



'''simple docstring'''






import unittest

from transformers import MraConfig, 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


if is_torch_available():
						import torch

						from transformers import (
						    MraForMaskedLM,
						    MraForMultipleChoice,
						    MraForQuestionAnswering,
						    MraForSequenceClassification,
						    MraForTokenClassification,
						    MraModel,
						)
						from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST

class 				SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	def __init__(						self			:     Optional[int]		,			__snake_case			:     str		,			__snake_case			:     Union[str, Any]=2		,			__snake_case			:     Optional[int]=8		,			__snake_case			:     Any=True		,			__snake_case			:     Union[str, Any]=True		,			__snake_case			:     Dict=True		,			__snake_case			:     int=True		,			__snake_case			:     List[Any]=99		,			__snake_case			:     str=16		,			__snake_case			:     Tuple=5		,			__snake_case			:     Tuple=2		,			__snake_case			:     str=36		,			__snake_case			:     Dict="gelu"		,			__snake_case			:     str=0.0		,			__snake_case			:     Optional[int]=0.0		,			__snake_case			:     Optional[int]=512		,			__snake_case			:     Optional[Any]=16		,			__snake_case			:     int=2		,			__snake_case			:     int=0.02		,			__snake_case			:     str=3		,			__snake_case			:     Dict=4		,			__snake_case			:     str=None		,			)			->		Optional[int]:
								UpperCAmelCase :       Optional[int] 							=		parent
								UpperCAmelCase :       Tuple 							=		batch_size
								UpperCAmelCase :       List[str] 							=		seq_length
								UpperCAmelCase :       List[Any] 							=		is_training
								UpperCAmelCase :       int 							=		use_input_mask
								UpperCAmelCase :       Any 							=		use_token_type_ids
								UpperCAmelCase :       str 							=		use_labels
								UpperCAmelCase :       Union[str, Any] 							=		vocab_size
								UpperCAmelCase :       List[str] 							=		hidden_size
								UpperCAmelCase :       Optional[Any] 							=		num_hidden_layers
								UpperCAmelCase :       Union[str, Any] 							=		num_attention_heads
								UpperCAmelCase :       Optional[Any] 							=		intermediate_size
								UpperCAmelCase :       Union[str, Any] 							=		hidden_act
								UpperCAmelCase :       int 							=		hidden_dropout_prob
								UpperCAmelCase :       Optional[int] 							=		attention_probs_dropout_prob
								UpperCAmelCase :       Union[str, Any] 							=		max_position_embeddings
								UpperCAmelCase :       str 							=		type_vocab_size
								UpperCAmelCase :       List[str] 							=		type_sequence_label_size
								UpperCAmelCase :       Tuple 							=		initializer_range
								UpperCAmelCase :       Optional[Any] 							=		num_labels
								UpperCAmelCase :       Optional[int] 							=		num_choices
								UpperCAmelCase :       Any 							=		scope







	def      A	(						self			:     Tuple		)			->		Union[str, Any]:
								UpperCAmelCase :       Optional[int] 							=		ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size		)

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

								UpperCAmelCase :       Dict 							=		None
								if self.use_token_type_ids:
															UpperCAmelCase :       Union[str, Any] 							=		ids_tensor([self.batch_size, self.seq_length]		,			self.type_vocab_size		)

								UpperCAmelCase :       str 							=		None
								UpperCAmelCase :       Tuple 							=		None
								UpperCAmelCase :       int 							=		None
								if self.use_labels:
															UpperCAmelCase :       str 							=		ids_tensor([self.batch_size]		,			self.type_sequence_label_size		)
															UpperCAmelCase :       Dict 							=		ids_tensor([self.batch_size, self.seq_length]		,			self.num_labels		)
															UpperCAmelCase :       str 							=		ids_tensor([self.batch_size]		,			self.num_choices		)

								UpperCAmelCase :       List[str] 							=		self.get_config()

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







	def      A	(						self			:     int		)			->		Tuple:
								return MraConfig(
								    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		,			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=__snake_case		,			initializer_range=self.initializer_range		,			)







	def      A	(						self			:     Optional[Any]		)			->		Any:
								UpperCAmelCase :       Optional[Any] 							=		self.get_config()
								UpperCAmelCase :       int 							=		300
								return config







	def      A	(						self			:     Optional[Any]		)			->		Any:
								(
								    (
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		
								) :       Tuple 							=		self.prepare_config_and_inputs()

