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        lowercase_   (					_lowercase		):



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



       def 	UpperCamelCase	(						self    ):
             _snake_case						:					Optional[Any]    =   ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size    )

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

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

             _snake_case						:					Optional[int]    =   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				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_    ):
             _snake_case						:					List[str]    =   SqueezeBertModel(config=__UpperCamelCase    )
             model.to(__UpperCamelCase    )
             model.eval()
             _snake_case						:					List[str]    =   model(__UpperCamelCase				,    __UpperCamelCase    )
             _snake_case						:					List[Any]    =   model(__UpperCamelCase    )
             self.parent.assertEqual(result.last_hidden_state.shape				,    (self.batch_size, self.seq_length, self.hidden_size)    )



       def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_    ):
             _snake_case						:					str    =   SqueezeBertForMaskedLM(config=__UpperCamelCase    )
             model.to(__UpperCamelCase    )
             model.eval()
             _snake_case						:					List[str]    =   model(__UpperCamelCase				,    attention_mask=__UpperCamelCase				,    labels=__UpperCamelCase    )
             self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.seq_length, self.vocab_size)    )



       def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_    ):
             _snake_case						:					Dict    =   SqueezeBertForQuestionAnswering(config=__UpperCamelCase    )
             model.to(__UpperCamelCase    )
             model.eval()
             _snake_case						:					Optional[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 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_    ):
             _snake_case						:					str    =   self.num_labels
             _snake_case						:					int    =   SqueezeBertForSequenceClassification(__UpperCamelCase    )
             model.to(__UpperCamelCase    )
             model.eval()
             _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 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_    ):
             _snake_case						:					str    =   self.num_labels
             _snake_case						:					int    =   SqueezeBertForTokenClassification(config=__UpperCamelCase    )
             model.to(__UpperCamelCase    )
             model.eval()

             _snake_case						:					Union[str, Any]    =   model(__UpperCamelCase				,    attention_mask=__UpperCamelCase				,    labels=__UpperCamelCase    )
             self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.seq_length, self.num_labels)    )



       def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_    ):
             _snake_case						:					int    =   self.num_choices
             _snake_case						:					Dict    =   SqueezeBertForMultipleChoice(config=__UpperCamelCase    )
             model.to(__UpperCamelCase    )
             model.eval()
             _snake_case						:					Dict    =   input_ids.unsqueeze(1    ).expand(-1				,    self.num_choices				,    -1    ).contiguous()
             _snake_case						:					Optional[Any]    =   input_mask.unsqueeze(1    ).expand(-1				,    self.num_choices				,    -1    ).contiguous()
             _snake_case						:					int    =   model(
                 __UpperCamelCase				,    attention_mask=__UpperCamelCase				,    labels=__UpperCamelCase				,    )
             self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.num_choices)    )



       def 	UpperCamelCase	(						self    ):
             _snake_case						:					Optional[int]    =   self.prepare_config_and_inputs()
             ((_snake_case)    ,(_snake_case)    ,(_snake_case)    ,(_snake_case)    ,(_snake_case)    ,(_snake_case))						:					Tuple    =   config_and_inputs
             _snake_case						:					Tuple    =   {"input_ids": input_ids, "attention_mask": input_mask}
             return config, inputs_dict




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



       def 	UpperCamelCase	(						self    ):
             _snake_case						:					Optional[int]    =   SqueezeBertModelTester(self    )
             _snake_case						:					Optional[int]    =   ConfigTester(self				,    config_class=__UpperCamelCase				,    dim=37    )



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



       def 	UpperCamelCase	(						self    ):
             _snake_case						:					str    =   self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_squeezebert_model(*__UpperCamelCase    )



       def 	UpperCamelCase	(						self    ):
             _snake_case						:					Optional[int]    =   self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_squeezebert_for_masked_lm(*__UpperCamelCase    )



       def 	UpperCamelCase	(						self    ):
             _snake_case						:					List[Any]    =   self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_squeezebert_for_question_answering(*__UpperCamelCase    )



       def 	UpperCamelCase	(						self    ):
             _snake_case						:					Union[str, Any]    =   self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_squeezebert_for_sequence_classification(*__UpperCamelCase    )



       def 	UpperCamelCase	(						self    ):
             _snake_case						:					Union[str, Any]    =   self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_squeezebert_for_token_classification(*__UpperCamelCase    )



       def 	UpperCamelCase	(						self    ):
             _snake_case						:					Optional[Any]    =   self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_squeezebert_for_multiple_choice(*__UpperCamelCase    )



       @slow
       def 	UpperCamelCase	(						self    ):
             for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   _snake_case						:					List[Any]    =   SqueezeBertModel.from_pretrained(__UpperCamelCase    )
                   self.assertIsNotNone(__UpperCamelCase    )




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



       @slow
       def 	UpperCamelCase	(						self    ):
             _snake_case						:					Any    =   SqueezeBertForSequenceClassification.from_pretrained("squeezebert/squeezebert-mnli"    )

             _snake_case						:					List[str]    =   torch.tensor([[1, 29_414, 232, 328, 740, 1_140, 12_695, 69, 13, 1_588, 2]]    )
             _snake_case						:					Dict    =   model(__UpperCamelCase    )[0]
             _snake_case						:					str    =   torch.Size((1, 3)    )
             self.assertEqual(output.shape				,    __UpperCamelCase    )
             _snake_case						:					List[str]    =   torch.tensor([[0.6_401, -0.0_349, -0.6_041]]    )
             self.assertTrue(torch.allclose(__UpperCamelCase				,    __UpperCamelCase				,    atol=1e-4    )    )
def  snake_case						(__lowercase						)	->	list:

						'''simple docstring'''



						return [
						    txt[:a] + txt[a].upper() + txt[a + 1 :]
						    for a in range(len(__lowercase						)						)
						    if txt[a].isalpha()
						]


if __name__ == "__main__":
				__import__('doctest').testmod()