


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        RemBertConfig,
        RemBertForCausalLM,
        RemBertForMaskedLM,
        RemBertForMultipleChoice,
        RemBertForQuestionAnswering,
        RemBertForSequenceClassification,
        RemBertForTokenClassification,
        RemBertModel,
    )
    from transformers.models.rembert.modeling_rembert import REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST







class      _lowercase					:


       """simple docstring"""




       def __init__(  self				:     List[Any]   , __lowerCamelCase				:     Tuple   , __lowerCamelCase				:     Any=13   , __lowerCamelCase				:     Dict=7   , __lowerCamelCase				:     str=True   , __lowerCamelCase				:     Dict=True   , __lowerCamelCase				:     Dict=True   , __lowerCamelCase				:     Union[str, Any]=True   , __lowerCamelCase				:     Tuple=99   , __lowerCamelCase				:     int=32   , __lowerCamelCase				:     Optional[int]=18   , __lowerCamelCase				:     int=43   , __lowerCamelCase				:     Optional[Any]=5   , __lowerCamelCase				:     str=4   , __lowerCamelCase				:     Optional[Any]=37   , __lowerCamelCase				:     Union[str, Any]="gelu"   , __lowerCamelCase				:     Dict=0.1   , __lowerCamelCase				:     Union[str, Any]=0.1   , __lowerCamelCase				:     Any=512   , __lowerCamelCase				:     Any=16   , __lowerCamelCase				:     List[str]=2   , __lowerCamelCase				:     Optional[Any]=0.0_2   , __lowerCamelCase				:     Dict=3   , __lowerCamelCase				:     Tuple=4   , __lowerCamelCase				:     Optional[int]=None   , ):


             '''simple docstring'''



             lowerCamelCase__						:							Any       =    parent
             lowerCamelCase__						:							str       =    batch_size
             lowerCamelCase__						:							List[Any]       =    seq_length
             lowerCamelCase__						:							List[Any]       =    is_training
             lowerCamelCase__						:							Optional[Any]       =    use_input_mask
             lowerCamelCase__						:							List[Any]       =    use_token_type_ids
             lowerCamelCase__						:							List[Any]       =    use_labels
             lowerCamelCase__						:							Union[str, Any]       =    vocab_size
             lowerCamelCase__						:							Dict       =    hidden_size
             lowerCamelCase__						:							Tuple       =    input_embedding_size
             lowerCamelCase__						:							Union[str, Any]       =    output_embedding_size
             lowerCamelCase__						:							Union[str, Any]       =    num_hidden_layers
             lowerCamelCase__						:							int       =    num_attention_heads
             lowerCamelCase__						:							Optional[int]       =    intermediate_size
             lowerCamelCase__						:							Union[str, Any]       =    hidden_act
             lowerCamelCase__						:							Any       =    hidden_dropout_prob
             lowerCamelCase__						:							Optional[int]       =    attention_probs_dropout_prob
             lowerCamelCase__						:							Tuple       =    max_position_embeddings
             lowerCamelCase__						:							Union[str, Any]       =    type_vocab_size
             lowerCamelCase__						:							List[str]       =    type_sequence_label_size
             lowerCamelCase__						:							str       =    initializer_range
             lowerCamelCase__						:							int       =    num_labels
             lowerCamelCase__						:							Any       =    num_choices
             lowerCamelCase__						:							Optional[int]       =    scope





       def 		lowerCAmelCase	(  self				:     int			):


             '''simple docstring'''



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

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

             lowerCamelCase__						:							Tuple       =    None
             if self.use_token_type_ids:
                   lowerCamelCase__						:							List[str]       =    ids_tensor([self.batch_size, self.seq_length]   , self.type_vocab_size			)

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

             lowerCamelCase__						:							Dict       =    RemBertConfig(
                 vocab_size=self.vocab_size   , hidden_size=self.hidden_size   , input_embedding_size=self.input_embedding_size   , output_embedding_size=self.output_embedding_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   , hidden_dropout_prob=self.hidden_dropout_prob   , attention_probs_dropout_prob=self.attention_probs_dropout_prob   , max_position_embeddings=self.max_position_embeddings   , type_vocab_size=self.type_vocab_size   , is_decoder=__lowerCamelCase   , initializer_range=self.initializer_range   , )

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





       def 		lowerCAmelCase	(  self				:     str			):


             '''simple docstring'''



