
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   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						def __init__(			self							:int		,    lowerCAmelCase__							:Optional[int]		,    lowerCAmelCase__							:int=13		,    lowerCAmelCase__							:Union[str, Any]=7		,    lowerCAmelCase__							:Dict=True		,    lowerCAmelCase__							:Dict=True		,    lowerCAmelCase__							:List[Any]=False		,    lowerCAmelCase__							:Dict=True		,    lowerCAmelCase__							:Optional[Any]=99		,    lowerCAmelCase__							:Optional[Any]=32		,    lowerCAmelCase__							:Dict=5		,    lowerCAmelCase__							:str=4		,    lowerCAmelCase__							:List[str]=64		,    lowerCAmelCase__							:Dict="gelu"		,    lowerCAmelCase__							:List[str]=0.1		,    lowerCAmelCase__							:int=0.1		,    lowerCAmelCase__							:Tuple=512		,    lowerCAmelCase__							:Dict=16		,    lowerCAmelCase__							:Tuple=2		,    lowerCAmelCase__							:Optional[Any]=0.02		,    lowerCAmelCase__							:str=3		,    lowerCAmelCase__							:List[str]=4		,    lowerCAmelCase__							:Optional[Any]=None		,    lowerCAmelCase__							:List[str]=2		,    lowerCAmelCase__							:int=2		,    lowerCAmelCase__							:List[str]=2		,    lowerCAmelCase__							:Dict=2		,    lowerCAmelCase__							:int=4		,    lowerCAmelCase__							:Optional[int]=1		,    )  ->Union[str, Any]:
										lowercase         =      parent
										lowercase         =      batch_size
										lowercase         =      seq_length
										lowercase         =      is_training
										lowercase         =      use_input_mask
										lowercase         =      use_token_type_ids
										lowercase         =      use_labels
										lowercase         =      vocab_size
										lowercase         =      hidden_size
										lowercase         =      num_hidden_layers
										lowercase         =      num_attention_heads
										lowercase         =      intermediate_size
										lowercase         =      hidden_act
										lowercase         =      hidden_dropout_prob
										lowercase         =      attention_probs_dropout_prob
										lowercase         =      max_position_embeddings
										lowercase         =      type_vocab_size
										lowercase         =      type_sequence_label_size
										lowercase         =      initializer_range
										lowercase         =      num_labels
										lowercase         =      num_choices
										lowercase         =      scope
										lowercase         =      q_groups
										lowercase         =      k_groups
										lowercase         =      v_groups
										lowercase         =      post_attention_groups
										lowercase         =      intermediate_groups
										lowercase         =      output_groups





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->Tuple:
										lowercase         =      ids_tensor([self.batch_size, self.seq_length]		,    self.vocab_size     )

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

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

										lowercase         =      self.get_config()

										return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels





						def        SCREAMING_SNAKE_CASE(			self							:Dict     )  ->str:
										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        SCREAMING_SNAKE_CASE(			self							:Optional[int]		,    lowerCAmelCase__							:Tuple		,    lowerCAmelCase__							:Tuple		,    lowerCAmelCase__							:Union[str, Any]		,    lowerCAmelCase__							:Union[str, Any]		,    lowerCAmelCase__							:Optional[int]		,    lowerCAmelCase__							:Tuple     )  ->Optional[Any]:
										lowercase         =      SqueezeBertModel(config=lowerCAmelCase__     )
										model.to(lowerCAmelCase__     )
										model.eval()
										lowercase         =      model(lowerCAmelCase__		,    lowerCAmelCase__     )
										lowercase         =      model(lowerCAmelCase__     )
										self.parent.assertEqual(result.last_hidden_state.shape		,    (self.batch_size, self.seq_length, self.hidden_size)     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]		,    lowerCAmelCase__							:str		,    lowerCAmelCase__							:str		,    lowerCAmelCase__							:Any		,    lowerCAmelCase__							:Dict		,    lowerCAmelCase__							:Optional[int]		,    lowerCAmelCase__							:int     )  ->List[str]:
										lowercase         =      SqueezeBertForMaskedLM(config=lowerCAmelCase__     )
										model.to(lowerCAmelCase__     )
										model.eval()
										lowercase         =      model(lowerCAmelCase__		,    attention_mask=lowerCAmelCase__		,    labels=lowerCAmelCase__     )
										self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.seq_length, self.vocab_size)     )





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    lowerCAmelCase__							:Union[str, Any]		,    lowerCAmelCase__							:List[str]		,    lowerCAmelCase__							:int		,    lowerCAmelCase__							:Union[str, Any]		,    lowerCAmelCase__							:Optional[Any]		,    lowerCAmelCase__							:int     )  ->int:
										lowercase         =      SqueezeBertForQuestionAnswering(config=lowerCAmelCase__     )
										model.to(lowerCAmelCase__     )
										model.eval()
										lowercase         =      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        SCREAMING_SNAKE_CASE(			self							:Dict		,    lowerCAmelCase__							:List[str]		,    lowerCAmelCase__							:Any		,    lowerCAmelCase__							:List[Any]		,    lowerCAmelCase__							:Any		,    lowerCAmelCase__							:List[Any]		,    lowerCAmelCase__							:Optional[Any]     )  ->Optional[Any]:
										lowercase         =      self.num_labels
										lowercase         =      SqueezeBertForSequenceClassification(lowerCAmelCase__     )
										model.to(lowerCAmelCase__     )
										model.eval()
										lowercase         =      model(lowerCAmelCase__		,    attention_mask=lowerCAmelCase__		,    labels=lowerCAmelCase__     )
										self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.num_labels)     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]		,    lowerCAmelCase__							:Dict		,    lowerCAmelCase__							:int		,    lowerCAmelCase__							:int		,    lowerCAmelCase__							:Union[str, Any]		,    lowerCAmelCase__							:Any		,    lowerCAmelCase__							:Optional[Any]     )  ->Optional[Any]:
										lowercase         =      self.num_labels
										lowercase         =      SqueezeBertForTokenClassification(config=lowerCAmelCase__     )
										model.to(lowerCAmelCase__     )
										model.eval()

