
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   __UpperCamelCase				:






     """simple docstring"""







     def __init__(							self    :    Optional[int]      ,		_A    :    str      ,		_A    :    List[Any]=13      ,		_A    :    int=7      ,		_A    :    Optional[Any]=True      ,		_A    :    Optional[Any]=True      ,		_A    :    Optional[int]=True      ,		_A    :    str=True      ,		_A    :    int=99      ,		_A    :    Dict=32      ,		_A    :    Dict=18      ,		_A    :    Any=43      ,		_A    :    Dict=5      ,		_A    :    Any=4      ,		_A    :    Optional[Any]=37      ,		_A    :    int="gelu"      ,		_A    :    Optional[int]=0.1      ,		_A    :    Union[str, Any]=0.1      ,		_A    :    Union[str, Any]=512      ,		_A    :    Dict=16      ,		_A    :    List[Any]=2      ,		_A    :    str=0.02      ,		_A    :    str=3      ,		_A    :    Tuple=4      ,		_A    :    Optional[Any]=None      ,		):







       """simple docstring"""




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



     def 				UpperCAmelCase__    (							self    :    Optional[int]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						ids_tensor([self.batch_size, self.seq_length]      ,		self.vocab_size				)

       __SCREAMING_SNAKE_CASE     :					List[str]			=						None
       if self.use_input_mask:
         __SCREAMING_SNAKE_CASE     :					Tuple			=						random_attention_mask([self.batch_size, self.seq_length]				)

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

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

       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						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=_A      ,		initializer_range=self.initializer_range      ,		)

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



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







       """simple docstring"""




       (
           (
           __SCREAMING_SNAKE_CASE
       ),						(
           __SCREAMING_SNAKE_CASE
       ),						(
           __SCREAMING_SNAKE_CASE
       ),						(
           __SCREAMING_SNAKE_CASE
       ),						(
           __SCREAMING_SNAKE_CASE
       ),						(
           __SCREAMING_SNAKE_CASE
       ),						(
           __SCREAMING_SNAKE_CASE
       ),						
       )     :					Union[str, Any]			=						self.prepare_config_and_inputs()

       __SCREAMING_SNAKE_CASE     :					List[Any]			=						True
       __SCREAMING_SNAKE_CASE     :					Dict			=						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
       __SCREAMING_SNAKE_CASE     :					Tuple			=						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 				UpperCAmelCase__    (							self    :    Optional[Any]      ,		_A    :    Optional[int]      ,		_A    :    Union[str, Any]      ,		_A    :    Optional[Any]      ,		_A    :    Optional[int]      ,		_A    :    List[Any]      ,		_A    :    Optional[int]      ,		_A    :    int				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					int			=						RemBertModel(config=_A				)
       model.to(_A				)
       model.eval()
       __SCREAMING_SNAKE_CASE     :					Any			=						model(_A      ,		attention_mask=_A      ,		token_type_ids=_A				)
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						model(_A      ,		token_type_ids=_A				)
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						model(_A				)
       self.parent.assertEqual(result.last_hidden_state.shape      ,		(self.batch_size, self.seq_length, self.hidden_size)				)



     def 				UpperCAmelCase__    (							self    :    int      ,		_A    :    Union[str, Any]      ,		_A    :    Optional[Any]      ,		_A    :    List[Any]      ,		_A    :    int      ,		_A    :    Dict      ,		_A    :    Dict      ,		_A    :    Union[str, Any]      ,		_A    :    Optional[int]      ,		_A    :    Dict      ,		):







       """simple docstring"""




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



     def 				UpperCAmelCase__    (							self    :    Dict      ,		_A    :    str      ,		_A    :    str      ,		_A    :    str      ,		_A    :    Union[str, Any]      ,		_A    :    List[str]      ,		_A    :    str      ,		_A    :    List[str]      ,		_A    :    Optional[Any]      ,		_A    :    str      ,		):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					str			=						RemBertForCausalLM(config=_A				)
       model.to(_A				)
       model.eval()
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						model(_A      ,		attention_mask=_A      ,		token_type_ids=_A      ,		labels=_A				)
       self.parent.assertEqual(result.logits.shape      ,		(self.batch_size, self.seq_length, self.vocab_size)				)



     def 				UpperCAmelCase__    (							self    :    str      ,		_A    :    Optional[Any]      ,		_A    :    Union[str, Any]      ,		_A    :    int      ,		_A    :    Any      ,		_A    :    Optional[Any]      ,		_A    :    Any      ,		_A    :    Dict				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					str			=						RemBertForMaskedLM(config=_A				)
       model.to(_A				)
       model.eval()
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						model(_A      ,		attention_mask=_A      ,		token_type_ids=_A      ,		labels=_A				)
       self.parent.assertEqual(result.logits.shape      ,		(self.batch_size, self.seq_length, self.vocab_size)				)



     def 				UpperCAmelCase__    (							self    :    Union[str, Any]      ,		_A    :    List[str]      ,		_A    :    str      ,		_A    :    List[str]      ,		_A    :    Tuple      ,		_A    :    Optional[Any]      ,		_A    :    Dict      ,		_A    :    Optional[Any]      ,		_A    :    str      ,		_A    :    Optional[int]      ,		):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						True
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						True
       __SCREAMING_SNAKE_CASE     :					Tuple			=						RemBertForCausalLM(config=_A				)
       model.to(_A				)
       model.eval()