             (
                 (
                 lowerCamelCase__
             )       ,  (
                 lowerCamelCase__
             )       ,  (
                 lowerCamelCase__
             )       ,  (
                 lowerCamelCase__
             )       ,  (
                 lowerCamelCase__
             )       ,  (
                 lowerCamelCase__
             )       ,  (
                 lowerCamelCase__
             )       ,  
             )						:							int       =    self.prepare_config_and_inputs()

             lowerCamelCase__						:							Optional[Any]       =    True
             lowerCamelCase__						:							int       =    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
             lowerCamelCase__						:							Dict       =    ids_tensor([self.batch_size, self.seq_length]   , vocab_size=2			)

             return (
                 config,
                 input_ids,
                 token_type_ids,
                 input_mask,
                 sequence_labels,
                 token_labels,
                 choice_labels,
                 encoder_hidden_states,
                 encoder_attention_mask,
             )





       def 		lowerCAmelCase	(  self				:     int   , __lowerCamelCase				:     Optional[Any]   , __lowerCamelCase				:     Optional[int]   , __lowerCamelCase				:     Dict   , __lowerCamelCase				:     List[Any]   , __lowerCamelCase				:     List[str]   , __lowerCamelCase				:     List[Any]   , __lowerCamelCase				:     Tuple			):


             '''simple docstring'''



             lowerCamelCase__						:							Any       =    RemBertModel(config=__lowerCamelCase			)
             model.to(__lowerCamelCase			)
             model.eval()
             lowerCamelCase__						:							Optional[Any]       =    model(__lowerCamelCase   , attention_mask=__lowerCamelCase   , token_type_ids=__lowerCamelCase			)
             lowerCamelCase__						:							str       =    model(__lowerCamelCase   , token_type_ids=__lowerCamelCase			)
             lowerCamelCase__						:							Dict       =    model(__lowerCamelCase			)
             self.parent.assertEqual(result.last_hidden_state.shape   , (self.batch_size, self.seq_length, self.hidden_size)			)





       def 		lowerCAmelCase	(  self				:     List[Any]   , __lowerCamelCase				:     List[Any]   , __lowerCamelCase				:     List[str]   , __lowerCamelCase				:     Tuple   , __lowerCamelCase				:     Any   , __lowerCamelCase				:     Any   , __lowerCamelCase				:     Any   , __lowerCamelCase				:     Optional[Any]   , __lowerCamelCase				:     Dict   , __lowerCamelCase				:     Tuple   , ):


             '''simple docstring'''



             lowerCamelCase__						:							str       =    True
             lowerCamelCase__						:							List[str]       =    RemBertModel(__lowerCamelCase			)
             model.to(__lowerCamelCase			)
             model.eval()
             lowerCamelCase__						:							Optional[Any]       =    model(
                 __lowerCamelCase   , attention_mask=__lowerCamelCase   , token_type_ids=__lowerCamelCase   , encoder_hidden_states=__lowerCamelCase   , encoder_attention_mask=__lowerCamelCase   , )
             lowerCamelCase__						:							Union[str, Any]       =    model(
                 __lowerCamelCase   , attention_mask=__lowerCamelCase   , token_type_ids=__lowerCamelCase   , encoder_hidden_states=__lowerCamelCase   , )
             lowerCamelCase__						:							List[str]       =    model(__lowerCamelCase   , attention_mask=__lowerCamelCase   , token_type_ids=__lowerCamelCase			)
             self.parent.assertEqual(result.last_hidden_state.shape   , (self.batch_size, self.seq_length, self.hidden_size)			)





       def 		lowerCAmelCase	(  self				:     Union[str, Any]   , __lowerCamelCase				:     Tuple   , __lowerCamelCase				:     List[Any]   , __lowerCamelCase				:     Dict   , __lowerCamelCase				:     str   , __lowerCamelCase				:     Tuple   , __lowerCamelCase				:     Any   , __lowerCamelCase				:     Union[str, Any]   , __lowerCamelCase				:     Optional[Any]   , __lowerCamelCase				:     Any   , ):


