






"""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        lowercase__      :


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


			def      UpperCAmelCase__						(				self					:  str ):
										lowerCamelCase_	:							Optional[Any]									=ids_tensor([self.batch_size, self.seq_length]       ,	self.vocab_size )

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

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

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

										lowerCamelCase_	:							Tuple									=self.get_config()

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


			def      UpperCAmelCase__						(				self					:  Union[str, Any] ):
										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=UpperCAmelCase_       ,	initializer_range=self.initializer_range       ,	)


			def      UpperCAmelCase__						(				self					:  Any ):
										lowerCamelCase_	:							Optional[int]									=self.get_config()
										lowerCamelCase_	:							Tuple									=300
										return config


			def      UpperCAmelCase__						(				self					:  str ):
										(
										    lowerCamelCase_	
										)	:							Optional[Any]									=self.prepare_config_and_inputs()

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


			def      UpperCAmelCase__						(				self					:  Union[str, Any]       ,	snake_case__					:  Any       ,	snake_case__					:  str       ,	snake_case__					:  Optional[int]       ,	snake_case__					:  Optional[int]       ,	snake_case__					:  int       ,	snake_case__					:  List[str]       ,	snake_case__					:  Any       ,	snake_case__					:  str       ,	snake_case__					:  Tuple       ,	):
										lowerCamelCase_	:							Any									=True
										lowerCamelCase_	:							int									=MraModel(UpperCAmelCase_ )
										model.to(UpperCAmelCase_ )
										model.eval()
										lowerCamelCase_	:							str									=model(
										    UpperCAmelCase_       ,	attention_mask=UpperCAmelCase_       ,	token_type_ids=UpperCAmelCase_       ,	encoder_hidden_states=UpperCAmelCase_       ,	encoder_attention_mask=UpperCAmelCase_       ,	)
										lowerCamelCase_	:							int									=model(
										    UpperCAmelCase_       ,	attention_mask=UpperCAmelCase_       ,	token_type_ids=UpperCAmelCase_       ,	encoder_hidden_states=UpperCAmelCase_       ,	)
										lowerCamelCase_	:							Tuple									=model(UpperCAmelCase_       ,	attention_mask=UpperCAmelCase_       ,	token_type_ids=UpperCAmelCase_ )
										self.parent.assertEqual(result.last_hidden_state.shape       ,	(self.batch_size, self.seq_length, self.hidden_size) )


			def      UpperCAmelCase__						(				self					:  Dict       ,	snake_case__					:  str       ,	snake_case__					:  Optional[int]       ,	snake_case__					:  Dict       ,	snake_case__					:  List[str]       ,	snake_case__					:  str       ,	snake_case__					:  Optional[Any]       ,	snake_case__					:  Dict ):
										lowerCamelCase_	:							Optional[Any]									=MraForMaskedLM(config=UpperCAmelCase_ )
										model.to(UpperCAmelCase_ )
										model.eval()
										lowerCamelCase_	:							Tuple									=model(UpperCAmelCase_       ,	attention_mask=UpperCAmelCase_       ,	token_type_ids=UpperCAmelCase_       ,	labels=UpperCAmelCase_ )
										self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.seq_length, self.vocab_size) )


			def      UpperCAmelCase__						(				self					:  Optional[Any]       ,	snake_case__					:  Optional[Any]       ,	snake_case__					:  str       ,	snake_case__					:  Optional[Any]       ,	snake_case__					:  int       ,	snake_case__					:  int       ,	snake_case__					:  int       ,	snake_case__					:  List[str] ):
										lowerCamelCase_	:							int									=MraForQuestionAnswering(config=UpperCAmelCase_ )
										model.to(UpperCAmelCase_ )
										model.eval()
										lowerCamelCase_	:							int									=model(
										    UpperCAmelCase_       ,	attention_mask=UpperCAmelCase_       ,	token_type_ids=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					:  Optional[int]       ,	snake_case__					:  Tuple       ,	snake_case__					:  int       ,	snake_case__					:  Optional[Any]       ,	snake_case__					:  Dict       ,	snake_case__					:  Optional[Any]       ,	snake_case__					:  List[Any]       ,	snake_case__					:  Tuple ):
										lowerCamelCase_	:							Optional[Any]									=self.num_labels
										lowerCamelCase_	:							List[Any]									=MraForSequenceClassification(UpperCAmelCase_ )
										model.to(UpperCAmelCase_ )
										model.eval()
										lowerCamelCase_	:							List[Any]									=model(UpperCAmelCase_       ,	attention_mask=UpperCAmelCase_       ,	token_type_ids=UpperCAmelCase_       ,	labels=UpperCAmelCase_ )
										self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.num_labels) )


			def      UpperCAmelCase__						(				self					:  Union[str, Any]       ,	snake_case__					:  Optional[int]       ,	snake_case__					:  Tuple       ,	snake_case__					:  List[str]       ,	snake_case__					:  str       ,	snake_case__					:  Any       ,	snake_case__					:  Dict       ,	snake_case__					:  List[Any] ):
										lowerCamelCase_	:							Optional[int]									=self.num_labels
										lowerCamelCase_	:							int									=MraForTokenClassification(config=UpperCAmelCase_ )
										model.to(UpperCAmelCase_ )
										model.eval()
										lowerCamelCase_	:							Union[str, Any]									=model(UpperCAmelCase_       ,	attention_mask=UpperCAmelCase_       ,	token_type_ids=UpperCAmelCase_       ,	labels=UpperCAmelCase_ )
										self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.seq_length, self.num_labels) )


