
"""simple docstring"""

import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							str							      =	""
      __SCREAMING_SNAKE_CASE   :							str							      =	(
          None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
      )
      __SCREAMING_SNAKE_CASE   :							str							      =	None  # compression type in fsspec. ex: "gzip"
      __SCREAMING_SNAKE_CASE   :							str							      =	None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz




      def __init__(							self   ,       lowercase_ = ""   ,       lowercase_ = None   ,       lowercase_ = None   ,       **lowercase_							)						->					str:
          super().__init__(self   ,       **lowercase_							)
          # always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
          UpperCAmelCase									=					fsspec.open(
              lowercase_   ,       mode='rb'   ,       protocol=lowercase_   ,       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 {})   ,       )
          UpperCAmelCase									=					os.path.basename(self.file.path.split('::'							)[0]							)
          UpperCAmelCase									=					(
              self.compressed_name[: self.compressed_name.rindex('.'							)]
              if '.' in self.compressed_name
              else self.compressed_name
          )
          UpperCAmelCase									=					None




      @classmethod
      def     a_      (							cls   ,       lowercase_							)						->					Union[str, Any]:
          # compressed file paths are always relative to the archive root
          return super()._strip_protocol(lowercase_							).lstrip('/'							)




      def     a_      (							self							)						->					int:
          if self.dir_cache is None:
              UpperCAmelCase									=					{**self.file.fs.info(self.file.path							), 'name': self.uncompressed_name}
              UpperCAmelCase									=					{f['name']: f}




      def     a_      (							self   ,       lowercase_							)						->					Any:
          return self.file.open().read()




      def     a_      (							self   ,       lowercase_   ,       lowercase_ = "rb"   ,       lowercase_=None   ,       lowercase_=True   ,       lowercase_=None   ,       **lowercase_   ,       )						->					Optional[Any]:
          UpperCAmelCase									=					self._strip_protocol(lowercase_							)
          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      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							List[Any]							      =	"bz2"
      __SCREAMING_SNAKE_CASE   :							Union[str, Any]							      =	"bz2"
      __SCREAMING_SNAKE_CASE   :							Union[str, Any]							      =	".bz2"

class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	"gzip"
      __SCREAMING_SNAKE_CASE   :							Any							      =	"gzip"
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	".gz"

class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	"lz4"
      __SCREAMING_SNAKE_CASE   :							List[Any]							      =	"lz4"
      __SCREAMING_SNAKE_CASE   :							Tuple							      =	".lz4"

class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	"xz"
      __SCREAMING_SNAKE_CASE   :							Union[str, Any]							      =	"xz"
      __SCREAMING_SNAKE_CASE   :							List[Any]							      =	".xz"

class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	"zstd"
      __SCREAMING_SNAKE_CASE   :							int							      =	"zstd"
      __SCREAMING_SNAKE_CASE   :							Tuple							      =	".zst"




      def __init__(							self   ,       lowercase_   ,       lowercase_ = "rb"   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = DEFAULT_BLOCK_SIZE   ,       **lowercase_   ,       )						->					str:
          super().__init__(
              fo=lowercase_   ,       mode=lowercase_   ,       target_protocol=lowercase_   ,       target_options=lowercase_   ,       block_size=lowercase_   ,       **lowercase_   ,       )
          # 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
          UpperCAmelCase									=					self.file.__enter__
          class      _UpperCAmelCase							:




                def __init__(							self   ,       lowercase_							)						->					List[Any]:
                    UpperCAmelCase									=					file_




                def __enter__(							self							)						->					List[str]:
                    self._file.__enter__()
                    return self




                def __exit__(							self   ,       *lowercase_   ,       **lowercase_							)						->					List[str]:
                    self._file.__exit__(*lowercase_   ,       **lowercase_							)




                def __iter__(							self							)						->					Optional[int]:
                    return iter(self._file							)




                def     a_      (							self							)						->					Tuple:
                    return next(self._file							)




                def __getattr__(							self   ,       lowercase_							)						->					Optional[int]:
                    return getattr(self._file   ,       lowercase_							)

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

          UpperCAmelCase									=					fixed_enter





"""simple docstring"""

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							(		SCREAMING_SNAKE_CASE_							):




      @slow
      @require_torch
      def     a_      (							self							)						->					List[Any]:
          UpperCAmelCase									=					EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'   ,       'prajjwal1/bert-tiny'							)
          UpperCAmelCase									=					BertTokenizer.from_pretrained('bert-base-uncased'							)

          UpperCAmelCase									=					bertabert.config.encoder.vocab_size
          UpperCAmelCase									=					tokenizer.sep_token_id
          UpperCAmelCase									=					tokenizer.cls_token_id
          UpperCAmelCase									=					1_2_8

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

          UpperCAmelCase									=					train_dataset.select(range(3_2							)							)
          UpperCAmelCase									=					val_dataset.select(range(1_6							)							)

          UpperCAmelCase									=					4

          def _map_to_encoder_decoder_inputs(lowercase_							):
              # Tokenizer will automatically set [BOS] <text> [EOS]
              UpperCAmelCase									=					tokenizer(batch['article']   ,       padding='max_length'   ,       truncation=lowercase_   ,       max_length=5_1_2							)
              UpperCAmelCase									=					tokenizer(batch['highlights']   ,       padding='max_length'   ,       truncation=lowercase_   ,       max_length=1_2_8							)
              UpperCAmelCase									=					inputs.input_ids
              UpperCAmelCase									=					inputs.attention_mask

              UpperCAmelCase									=					outputs.input_ids
              UpperCAmelCase									=					outputs.input_ids.copy()
              UpperCAmelCase									=					[
                  [-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
              ]
              UpperCAmelCase									=					outputs.attention_mask

              assert all(len(lowercase_							) == 5_1_2 for x in inputs.input_ids							)
              assert all(len(lowercase_							) == 1_2_8 for x in outputs.input_ids							)

              return batch

          def _compute_metrics(lowercase_							):
              UpperCAmelCase									=					pred.label_ids
              UpperCAmelCase									=					pred.predictions

              # all unnecessary tokens are removed
              UpperCAmelCase									=					tokenizer.batch_decode(lowercase_   ,       skip_special_tokens=lowercase_							)
              UpperCAmelCase									=					tokenizer.batch_decode(lowercase_   ,       skip_special_tokens=lowercase_							)

              UpperCAmelCase									=					sum([int(pred_str[i] == label_str[i]							) for i in range(len(lowercase_							)							)]							) / len(lowercase_							)

              return {"accuracy": accuracy}

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

          UpperCAmelCase									=					self.get_auto_remove_tmp_dir()

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

          # instantiate trainer
          UpperCAmelCase									=					SeqaSeqTrainer(
              model=lowercase_   ,       args=lowercase_   ,       compute_metrics=_compute_metrics   ,       train_dataset=lowercase_   ,       eval_dataset=lowercase_   ,       tokenizer=lowercase_   ,       )

          # start training
          trainer.train()