										lowercase         =      model(lowerCAmelCase__		,    attention_mask=lowerCAmelCase__		,    labels=lowerCAmelCase__     )
										self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.seq_length, self.num_labels)     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]		,    lowerCAmelCase__							:Union[str, Any]		,    lowerCAmelCase__							:Dict		,    lowerCAmelCase__							:List[Any]		,    lowerCAmelCase__							:Dict		,    lowerCAmelCase__							:Any		,    lowerCAmelCase__							:Dict     )  ->List[str]:
										lowercase         =      self.num_choices
										lowercase         =      SqueezeBertForMultipleChoice(config=lowerCAmelCase__     )
										model.to(lowerCAmelCase__     )
										model.eval()
										lowercase         =      input_ids.unsqueeze(1     ).expand(-1		,    self.num_choices		,    -1     ).contiguous()
										lowercase         =      input_mask.unsqueeze(1     ).expand(-1		,    self.num_choices		,    -1     ).contiguous()
										lowercase         =      model(
										    lowerCAmelCase__		,    attention_mask=lowerCAmelCase__		,    labels=lowerCAmelCase__		,    )
										self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.num_choices)     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->int:
										lowercase         =      self.prepare_config_and_inputs()
										((lowercase)     ,	(lowercase)     ,	(lowercase)     ,	(lowercase)     ,	(lowercase)     ,	(lowercase))         =      config_and_inputs
										lowercase         =      {"input_ids": input_ids, "attention_mask": input_mask}
										return config, inputs_dict





@require_torch
class   UpperCamelCase_      (      __a		,    __a		,    unittest.TestCase		):







						'''simple docstring'''



						UpperCamelCase      :  Dict				   =					(
						    (
						        SqueezeBertModel,
						        SqueezeBertForMaskedLM,
						        SqueezeBertForMultipleChoice,
						        SqueezeBertForQuestionAnswering,
						        SqueezeBertForSequenceClassification,
						        SqueezeBertForTokenClassification,
						    )
						    if is_torch_available()
						    else None
						)
						UpperCamelCase      :  Tuple				   =					(
						    {
						        '''feature-extraction''': SqueezeBertModel,
						        '''fill-mask''': SqueezeBertForMaskedLM,
						        '''question-answering''': SqueezeBertForQuestionAnswering,
						        '''text-classification''': SqueezeBertForSequenceClassification,
						        '''token-classification''': SqueezeBertForTokenClassification,
						        '''zero-shot''': SqueezeBertForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						UpperCamelCase      :  str				   =					False
						UpperCamelCase      :  List[str]				   =					True
						UpperCamelCase      :  Tuple				   =					False





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->List[Any]:
										lowercase         =      SqueezeBertModelTester(self     )
										lowercase         =      ConfigTester(self		,    config_class=lowerCAmelCase__		,    dim=37     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->Union[str, Any]:
										self.config_tester.run_common_tests()





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->int:
										lowercase         =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_model(*lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->int:
										lowercase         =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_masked_lm(*lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->int:
										lowercase         =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_question_answering(*lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->Dict:
										lowercase         =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_sequence_classification(*lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->Any:
										lowercase         =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_token_classification(*lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->Tuple:
										lowercase         =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_multiple_choice(*lowerCAmelCase__     )





						@slow
						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->Any:
										for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														lowercase         =      SqueezeBertModel.from_pretrained(lowerCAmelCase__     )
														self.assertIsNotNone(lowerCAmelCase__     )





@require_sentencepiece
@require_tokenizers
@require_torch
class   UpperCamelCase_      (      unittest.TestCase		):







						'''simple docstring'''



						@slow
						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->int:
										lowercase         =      SqueezeBertForSequenceClassification.from_pretrained("squeezebert/squeezebert-mnli"     )

										lowercase         =      torch.tensor([[1, 29414, 232, 328, 740, 1140, 12695, 69, 13, 1588, 2]]     )
										lowercase         =      model(lowerCAmelCase__     )[0]
										lowercase         =      torch.Size((1, 3)     )
										self.assertEqual(output.shape		,    lowerCAmelCase__     )
										lowercase         =      torch.tensor([[0.64_01, -0.03_49, -0.60_41]]     )
										self.assertTrue(torch.allclose(lowerCAmelCase__		,    lowerCAmelCase__		,    atol=1E-4     )     )


from __future__ import annotations

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				if len(__magic_name__						) == 0:
								return []
				lowercase     ,	lowercase         =      min(__magic_name__						), max(__magic_name__						)
				lowercase         =      int(max_value - min_value						) + 1
				lowercase         =      [[] for _ in range(__magic_name__						)]

				for i in my_list:
								buckets[int(i - min_value						)].append(__magic_name__						)

				return [v for bucket in buckets for v in sorted(__magic_name__						)]


if __name__ == "__main__":
						from doctest import testmod

						testmod()
						assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
						assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]
