCase_								=     len(__lowercase		)

						if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
							UpperCAmelCase_								=     ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

						UpperCAmelCase_								=     padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__lowercase		) < max_length

						if return_attention_mask and "attention_mask" not in processed_features:
							UpperCAmelCase_								=     np.ones(len(__lowercase		) ,					dtype=np.intaa		)

						if needs_to_be_padded:
							UpperCAmelCase_								=     max_length - len(__lowercase		)
							if self.padding_side == "right":
								if return_attention_mask:
									UpperCAmelCase_								=     np.pad(
									    processed_features["""attention_mask"""] ,					(0, difference)		)
								UpperCAmelCase_								=     ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
								UpperCAmelCase_								=     np.pad(
								    __lowercase ,					__lowercase ,					"""constant""" ,					constant_values=self.padding_value		)
							elif self.padding_side == "left":
								if return_attention_mask:
									UpperCAmelCase_								=     np.pad(
									    processed_features["""attention_mask"""] ,					(difference, 0)		)
								UpperCAmelCase_								=     ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
								UpperCAmelCase_								=     np.pad(
								    __lowercase ,					__lowercase ,					"""constant""" ,					constant_values=self.padding_value		)
							else:
								raise ValueError("""Invalid padding strategy:""" + str(self.padding_side		)		)

						return processed_features





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[int] ,					__lowercase    :  Union[Dict[str, np.ndarray], BatchFeature] ,					__lowercase    :  Optional[int] = None ,					__lowercase    :  Optional[int] = None ,					__lowercase    :  Optional[bool] = None ,					):






						'''simple docstring'''







						if not truncation:
							return processed_features
						elif truncation and max_length is None:
							raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined."""		)

						UpperCAmelCase_								=     processed_features[self.model_input_names[0]]

						# find `max_length` that fits `pad_to_multiple_of`
						if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
							UpperCAmelCase_								=     ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

						UpperCAmelCase_								=     len(__lowercase		) > max_length

						if needs_to_be_truncated:
							UpperCAmelCase_								=     processed_features[self.model_input_names[0]][:max_length]
							if "attention_mask" in processed_features:
								UpperCAmelCase_								=     processed_features["""attention_mask"""][:max_length]

						return processed_features





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any] ,					__lowercase    :  int=False ,					__lowercase    :  List[str]=None		):






						'''simple docstring'''







						if padding is not False:
							if padding is True:
								UpperCAmelCase_								=     PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
							elif not isinstance(__lowercase ,					__lowercase		):
								UpperCAmelCase_								=     PaddingStrategy(__lowercase		)
							elif isinstance(__lowercase ,					__lowercase		):
								UpperCAmelCase_								=     padding
						else:
							UpperCAmelCase_								=     PaddingStrategy.DO_NOT_PAD

						# Set max length if needed
						if max_length is None:
							if padding_strategy == PaddingStrategy.MAX_LENGTH:
								raise ValueError(
								    F"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"""		)

        # Test if we have a padding value
						if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
							raise ValueError(
							    """Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"""
							    """ as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."""		)

						return padding_strategy