             '''simple docstring'''



             lowerCamelCase__						:							Any       =    RemBertForCausalLM(config=__lowerCamelCase			)
             model.to(__lowerCamelCase			)
             model.eval()
             lowerCamelCase__						:							Dict       =    model(__lowerCamelCase   , attention_mask=__lowerCamelCase   , token_type_ids=__lowerCamelCase   , labels=__lowerCamelCase			)
             self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.seq_length, self.vocab_size)			)





       def 		lowerCAmelCase	(  self				:     str   , __lowerCamelCase				:     Optional[int]   , __lowerCamelCase				:     List[str]   , __lowerCamelCase				:     int   , __lowerCamelCase				:     Union[str, Any]   , __lowerCamelCase				:     Tuple   , __lowerCamelCase				:     List[str]   , __lowerCamelCase				:     Optional[Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							Tuple       =    RemBertForMaskedLM(config=__lowerCamelCase			)
             model.to(__lowerCamelCase			)
             model.eval()
             lowerCamelCase__						:							Optional[Any]       =    model(__lowerCamelCase   , attention_mask=__lowerCamelCase   , token_type_ids=__lowerCamelCase   , labels=__lowerCamelCase			)
             self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.seq_length, self.vocab_size)			)





       def 		lowerCAmelCase	(  self				:     Dict   , __lowerCamelCase				:     Tuple   , __lowerCamelCase				:     str   , __lowerCamelCase				:     Union[str, Any]   , __lowerCamelCase				:     Any   , __lowerCamelCase				:     List[Any]   , __lowerCamelCase				:     Union[str, Any]   , __lowerCamelCase				:     List[str]   , __lowerCamelCase				:     List[str]   , __lowerCamelCase				:     Dict   , ):


             '''simple docstring'''



             lowerCamelCase__						:							Tuple       =    True
             lowerCamelCase__						:							Any       =    True
             lowerCamelCase__						:							Tuple       =    RemBertForCausalLM(config=__lowerCamelCase			)
             model.to(__lowerCamelCase			)
             model.eval()

             # first forward pass
             lowerCamelCase__						:							Optional[Any]       =    model(
                 __lowerCamelCase   , attention_mask=__lowerCamelCase   , encoder_hidden_states=__lowerCamelCase   , encoder_attention_mask=__lowerCamelCase   , use_cache=__lowerCamelCase   , )
             lowerCamelCase__						:							Dict       =    outputs.past_key_values

             # create hypothetical multiple next token and extent to next_input_ids
             lowerCamelCase__						:							Dict       =    ids_tensor((self.batch_size, 3)   , config.vocab_size			)
             lowerCamelCase__						:							Optional[Any]       =    ids_tensor((self.batch_size, 3)   , vocab_size=2			)

             # append to next input_ids and
             lowerCamelCase__						:							Dict       =    torch.cat([input_ids, next_tokens]   , dim=-1			)
             lowerCamelCase__						:							int       =    torch.cat([input_mask, next_mask]   , dim=-1			)

             lowerCamelCase__						:							Dict       =    model(
                 __lowerCamelCase   , attention_mask=__lowerCamelCase   , encoder_hidden_states=__lowerCamelCase   , encoder_attention_mask=__lowerCamelCase   , output_hidden_states=__lowerCamelCase   , )["hidden_states"][0]
             lowerCamelCase__						:							int       =    model(
                 __lowerCamelCase   , attention_mask=__lowerCamelCase   , encoder_hidden_states=__lowerCamelCase   , encoder_attention_mask=__lowerCamelCase   , past_key_values=__lowerCamelCase   , output_hidden_states=__lowerCamelCase   , )["hidden_states"][0]

             # select random slice
             lowerCamelCase__						:							Tuple       =    ids_tensor((1,)   , output_from_past.shape[-1]			).item()
             lowerCamelCase__						:							Tuple       =    output_from_no_past[:, -3:, random_slice_idx].detach()
             lowerCamelCase__						:							List[Any]       =    output_from_past[:, :, random_slice_idx].detach()

             self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]			)

             # test that outputs are equal for slice
             self.parent.assertTrue(torch.allclose(__lowerCamelCase   , __lowerCamelCase   , atol=1E-3			)			)





       def 		lowerCAmelCase	(  self				:     Tuple   , __lowerCamelCase				:     Dict   , __lowerCamelCase				:     Tuple   , __lowerCamelCase				:     List[Any]   , __lowerCamelCase				:     Optional[int]   , __lowerCamelCase				:     Any   , __lowerCamelCase				:     Optional[int]   , __lowerCamelCase				:     Tuple			):


