






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     lowerCamelCase_					(   lowerCamelCase			):







				def __init__(    self							,       __lowerCAmelCase							,       __lowerCAmelCase=1_3							,       __lowerCAmelCase=7							,       __lowerCAmelCase=True							,       __lowerCAmelCase=True							,       __lowerCAmelCase=False							,       __lowerCAmelCase=True							,       __lowerCAmelCase=9_9							,       __lowerCAmelCase=3_2							,       __lowerCAmelCase=5							,       __lowerCAmelCase=4							,       __lowerCAmelCase=6_4							,       __lowerCAmelCase="gelu"							,       __lowerCAmelCase=0.1							,       __lowerCAmelCase=0.1							,       __lowerCAmelCase=5_1_2							,       __lowerCAmelCase=1_6							,       __lowerCAmelCase=2							,       __lowerCAmelCase=0.02							,       __lowerCAmelCase=3							,       __lowerCAmelCase=4							,       __lowerCAmelCase=None							,       __lowerCAmelCase=2							,       __lowerCAmelCase=2							,       __lowerCAmelCase=2							,       __lowerCAmelCase=2							,       __lowerCAmelCase=4							,       __lowerCAmelCase=1							,       ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]   =   parent
								__magic_name__		:int   =   batch_size
								__magic_name__		:int   =   seq_length
								__magic_name__		:List[Any]   =   is_training
								__magic_name__		:Any   =   use_input_mask
								__magic_name__		:List[Any]   =   use_token_type_ids
								__magic_name__		:Any   =   use_labels
								__magic_name__		:str   =   vocab_size
								__magic_name__		:Tuple   =   hidden_size
								__magic_name__		:List[str]   =   num_hidden_layers
								__magic_name__		:Dict   =   num_attention_heads
								__magic_name__		:str   =   intermediate_size
								__magic_name__		:Optional[int]   =   hidden_act
								__magic_name__		:List[Any]   =   hidden_dropout_prob
								__magic_name__		:List[str]   =   attention_probs_dropout_prob
								__magic_name__		:List[str]   =   max_position_embeddings
								__magic_name__		:Union[str, Any]   =   type_vocab_size
								__magic_name__		:int   =   type_sequence_label_size
								__magic_name__		:Optional[Any]   =   initializer_range
								__magic_name__		:int   =   num_labels
								__magic_name__		:Tuple   =   num_choices
								__magic_name__		:Optional[int]   =   scope
								__magic_name__		:Tuple   =   q_groups
								__magic_name__		:List[str]   =   k_groups
								__magic_name__		:str   =   v_groups
								__magic_name__		:Optional[Any]   =   post_attention_groups
								__magic_name__		:List[str]   =   intermediate_groups
								__magic_name__		:int   =   output_groups







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Tuple   =   ids_tensor([self.batch_size, self.seq_length]							,       self.vocab_size  )

								__magic_name__		:List[Any]   =   None
								if self.use_input_mask:
												__magic_name__		:Dict   =   random_attention_mask([self.batch_size, self.seq_length]  )

								__magic_name__		:int   =   None
								__magic_name__		:Optional[Any]   =   None
								__magic_name__		:Union[str, Any]   =   None
								if self.use_labels:
												__magic_name__		:Optional[int]   =   ids_tensor([self.batch_size]							,       self.type_sequence_label_size  )
												__magic_name__		:List[str]   =   ids_tensor([self.batch_size, self.seq_length]							,       self.num_labels  )
												__magic_name__		:Dict   =   ids_tensor([self.batch_size]							,       self.num_choices  )

								__magic_name__		:Optional[int]   =   self.get_config()

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







				def       A						(    self  ):
								"""simple docstring"""
								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       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   SqueezeBertModel(config=__lowerCAmelCase  )
								model.to(__lowerCAmelCase  )
								model.eval()
								__magic_name__		:str   =   model(__lowerCAmelCase							,       __lowerCAmelCase  )
								__magic_name__		:List[Any]   =   model(__lowerCAmelCase  )
								self.parent.assertEqual(result.last_hidden_state.shape							,       (self.batch_size, self.seq_length, self.hidden_size)  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:str   =   SqueezeBertForMaskedLM(config=__lowerCAmelCase  )
								model.to(__lowerCAmelCase  )
								model.eval()
								__magic_name__		:Optional[int]   =   model(__lowerCAmelCase							,       attention_mask=__lowerCAmelCase							,       labels=__lowerCAmelCase  )
								self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.seq_length, self.vocab_size)  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   SqueezeBertForQuestionAnswering(config=__lowerCAmelCase  )
								model.to(__lowerCAmelCase  )
								model.eval()
								__magic_name__		:Union[str, Any]   =   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       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   self.num_labels
								__magic_name__		:int   =   SqueezeBertForSequenceClassification(__lowerCAmelCase  )
								model.to(__lowerCAmelCase  )
								model.eval()
								__magic_name__		:Optional[Any]   =   model(__lowerCAmelCase							,       attention_mask=__lowerCAmelCase							,       labels=__lowerCAmelCase  )
								self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.num_labels)  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   self.num_labels
								__magic_name__		:Union[str, Any]   =   SqueezeBertForTokenClassification(config=__lowerCAmelCase  )
								model.to(__lowerCAmelCase  )
								model.eval()

