


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  (   UpperCamelCase_     ):






      def __init__(					self						:     Optional[int]			,   lowercase_						:     Dict			,   lowercase_						:     int=13			,   lowercase_						:     Optional[Any]=7			,   lowercase_						:     Optional[int]=True			,   lowercase_						:     Dict=True			,   lowercase_						:     List[Any]=False			,   lowercase_						:     List[str]=True			,   lowercase_						:     Union[str, Any]=99			,   lowercase_						:     Any=32			,   lowercase_						:     Tuple=5			,   lowercase_						:     Union[str, Any]=4			,   lowercase_						:     Dict=64			,   lowercase_						:     Optional[Any]="gelu"			,   lowercase_						:     Union[str, Any]=0.1			,   lowercase_						:     Union[str, Any]=0.1			,   lowercase_						:     List[str]=512			,   lowercase_						:     Dict=16			,   lowercase_						:     Optional[Any]=2			,   lowercase_						:     List[str]=0.02			,   lowercase_						:     List[str]=3			,   lowercase_						:     List[Any]=4			,   lowercase_						:     Optional[Any]=None			,   lowercase_						:     List[Any]=2			,   lowercase_						:     List[Any]=2			,   lowercase_						:     int=2			,   lowercase_						:     int=2			,   lowercase_						:     Optional[Any]=4			,   lowercase_						:     Tuple=1			,   )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		str		=parent
             _lowerCamelCase       :		Optional[Any]		=batch_size
             _lowerCamelCase       :		Union[str, Any]		=seq_length
             _lowerCamelCase       :		Optional[int]		=is_training
             _lowerCamelCase       :		Dict		=use_input_mask
             _lowerCamelCase       :		str		=use_token_type_ids
             _lowerCamelCase       :		List[str]		=use_labels
             _lowerCamelCase       :		Optional[Any]		=vocab_size
             _lowerCamelCase       :		Dict		=hidden_size
             _lowerCamelCase       :		List[Any]		=num_hidden_layers
             _lowerCamelCase       :		Optional[Any]		=num_attention_heads
             _lowerCamelCase       :		Union[str, Any]		=intermediate_size
             _lowerCamelCase       :		List[str]		=hidden_act
             _lowerCamelCase       :		str		=hidden_dropout_prob
             _lowerCamelCase       :		List[Any]		=attention_probs_dropout_prob
             _lowerCamelCase       :		Optional[Any]		=max_position_embeddings
             _lowerCamelCase       :		int		=type_vocab_size
             _lowerCamelCase       :		Any		=type_sequence_label_size
             _lowerCamelCase       :		List[Any]		=initializer_range
             _lowerCamelCase       :		Optional[Any]		=num_labels
             _lowerCamelCase       :		int		=num_choices
             _lowerCamelCase       :		Optional[int]		=scope
             _lowerCamelCase       :		Union[str, Any]		=q_groups
             _lowerCamelCase       :		List[str]		=k_groups
             _lowerCamelCase       :		Tuple		=v_groups
             _lowerCamelCase       :		Tuple		=post_attention_groups
             _lowerCamelCase       :		Optional[Any]		=intermediate_groups
             _lowerCamelCase       :		List[str]		=output_groups






      def        lowerCamelCase       (					self						:     Tuple )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		str		=ids_tensor([self.batch_size, self.seq_length]			,   self.vocab_size )

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

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

             _lowerCamelCase       :		Tuple		=self.get_config()

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






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						int:
             """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        lowerCamelCase       (					self						:     str			,   lowercase_						:     str			,   lowercase_						:     Tuple			,   lowercase_						:     List[Any]			,   lowercase_						:     Union[str, Any]			,   lowercase_						:     str			,   lowercase_						:     int )					->						List[str]:
             """simple docstring"""