             '''simple docstring'''



             lowerCamelCase__						:							Any       =    RemBertForQuestionAnswering(config=__lowerCamelCase			)
             model.to(__lowerCamelCase			)
             model.eval()
             lowerCamelCase__						:							int       =    model(
                 __lowerCamelCase   , attention_mask=__lowerCamelCase   , token_type_ids=__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 		lowerCAmelCase	(  self				:     Tuple   , __lowerCamelCase				:     Union[str, Any]   , __lowerCamelCase				:     List[str]   , __lowerCamelCase				:     Optional[Any]   , __lowerCamelCase				:     Dict   , __lowerCamelCase				:     Optional[Any]   , __lowerCamelCase				:     int   , __lowerCamelCase				:     int			):


             '''simple docstring'''



             lowerCamelCase__						:							Any       =    self.num_labels
             lowerCamelCase__						:							Any       =    RemBertForSequenceClassification(__lowerCamelCase			)
             model.to(__lowerCamelCase			)
             model.eval()
             lowerCamelCase__						:							Union[str, Any]       =    model(__lowerCamelCase   , attention_mask=__lowerCamelCase   , token_type_ids=__lowerCamelCase   , labels=__lowerCamelCase			)
             self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.num_labels)			)





       def 		lowerCAmelCase	(  self				:     List[Any]   , __lowerCamelCase				:     int   , __lowerCamelCase				:     List[str]   , __lowerCamelCase				:     List[str]   , __lowerCamelCase				:     str   , __lowerCamelCase				:     int   , __lowerCamelCase				:     Optional[int]   , __lowerCamelCase				:     Optional[Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							Tuple       =    self.num_labels
             lowerCamelCase__						:							Optional[int]       =    RemBertForTokenClassification(config=__lowerCamelCase			)
             model.to(__lowerCamelCase			)
             model.eval()
             lowerCamelCase__						:							Union[str, Any]       =    model(__lowerCamelCase   , attention_mask=__lowerCamelCase   , token_type_ids=__lowerCamelCase   , labels=__lowerCamelCase			)
             self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.seq_length, self.num_labels)			)





       def 		lowerCAmelCase	(  self				:     Any   , __lowerCamelCase				:     int   , __lowerCamelCase				:     List[str]   , __lowerCamelCase				:     int   , __lowerCamelCase				:     Optional[int]   , __lowerCamelCase				:     Optional[Any]   , __lowerCamelCase				:     Dict   , __lowerCamelCase				:     Tuple			):


             '''simple docstring'''



             lowerCamelCase__						:							Dict       =    self.num_choices
             lowerCamelCase__						:							int       =    RemBertForMultipleChoice(config=__lowerCamelCase			)
             model.to(__lowerCamelCase			)
             model.eval()
             lowerCamelCase__						:							Optional[int]       =    input_ids.unsqueeze(1			).expand(-1   , self.num_choices   , -1			).contiguous()
             lowerCamelCase__						:							Optional[int]       =    token_type_ids.unsqueeze(1			).expand(-1   , self.num_choices   , -1			).contiguous()
             lowerCamelCase__						:							Tuple       =    input_mask.unsqueeze(1			).expand(-1   , self.num_choices   , -1			).contiguous()
             lowerCamelCase__						:							Optional[int]       =    model(
                 __lowerCamelCase   , attention_mask=__lowerCamelCase   , token_type_ids=__lowerCamelCase   , labels=__lowerCamelCase   , )
             self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.num_choices)			)





       def 		lowerCAmelCase	(  self				:     Union[str, Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							Union[str, Any]       =    self.prepare_config_and_inputs()
             (
                 (
                 lowerCamelCase__
             )       ,  (
                 lowerCamelCase__
             )       ,  (
                 lowerCamelCase__
             )       ,  (
                 lowerCamelCase__
             )       ,  (
                 lowerCamelCase__
             )       ,  (
                 lowerCamelCase__
             )       ,  (
                 lowerCamelCase__
             )       ,  
             )						:							Optional[Any]       =    config_and_inputs
             lowerCamelCase__						:							int       =    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
             return config, inputs_dict







@require_torch
class      _lowercase					(				lowercase__				,    lowercase__				,    unittest.TestCase):


       """simple docstring"""




