




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 (     _snake_case  ):
      """simple docstring"""

      def __init__( self :     Optional[Any]				,							lowerCamelCase__ :     List[str]				,							lowerCamelCase__ :     Union[str, Any]=13				,							lowerCamelCase__ :     List[Any]=7				,							lowerCamelCase__ :     Dict=True				,							lowerCamelCase__ :     int=True				,							lowerCamelCase__ :     List[str]=False				,							lowerCamelCase__ :     Union[str, Any]=True				,							lowerCamelCase__ :     str=99				,							lowerCamelCase__ :     int=32				,							lowerCamelCase__ :     Optional[Any]=5				,							lowerCamelCase__ :     Tuple=4				,							lowerCamelCase__ :     Optional[int]=64				,							lowerCamelCase__ :     int="gelu"				,							lowerCamelCase__ :     Optional[int]=0.1				,							lowerCamelCase__ :     int=0.1				,							lowerCamelCase__ :     Any=512				,							lowerCamelCase__ :     Dict=16				,							lowerCamelCase__ :     List[str]=2				,							lowerCamelCase__ :     Tuple=0.0_2				,							lowerCamelCase__ :     Any=3				,							lowerCamelCase__ :     Optional[int]=4				,							lowerCamelCase__ :     List[str]=None				,							lowerCamelCase__ :     Union[str, Any]=2				,							lowerCamelCase__ :     Dict=2				,							lowerCamelCase__ :     Dict=2				,							lowerCamelCase__ :     int=2				,							lowerCamelCase__ :     List[str]=4				,							lowerCamelCase__ :     Any=1				,							)    ->						List[str]:
             """simple docstring"""


             __lowercase      =    parent
             __lowercase      =    batch_size
             __lowercase      =    seq_length
             __lowercase      =    is_training
             __lowercase      =    use_input_mask
             __lowercase      =    use_token_type_ids
             __lowercase      =    use_labels
             __lowercase      =    vocab_size
             __lowercase      =    hidden_size
             __lowercase      =    num_hidden_layers
             __lowercase      =    num_attention_heads
             __lowercase      =    intermediate_size
             __lowercase      =    hidden_act
             __lowercase      =    hidden_dropout_prob
             __lowercase      =    attention_probs_dropout_prob
             __lowercase      =    max_position_embeddings
             __lowercase      =    type_vocab_size
             __lowercase      =    type_sequence_label_size
             __lowercase      =    initializer_range
             __lowercase      =    num_labels
             __lowercase      =    num_choices
             __lowercase      =    scope
             __lowercase      =    q_groups
             __lowercase      =    k_groups
             __lowercase      =    v_groups
             __lowercase      =    post_attention_groups
             __lowercase      =    intermediate_groups
             __lowercase      =    output_groups






      def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						str:
             """simple docstring"""


             __lowercase      =    ids_tensor([self.batch_size, self.seq_length]				,							self.vocab_size				)

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

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

             __lowercase      =    self.get_config()

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






      def     UpperCAmelCase_      ( self :     str				)    ->						Optional[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     UpperCAmelCase_      ( self :     Union[str, Any]				,							lowerCamelCase__ :     List[str]				,							lowerCamelCase__ :     Any				,							lowerCamelCase__ :     Tuple				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     int				,							lowerCamelCase__ :     Any				)    ->						Optional[Any]:
             """simple docstring"""


             __lowercase      =    SqueezeBertModel(config=lowerCAmelCase__				)
             model.to(lowerCAmelCase__				)
             model.eval()
             __lowercase      =    model(lowerCAmelCase__				,							lowerCAmelCase__				)
             __lowercase      =    model(lowerCAmelCase__				)
             self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, self.seq_length, self.hidden_size)				)






      def     UpperCAmelCase_      ( self :     Tuple				,							lowerCamelCase__ :     List[Any]				,							lowerCamelCase__ :     int				,							lowerCamelCase__ :     Dict				,							lowerCamelCase__ :     Optional[Any]				,							lowerCamelCase__ :     Optional[Any]				,							lowerCamelCase__ :     Dict				)    ->						Optional[int]:
             """simple docstring"""


             __lowercase      =    SqueezeBertForMaskedLM(config=lowerCAmelCase__				)
             model.to(lowerCAmelCase__				)
             model.eval()
             __lowercase      =    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 :     Optional[int]				,							lowerCamelCase__ :     Dict				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     Any				,							lowerCamelCase__ :     List[Any]				,							lowerCamelCase__ :     Union[str, Any]				,							lowerCamelCase__ :     Optional[Any]				)    ->						int:
             """simple docstring"""


             __lowercase      =    SqueezeBertForQuestionAnswering(config=lowerCAmelCase__				)
             model.to(lowerCAmelCase__				)
             model.eval()
             __lowercase      =    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 :     Dict				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     List[Any]				,							lowerCamelCase__ :     int				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     Any				)    ->						List[Any]:
             """simple docstring"""


             __lowercase      =    self.num_labels
             __lowercase      =    SqueezeBertForSequenceClassification(lowerCAmelCase__				)
             model.to(lowerCAmelCase__				)
             model.eval()
             __lowercase      =    model(lowerCAmelCase__				,							attention_mask=lowerCAmelCase__				,							labels=lowerCAmelCase__				)
             self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.num_labels)				)






