






import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
							    SqueezeBertForMaskedLM,
							    SqueezeBertForMultipleChoice,
							    SqueezeBertForQuestionAnswering,
							    SqueezeBertForSequenceClassification,
							    SqueezeBertForTokenClassification,
							    SqueezeBertModel,
							)


class 	__SCREAMING_SNAKE_CASE(						a_						):
							def __init__(  self:   Any							,						UpperCamelCase:   Optional[int]							,						UpperCamelCase:   List[Any]=13							,						UpperCamelCase:   List[str]=7							,						UpperCamelCase:   Union[str, Any]=True							,						UpperCamelCase:   Union[str, Any]=True							,						UpperCamelCase:   Union[str, Any]=False							,						UpperCamelCase:   Optional[int]=True							,						UpperCamelCase:   int=99							,						UpperCamelCase:   int=32							,						UpperCamelCase:   List[Any]=5							,						UpperCamelCase:   Dict=4							,						UpperCamelCase:   Tuple=64							,						UpperCamelCase:   Union[str, Any]="gelu"							,						UpperCamelCase:   Tuple=0.1							,						UpperCamelCase:   str=0.1							,						UpperCamelCase:   Optional[int]=5_12							,						UpperCamelCase:   Dict=16							,						UpperCamelCase:   List[str]=2							,						UpperCamelCase:   Optional[Any]=0.02							,						UpperCamelCase:   List[Any]=3							,						UpperCamelCase:   List[str]=4							,						UpperCamelCase:   Any=None							,						UpperCamelCase:   Dict=2							,						UpperCamelCase:   int=2							,						UpperCamelCase:   Tuple=2							,						UpperCamelCase:   Union[str, Any]=2							,						UpperCamelCase:   List[str]=4							,						UpperCamelCase:   Optional[Any]=1							,						)		->		Tuple:
											snake_case__						=  parent
											snake_case__						=  batch_size
											snake_case__						=  seq_length
											snake_case__						=  is_training
											snake_case__						=  use_input_mask
											snake_case__						=  use_token_type_ids
											snake_case__						=  use_labels
											snake_case__						=  vocab_size
											snake_case__						=  hidden_size
											snake_case__						=  num_hidden_layers
											snake_case__						=  num_attention_heads
											snake_case__						=  intermediate_size
											snake_case__						=  hidden_act
											snake_case__						=  hidden_dropout_prob
											snake_case__						=  attention_probs_dropout_prob
											snake_case__						=  max_position_embeddings
											snake_case__						=  type_vocab_size
											snake_case__						=  type_sequence_label_size
											snake_case__						=  initializer_range
											snake_case__						=  num_labels
											snake_case__						=  num_choices
											snake_case__						=  scope
											snake_case__						=  q_groups
											snake_case__						=  k_groups
											snake_case__						=  v_groups
											snake_case__						=  post_attention_groups
											snake_case__						=  intermediate_groups
											snake_case__						=  output_groups
							def lowerCAmelCase_			(  self:   int    )		->		Dict:
											snake_case__						=  ids_tensor([self.batch_size, self.seq_length]							,						self.vocab_size    )

											snake_case__						=  None
											if self.use_input_mask:
															snake_case__						=  random_attention_mask([self.batch_size, self.seq_length]    )

											snake_case__						=  None
											snake_case__						=  None
											snake_case__						=  None
											if self.use_labels:
															snake_case__						=  ids_tensor([self.batch_size]							,						self.type_sequence_label_size    )
															snake_case__						=  ids_tensor([self.batch_size, self.seq_length]							,						self.num_labels    )
															snake_case__						=  ids_tensor([self.batch_size]							,						self.num_choices    )

											snake_case__						=  self.get_config()