       A__   		=     (
           (
               RemBertModel,
               RemBertForMaskedLM,
               RemBertForCausalLM,
               RemBertForMultipleChoice,
               RemBertForQuestionAnswering,
               RemBertForSequenceClassification,
               RemBertForTokenClassification,
           )
           if is_torch_available()
           else ()
       )
       A__   		=     (RemBertForCausalLM,) if is_torch_available() else ()
       A__   		=     (
           {
               "feature-extraction": RemBertModel,
               "fill-mask": RemBertForMaskedLM,
               "question-answering": RemBertForQuestionAnswering,
               "text-classification": RemBertForSequenceClassification,
               "text-generation": RemBertForCausalLM,
               "token-classification": RemBertForTokenClassification,
               "zero-shot": RemBertForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )





       def 		lowerCAmelCase	(  self				:     str			):


             '''simple docstring'''



             lowerCamelCase__						:							Optional[int]       =    RemBertModelTester(self			)
             lowerCamelCase__						:							Any       =    ConfigTester(self   , config_class=__lowerCamelCase   , hidden_size=37			)





       def 		lowerCAmelCase	(  self				:     int			):


             '''simple docstring'''



             self.config_tester.run_common_tests()





       def 		lowerCAmelCase	(  self				:     Any			):


             '''simple docstring'''



             lowerCamelCase__						:							Any       =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     str			):


             '''simple docstring'''



             lowerCamelCase__						:							Dict       =    self.model_tester.prepare_config_and_inputs()
             for type in ["absolute", "relative_key", "relative_key_query"]:
                   lowerCamelCase__						:							Union[str, Any]       =    type
                   self.model_tester.create_and_check_model(*__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     Optional[int]			):


             '''simple docstring'''



             lowerCamelCase__						:							Dict       =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     Union[str, Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							Any       =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_multiple_choice(*__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     Dict			):


             '''simple docstring'''



             lowerCamelCase__						:							List[str]       =    self.model_tester.prepare_config_and_inputs_for_decoder()
             self.model_tester.create_and_check_decoder_model_past_large_inputs(*__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     List[str]			):


             '''simple docstring'''



             lowerCamelCase__						:							List[Any]       =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     Dict			):


             '''simple docstring'''



             lowerCamelCase__						:							Any       =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_sequence_classification(*__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     int			):


             '''simple docstring'''



             lowerCamelCase__						:							Any       =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     Union[str, Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							Dict       =    self.model_tester.prepare_config_and_inputs_for_decoder()
             self.model_tester.create_and_check_model_as_decoder(*__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     Optional[Any]			):


             '''simple docstring'''



             (
                 (
                 lowerCamelCase__
             )       ,  (
                 lowerCamelCase__
             )       ,  (
                 lowerCamelCase__
             )       ,  (
                 lowerCamelCase__
             )       ,  (
                 lowerCamelCase__
             )       ,  (
                 lowerCamelCase__
             )       ,  (
                 lowerCamelCase__
             )       ,  (
                 lowerCamelCase__
             )       ,  (
                 lowerCamelCase__
             )       ,  
             )						:							Dict       =    self.model_tester.prepare_config_and_inputs_for_decoder()

             lowerCamelCase__						:							Any       =    None

             self.model_tester.create_and_check_model_as_decoder(
                 __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , )





       @slow
       def 		lowerCAmelCase	(  self				:     Optional[int]			):


             '''simple docstring'''



             for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   lowerCamelCase__						:							Dict       =    RemBertModel.from_pretrained(__lowerCamelCase			)
                   self.assertIsNotNone(__lowerCamelCase			)







@require_torch
class      _lowercase					(				unittest.TestCase):


       """simple docstring"""




       @slow
       def 		lowerCAmelCase	(  self				:     Optional[Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							Optional[int]       =    RemBertModel.from_pretrained("google/rembert"			)
             lowerCamelCase__						:							str       =    torch.tensor([[312, 56498, 313, 2125, 313]]			)
             lowerCamelCase__						:							List[str]       =    torch.tensor([[0, 0, 0, 1, 1]]			)
             with torch.no_grad():
                   lowerCamelCase__						:							Union[str, Any]       =    model(__lowerCamelCase   , token_type_ids=__lowerCamelCase   , output_hidden_states=__lowerCamelCase			)

             lowerCamelCase__						:							int       =    1152

             lowerCamelCase__						:							Any       =    torch.Size((1, 5, hidden_size)			)
             self.assertEqual(output["last_hidden_state"].shape   , __lowerCamelCase			)

             lowerCamelCase__						:							int       =    torch.tensor(
                 [
                     [
                         [0.0_7_5_4, -0.2_0_2_2, 0.1_9_0_4],
                         [-0.3_3_5_4, -0.3_6_9_2, -0.4_7_9_1],
                         [-0.2_3_1_4, -0.6_7_2_9, -0.0_7_4_9],
                         [-0.0_3_9_6, -0.3_1_0_5, -0.4_2_3_4],
                         [-0.1_5_7_1, -0.0_5_2_5, 0.5_3_5_3],
                     ]
                 ]			)