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


			def      UpperCAmelCase__						(				self					:  Dict ):
										lowerCamelCase_	:							Optional[Any]									=self.prepare_config_and_inputs()
										(
										    lowerCamelCase_	
										)	:							List[Any]									=config_and_inputs
										lowerCamelCase_	:							Optional[Any]									={"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
										return config, inputs_dict







@require_torch
class        lowercase__      (							snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:int             =			(
			    (
			        MraModel,
			        MraForMaskedLM,
			        MraForMultipleChoice,
			        MraForQuestionAnswering,
			        MraForSequenceClassification,
			        MraForTokenClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			_UpperCAmelCase		:Any             =			False
			_UpperCAmelCase		:Dict             =			False
			_UpperCAmelCase		:Optional[Any]             =			False
			_UpperCAmelCase		:List[Any]             =			False

			_UpperCAmelCase		:List[Any]             =			()


			def      UpperCAmelCase__						(				self					:  List[str] ):
										lowerCamelCase_	:							Any									=MraModelTester(self )
										lowerCamelCase_	:							Tuple									=ConfigTester(self       ,	config_class=UpperCAmelCase_       ,	hidden_size=37 )


			def      UpperCAmelCase__						(				self					:  Dict ):
										self.config_tester.run_common_tests()


			def      UpperCAmelCase__						(				self					:  Tuple ):
										lowerCamelCase_	:							Union[str, Any]									=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*UpperCAmelCase_ )


			def      UpperCAmelCase__						(				self					:  Dict ):
										lowerCamelCase_	:							Any									=self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
																	lowerCamelCase_	:							List[Any]									=type
																	self.model_tester.create_and_check_model(*UpperCAmelCase_ )


			def      UpperCAmelCase__						(				self					:  Dict ):
										lowerCamelCase_	:							Union[str, Any]									=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase_ )


			def      UpperCAmelCase__						(				self					:  Tuple ):
										lowerCamelCase_	:							List[str]									=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_multiple_choice(*UpperCAmelCase_ )


			def      UpperCAmelCase__						(				self					:  str ):
										lowerCamelCase_	:							Union[str, Any]									=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase_ )


			def      UpperCAmelCase__						(				self					:  Tuple ):
										lowerCamelCase_	:							int									=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase_ )


			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										lowerCamelCase_	:							Tuple									=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase_ )


			@slow
			def      UpperCAmelCase__						(				self					:  Tuple ):
										for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowerCamelCase_	:							List[str]									=MraModel.from_pretrained(UpperCAmelCase_ )
																	self.assertIsNotNone(UpperCAmelCase_ )


			@unittest.skip(reason="MRA does not output attentions" )
			def      UpperCAmelCase__						(				self					:  List[str] ):
										return







@require_torch
class        lowercase__      (							unittest.TestCase       ):


			@slow
			def      UpperCAmelCase__						(				self					:  List[str] ):
										lowerCamelCase_	:							Dict									=MraModel.from_pretrained("uw-madison/mra-base-512-4" )
										lowerCamelCase_	:							Dict									=torch.arange(256 ).unsqueeze(0 )

										with torch.no_grad():
																	lowerCamelCase_	:							Union[str, Any]									=model(UpperCAmelCase_ )[0]

										lowerCamelCase_	:							Tuple									=torch.Size((1, 256, 768) )
										self.assertEqual(output.shape       ,	UpperCAmelCase_ )

										lowerCamelCase_	:							Optional[Any]									=torch.tensor(
										    [[[-0.0_140, 0.0_830, -0.0_381], [0.1_546, 0.1_402, 0.0_220], [0.1_162, 0.0_851, 0.0_165]]] )

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


			@slow
			def      UpperCAmelCase__						(				self					:  int ):
										lowerCamelCase_	:							Optional[int]									=MraForMaskedLM.from_pretrained("uw-madison/mra-base-512-4" )
										lowerCamelCase_	:							Dict									=torch.arange(256 ).unsqueeze(0 )

										with torch.no_grad():
																	lowerCamelCase_	:							Tuple									=model(UpperCAmelCase_ )[0]

										lowerCamelCase_	:							Any									=5_0265

										lowerCamelCase_	:							List[Any]									=torch.Size((1, 256, vocab_size) )
										self.assertEqual(output.shape       ,	UpperCAmelCase_ )

										lowerCamelCase_	:							int									=torch.tensor(
										    [[[9.2_595, -3.6_038, 11.8_819], [9.3_869, -3.2_693, 11.0_956], [11.8_524, -3.4_938, 13.1_210]]] )

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


			@slow
			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										lowerCamelCase_	:							List[Any]									=MraForMaskedLM.from_pretrained("uw-madison/mra-base-4096-8-d3" )
										lowerCamelCase_	:							Dict									=torch.arange(4096 ).unsqueeze(0 )

										with torch.no_grad():
																	lowerCamelCase_	:							int									=model(UpperCAmelCase_ )[0]

										lowerCamelCase_	:							List[str]									=5_0265

										lowerCamelCase_	:							Dict									=torch.Size((1, 4096, vocab_size) )
										self.assertEqual(output.shape       ,	UpperCAmelCase_ )

										lowerCamelCase_	:							Union[str, Any]									=torch.tensor(
										    [[[5.4_789, -2.3_564, 7.5_064], [7.9_067, -1.3_369, 9.9_668], [9.0_712, -1.8_106, 7.0_380]]] )

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













"""simple docstring"""



import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


A__	:    str			 = logging.get_logger(__name__)







class        lowercase__      (							snake_case__       ):


			def __init__(				self					:  Optional[Any]       ,	*snake_case__					:  int       ,	**snake_case__					:  Any ):
										warnings.warn(
										    "The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
										    " Please use PerceiverImageProcessor instead."       ,	snake_case__       ,	)
										super().__init__(*snake_case__       ,	**snake_case__ )