											return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
							def lowerCAmelCase_			(  self:   Union[str, Any]    )		->		Any:
											return SqueezeBertConfig(
											    embedding_size=self.hidden_size							,						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							,						attention_probs_dropout_prob=self.hidden_dropout_prob							,						attention_dropout=self.attention_probs_dropout_prob							,						max_position_embeddings=self.max_position_embeddings							,						initializer_range=self.initializer_range							,						q_groups=self.q_groups							,						k_groups=self.k_groups							,						v_groups=self.v_groups							,						post_attention_groups=self.post_attention_groups							,						intermediate_groups=self.intermediate_groups							,						output_groups=self.output_groups							,						)
							def lowerCAmelCase_			(  self:   List[str]							,						UpperCamelCase:   List[str]							,						UpperCamelCase:   Optional[int]							,						UpperCamelCase:   List[Any]							,						UpperCamelCase:   Any							,						UpperCamelCase:   Any							,						UpperCamelCase:   Any    )		->		List[Any]:
											snake_case__						=  SqueezeBertModel(config=lowerCamelCase__    )
											model.to(lowerCamelCase__    )
											model.eval()
											snake_case__						=  model(lowerCamelCase__							,						lowerCamelCase__    )
											snake_case__						=  model(lowerCamelCase__    )
											self.parent.assertEqual(result.last_hidden_state.shape							,						(self.batch_size, self.seq_length, self.hidden_size)    )
							def lowerCAmelCase_			(  self:   Tuple							,						UpperCamelCase:   Union[str, Any]							,						UpperCamelCase:   int							,						UpperCamelCase:   Dict							,						UpperCamelCase:   Optional[int]							,						UpperCamelCase:   Optional[Any]							,						UpperCamelCase:   Union[str, Any]    )		->		List[Any]:
											snake_case__						=  SqueezeBertForMaskedLM(config=lowerCamelCase__    )
											model.to(lowerCamelCase__    )
											model.eval()
											snake_case__						=  model(lowerCamelCase__							,						attention_mask=lowerCamelCase__							,						labels=lowerCamelCase__    )
											self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.seq_length, self.vocab_size)    )
							def lowerCAmelCase_			(  self:   Union[str, Any]							,						UpperCamelCase:   Any							,						UpperCamelCase:   int							,						UpperCamelCase:   int							,						UpperCamelCase:   List[str]							,						UpperCamelCase:   List[str]							,						UpperCamelCase:   Dict    )		->		str:
											snake_case__						=  SqueezeBertForQuestionAnswering(config=lowerCamelCase__    )
											model.to(lowerCamelCase__    )
											model.eval()
											snake_case__						=  model(
											    lowerCamelCase__							,						attention_mask=lowerCamelCase__							,						start_positions=lowerCamelCase__							,						end_positions=lowerCamelCase__    )
											self.parent.assertEqual(result.start_logits.shape							,						(self.batch_size, self.seq_length)    )
											self.parent.assertEqual(result.end_logits.shape							,						(self.batch_size, self.seq_length)    )
							def lowerCAmelCase_			(  self:   Any							,						UpperCamelCase:   List[Any]							,						UpperCamelCase:   Tuple							,						UpperCamelCase:   Union[str, Any]							,						UpperCamelCase:   List[str]							,						UpperCamelCase:   Union[str, Any]							,						UpperCamelCase:   int    )		->		List[str]:
											snake_case__						=  self.num_labels
											snake_case__						=  SqueezeBertForSequenceClassification(lowerCamelCase__    )
											model.to(lowerCamelCase__    )
											model.eval()
											snake_case__						=  model(lowerCamelCase__							,						attention_mask=lowerCamelCase__							,						labels=lowerCamelCase__    )
											self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.num_labels)    )
							def lowerCAmelCase_			(  self:   str							,						UpperCamelCase:   List[str]							,						UpperCamelCase:   List[str]							,						UpperCamelCase:   str							,						UpperCamelCase:   Any							,						UpperCamelCase:   Optional[Any]							,						UpperCamelCase:   List[Any]    )		->		Dict:
											snake_case__						=  self.num_labels
											snake_case__						=  SqueezeBertForTokenClassification(config=lowerCamelCase__    )
											model.to(lowerCamelCase__    )
											model.eval()

											snake_case__						=  model(lowerCamelCase__							,						attention_mask=lowerCamelCase__							,						labels=lowerCamelCase__    )
											self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.seq_length, self.num_labels)    )
							def lowerCAmelCase_			(  self:   Dict							,						UpperCamelCase:   int							,						UpperCamelCase:   Tuple							,						UpperCamelCase:   Any							,						UpperCamelCase:   Dict							,						UpperCamelCase:   Dict							,						UpperCamelCase:   List[str]    )		->		Tuple:
											snake_case__						=  self.num_choices
											snake_case__						=  SqueezeBertForMultipleChoice(config=lowerCamelCase__    )
											model.to(lowerCamelCase__    )
											model.eval()
											snake_case__						=  input_ids.unsqueeze(1    ).expand(-1							,						self.num_choices							,						-1    ).contiguous()
											snake_case__						=  input_mask.unsqueeze(1    ).expand(-1							,						self.num_choices							,						-1    ).contiguous()
											snake_case__						=  model(
											    lowerCamelCase__							,						attention_mask=lowerCamelCase__							,						labels=lowerCamelCase__							,						)
											self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.num_choices)    )