             _lowerCamelCase       :		List[Any]		=SqueezeBertModel(config=lowercase_ )
             model.to(lowercase_ )
             model.eval()
             _lowerCamelCase       :		Tuple		=model(lowercase_			,   lowercase_ )
             _lowerCamelCase       :		List[Any]		=model(lowercase_ )
             self.parent.assertEqual(result.last_hidden_state.shape			,   (self.batch_size, self.seq_length, self.hidden_size) )






      def        lowerCamelCase       (					self						:     Union[str, Any]			,   lowercase_						:     str			,   lowercase_						:     Tuple			,   lowercase_						:     List[Any]			,   lowercase_						:     Dict			,   lowercase_						:     List[Any]			,   lowercase_						:     Tuple )					->						List[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Optional[Any]		=SqueezeBertForMaskedLM(config=lowercase_ )
             model.to(lowercase_ )
             model.eval()
             _lowerCamelCase       :		int		=model(lowercase_			,   attention_mask=lowercase_			,   labels=lowercase_ )
             self.parent.assertEqual(result.logits.shape			,   (self.batch_size, self.seq_length, self.vocab_size) )






      def        lowerCamelCase       (					self						:     Optional[Any]			,   lowercase_						:     List[Any]			,   lowercase_						:     Any			,   lowercase_						:     Union[str, Any]			,   lowercase_						:     Any			,   lowercase_						:     List[Any]			,   lowercase_						:     int )					->						List[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		=SqueezeBertForQuestionAnswering(config=lowercase_ )
             model.to(lowercase_ )
             model.eval()
             _lowerCamelCase       :		Optional[Any]		=model(
                 lowercase_			,   attention_mask=lowercase_			,   start_positions=lowercase_			,   end_positions=lowercase_ )
             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        lowerCamelCase       (					self						:     List[Any]			,   lowercase_						:     List[str]			,   lowercase_						:     Union[str, Any]			,   lowercase_						:     int			,   lowercase_						:     str			,   lowercase_						:     Optional[int]			,   lowercase_						:     int )					->						List[Any]:
             """simple docstring"""




             _lowerCamelCase       :		int		=self.num_labels
             _lowerCamelCase       :		Any		=SqueezeBertForSequenceClassification(lowercase_ )
             model.to(lowercase_ )
             model.eval()
             _lowerCamelCase       :		Tuple		=model(lowercase_			,   attention_mask=lowercase_			,   labels=lowercase_ )
             self.parent.assertEqual(result.logits.shape			,   (self.batch_size, self.num_labels) )






      def        lowerCamelCase       (					self						:     Dict			,   lowercase_						:     Tuple			,   lowercase_						:     Optional[Any]			,   lowercase_						:     int			,   lowercase_						:     int			,   lowercase_						:     Any			,   lowercase_						:     Optional[Any] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		Any		=self.num_labels
             _lowerCamelCase       :		str		=SqueezeBertForTokenClassification(config=lowercase_ )
             model.to(lowercase_ )
             model.eval()

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






      def        lowerCamelCase       (					self						:     List[str]			,   lowercase_						:     Optional[int]			,   lowercase_						:     List[Any]			,   lowercase_						:     Union[str, Any]			,   lowercase_						:     Optional[int]			,   lowercase_						:     Dict			,   lowercase_						:     str )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		Tuple		=self.num_choices
             _lowerCamelCase       :		List[Any]		=SqueezeBertForMultipleChoice(config=lowercase_ )
             model.to(lowercase_ )
             model.eval()
             _lowerCamelCase       :		Dict		=input_ids.unsqueeze(1 ).expand(-1			,   self.num_choices			,   -1 ).contiguous()
             _lowerCamelCase       :		Union[str, Any]		=input_mask.unsqueeze(1 ).expand(-1			,   self.num_choices			,   -1 ).contiguous()
             _lowerCamelCase       :		Tuple		=model(
                 lowercase_			,   attention_mask=lowercase_			,   labels=lowercase_			,   )
             self.parent.assertEqual(result.logits.shape			,   (self.batch_size, self.num_choices) )






      def        lowerCamelCase       (					self						:     int )					->						str:
             """simple docstring"""




             _lowerCamelCase       :		Dict		=self.prepare_config_and_inputs()
             ((_lowerCamelCase)	,     (_lowerCamelCase)	,     (_lowerCamelCase)	,     (_lowerCamelCase)	,     (_lowerCamelCase)	,     (_lowerCamelCase))       :		Tuple		=config_and_inputs
             _lowerCamelCase       :		Dict		={'input_ids': input_ids, 'attention_mask': input_mask}
             return config, inputs_dict




