

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_						(     __lowercase  ):
							"""simple docstring"""







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


							def 							__lowercase							(     self			)       ->       Optional[int]:
												_a      :       Tuple   =						ids_tensor([self.batch_size, self.seq_length]			,				self.vocab_size			)

												_a      :       Tuple   =						None
												if self.use_input_mask:
																	_a      :       Optional[Any]   =						random_attention_mask([self.batch_size, self.seq_length]			)

												_a      :       Tuple   =						None
												_a      :       List[str]   =						None
												_a      :       Tuple   =						None
												if self.use_labels:
																	_a      :       int   =						ids_tensor([self.batch_size]			,				self.type_sequence_label_size			)
																	_a      :       Optional[int]   =						ids_tensor([self.batch_size, self.seq_length]			,				self.num_labels			)
																	_a      :       int   =						ids_tensor([self.batch_size]			,				self.num_choices			)

												_a      :       List[str]   =						self.get_config()

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


							def 							__lowercase							(     self			)       ->       Optional[int]:
												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 							__lowercase							(     self			,				_a			,				_a			,				_a			,				_a			,				_a			,				_a			)       ->       List[str]:
												_a      :       Any   =						SqueezeBertModel(config=SCREAMING_SNAKE_CASE_			)
												model.to(SCREAMING_SNAKE_CASE_			)
												model.eval()
												_a      :       Optional[Any]   =						model(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
												_a      :       Dict   =						model(SCREAMING_SNAKE_CASE_			)
												self.parent.assertEqual(result.last_hidden_state.shape			,				(self.batch_size, self.seq_length, self.hidden_size)			)


							def 							__lowercase							(     self			,				_a			,				_a			,				_a			,				_a			,				_a			,				_a			)       ->       Dict:
												_a      :       List[Any]   =						SqueezeBertForMaskedLM(config=SCREAMING_SNAKE_CASE_			)
												model.to(SCREAMING_SNAKE_CASE_			)
												model.eval()
												_a      :       Any   =						model(SCREAMING_SNAKE_CASE_			,				attention_mask=SCREAMING_SNAKE_CASE_			,				labels=SCREAMING_SNAKE_CASE_			)
												self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.seq_length, self.vocab_size)			)


							def 							__lowercase							(     self			,				_a			,				_a			,				_a			,				_a			,				_a			,				_a			)       ->       List[Any]:
												_a      :       List[Any]   =						SqueezeBertForQuestionAnswering(config=SCREAMING_SNAKE_CASE_			)
												model.to(SCREAMING_SNAKE_CASE_			)
												model.eval()
												_a      :       int   =						model(
												    SCREAMING_SNAKE_CASE_			,				attention_mask=SCREAMING_SNAKE_CASE_			,				start_positions=SCREAMING_SNAKE_CASE_			,				end_positions=SCREAMING_SNAKE_CASE_			)
												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 							__lowercase							(     self			,				_a			,				_a			,				_a			,				_a			,				_a			,				_a			)       ->       Optional[Any]:
												_a      :       Any   =						self.num_labels
												_a      :       Dict   =						SqueezeBertForSequenceClassification(SCREAMING_SNAKE_CASE_			)
												model.to(SCREAMING_SNAKE_CASE_			)
												model.eval()
												_a      :       Optional[int]   =						model(SCREAMING_SNAKE_CASE_			,				attention_mask=SCREAMING_SNAKE_CASE_			,				labels=SCREAMING_SNAKE_CASE_			)
												self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_labels)			)


							def 							__lowercase							(     self			,				_a			,				_a			,				_a			,				_a			,				_a			,				_a			)       ->       Optional[int]:
												_a      :       int   =						self.num_labels
												_a      :       Dict   =						SqueezeBertForTokenClassification(config=SCREAMING_SNAKE_CASE_			)
												model.to(SCREAMING_SNAKE_CASE_			)
												model.eval()

												_a      :       Optional[int]   =						model(SCREAMING_SNAKE_CASE_			,				attention_mask=SCREAMING_SNAKE_CASE_			,				labels=SCREAMING_SNAKE_CASE_			)
												self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.seq_length, self.num_labels)			)