								UpperCAmelCase :       Dict 							=		True
								UpperCAmelCase :       Tuple 							=		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
								UpperCAmelCase :       str 							=		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      A	(						self			:     Union[str, Any]		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     int		,			__snake_case			:     Optional[int]		,			__snake_case			:     int		,			__snake_case			:     Dict		,			__snake_case			:     Tuple		,			__snake_case			:     Optional[Any]		)			->		List[str]:
								UpperCAmelCase :       int 							=		MraModel(config=__snake_case		)
								model.to(__snake_case		)
								model.eval()
								UpperCAmelCase :       Tuple 							=		model(__snake_case		,			attention_mask=__snake_case		,			token_type_ids=__snake_case		)
								UpperCAmelCase :       Optional[int] 							=		model(__snake_case		,			token_type_ids=__snake_case		)
								UpperCAmelCase :       Dict 							=		model(__snake_case		)
								self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, self.seq_length, self.hidden_size)		)







	def      A	(						self			:     Tuple		,			__snake_case			:     Optional[Any]		,			__snake_case			:     List[str]		,			__snake_case			:     List[str]		,			__snake_case			:     int		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     Optional[Any]		,			__snake_case			:     Any		,			__snake_case			:     List[Any]		,			__snake_case			:     Optional[Any]		,			)			->		Tuple:
								UpperCAmelCase :       str 							=		True
								UpperCAmelCase :       Tuple 							=		MraModel(__snake_case		)
								model.to(__snake_case		)
								model.eval()
								UpperCAmelCase :       Optional[int] 							=		model(
								    __snake_case		,			attention_mask=__snake_case		,			token_type_ids=__snake_case		,			encoder_hidden_states=__snake_case		,			encoder_attention_mask=__snake_case		,			)
								UpperCAmelCase :       Optional[Any] 							=		model(
								    __snake_case		,			attention_mask=__snake_case		,			token_type_ids=__snake_case		,			encoder_hidden_states=__snake_case		,			)
								UpperCAmelCase :       str 							=		model(__snake_case		,			attention_mask=__snake_case		,			token_type_ids=__snake_case		)
								self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, self.seq_length, self.hidden_size)		)







	def      A	(						self			:     Tuple		,			__snake_case			:     str		,			__snake_case			:     Optional[Any]		,			__snake_case			:     List[str]		,			__snake_case			:     Tuple		,			__snake_case			:     Optional[Any]		,			__snake_case			:     List[str]		,			__snake_case			:     int		)			->		Any:
								UpperCAmelCase :       Dict 							=		MraForMaskedLM(config=__snake_case		)
								model.to(__snake_case		)
								model.eval()
								UpperCAmelCase :       Optional[int] 							=		model(__snake_case		,			attention_mask=__snake_case		,			token_type_ids=__snake_case		,			labels=__snake_case		)
								self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.seq_length, self.vocab_size)		)







	def      A	(						self			:     Tuple		,			__snake_case			:     Tuple		,			__snake_case			:     Dict		,			__snake_case			:     Dict		,			__snake_case			:     Any		,			__snake_case			:     int		,			__snake_case			:     Optional[Any]		,			__snake_case			:     Tuple		)			->		Optional[int]:
								UpperCAmelCase :       List[str] 							=		MraForQuestionAnswering(config=__snake_case		)
								model.to(__snake_case		)
								model.eval()
								UpperCAmelCase :       List[Any] 							=		model(
								    __snake_case		,			attention_mask=__snake_case		,			token_type_ids=__snake_case		,			start_positions=__snake_case		,			end_positions=__snake_case		,			)
								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      A	(						self			:     str		,			__snake_case			:     Optional[int]		,			__snake_case			:     List[str]		,			__snake_case			:     str		,			__snake_case			:     int		,			__snake_case			:     Optional[Any]		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     List[Any]		)			->		int:
								UpperCAmelCase :       int 							=		self.num_labels
								UpperCAmelCase :       Union[str, Any] 							=		MraForSequenceClassification(__snake_case		)
								model.to(__snake_case		)
								model.eval()
								UpperCAmelCase :       List[str] 							=		model(__snake_case		,			attention_mask=__snake_case		,			token_type_ids=__snake_case		,			labels=__snake_case		)
								self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.num_labels)		)







