






from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
		import datasets



class       __UpperCAmelCase		(_UpperCAmelCase    ):







			@slow
			@require_torch
			def 							UpperCamelCase      (	self:  Optional[int]     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny"""      ,	"""prajjwal1/bert-tiny"""     )
									_SCREAMING_SNAKE_CASE	      =  BertTokenizer.from_pretrained("""bert-base-uncased"""     )

									_SCREAMING_SNAKE_CASE	      =  bertabert.config.encoder.vocab_size
									_SCREAMING_SNAKE_CASE	      =  tokenizer.sep_token_id
									_SCREAMING_SNAKE_CASE	      =  tokenizer.cls_token_id
									_SCREAMING_SNAKE_CASE	      =  128

									_SCREAMING_SNAKE_CASE	      =  datasets.load_dataset("""cnn_dailymail"""      ,	"""3.0.0"""      ,	split="""train[:1%]"""     )
									_SCREAMING_SNAKE_CASE	      =  datasets.load_dataset("""cnn_dailymail"""      ,	"""3.0.0"""      ,	split="""validation[:1%]"""     )

									_SCREAMING_SNAKE_CASE	      =  train_dataset.select(range(32     )     )
									_SCREAMING_SNAKE_CASE	      =  val_dataset.select(range(16     )     )

									_SCREAMING_SNAKE_CASE	      =  4

									def _map_to_encoder_decoder_inputs(UpperCAmelCase_:  str     ):
															# Tokenizer will automatically set [BOS] <text> [EOS]
															_SCREAMING_SNAKE_CASE	      =  tokenizer(batch["""article"""]      ,	padding="""max_length"""      ,	truncation=UpperCAmelCase_      ,	max_length=512     )
															_SCREAMING_SNAKE_CASE	      =  tokenizer(batch["""highlights"""]      ,	padding="""max_length"""      ,	truncation=UpperCAmelCase_      ,	max_length=128     )
															_SCREAMING_SNAKE_CASE	      =  inputs.input_ids
															_SCREAMING_SNAKE_CASE	      =  inputs.attention_mask

															_SCREAMING_SNAKE_CASE	      =  outputs.input_ids
															_SCREAMING_SNAKE_CASE	      =  outputs.input_ids.copy()
															_SCREAMING_SNAKE_CASE	      =  [
															    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
															]
															_SCREAMING_SNAKE_CASE	      =  outputs.attention_mask

															assert all(len(UpperCAmelCase_     ) == 512 for x in inputs.input_ids     )
															assert all(len(UpperCAmelCase_     ) == 128 for x in outputs.input_ids     )

															return batch

									def _compute_metrics(UpperCAmelCase_:  int     ):
															_SCREAMING_SNAKE_CASE	      =  pred.label_ids
															_SCREAMING_SNAKE_CASE	      =  pred.predictions

															# all unnecessary tokens are removed
															_SCREAMING_SNAKE_CASE	      =  tokenizer.batch_decode(UpperCAmelCase_      ,	skip_special_tokens=UpperCAmelCase_     )
															_SCREAMING_SNAKE_CASE	      =  tokenizer.batch_decode(UpperCAmelCase_      ,	skip_special_tokens=UpperCAmelCase_     )

															_SCREAMING_SNAKE_CASE	      =  sum([int(pred_str[i] == label_str[i]     ) for i in range(len(UpperCAmelCase_     )     )]     ) / len(UpperCAmelCase_     )

															return {"accuracy": accuracy}

									# map train dataset
									_SCREAMING_SNAKE_CASE	      =  train_dataset.map(
									    _map_to_encoder_decoder_inputs      ,	batched=UpperCAmelCase_      ,	batch_size=UpperCAmelCase_      ,	remove_columns=["""article""", """highlights"""]      ,	)
									train_dataset.set_format(
									    type="""torch"""      ,	columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]      ,	)

									# same for validation dataset
									_SCREAMING_SNAKE_CASE	      =  val_dataset.map(
									    _map_to_encoder_decoder_inputs      ,	batched=UpperCAmelCase_      ,	batch_size=UpperCAmelCase_      ,	remove_columns=["""article""", """highlights"""]      ,	)
									val_dataset.set_format(
									    type="""torch"""      ,	columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]      ,	)

									_SCREAMING_SNAKE_CASE	      =  self.get_auto_remove_tmp_dir()

									_SCREAMING_SNAKE_CASE	      =  SeqaSeqTrainingArguments(
									    output_dir=UpperCAmelCase_      ,	per_device_train_batch_size=UpperCAmelCase_      ,	per_device_eval_batch_size=UpperCAmelCase_      ,	predict_with_generate=UpperCAmelCase_      ,	evaluation_strategy="""steps"""      ,	do_train=UpperCAmelCase_      ,	do_eval=UpperCAmelCase_      ,	warmup_steps=0      ,	eval_steps=2      ,	logging_steps=2      ,	)

									# instantiate trainer
									_SCREAMING_SNAKE_CASE	      =  SeqaSeqTrainer(
									    model=UpperCAmelCase_      ,	args=UpperCAmelCase_      ,	compute_metrics=_compute_metrics      ,	train_dataset=UpperCAmelCase_      ,	eval_dataset=UpperCAmelCase_      ,	tokenizer=UpperCAmelCase_      ,	)

									# start training
									trainer.train()





'''simple docstring'''

# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser





def __snake_case					(      ):
 lowerCamelCase_      =  ArgumentParser("Accelerate CLI tool"					,				usage="accelerate <command> [<args>]"					,				allow_abbrev=UpperCAmelCase_		)
 lowerCamelCase_      =  parser.add_subparsers(help="accelerate command helpers"		)

 # Register commands
 get_config_parser(subparsers=UpperCAmelCase_		)
 env_command_parser(subparsers=UpperCAmelCase_		)
 launch_command_parser(subparsers=UpperCAmelCase_		)
 tpu_command_parser(subparsers=UpperCAmelCase_		)
 test_command_parser(subparsers=UpperCAmelCase_		)

 # Let's go
 lowerCamelCase_      =  parser.parse_args()

 if not hasattr(UpperCAmelCase_					,				"func"		):
  parser.print_help()
  exit(1		)

 # Run
 args.func(UpperCAmelCase_		)


if __name__ == "__main__":
     main()