								__magic_name__		:Tuple   =   model(__lowerCAmelCase							,       attention_mask=__lowerCAmelCase							,       labels=__lowerCAmelCase  )
								self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.seq_length, self.num_labels)  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:str   =   self.num_choices
								__magic_name__		:Dict   =   SqueezeBertForMultipleChoice(config=__lowerCAmelCase  )
								model.to(__lowerCAmelCase  )
								model.eval()
								__magic_name__		:Any   =   input_ids.unsqueeze(1  ).expand(-1							,       self.num_choices							,       -1  ).contiguous()
								__magic_name__		:Tuple   =   input_mask.unsqueeze(1  ).expand(-1							,       self.num_choices							,       -1  ).contiguous()
								__magic_name__		:Optional[int]   =   model(
								    __lowerCAmelCase							,       attention_mask=__lowerCAmelCase							,       labels=__lowerCAmelCase							,       )
								self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.num_choices)  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   self.prepare_config_and_inputs()
								((__magic_name__)  ,					(__magic_name__)  ,					(__magic_name__)  ,					(__magic_name__)  ,					(__magic_name__)  ,					(__magic_name__))		:Tuple   =   config_and_inputs
								__magic_name__		:Dict   =   {'''input_ids''': input_ids, '''attention_mask''': input_mask}
								return config, inputs_dict







@require_torch
class     lowerCamelCase_					(   lowerCamelCase      ,					lowerCamelCase      ,					unittest.TestCase			):
				a__       							=	(
				    (
				        SqueezeBertModel,
				        SqueezeBertForMaskedLM,
				        SqueezeBertForMultipleChoice,
				        SqueezeBertForQuestionAnswering,
				        SqueezeBertForSequenceClassification,
				        SqueezeBertForTokenClassification,
				    )
				    if is_torch_available()
				    else None
				)
				a__       							=	(
				    {
				        '''feature-extraction''': SqueezeBertModel,
				        '''fill-mask''': SqueezeBertForMaskedLM,
				        '''question-answering''': SqueezeBertForQuestionAnswering,
				        '''text-classification''': SqueezeBertForSequenceClassification,
				        '''token-classification''': SqueezeBertForTokenClassification,
				        '''zero-shot''': SqueezeBertForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				a__       							=	False
				a__       							=	True
				a__       							=	False







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Any   =   SqueezeBertModelTester(self  )
								__magic_name__		:Optional[int]   =   ConfigTester(self							,       config_class=__lowerCAmelCase							,       dim=3_7  )







				def       A						(    self  ):
								"""simple docstring"""
								self.config_tester.run_common_tests()







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]   =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_squeezebert_model(*__lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[str]   =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_squeezebert_for_masked_lm(*__lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Dict   =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_squeezebert_for_question_answering(*__lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[int]   =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_squeezebert_for_sequence_classification(*__lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Tuple   =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_squeezebert_for_token_classification(*__lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_squeezebert_for_multiple_choice(*__lowerCAmelCase  )







				@slow
				def       A						(    self  ):
								"""simple docstring"""
								for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												__magic_name__		:int   =   SqueezeBertModel.from_pretrained(__lowerCAmelCase  )
												self.assertIsNotNone(__lowerCAmelCase  )







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







				@slow
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:int   =   SqueezeBertForSequenceClassification.from_pretrained('''squeezebert/squeezebert-mnli'''  )

								__magic_name__		:Optional[int]   =   torch.tensor([[1, 2_9_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 1_3, 1_5_8_8, 2]]  )
								__magic_name__		:Tuple   =   model(__lowerCAmelCase  )[0]
								__magic_name__		:Any   =   torch.Size((1, 3)  )
								self.assertEqual(output.shape							,       __lowerCAmelCase  )
								__magic_name__		:List[Any]   =   torch.tensor([[0.6401, -0.0349, -0.6041]]  )
								self.assertTrue(torch.allclose(__lowerCAmelCase							,       __lowerCAmelCase							,       atol=1E-4  )  )














def   __lowercase       (			snake_case			):







				"""simple docstring"""






				if not isinstance(snake_case,	snake_case			):
								raise ValueError('''multiplicative_persistence() only accepts integral values'''			)
				if num < 0:
								raise ValueError('''multiplicative_persistence() does not accept negative values'''			)

				__magic_name__		:str   =   0
				__magic_name__		:Dict   =   str(snake_case			)

				while len(snake_case			) != 1:
								__magic_name__		:Optional[Any]   =   [int(snake_case			) for i in num_string]

								__magic_name__		:Dict   =   1
								for i in range(0,	len(snake_case			)			):
												total *= numbers[i]

								__magic_name__		:int   =   str(snake_case			)

								steps += 1
				return steps

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				if not isinstance(snake_case,	snake_case			):
								raise ValueError('''additive_persistence() only accepts integral values'''			)
				if num < 0:
								raise ValueError('''additive_persistence() does not accept negative values'''			)

				__magic_name__		:str   =   0
				__magic_name__		:Union[str, Any]   =   str(snake_case			)

				while len(snake_case			) != 1:
								__magic_name__		:str   =   [int(snake_case			) for i in num_string]

								__magic_name__		:Optional[int]   =   0
								for i in range(0,	len(snake_case			)			):
												total += numbers[i]

								__magic_name__		:int   =   str(snake_case			)

								steps += 1
				return steps


if __name__ == "__main__":
					import doctest

					doctest.testmod()