	def      A	(						self			:     str		,			__snake_case			:     Dict		,			__snake_case			:     Optional[Any]		,			__snake_case			:     Dict		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     Dict		)			->		int:
								UpperCAmelCase :       Tuple 							=		self.num_labels
								UpperCAmelCase :       List[str] 							=		MraForTokenClassification(config=__snake_case		)
								model.to(__snake_case		)
								model.eval()
								UpperCAmelCase :       str 							=		model(__snake_case		,			attention_mask=__snake_case		,			token_type_ids=__snake_case		,			labels=__snake_case		)
								self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.seq_length, self.num_labels)		)







	def      A	(						self			:     str		,			__snake_case			:     int		,			__snake_case			:     Any		,			__snake_case			:     Tuple		,			__snake_case			:     Optional[int]		,			__snake_case			:     List[str]		,			__snake_case			:     str		,			__snake_case			:     Union[str, Any]		)			->		Optional[Any]:
								UpperCAmelCase :       Tuple 							=		self.num_choices
								UpperCAmelCase :       int 							=		MraForMultipleChoice(config=__snake_case		)
								model.to(__snake_case		)
								model.eval()
								UpperCAmelCase :       str 							=		input_ids.unsqueeze(1		).expand(-1		,			self.num_choices		,			-1		).contiguous()
								UpperCAmelCase :       List[Any] 							=		token_type_ids.unsqueeze(1		).expand(-1		,			self.num_choices		,			-1		).contiguous()
								UpperCAmelCase :       List[str] 							=		input_mask.unsqueeze(1		).expand(-1		,			self.num_choices		,			-1		).contiguous()
								UpperCAmelCase :       List[str] 							=		model(
								    __snake_case		,			attention_mask=__snake_case		,			token_type_ids=__snake_case		,			labels=__snake_case		,			)
								self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.num_choices)		)







	def      A	(						self			:     str		)			->		Dict:
								UpperCAmelCase :       Any 							=		self.prepare_config_and_inputs()
								(
								    (
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		
								) :       List[str] 							=		config_and_inputs
								UpperCAmelCase :       Any 							=		{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
								return config, inputs_dict

@require_torch
class 				SCREAMING_SNAKE_CASE(							A__							,		unittest.TestCase			):







	"""simple docstring"""






	lowerCamelCase__			 =       (
	    (
	        MraModel,
	        MraForMaskedLM,
	        MraForMultipleChoice,
	        MraForQuestionAnswering,
	        MraForSequenceClassification,
	        MraForTokenClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	lowerCamelCase__			 =       False
	lowerCamelCase__			 =       False
	lowerCamelCase__			 =       False
	lowerCamelCase__			 =       False

	lowerCamelCase__			 =       ()







	def      A	(						self			:     int		)			->		Union[str, Any]:
								UpperCAmelCase :       List[str] 							=		MraModelTester(self		)
								UpperCAmelCase :       Optional[int] 							=		ConfigTester(self		,			config_class=__snake_case		,			hidden_size=37		)







	def      A	(						self			:     Optional[Any]		)			->		str:
								self.config_tester.run_common_tests()







	def      A	(						self			:     Tuple		)			->		Optional[Any]:
								UpperCAmelCase :       Optional[int] 							=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__snake_case		)







	def      A	(						self			:     List[Any]		)			->		Optional[Any]:
								UpperCAmelCase :       Tuple 							=		self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
															UpperCAmelCase :       List[Any] 							=		type
															self.model_tester.create_and_check_model(*__snake_case		)







	def      A	(						self			:     Tuple		)			->		Dict:
								UpperCAmelCase :       List[Any] 							=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*__snake_case		)







	def      A	(						self			:     Tuple		)			->		List[str]:
								UpperCAmelCase :       Tuple 							=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_multiple_choice(*__snake_case		)







	def      A	(						self			:     int		)			->		Dict:
								UpperCAmelCase :       Optional[int] 							=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*__snake_case		)







	def      A	(						self			:     Dict		)			->		Optional[int]:
								UpperCAmelCase :       Any 							=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*__snake_case		)







	def      A	(						self			:     Any		)			->		Optional[int]:
								UpperCAmelCase :       List[str] 							=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*__snake_case		)







	@slow
	def      A	(						self			:     Dict		)			->		Any:
								for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															UpperCAmelCase :       str 							=		MraModel.from_pretrained(__snake_case		)
															self.assertIsNotNone(__snake_case		)







	@unittest.skip(reason='''MRA does not output attentions'''		)
	def      A	(						self			:     str		)			->		Optional[Any]:
								return