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


             __lowercase      =    self.num_labels
             __lowercase      =    SqueezeBertForTokenClassification(config=lowerCAmelCase__				)
             model.to(lowerCAmelCase__				)
             model.eval()

             __lowercase      =    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 :     Optional[int]				,							lowerCamelCase__ :     List[str]				,							lowerCamelCase__ :     Dict				,							lowerCamelCase__ :     Union[str, Any]				,							lowerCamelCase__ :     Any				,							lowerCamelCase__ :     Optional[Any]				,							lowerCamelCase__ :     Dict				)    ->						Any:
             """simple docstring"""


             __lowercase      =    self.num_choices
             __lowercase      =    SqueezeBertForMultipleChoice(config=lowerCAmelCase__				)
             model.to(lowerCAmelCase__				)
             model.eval()
             __lowercase      =    input_ids.unsqueeze(1				).expand(-1				,							self.num_choices				,							-1				).contiguous()
             __lowercase      =    input_mask.unsqueeze(1				).expand(-1				,							self.num_choices				,							-1				).contiguous()
             __lowercase      =    model(
                 lowerCAmelCase__				,							attention_mask=lowerCAmelCase__				,							labels=lowerCAmelCase__				,							)
             self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.num_choices)				)






      def     UpperCAmelCase_      ( self :     int				)    ->						Dict:
             """simple docstring"""


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







@require_torch
class a (     _snake_case       ,						_snake_case       ,						unittest.TestCase  ):
      """simple docstring"""

      UpperCamelCase_	:       Optional[int]       	=		(
          (
              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_	:       int       	=		False
      UpperCamelCase_	:       Union[str, Any]       	=		True
      UpperCamelCase_	:       List[str]       	=		False






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


             __lowercase      =    SqueezeBertModelTester(self				)
             __lowercase      =    ConfigTester(self				,							config_class=lowerCAmelCase__				,							dim=37				)






      def     UpperCAmelCase_      ( self :     str				)    ->						Any:
             """simple docstring"""


             self.config_tester.run_common_tests()






      def     UpperCAmelCase_      ( self :     Any				)    ->						Optional[int]:
             """simple docstring"""


             __lowercase      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_squeezebert_model(*lowerCAmelCase__				)






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


             __lowercase      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_squeezebert_for_masked_lm(*lowerCAmelCase__				)






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


             __lowercase      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_squeezebert_for_question_answering(*lowerCAmelCase__				)






      def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						List[str]:
             """simple docstring"""


             __lowercase      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_squeezebert_for_sequence_classification(*lowerCAmelCase__				)






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


             __lowercase      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_squeezebert_for_token_classification(*lowerCAmelCase__				)






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


             __lowercase      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_squeezebert_for_multiple_choice(*lowerCAmelCase__				)






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


             for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    __lowercase      =    SqueezeBertModel.from_pretrained(lowerCAmelCase__				)
                    self.assertIsNotNone(lowerCAmelCase__				)







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

      @slow
      def     UpperCAmelCase_      ( self :     Any				)    ->						Any:
             """simple docstring"""


             __lowercase      =    SqueezeBertForSequenceClassification.from_pretrained('''squeezebert/squeezebert-mnli'''				)

             __lowercase      =    torch.tensor([[1, 29_414, 232, 328, 740, 1_140, 12_695, 69, 13, 1_588, 2]]				)
             __lowercase      =    model(lowerCAmelCase__				)[0]
             __lowercase      =    torch.Size((1, 3)				)
             self.assertEqual(output.shape				,							lowerCAmelCase__				)
             __lowercase      =    torch.tensor([[0.6_4_0_1, -0.0_3_4_9, -0.6_0_4_1]]				)
             self.assertTrue(torch.allclose(lowerCAmelCase__				,							lowerCAmelCase__				,							atol=1e-4				)				)












import os







def 		_A(       UpperCamelCase__							:			str = "matrix.txt"				)    -> int:
							'''simple docstring'''


							with open(os.path.join(os.path.dirname(UpperCamelCase__				)				,       UpperCamelCase__				)				) as in_file:
														__lowercase      =    in_file.read()

							__lowercase      =    [[int(UpperCamelCase__				) for cell in row.split(''','''				)] for row in data.strip().splitlines()]
							__lowercase      =    [[0 for cell in row] for row in grid]
							__lowercase      =    len(grid[0]				)

							__lowercase      =    [[0 for i in range(UpperCamelCase__				)] for j in range(UpperCamelCase__				)]
							__lowercase      =    grid[0][0]
							for i in range(1				,       UpperCamelCase__				):
														__lowercase      =    grid[0][i] + dp[0][i - 1]
							for i in range(1				,       UpperCamelCase__				):
														__lowercase      =    grid[i][0] + dp[i - 1][0]

							for i in range(1				,       UpperCamelCase__				):
														for j in range(1				,       UpperCamelCase__				):
																					__lowercase      =    grid[i][j] + min(dp[i - 1][j]				,       dp[i][j - 1]				)

							return dp[-1][-1]


if __name__ == "__main__":
			print(F"""{solution() = }""")