@require_torch
class A  (   UpperCamelCase_    ,							UpperCamelCase_    ,							unittest.TestCase     ):
      UpperCamelCase__							:  Union[str, Any]      =(
          (
              SqueezeBertModel,
              SqueezeBertForMaskedLM,
              SqueezeBertForMultipleChoice,
              SqueezeBertForQuestionAnswering,
              SqueezeBertForSequenceClassification,
              SqueezeBertForTokenClassification,
          )
          if is_torch_available()
          else None
      )
      UpperCamelCase__							:  int      =(
          {
              'feature-extraction': SqueezeBertModel,
              'fill-mask': SqueezeBertForMaskedLM,
              'question-answering': SqueezeBertForQuestionAnswering,
              'text-classification': SqueezeBertForSequenceClassification,
              'token-classification': SqueezeBertForTokenClassification,
              'zero-shot': SqueezeBertForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      UpperCamelCase__							:  Tuple      =False
      UpperCamelCase__							:  str      =True
      UpperCamelCase__							:  int      =False






      def        lowerCamelCase       (					self						:     List[str] )					->						List[str]:
             """simple docstring"""




             _lowerCamelCase       :		str		=SqueezeBertModelTester(self )
             _lowerCamelCase       :		Dict		=ConfigTester(self			,   config_class=lowercase_			,   dim=37 )






      def        lowerCamelCase       (					self						:     Dict )					->						List[Any]:
             """simple docstring"""




             self.config_tester.run_common_tests()






      def        lowerCamelCase       (					self						:     Optional[int] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		List[Any]		=self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_squeezebert_model(*lowercase_ )






      def        lowerCamelCase       (					self						:     List[Any] )					->						List[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Dict		=self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_squeezebert_for_masked_lm(*lowercase_ )






      def        lowerCamelCase       (					self						:     List[Any] )					->						Any:
             """simple docstring"""




             _lowerCamelCase       :		int		=self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_squeezebert_for_question_answering(*lowercase_ )






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		=self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_squeezebert_for_sequence_classification(*lowercase_ )






      def        lowerCamelCase       (					self						:     str )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Tuple		=self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_squeezebert_for_token_classification(*lowercase_ )






      def        lowerCamelCase       (					self						:     int )					->						List[str]:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		=self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_squeezebert_for_multiple_choice(*lowercase_ )






      @slow
      def        lowerCamelCase       (					self						:     str )					->						Dict:
             """simple docstring"""




             for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    _lowerCamelCase       :		List[Any]		=SqueezeBertModel.from_pretrained(lowercase_ )
                    self.assertIsNotNone(lowercase_ )




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






      @slow
      def        lowerCamelCase       (					self						:     Optional[int] )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Tuple		=SqueezeBertForSequenceClassification.from_pretrained('squeezebert/squeezebert-mnli' )

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






def 	a_      (    SCREAMING_SNAKE_CASE__					:      str		,			SCREAMING_SNAKE_CASE__					:      str     ):
       '''simple docstring'''





       assert x is not None
       assert y is not None

       _lowerCamelCase       :		Dict		=len(SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		Optional[Any]		=len(SCREAMING_SNAKE_CASE__     )

       # declaring the array for storing the dp values
       _lowerCamelCase       :		Optional[int]		=[[0] * (n + 1) for _ in range(m + 1     )]  # noqa: E741

       for i in range(1		,			m + 1     ):
              for j in range(1		,			n + 1     ):
                     _lowerCamelCase       :		int		=1 if x[i - 1] == y[j - 1] else 0

                     _lowerCamelCase       :		Union[str, Any]		=max(l[i - 1][j]		,			l[i][j - 1]		,			l[i - 1][j - 1] + match     )

       _lowerCamelCase       :		Optional[int]		=''
       _lowerCamelCase	,     _lowerCamelCase       :		Dict		=m, n
       while i > 0 and j > 0:
              _lowerCamelCase       :		int		=1 if x[i - 1] == y[j - 1] else 0

              if l[i][j] == l[i - 1][j - 1] + match:
                     if match == 1:
                            _lowerCamelCase       :		Union[str, Any]		=x[i - 1] + seq
                     i -= 1
                     j -= 1
              elif l[i][j] == l[i - 1][j]:
                     i -= 1
              else:
                     j -= 1

       return l[m][n], seq


if __name__ == "__main__":
 lowerCamelCase											=       'AGGTAB'
 lowerCamelCase											=       'GXTXAYB'
 lowerCamelCase											=       4
 lowerCamelCase											=       'GTAB'

 lowerCamelCase					,				lowerCamelCase											=       longest_common_subsequence(a, b)
 print('len =', ln, ', sub-sequence =', subseq)
 import doctest

 doctest.testmod()