       # first forward pass
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						model(
           _A      ,		attention_mask=_A      ,		encoder_hidden_states=_A      ,		encoder_attention_mask=_A      ,		use_cache=_A      ,		)
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						outputs.past_key_values

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

       # append to next input_ids and
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						torch.cat([input_ids, next_tokens]      ,		dim=-1				)
       __SCREAMING_SNAKE_CASE     :					str			=						torch.cat([input_mask, next_mask]      ,		dim=-1				)

       __SCREAMING_SNAKE_CASE     :					int			=						model(
           _A      ,		attention_mask=_A      ,		encoder_hidden_states=_A      ,		encoder_attention_mask=_A      ,		output_hidden_states=_A      ,		)['''hidden_states'''][0]
       __SCREAMING_SNAKE_CASE     :					Any			=						model(
           _A      ,		attention_mask=_A      ,		encoder_hidden_states=_A      ,		encoder_attention_mask=_A      ,		past_key_values=_A      ,		output_hidden_states=_A      ,		)['''hidden_states'''][0]

       # select random slice
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						ids_tensor((1,)      ,		output_from_past.shape[-1]				).item()
       __SCREAMING_SNAKE_CASE     :					Tuple			=						output_from_no_past[:, -3:, random_slice_idx].detach()
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						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(_A      ,		_A      ,		atol=1e-3				)				)



     def 				UpperCAmelCase__    (							self    :    Optional[int]      ,		_A    :    Any      ,		_A    :    Any      ,		_A    :    Tuple      ,		_A    :    Optional[Any]      ,		_A    :    Optional[int]      ,		_A    :    Union[str, Any]      ,		_A    :    Union[str, Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Tuple			=						RemBertForQuestionAnswering(config=_A				)
       model.to(_A				)
       model.eval()
       __SCREAMING_SNAKE_CASE     :					Any			=						model(
           _A      ,		attention_mask=_A      ,		token_type_ids=_A      ,		start_positions=_A      ,		end_positions=_A      ,		)
       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    :    Optional[Any]      ,		_A    :    Union[str, Any]      ,		_A    :    List[Any]      ,		_A    :    Optional[Any]      ,		_A    :    List[str]      ,		_A    :    Tuple      ,		_A    :    List[Any]      ,		_A    :    int				):







       """simple docstring"""




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



     def 				UpperCAmelCase__    (							self    :    Optional[Any]      ,		_A    :    Any      ,		_A    :    str      ,		_A    :    Tuple      ,		_A    :    int      ,		_A    :    Optional[Any]      ,		_A    :    Dict      ,		_A    :    Optional[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						self.num_labels
       __SCREAMING_SNAKE_CASE     :					Dict			=						RemBertForTokenClassification(config=_A				)
       model.to(_A				)
       model.eval()
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						model(_A      ,		attention_mask=_A      ,		token_type_ids=_A      ,		labels=_A				)
       self.parent.assertEqual(result.logits.shape      ,		(self.batch_size, self.seq_length, self.num_labels)				)



     def 				UpperCAmelCase__    (							self    :    List[str]      ,		_A    :    Tuple      ,		_A    :    Union[str, Any]      ,		_A    :    Dict      ,		_A    :    Optional[int]      ,		_A    :    List[str]      ,		_A    :    Tuple      ,		_A    :    str				):







       """simple docstring"""




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




     def 				UpperCAmelCase__    (							self    :    Tuple				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						self.prepare_config_and_inputs()
       (
           (
           __SCREAMING_SNAKE_CASE
       ),						(
           __SCREAMING_SNAKE_CASE
       ),						(
           __SCREAMING_SNAKE_CASE
       ),						(
           __SCREAMING_SNAKE_CASE
       ),						(
           __SCREAMING_SNAKE_CASE
       ),						(
           __SCREAMING_SNAKE_CASE
       ),						(
           __SCREAMING_SNAKE_CASE
       ),						
       )     :					Dict			=						config_and_inputs
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
       return config, inputs_dict






@require_torch
class   __UpperCamelCase				(    lowerCAmelCase__				,		lowerCAmelCase__				,		unittest.TestCase   ):






