




"""simple docstring"""






import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
			from transformers.models.bert.modeling_flax_bert import (
			    FlaxBertForMaskedLM,
			    FlaxBertForMultipleChoice,
			    FlaxBertForNextSentencePrediction,
			    FlaxBertForPreTraining,
			    FlaxBertForQuestionAnswering,
			    FlaxBertForSequenceClassification,
			    FlaxBertForTokenClassification,
			    FlaxBertModel,
			)






class lowerCAmelCase__			(  unittest.TestCase		):






			'''simple docstring'''



			def __init__(						self						,   lowercase						,   lowercase=13						,   lowercase=7						,   lowercase=True						,   lowercase=True						,   lowercase=True						,   lowercase=True						,   lowercase=99						,   lowercase=32						,   lowercase=5						,   lowercase=4						,   lowercase=37						,   lowercase="gelu"						,   lowercase=0.1						,   lowercase=0.1						,   lowercase=512						,   lowercase=16						,   lowercase=2						,   lowercase=0.02						,   lowercase=4						,   ):
							_lowerCamelCase  :    Optional[int]					=					parent
							_lowerCamelCase  :    Optional[int]					=					batch_size
							_lowerCamelCase  :    Optional[Any]					=					seq_length
							_lowerCamelCase  :    List[str]					=					is_training
							_lowerCamelCase  :    Optional[int]					=					use_attention_mask
							_lowerCamelCase  :    Any					=					use_token_type_ids
							_lowerCamelCase  :    Optional[int]					=					use_labels
							_lowerCamelCase  :    Optional[int]					=					vocab_size
							_lowerCamelCase  :    str					=					hidden_size
							_lowerCamelCase  :    int					=					num_hidden_layers
							_lowerCamelCase  :    List[Any]					=					num_attention_heads
							_lowerCamelCase  :    int					=					intermediate_size
							_lowerCamelCase  :    Optional[Any]					=					hidden_act
							_lowerCamelCase  :    Optional[Any]					=					hidden_dropout_prob
							_lowerCamelCase  :    List[Any]					=					attention_probs_dropout_prob
							_lowerCamelCase  :    Optional[int]					=					max_position_embeddings
							_lowerCamelCase  :    Union[str, Any]					=					type_vocab_size
							_lowerCamelCase  :    str					=					type_sequence_label_size
							_lowerCamelCase  :    int					=					initializer_range
							_lowerCamelCase  :    Tuple					=					num_choices
			def       A_					(						self						):
							_lowerCamelCase  :    List[Any]					=					ids_tensor([self.batch_size, self.seq_length]						,   self.vocab_size						)

							_lowerCamelCase  :    Optional[int]					=					None
							if self.use_attention_mask:
											_lowerCamelCase  :    List[str]					=					random_attention_mask([self.batch_size, self.seq_length]						)

							_lowerCamelCase  :    Optional[Any]					=					None
							if self.use_token_type_ids:
											_lowerCamelCase  :    int					=					ids_tensor([self.batch_size, self.seq_length]						,   self.type_vocab_size						)

							_lowerCamelCase  :    int					=					BertConfig(
							    vocab_size=self.vocab_size						,   hidden_size=self.hidden_size						,   num_hidden_layers=self.num_hidden_layers						,   num_attention_heads=self.num_attention_heads						,   intermediate_size=self.intermediate_size						,   hidden_act=self.hidden_act						,   hidden_dropout_prob=self.hidden_dropout_prob						,   attention_probs_dropout_prob=self.attention_probs_dropout_prob						,   max_position_embeddings=self.max_position_embeddings						,   type_vocab_size=self.type_vocab_size						,   is_decoder=lowerCAmelCase_						,   initializer_range=self.initializer_range						,   )

							return config, input_ids, token_type_ids, attention_mask
			def       A_					(						self						):
							_lowerCamelCase  :    Optional[int]					=					self.prepare_config_and_inputs()
							_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase  :    List[Any]					=					config_and_inputs
							_lowerCamelCase  :    Dict					=					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
							return config, inputs_dict






			def       A_					(						self						):
							_lowerCamelCase  :    Union[str, Any]					=					self.prepare_config_and_inputs()
							_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase  :    Tuple					=					config_and_inputs

							_lowerCamelCase  :    Optional[Any]					=					True
							_lowerCamelCase  :    Optional[Any]					=					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
							_lowerCamelCase  :    Optional[Any]					=					ids_tensor([self.batch_size, self.seq_length]						,   vocab_size=2						)

							return (
							    config,
							    input_ids,
							    attention_mask,
							    encoder_hidden_states,
							    encoder_attention_mask,
							)






@require_flax
class lowerCAmelCase__			(  __a,			unittest.TestCase		):






			'''simple docstring'''



			lowerCamelCase__   =    True

			lowerCamelCase__   =    (
			    (
			        FlaxBertModel,
			        FlaxBertForPreTraining,
			        FlaxBertForMaskedLM,
			        FlaxBertForMultipleChoice,
			        FlaxBertForQuestionAnswering,
			        FlaxBertForNextSentencePrediction,
			        FlaxBertForSequenceClassification,
			        FlaxBertForTokenClassification,
			        FlaxBertForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)
			def       A_					(						self						):
							_lowerCamelCase  :    Tuple					=					FlaxBertModelTester(self						)






			@slow
			def       A_					(						self						):
							_lowerCamelCase  :    str					=					FlaxBertModel.from_pretrained('bert-base-cased'						)
							_lowerCamelCase  :    Union[str, Any]					=					model(np.ones((1, 1)						)						)
							self.assertIsNotNone(lowerCAmelCase_						)











"""simple docstring"""






from pathlib import Path

import fire







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):
				_lowerCamelCase  :    int					=					Path(lowercase__	)
				_lowerCamelCase  :    List[Any]					=					Path(lowercase__	)
				dest_dir.mkdir(exist_ok=lowercase__	)
				for path in src_dir.iterdir():
								_lowerCamelCase  :    str					=					[x.rstrip() for x in list(path.open().readlines()	)][:n]
								_lowerCamelCase  :    Any					=					dest_dir.joinpath(path.name	)
								print(lowercase__	)
								dest_path.open('w'	).write('\n'.join(lowercase__	)	)


if __name__ == "__main__":
			fire.Fire(minify)