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




							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       str   =						self.prepare_config_and_inputs()
												((_a) ,   (_a) ,   (_a) ,   (_a) ,   (_a) ,   (_a))      :       str   =						config_and_inputs
												_a      :       Optional[int]   =						{'''input_ids''': input_ids, '''attention_mask''': input_mask}
												return config, inputs_dict



@require_torch
class 							UpperCAmelCase_						(     __lowercase							,			__lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Any             =       (
							    (
							        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__			:		Union[str, Any]             =       False
							UpperCAmelCase__			:		Union[str, Any]             =       True
							UpperCAmelCase__			:		Optional[Any]             =       False


							def 							__lowercase							(     self			)       ->       Tuple:
												_a      :       Tuple   =						SqueezeBertModelTester(self			)
												_a      :       Tuple   =						ConfigTester(self			,				config_class=SCREAMING_SNAKE_CASE_			,				dim=3_7			)


							def 							__lowercase							(     self			)       ->       Tuple:
												self.config_tester.run_common_tests()


							def 							__lowercase							(     self			)       ->       List[str]:
												_a      :       int   =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_squeezebert_model(*SCREAMING_SNAKE_CASE_			)


							def 							__lowercase							(     self			)       ->       Any:
												_a      :       List[str]   =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_squeezebert_for_masked_lm(*SCREAMING_SNAKE_CASE_			)


							def 							__lowercase							(     self			)       ->       Dict:
												_a      :       Any   =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_squeezebert_for_question_answering(*SCREAMING_SNAKE_CASE_			)


							def 							__lowercase							(     self			)       ->       int:
												_a      :       List[str]   =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_squeezebert_for_sequence_classification(*SCREAMING_SNAKE_CASE_			)


							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       Union[str, Any]   =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_squeezebert_for_token_classification(*SCREAMING_SNAKE_CASE_			)


							def 							__lowercase							(     self			)       ->       Dict:
												_a      :       Tuple   =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_squeezebert_for_multiple_choice(*SCREAMING_SNAKE_CASE_			)




							@slow
							def 							__lowercase							(     self			)       ->       Dict:
												for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_a      :       Optional[Any]   =						SqueezeBertModel.from_pretrained(SCREAMING_SNAKE_CASE_			)
																	self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)



@require_sentencepiece
@require_tokenizers
@require_torch
class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







							@slow
							def 							__lowercase							(     self			)       ->       Dict:
												_a      :       Any   =						SqueezeBertForSequenceClassification.from_pretrained('''squeezebert/squeezebert-mnli'''			)

												_a      :       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]]			)
												_a      :       str   =						model(SCREAMING_SNAKE_CASE_			)[0]
												_a      :       str   =						torch.Size((1, 3)			)
												self.assertEqual(output.shape			,				SCREAMING_SNAKE_CASE_			)
												_a      :       Any   =						torch.tensor([[0.6401, -0.0349, -0.6041]]			)
												self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				atol=1e-4			)			)





from __future__ import annotations




def 							a		(				snake_case__:   Optional[int]		,  snake_case__:   Optional[int]		,  snake_case__:   Any		,  snake_case__:   Optional[int]   ):  # noqa: E741


				'''simple docstring'''
				while r - l > 1:
								lowercase_	    =   (l + r) // 2
								if v[m] >= key:
												lowercase_	    =   m
								else:
												lowercase_	    =   m  # noqa: E741
				return r




def 							a		(				snake_case__:   list[int]   ):


				'''simple docstring'''
				if len(snake_case__   ) == 0:
								return 0

				lowercase_	    =   [0] * len(snake_case__   )
				lowercase_	    =   1

				lowercase_	    =   v[0]

				for i in range(1		,  len(snake_case__   )   ):
								if v[i] < tail[0]:
												lowercase_	    =   v[i]
								elif v[i] > tail[length - 1]:
												lowercase_	    =   v[i]
												length += 1
								else:
												lowercase_	    =   v[i]

				return length


if __name__ == "__main__":
						import doctest

						doctest.testmod()