             # Running on the original tf implementation gives slightly different results here.
             # Not clear why this variations is present
             # TODO: Find reason for discrepancy
             # expected_original_implementation = [[
             #     [0.07630594074726105, -0.20146065950393677, 0.19107051193714142],
             #     [-0.3405614495277405, -0.36971670389175415, -0.4808273911476135],
             #     [-0.22587086260318756, -0.6656315922737122, -0.07844287157058716],
             #     [-0.04145475849509239, -0.3077218234539032, -0.42316967248916626],
             #     [-0.15887849032878876, -0.054529931396245956, 0.5356100797653198]
             # ]]

             self.assertTrue(torch.allclose(output["last_hidden_state"][:, :, :3]   , __lowerCamelCase   , atol=1E-4			)			)






import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata

def  lowercase_						(						_A     :       str		):

      """simple docstring"""




      lowerCamelCase__						:							Optional[Any]       =    min(re.search("^ +"       ,   _A		).end() if t.startswith(" "		) else 0 for t in string.splitlines()		)
      return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(_A		)]		)


A :       Optional[int]						=   "\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n"

A :       str						=   "\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n"


A :       Optional[Any]						=   "\\n# Begin of markdown\n\nSome cool dataset card\n"







class      _lowercase					(				unittest.TestCase):


       """simple docstring"""




       def 		lowerCAmelCase	(  self				:     List[Any]			):


             '''simple docstring'''



             with tempfile.TemporaryDirectory() as tmp_dir:
                   lowerCamelCase__						:							List[str]       =    Path(__lowerCamelCase			) / "README.md"
                   with open(__lowerCamelCase   , "w+"			) as readme_file:
                         readme_file.write(__lowerCamelCase			)
                   lowerCamelCase__						:							Dict       =    DatasetMetadata.from_readme(__lowerCamelCase			)
                   self.assertDictEqual(__lowerCamelCase   , {"language": ["zh", "en"], "task_ids": ["sentiment-classification"]}			)

                   with open(__lowerCamelCase   , "w+"			) as readme_file:
                         readme_file.write(__lowerCamelCase			)
                   lowerCamelCase__						:							Optional[Any]       =    DatasetMetadata.from_readme(__lowerCamelCase			)
                   self.assertDictEqual(__lowerCamelCase   , {}			)

                   with open(__lowerCamelCase   , "w+"			) as readme_file:
                         readme_file.write(__lowerCamelCase			)
                   lowerCamelCase__						:							int       =    DatasetMetadata.from_readme(__lowerCamelCase			)
                   self.assertEqual(__lowerCamelCase   , {}			)





       def 		lowerCAmelCase	(  self				:     Any			):


             '''simple docstring'''



             lowerCamelCase__						:							List[str]       =    _dedent(
                 "\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            "			)
             assert DatasetMetadata.from_yaml_string(__lowerCamelCase			)

             lowerCamelCase__						:							Dict       =    _dedent(
                 "\\n            annotations_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            task_ids:\n            - open-domain-qa\n            "			)
             with self.assertRaises(__lowerCamelCase			):
                   DatasetMetadata.from_yaml_string(__lowerCamelCase			)

             lowerCamelCase__						:							Optional[Any]       =    _dedent(
                 "\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - text-classification\n            task_ids:\n            - multi-class-classification\n            paperswithcode_id:\n            - squad\n            configs:\n            - en\n            train-eval-index:\n            - config: en\n              task: text-classification\n              task_id: multi_class_classification\n              splits:\n                train_split: train\n                eval_split: test\n              col_mapping:\n                text: text\n                label: target\n              metrics:\n                - type: accuracy\n                  name: Accuracy\n            extra_gated_prompt: |\n              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:\n              [RESEARCHER_FULLNAME] (the \"Researcher\") has requested permission to use the ImageNet database (the \"Database\") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n              1. Researcher shall use the Database only for non-commercial research and educational purposes.\n            extra_gated_fields:\n              Company: text\n              Country: text\n              I agree to use this model for non-commerical use ONLY: checkbox\n            "			)
             assert DatasetMetadata.from_yaml_string(__lowerCamelCase			)