							def lowerCAmelCase_			(  self:   List[str]    )		->		Dict:
											snake_case__						=  self.prepare_config_and_inputs()
											(snake_case__)						=  config_and_inputs
											snake_case__						=  {'input_ids': input_ids, 'attention_mask': input_mask}
											return config, inputs_dict


@require_torch
class 	__SCREAMING_SNAKE_CASE(						a_     ,	a_     ,	unittest.TestCase						):
							_UpperCAmelCase			     =							(
							    (
							        SqueezeBertModel,
							        SqueezeBertForMaskedLM,
							        SqueezeBertForMultipleChoice,
							        SqueezeBertForQuestionAnswering,
							        SqueezeBertForSequenceClassification,
							        SqueezeBertForTokenClassification,
							    )
							    if is_torch_available()
							    else None
							)
							_UpperCAmelCase			     =							(
							    {
							        "feature-extraction": SqueezeBertModel,
							        "fill-mask": SqueezeBertForMaskedLM,
							        "question-answering": SqueezeBertForQuestionAnswering,
							        "text-classification": SqueezeBertForSequenceClassification,
							        "token-classification": SqueezeBertForTokenClassification,
							        "zero-shot": SqueezeBertForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							_UpperCAmelCase			     =							False
							_UpperCAmelCase			     =							True
							_UpperCAmelCase			     =							False
							def lowerCAmelCase_			(  self:   Any    )		->		Dict:
											snake_case__						=  SqueezeBertModelTester(self    )
											snake_case__						=  ConfigTester(self							,						config_class=lowerCamelCase__							,						dim=37    )
							def lowerCAmelCase_			(  self:   str    )		->		Optional[Any]:
											self.config_tester.run_common_tests()
							def lowerCAmelCase_			(  self:   Optional[int]    )		->		Union[str, Any]:
											snake_case__						=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_squeezebert_model(*lowerCamelCase__    )
							def lowerCAmelCase_			(  self:   int    )		->		List[str]:
											snake_case__						=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_squeezebert_for_masked_lm(*lowerCamelCase__    )
							def lowerCAmelCase_			(  self:   Union[str, Any]    )		->		Optional[int]:
											snake_case__						=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_squeezebert_for_question_answering(*lowerCamelCase__    )
							def lowerCAmelCase_			(  self:   List[Any]    )		->		Optional[Any]:
											snake_case__						=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_squeezebert_for_sequence_classification(*lowerCamelCase__    )
							def lowerCAmelCase_			(  self:   Optional[Any]    )		->		Dict:
											snake_case__						=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_squeezebert_for_token_classification(*lowerCamelCase__    )
							def lowerCAmelCase_			(  self:   List[str]    )		->		List[Any]:
											snake_case__						=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_squeezebert_for_multiple_choice(*lowerCamelCase__    )


							@slow
							def lowerCAmelCase_			(  self:   Optional[Any]    )		->		str:
											for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case__						=  SqueezeBertModel.from_pretrained(lowerCamelCase__    )
															self.assertIsNotNone(lowerCamelCase__    )


@require_sentencepiece
@require_tokenizers
@require_torch
class 	__SCREAMING_SNAKE_CASE(						unittest.TestCase						):
							@slow
							def lowerCAmelCase_			(  self:   Optional[int]    )		->		int:
											snake_case__						=  SqueezeBertForSequenceClassification.from_pretrained('squeezebert/squeezebert-mnli'    )

											snake_case__						=  torch.tensor([[1, 2_94_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 13, 15_88, 2]]    )
											snake_case__						=  model(lowerCamelCase__    )[0]
											snake_case__						=  torch.Size((1, 3)    )
											self.assertEqual(output.shape							,						lowerCamelCase__    )
											snake_case__						=  torch.tensor([[0.6_401, -0.0_349, -0.6_041]]    )
											self.assertTrue(torch.allclose(lowerCamelCase__							,						lowerCamelCase__							,						atol=1e-4    )    )









'''simple docstring'''
from pathlib import Path

import fire






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


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


