





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__ (						UpperCamelCase__      ):



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



 def 				UpperCamelCase			(							self ):
        A__											=						ids_tensor([self.batch_size, self.seq_length],self.vocab_size )

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

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

        A__											=						self.get_config()

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



 def 				UpperCamelCase			(							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 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
        A__											=						SqueezeBertModel(config=__lowerCamelCase )
        model.to(__lowerCamelCase )
        model.eval()
        A__											=						model(__lowerCamelCase,__lowerCamelCase )
        A__											=						model(__lowerCamelCase )
        self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size) )



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
        A__											=						SqueezeBertForMaskedLM(config=__lowerCamelCase )
        model.to(__lowerCamelCase )
        model.eval()
        A__											=						model(__lowerCamelCase,attention_mask=__lowerCamelCase,labels=__lowerCamelCase )
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.vocab_size) )



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
        A__											=						SqueezeBertForQuestionAnswering(config=__lowerCamelCase )
        model.to(__lowerCamelCase )
        model.eval()
        A__											=						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 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
        A__											=						self.num_labels
        A__											=						SqueezeBertForSequenceClassification(__lowerCamelCase )
        model.to(__lowerCamelCase )
        model.eval()
        A__											=						model(__lowerCamelCase,attention_mask=__lowerCamelCase,labels=__lowerCamelCase )
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels) )



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
        A__											=						self.num_labels
        A__											=						SqueezeBertForTokenClassification(config=__lowerCamelCase )
        model.to(__lowerCamelCase )
        model.eval()

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



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
        A__											=						self.num_choices
        A__											=						SqueezeBertForMultipleChoice(config=__lowerCamelCase )
        model.to(__lowerCamelCase )
        model.eval()
        A__											=						input_ids.unsqueeze(1 ).expand(-1,self.num_choices,-1 ).contiguous()
        A__											=						input_mask.unsqueeze(1 ).expand(-1,self.num_choices,-1 ).contiguous()
        A__											=						model(
            __lowerCamelCase,attention_mask=__lowerCamelCase,labels=__lowerCamelCase,)
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_choices) )




 def 				UpperCamelCase			(							self ):
        A__											=						self.prepare_config_and_inputs()
        ((A__)					,	(A__)					,	(A__)					,	(A__)					,	(A__)					,	(A__))											=						config_and_inputs
        A__											=						{'''input_ids''': input_ids, '''attention_mask''': input_mask}
        return config, inputs_dict




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



 def 				UpperCamelCase			(							self ):
        A__											=						SqueezeBertModelTester(self )
        A__											=						ConfigTester(self,config_class=__lowerCamelCase,dim=37 )



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



 def 				UpperCamelCase			(							self ):
        A__											=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_squeezebert_model(*__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__											=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_squeezebert_for_masked_lm(*__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__											=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_squeezebert_for_question_answering(*__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__											=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_squeezebert_for_sequence_classification(*__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__											=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_squeezebert_for_token_classification(*__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__											=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_squeezebert_for_multiple_choice(*__lowerCamelCase )




 @slow
 def 				UpperCamelCase			(							self ):
        for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               A__											=						SqueezeBertModel.from_pretrained(__lowerCamelCase )
               self.assertIsNotNone(__lowerCamelCase )




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



 @slow
 def 				UpperCamelCase			(							self ):
        A__											=						SqueezeBertForSequenceClassification.from_pretrained('''squeezebert/squeezebert-mnli''' )

        A__											=						torch.tensor([[1, 2_9414, 232, 328, 740, 1140, 1_2695, 69, 13, 1588, 2]] )
        A__											=						model(__lowerCamelCase )[0]
        A__											=						torch.Size((1, 3) )
        self.assertEqual(output.shape,__lowerCamelCase )
        A__											=						torch.tensor([[0.6401, -0.0349, -0.6041]] )
        self.assertTrue(torch.allclose(__lowerCamelCase,__lowerCamelCase,atol=1E-4 ) )












import random


def 	UpperCamelCase__(       UpperCamelCase__			:       list ,     UpperCamelCase__			:       List[Any]  )->tuple:
       A__					,	A__					,	A__											=						[], [], []
       for element in data:
              if element < pivot:
                     less.append(UpperCamelCase__  )
              elif element > pivot:
                     greater.append(UpperCamelCase__  )
              else:
                     equal.append(UpperCamelCase__  )
       return less, equal, greater


def 	UpperCamelCase__(       UpperCamelCase__			:       list ,     UpperCamelCase__			:       int  )->Optional[int]:
       # index = len(items) // 2 when trying to find the median
       #   (value of index when items is sorted)

       # invalid input
       if index >= len(UpperCamelCase__  ) or index < 0:
              return None

       A__											=						items[random.randint(0 ,     len(UpperCamelCase__  ) - 1  )]
       A__											=						0
       A__					,	A__					,	A__											=						_partition(UpperCamelCase__ ,     UpperCamelCase__  )
       A__											=						len(UpperCamelCase__  )
       A__											=						len(UpperCamelCase__  )

       # index is the pivot
       if m <= index < m + count:
              return pivot
       # must be in smaller
       elif m > index:
              return quick_select(UpperCamelCase__ ,     UpperCamelCase__  )
       # must be in larger
       else:
              return quick_select(UpperCamelCase__ ,     index - (m + count)  )





