


import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE






class     _lowerCamelCase					(   A__     ):




       """simple docstring"""
       snake_case    =       ""
       snake_case    =       (
           None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
       )
       snake_case    =       None  # compression type in fsspec. ex: "gzip"
       snake_case    =       None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz



       def __init__( self							,			_SCREAMING_SNAKE_CASE = ""							,			_SCREAMING_SNAKE_CASE = None							,			_SCREAMING_SNAKE_CASE = None							,			**_SCREAMING_SNAKE_CASE						)->Any:




         '''simple docstring'''





         super().__init__(self							,			**_SCREAMING_SNAKE_CASE						)
         # always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
         A_ :							Optional[int]							=   fsspec.open(
             _SCREAMING_SNAKE_CASE							,			mode='''rb'''							,			protocol=_SCREAMING_SNAKE_CASE							,			compression=self.compression							,			client_kwargs={
                 '''requote_redirect_url''': False,  # see https://github.com/huggingface/datasets/pull/5459
                 '''trust_env''': True,  # Enable reading proxy env variables.
                 **(target_options or {}).pop('''client_kwargs'''							,			{}						),  # To avoid issues if it was already passed.
             }							,			**(target_options or {})							,			)
         A_ :							str							=   os.path.basename(self.file.path.split('''::'''						)[0]						)
         A_ :							Dict							=   (
             self.compressed_name[: self.compressed_name.rindex('''.'''						)]
             if '''.''' in self.compressed_name
             else self.compressed_name
         )
         A_ :							Optional[int]							=   None



       @classmethod
       def 			_snake_case			( cls							,			_SCREAMING_SNAKE_CASE						)->Union[str, Any]:




         '''simple docstring'''





         return super()._strip_protocol(_SCREAMING_SNAKE_CASE						).lstrip('''/'''						)



       def 			_snake_case			( self						)->Optional[Any]:




         '''simple docstring'''





         if self.dir_cache is None:
           A_ :							Any							=   {**self.file.fs.info(self.file.path						), '''name''': self.uncompressed_name}
           A_ :							List[Any]							=   {f['''name''']: f}



       def 			_snake_case			( self							,			_SCREAMING_SNAKE_CASE						)->Any:




         '''simple docstring'''





         return self.file.open().read()







       def 			_snake_case			( self							,			_SCREAMING_SNAKE_CASE							,			_SCREAMING_SNAKE_CASE = "rb"							,			_SCREAMING_SNAKE_CASE=None							,			_SCREAMING_SNAKE_CASE=True							,			_SCREAMING_SNAKE_CASE=None							,			**_SCREAMING_SNAKE_CASE							,			)->Union[str, Any]:




         '''simple docstring'''





         A_ :							Optional[Any]							=   self._strip_protocol(_SCREAMING_SNAKE_CASE						)
         if mode != "rb":
           raise ValueError(F'''Tried to read with mode {mode} on file {self.file.path} opened with mode \'rb\''''						)
         return self.file.open()






class     _lowerCamelCase					(   A__     ):




       """simple docstring"""
       snake_case    =       "bz2"
       snake_case    =       "bz2"
       snake_case    =       ".bz2"






class     _lowerCamelCase					(   A__     ):




       """simple docstring"""
       snake_case    =       "gzip"
       snake_case    =       "gzip"
       snake_case    =       ".gz"






class     _lowerCamelCase					(   A__     ):




       """simple docstring"""
       snake_case    =       "lz4"
       snake_case    =       "lz4"
       snake_case    =       ".lz4"






class     _lowerCamelCase					(   A__     ):




       """simple docstring"""
       snake_case    =       "xz"
       snake_case    =       "xz"
       snake_case    =       ".xz"






class     _lowerCamelCase					(   A__     ):




       """simple docstring"""
       snake_case    =       "zstd"
       snake_case    =       "zstd"
       snake_case    =       ".zst"



       def __init__( self							,			_SCREAMING_SNAKE_CASE							,			_SCREAMING_SNAKE_CASE = "rb"							,			_SCREAMING_SNAKE_CASE = None							,			_SCREAMING_SNAKE_CASE = None							,			_SCREAMING_SNAKE_CASE = DEFAULT_BLOCK_SIZE							,			**_SCREAMING_SNAKE_CASE							,			)->List[Any]:




         '''simple docstring'''





         super().__init__(
             fo=_SCREAMING_SNAKE_CASE							,			mode=_SCREAMING_SNAKE_CASE							,			target_protocol=_SCREAMING_SNAKE_CASE							,			target_options=_SCREAMING_SNAKE_CASE							,			block_size=_SCREAMING_SNAKE_CASE							,			**_SCREAMING_SNAKE_CASE							,			)
         # We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
         #
         # File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
         #     out.close = close
         # AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
         #
         # see https://github.com/intake/filesystem_spec/issues/725
         A_ :							List[Any]							=   self.file.__enter__






         class     _lowerCamelCase					:




                """simple docstring"""



                def __init__( self							,			_SCREAMING_SNAKE_CASE						)->Tuple:




                  '''simple docstring'''





                  A_ :							str							=   file_



                def __enter__( self						)->List[Any]:




                  '''simple docstring'''





                  self._file.__enter__()
                  return self



                def __exit__( self							,			*_SCREAMING_SNAKE_CASE							,			**_SCREAMING_SNAKE_CASE						)->Optional[int]:




                  '''simple docstring'''





                  self._file.__exit__(*_SCREAMING_SNAKE_CASE							,			**_SCREAMING_SNAKE_CASE						)



                def __iter__( self						)->str:




                  '''simple docstring'''





                  return iter(self._file						)



                def 			_snake_case			( self						)->Any:




                  '''simple docstring'''





                  return next(self._file						)







                def __getattr__( self							,			_SCREAMING_SNAKE_CASE						)->int:




                  '''simple docstring'''





                  return getattr(self._file							,			_SCREAMING_SNAKE_CASE						)

         def fixed_enter(*_SCREAMING_SNAKE_CASE							,			**_SCREAMING_SNAKE_CASE						):
           return WrappedFile(_enter(*_SCREAMING_SNAKE_CASE							,			**_SCREAMING_SNAKE_CASE						)						)

         A_ :							int							=   fixed_enter












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   (			A__      ):

				"""simple docstring"""




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

								'''simple docstring'''
								lowercase__  					=     EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny''',    '''prajjwal1/bert-tiny'''    )
								lowercase__  					=     BertTokenizer.from_pretrained('''bert-base-uncased'''    )

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

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

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

								lowercase__  					=     4

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

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

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

												return batch

								def _compute_metrics(lowerCamelCase		:       Any    ):
												lowercase__  					=     pred.label_ids
												lowercase__  					=     pred.predictions

												# all unnecessary tokens are removed
												lowercase__  					=     tokenizer.batch_decode(lowerCamelCase,    skip_special_tokens=lowerCamelCase    )
												lowercase__  					=     tokenizer.batch_decode(lowerCamelCase,    skip_special_tokens=lowerCamelCase    )

												lowercase__  					=     sum([int(pred_str[i] == label_str[i]    ) for i in range(len(lowerCamelCase    )    )]    ) / len(lowerCamelCase    )

												return {"accuracy": accuracy}

								# map train dataset
								lowercase__  					=     train_dataset.map(
								    _map_to_encoder_decoder_inputs,    batched=lowerCamelCase,    batch_size=lowerCamelCase,    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
								lowercase__  					=     val_dataset.map(
								    _map_to_encoder_decoder_inputs,    batched=lowerCamelCase,    batch_size=lowerCamelCase,    remove_columns=['''article''', '''highlights'''],    )
								val_dataset.set_format(
								    type='''torch''',    columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''],    )

								lowercase__  					=     self.get_auto_remove_tmp_dir()

								lowercase__  					=     SeqaSeqTrainingArguments(
								    output_dir=lowerCamelCase,    per_device_train_batch_size=lowerCamelCase,    per_device_eval_batch_size=lowerCamelCase,    predict_with_generate=lowerCamelCase,    evaluation_strategy='''steps''',    do_train=lowerCamelCase,    do_eval=lowerCamelCase,    warmup_steps=0,    eval_steps=2,    logging_steps=2,    )

								# instantiate trainer
								lowercase__  					=     SeqaSeqTrainer(
								    model=lowerCamelCase,    args=lowerCamelCase,    compute_metrics=_compute_metrics,    train_dataset=lowerCamelCase,    eval_dataset=lowerCamelCase,    tokenizer=lowerCamelCase,    )

								# start training
								trainer.train()




