






'''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 _lowercase	(				_lowercase		):




							@slow
							@require_torch
							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														lowerCamelCase__					:	List[Any]         =       EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny"""			,							"""prajjwal1/bert-tiny"""     )
														lowerCamelCase__					:	str         =       BertTokenizer.from_pretrained("""bert-base-uncased"""     )

														lowerCamelCase__					:	int         =       bertabert.config.encoder.vocab_size
														lowerCamelCase__					:	int         =       tokenizer.sep_token_id
														lowerCamelCase__					:	List[Any]         =       tokenizer.cls_token_id
														lowerCamelCase__					:	List[Any]         =       128

														lowerCamelCase__					:	Optional[Any]         =       datasets.load_dataset("""cnn_dailymail"""			,							"""3.0.0"""			,							split="""train[:1%]"""     )
														lowerCamelCase__					:	Optional[int]         =       datasets.load_dataset("""cnn_dailymail"""			,							"""3.0.0"""			,							split="""validation[:1%]"""     )

														lowerCamelCase__					:	Dict         =       train_dataset.select(range(32     )     )
														lowerCamelCase__					:	Any         =       val_dataset.select(range(16     )     )

														lowerCamelCase__					:	List[Any]         =       4

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

																					lowerCamelCase__					:	str         =       outputs.input_ids
																					lowerCamelCase__					:	Dict         =       outputs.input_ids.copy()
																					lowerCamelCase__					:	Optional[int]         =       [
																					    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
																					]
																					lowerCamelCase__					:	Optional[Any]         =       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__:				Tuple     ):
																					lowerCamelCase__					:	Any         =       pred.label_ids
																					lowerCamelCase__					:	Tuple         =       pred.predictions

																					# all unnecessary tokens are removed
																					lowerCamelCase__					:	Optional[int]         =       tokenizer.batch_decode(UpperCamelCase__			,							skip_special_tokens=UpperCamelCase__     )
																					lowerCamelCase__					:	Optional[int]         =       tokenizer.batch_decode(UpperCamelCase__			,							skip_special_tokens=UpperCamelCase__     )

																					lowerCamelCase__					:	Any         =       sum([int(pred_str[i] == label_str[i]     ) for i in range(len(UpperCamelCase__     )     )]     ) / len(UpperCamelCase__     )

																					return {"accuracy": accuracy}

														# map train dataset
														lowerCamelCase__					:	Tuple         =       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
														lowerCamelCase__					:	List[Any]         =       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"""]			,							)

														lowerCamelCase__					:	Any         =       self.get_auto_remove_tmp_dir()

														lowerCamelCase__					:	List[str]         =       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
														lowerCamelCase__					:	int         =       SeqaSeqTrainer(
														    model=UpperCamelCase__			,							args=UpperCamelCase__			,							compute_metrics=_compute_metrics			,							train_dataset=UpperCamelCase__			,							eval_dataset=UpperCamelCase__			,							tokenizer=UpperCamelCase__			,							)

														# start training
														trainer.train()








import os

import pytest

from transformers.dynamic_module_utils import get_imports


snake_case_				=			'\nimport os\n'

snake_case_				=			'\ndef foo():\n    import os\n    return False\n'

snake_case_				=			'\ndef foo():\n    def bar():\n        if True:\n            import os\n        return False\n    return bar()\n'

snake_case_				=			'\nimport os\n\ntry:\n    import bar\nexcept ImportError:\n    raise ValueError()\n'

snake_case_				=			'\nimport os\n\ndef foo():\n    try:\n        import bar\n    except ImportError:\n        raise ValueError()\n'

snake_case_				=			'\nimport os\n\ntry:\n    import bar\nexcept (ImportError, AttributeError):\n    raise ValueError()\n'

snake_case_				=			'\nimport os\n\ntry:\n    import bar\nexcept ImportError as e:\n    raise ValueError()\n'

snake_case_				=			'\nimport os\n\ntry:\n    import bar\nexcept:\n    raise ValueError()\n'

snake_case_				=			'\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    raise ValueError()\n'

snake_case_				=			'\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    x = 1\n    raise ValueError()\n'

snake_case_				=			[
    TOP_LEVEL_IMPORT,
    IMPORT_IN_FUNCTION,
    DEEPLY_NESTED_IMPORT,
    TOP_LEVEL_TRY_IMPORT,
    GENERIC_EXCEPT_IMPORT,
    MULTILINE_TRY_IMPORT,
    MULTILINE_BOTH_IMPORT,
    MULTIPLE_EXCEPTS_IMPORT,
    EXCEPT_AS_IMPORT,
    TRY_IMPORT_IN_FUNCTION,
]






@pytest.mark.parametrize('''case'''       ,      snake_case_       )
def lowerCamelCase__    (							snake_case_	:  str       ,      snake_case_	:  Optional[int]       )      ->				Dict:
   __snake_case							       =   os.path.join(snake_case_       ,      '''test_file.py'''       )
   with open(snake_case_       ,      '''w'''       ) as _tmp_file:
      _tmp_file.write(snake_case_       )

   __snake_case							       =   get_imports(snake_case_       )
   assert parsed_imports == ["os"]

