






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 							__a							(  __A						):





		'''simple docstring'''



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


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Tuple													=   ids_tensor([self.batch_size, self.seq_length] ,    self.vocab_size       )

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

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

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.get_config()

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


		def   __snake_case			(						self       ):
						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   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   SqueezeBertModel(config=UpperCamelCase__       )
						model.to(UpperCamelCase__       )
						model.eval()
						SCREAMING_SNAKE_CASE_   :	int													=   model(UpperCamelCase__ ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Dict													=   model(UpperCamelCase__       )
						self.parent.assertEqual(result.last_hidden_state.shape ,    (self.batch_size, self.seq_length, self.hidden_size)       )


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


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   SqueezeBertForQuestionAnswering(config=UpperCamelCase__       )
						model.to(UpperCamelCase__       )
						model.eval()
						SCREAMING_SNAKE_CASE_   :	Any													=   model(
						    UpperCamelCase__ ,    attention_mask=UpperCamelCase__ ,    start_positions=UpperCamelCase__ ,    end_positions=UpperCamelCase__       )
						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   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   self.num_labels
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   SqueezeBertForSequenceClassification(UpperCamelCase__       )
						model.to(UpperCamelCase__       )
						model.eval()
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   model(UpperCamelCase__ ,    attention_mask=UpperCamelCase__ ,    labels=UpperCamelCase__       )
						self.parent.assertEqual(result.logits.shape ,    (self.batch_size, self.num_labels)       )


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.num_labels
						SCREAMING_SNAKE_CASE_   :	str													=   SqueezeBertForTokenClassification(config=UpperCamelCase__       )
						model.to(UpperCamelCase__       )
						model.eval()

						SCREAMING_SNAKE_CASE_   :	Any													=   model(UpperCamelCase__ ,    attention_mask=UpperCamelCase__ ,    labels=UpperCamelCase__       )
						self.parent.assertEqual(result.logits.shape ,    (self.batch_size, self.seq_length, self.num_labels)       )


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






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






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





		'''simple docstring'''



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


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   SqueezeBertModelTester(self       )
						SCREAMING_SNAKE_CASE_   :	str													=   ConfigTester(self ,    config_class=UpperCamelCase__ ,    dim=37       )


		def   __snake_case			(						self       ):
						self.config_tester.run_common_tests()


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_squeezebert_model(*UpperCamelCase__       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_squeezebert_for_masked_lm(*UpperCamelCase__       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_squeezebert_for_question_answering(*UpperCamelCase__       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_squeezebert_for_sequence_classification(*UpperCamelCase__       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_squeezebert_for_token_classification(*UpperCamelCase__       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Tuple													=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_squeezebert_for_multiple_choice(*UpperCamelCase__       )






		@slow
		def   __snake_case			(						self       ):
						for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   SqueezeBertModel.from_pretrained(UpperCamelCase__       )
										self.assertIsNotNone(UpperCamelCase__       )






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





		'''simple docstring'''



		@slow
		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	int													=   SqueezeBertForSequenceClassification.from_pretrained('squeezebert/squeezebert-mnli'       )

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







from __future__ import annotations

from collections import Counter
from random import random


class 							__a							:





		'''simple docstring'''



		def __init__(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   {}


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	int													=   {}


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						if nodea not in self.connections:
										self.add_node(UpperCamelCase__       )
						if nodea not in self.connections:
										self.add_node(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   probability


		def   __snake_case			(						self       ):
						return list(self.connections       )






		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   0
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   random()

						for dest in self.connections[node]:
										current_probability += self.connections[node][dest]
										if current_probability > random_value:
														return dest
						return ""






def 					_lowerCamelCase(			lowerCAmelCase__				:		str    ,    lowerCAmelCase__				:		list[tuple[str, str, float]]    ,    lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   MarkovChainGraphUndirectedUnweighted()

				for nodea, nodea, probability in transitions:
								graph.add_transition_probability(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )

				SCREAMING_SNAKE_CASE_   :	List[str]													=   Counter(graph.get_nodes()      )
				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   start

				for _ in range(lowerCAmelCase__      ):
								SCREAMING_SNAKE_CASE_   :	Dict													=   graph.transition(lowerCAmelCase__      )
								visited[node] += 1

				return visited


if __name__ == "__main__":
				import doctest

				doctest.testmod()