     """simple docstring"""







     lowerCAmelCase_     =	(
         (
             RemBertModel,
             RemBertForMaskedLM,
             RemBertForCausalLM,
             RemBertForMultipleChoice,
             RemBertForQuestionAnswering,
             RemBertForSequenceClassification,
             RemBertForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     lowerCAmelCase_     =	(RemBertForCausalLM,) if is_torch_available() else ()
     lowerCAmelCase_     =	(
         {
             '''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 				UpperCAmelCase__    (							self    :    Optional[int]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						RemBertModelTester(self				)
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						ConfigTester(self      ,		config_class=_A      ,		hidden_size=37				)



     def 				UpperCAmelCase__    (							self    :    Any				):







       """simple docstring"""




       self.config_tester.run_common_tests()



     def 				UpperCAmelCase__    (							self    :    str				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_A				)



     def 				UpperCAmelCase__    (							self    :    Tuple				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[Any]			=						self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
         __SCREAMING_SNAKE_CASE     :					int			=						type
         self.model_tester.create_and_check_model(*_A				)



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







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*_A				)



     def 				UpperCAmelCase__    (							self    :    Any				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*_A				)



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







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[Any]			=						self.model_tester.prepare_config_and_inputs_for_decoder()
       self.model_tester.create_and_check_decoder_model_past_large_inputs(*_A				)



     def 				UpperCAmelCase__    (							self    :    Tuple				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*_A				)



     def 				UpperCAmelCase__    (							self    :    Optional[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*_A				)



     def 				UpperCAmelCase__    (							self    :    Tuple				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*_A				)



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







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						self.model_tester.prepare_config_and_inputs_for_decoder()
       self.model_tester.create_and_check_model_as_decoder(*_A				)



     def 				UpperCAmelCase__    (							self    :    Optional[int]				):







       """simple docstring"""




       (
           (
           __SCREAMING_SNAKE_CASE
       ),						(
           __SCREAMING_SNAKE_CASE
       ),						(
           __SCREAMING_SNAKE_CASE
       ),						(
           __SCREAMING_SNAKE_CASE
       ),						(
           __SCREAMING_SNAKE_CASE
       ),						(
           __SCREAMING_SNAKE_CASE
       ),						(
           __SCREAMING_SNAKE_CASE
       ),						(
           __SCREAMING_SNAKE_CASE
       ),						(
           __SCREAMING_SNAKE_CASE
       ),						
       )     :					List[str]			=						self.model_tester.prepare_config_and_inputs_for_decoder()

       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						None

       self.model_tester.create_and_check_model_as_decoder(
           _A      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A      ,		)




     @slow
     def 				UpperCAmelCase__    (							self    :    str				):







       """simple docstring"""




       for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __SCREAMING_SNAKE_CASE     :					str			=						RemBertModel.from_pretrained(_A				)
         self.assertIsNotNone(_A				)






@require_torch
class   __UpperCamelCase				(    unittest.TestCase   ):






     """simple docstring"""







     @slow
     def 				UpperCAmelCase__    (							self    :    str				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Any			=						RemBertModel.from_pretrained('''google/rembert'''				)
       __SCREAMING_SNAKE_CASE     :					int			=						torch.tensor([[312, 5_6498, 313, 2125, 313]]				)
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						torch.tensor([[0, 0, 0, 1, 1]]				)
       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						model(_A      ,		token_type_ids=_A      ,		output_hidden_states=_A				)

       __SCREAMING_SNAKE_CASE     :					List[str]			=						1152

       __SCREAMING_SNAKE_CASE     :					Tuple			=						torch.Size((1, 5, hidden_size)				)
       self.assertEqual(output['''last_hidden_state'''].shape      ,		_A				)

       __SCREAMING_SNAKE_CASE     :					List[Any]			=						torch.tensor(
           [
               [
                   [0.07_54, -0.20_22, 0.19_04],
                   [-0.33_54, -0.36_92, -0.47_91],
                   [-0.23_14, -0.67_29, -0.07_49],
                   [-0.03_96, -0.31_05, -0.42_34],
                   [-0.15_71, -0.05_25, 0.53_53],
               ]
           ]				)

       # 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]      ,		_A      ,		atol=1e-4				)				)


import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


lowercase_											=	"""
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.

In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]

On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
"""





class   __UpperCamelCase				(    unittest.TestCase				,		lowerCAmelCase__   ):






     """simple docstring"""







     def 				UpperCAmelCase__    (							self    :    Optional[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					int			=						load_tool('''summarization'''				)
       self.tool.setup()
       __SCREAMING_SNAKE_CASE     :					Any			=						load_tool('''summarization'''      ,		remote=_A				)



     def 				UpperCAmelCase__    (							self    :    Tuple				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						self.tool(_A				)
       self.assertEqual(
           _A      ,		'''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''      ,		)



     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.remote_tool(_A				)
       self.assertEqual(
           _A      ,		'''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''      ,		)



     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						self.tool(text=_A				)
       self.assertEqual(
           _A      ,		'''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''      ,		)




     def 				UpperCAmelCase__    (							self    :    Optional[int]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					str			=						self.remote_tool(text=_A				)
       self.assertEqual(
           _A      ,		'''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''      ,		)