@require_torch
class 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	@slow
	def      A	(						self			:     Tuple		)			->		List[Any]:
								UpperCAmelCase :       int 							=		MraModel.from_pretrained('''uw-madison/mra-base-512-4'''		)
								UpperCAmelCase :       Optional[Any] 							=		torch.arange(256		).unsqueeze(0		)

								with torch.no_grad():
															UpperCAmelCase :       List[Any] 							=		model(__snake_case		)[0]

								UpperCAmelCase :       Optional[Any] 							=		torch.Size((1, 256, 768)		)
								self.assertEqual(output.shape		,			__snake_case		)

								UpperCAmelCase :       Any 							=		torch.tensor(
								    [[[-0.01_40, 0.08_30, -0.03_81], [0.15_46, 0.14_02, 0.02_20], [0.11_62, 0.08_51, 0.01_65]]]		)

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







	@slow
	def      A	(						self			:     Optional[Any]		)			->		Any:
								UpperCAmelCase :       Optional[int] 							=		MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4'''		)
								UpperCAmelCase :       Dict 							=		torch.arange(256		).unsqueeze(0		)

								with torch.no_grad():
															UpperCAmelCase :       List[Any] 							=		model(__snake_case		)[0]

								UpperCAmelCase :       int 							=		50265

								UpperCAmelCase :       int 							=		torch.Size((1, 256, vocab_size)		)
								self.assertEqual(output.shape		,			__snake_case		)

								UpperCAmelCase :       Union[str, Any] 							=		torch.tensor(
								    [[[9.25_95, -3.60_38, 11.88_19], [9.38_69, -3.26_93, 11.09_56], [11.85_24, -3.49_38, 13.12_10]]]		)

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







	@slow
	def      A	(						self			:     str		)			->		List[Any]:
								UpperCAmelCase :       List[Any] 							=		MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3'''		)
								UpperCAmelCase :       List[Any] 							=		torch.arange(4096		).unsqueeze(0		)

								with torch.no_grad():
															UpperCAmelCase :       Tuple 							=		model(__snake_case		)[0]

								UpperCAmelCase :       Optional[int] 							=		50265

								UpperCAmelCase :       Tuple 							=		torch.Size((1, 4096, vocab_size)		)
								self.assertEqual(output.shape		,			__snake_case		)

								UpperCAmelCase :       Optional[int] 							=		torch.tensor(
								    [[[5.47_89, -2.35_64, 7.50_64], [7.90_67, -1.33_69, 9.96_68], [9.07_12, -1.81_06, 7.03_80]]]		)

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










from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_   :Optional[int]        =      logging.get_logger(__name__)

a_   :Dict        =      {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	"""openai-gpt"""
     _SCREAMING_SNAKE_CASE						=	{
         """max_position_embeddings""": """n_positions""",
         """hidden_size""": """n_embd""",
         """num_attention_heads""": """n_head""",
         """num_hidden_layers""": """n_layer""",
     }






     def __init__(       self  :  Optional[int], _snake_case  :  Dict=4_0_4_7_8, _snake_case  :  str=5_1_2, _snake_case  :  int=7_6_8, _snake_case  :  Tuple=1_2, _snake_case  :  Any=1_2, _snake_case  :  str="gelu", _snake_case  :  List[str]=0.1, _snake_case  :  Any=0.1, _snake_case  :  Dict=0.1, _snake_case  :  int=1e-5, _snake_case  :  Optional[Any]=0.0_2, _snake_case  :  List[Any]="cls_index", _snake_case  :  Any=True, _snake_case  :  Any=None, _snake_case  :  int=True, _snake_case  :  Optional[Any]=0.1, **_snake_case  :  List[Any], )   ->Optional[int]:
            snake_case__							:	int   	=	vocab_size
            snake_case__							:	Dict   	=	n_positions
            snake_case__							:	str   	=	n_embd
            snake_case__							:	str   	=	n_layer
            snake_case__							:	List[Any]   	=	n_head
            snake_case__							:	List[Any]   	=	afn
            snake_case__							:	Optional[Any]   	=	resid_pdrop
            snake_case__							:	List[str]   	=	embd_pdrop
            snake_case__							:	List[Any]   	=	attn_pdrop
            snake_case__							:	Optional[int]   	=	layer_norm_epsilon
            snake_case__							:	str   	=	initializer_range
            snake_case__							:	List[str]   	=	summary_type
            snake_case__							:	Optional[int]   	=	summary_use_proj
            snake_case__							:	List[str]   	=	summary_activation
            snake_case__							:	Optional[Any]   	=	summary_first_dropout
            snake_case__							:	int   	=	summary_proj_to_labels
            super().__init__(**_snake_case					)






