




"""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   UpperCAmelCase_    :
		def __init__(						self				,						UpperCamelCase_				,						UpperCamelCase_=2				,						UpperCamelCase_=8				,						UpperCamelCase_=True				,						UpperCamelCase_=True				,						UpperCamelCase_=True				,						UpperCamelCase_=True				,						UpperCamelCase_=99				,						UpperCamelCase_=16				,						UpperCamelCase_=5				,						UpperCamelCase_=2				,						UpperCamelCase_=36				,						UpperCamelCase_="gelu"				,						UpperCamelCase_=0.0				,						UpperCamelCase_=0.0				,						UpperCamelCase_=5_12				,						UpperCamelCase_=16				,						UpperCamelCase_=2				,						UpperCamelCase_=0.0_2				,						UpperCamelCase_=3				,						UpperCamelCase_=4				,						UpperCamelCase_=None				,						)						->       Optional[int]:
					__lowercase			:  Dict								=       parent
					__lowercase			:  Dict								=       batch_size
					__lowercase			:  List[Any]								=       seq_length
					__lowercase			:  List[str]								=       is_training
					__lowercase			:  int								=       use_input_mask
					__lowercase			:  List[Any]								=       use_token_type_ids
					__lowercase			:  int								=       use_labels
					__lowercase			:  Union[str, Any]								=       vocab_size
					__lowercase			:  int								=       hidden_size
					__lowercase			:  Any								=       num_hidden_layers
					__lowercase			:  int								=       num_attention_heads
					__lowercase			:  Optional[int]								=       intermediate_size
					__lowercase			:  Optional[Any]								=       hidden_act
					__lowercase			:  int								=       hidden_dropout_prob
					__lowercase			:  Tuple								=       attention_probs_dropout_prob
					__lowercase			:  Tuple								=       max_position_embeddings
					__lowercase			:  Tuple								=       type_vocab_size
					__lowercase			:  Any								=       type_sequence_label_size
					__lowercase			:  Any								=       initializer_range
					__lowercase			:  int								=       num_labels
					__lowercase			:  List[str]								=       num_choices
					__lowercase			:  Optional[Any]								=       scope
		def 	_lowerCamelCase							(						self       )						->       List[Any]:
					__lowercase			:  int								=       ids_tensor([self.batch_size, self.seq_length]				,						self.vocab_size       )

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

					__lowercase			:  Dict								=       None
					if self.use_token_type_ids:
								__lowercase			:  Tuple								=       ids_tensor([self.batch_size, self.seq_length]				,						self.type_vocab_size       )

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

					__lowercase			:  Optional[Any]								=       self.get_config()

					return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
		def 	_lowerCamelCase							(						self       )						->       List[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 	_lowerCamelCase							(						self       )						->       Optional[int]:
					__lowercase			:  Dict								=       self.get_config()
					__lowercase			:  str								=       3_00
					return config
		def 	_lowerCamelCase							(						self       )						->       Tuple:
					(
					    (
					    __lowercase					
					)			,(
					    __lowercase					
					)			,(
					    __lowercase					
					)			,(
					    __lowercase					
					)			,(
					    __lowercase					
					)			,(
					    __lowercase					
					)			,(
					    __lowercase					
					)			,					
					)			:  Optional[Any]								=       self.prepare_config_and_inputs()

					__lowercase			:  Optional[int]								=       True
					__lowercase			:  List[Any]								=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
					__lowercase			:  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 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_       )						->       Tuple:
					__lowercase			:  List[Any]								=       MraModel(config=UpperCamelCase_       )
					model.to(UpperCamelCase_       )
					model.eval()
					__lowercase			:  Union[str, Any]								=       model(UpperCamelCase_				,						attention_mask=UpperCamelCase_				,						token_type_ids=UpperCamelCase_       )
					__lowercase			:  str								=       model(UpperCamelCase_				,						token_type_ids=UpperCamelCase_       )
					__lowercase			:  Dict								=       model(UpperCamelCase_       )
					self.parent.assertEqual(result.last_hidden_state.shape				,						(self.batch_size, self.seq_length, self.hidden_size)       )
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						)						->       Tuple:
					__lowercase			:  List[str]								=       True
					__lowercase			:  Tuple								=       MraModel(UpperCamelCase_       )
					model.to(UpperCamelCase_       )
					model.eval()
					__lowercase			:  List[str]								=       model(
					    UpperCamelCase_				,						attention_mask=UpperCamelCase_				,						token_type_ids=UpperCamelCase_				,						encoder_hidden_states=UpperCamelCase_				,						encoder_attention_mask=UpperCamelCase_				,						)
					__lowercase			:  Dict								=       model(
					    UpperCamelCase_				,						attention_mask=UpperCamelCase_				,						token_type_ids=UpperCamelCase_				,						encoder_hidden_states=UpperCamelCase_				,						)
					__lowercase			:  Optional[int]								=       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 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_       )						->       int:
					__lowercase			:  str								=       MraForMaskedLM(config=UpperCamelCase_       )
					model.to(UpperCamelCase_       )
					model.eval()
					__lowercase			:  Dict								=       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 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_       )						->       Dict:
					__lowercase			:  Dict								=       MraForQuestionAnswering(config=UpperCamelCase_       )
					model.to(UpperCamelCase_       )
					model.eval()
					__lowercase			:  Dict								=       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 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_       )						->       List[Any]:
					__lowercase			:  Tuple								=       self.num_labels
					__lowercase			:  Optional[int]								=       MraForSequenceClassification(UpperCamelCase_       )
					model.to(UpperCamelCase_       )
					model.eval()
					__lowercase			:  str								=       model(UpperCamelCase_				,						attention_mask=UpperCamelCase_				,						token_type_ids=UpperCamelCase_				,						labels=UpperCamelCase_       )
					self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.num_labels)       )
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_       )						->       Dict:
					__lowercase			:  str								=       self.num_labels
					__lowercase			:  Optional[int]								=       MraForTokenClassification(config=UpperCamelCase_       )
					model.to(UpperCamelCase_       )
					model.eval()
					__lowercase			:  List[str]								=       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 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_       )						->       int:
					__lowercase			:  Any								=       self.num_choices
					__lowercase			:  Optional[int]								=       MraForMultipleChoice(config=UpperCamelCase_       )
					model.to(UpperCamelCase_       )
					model.eval()
					__lowercase			:  List[str]								=       input_ids.unsqueeze(1       ).expand(-1				,						self.num_choices				,						-1       ).contiguous()
					__lowercase			:  str								=       token_type_ids.unsqueeze(1       ).expand(-1				,						self.num_choices				,						-1       ).contiguous()
					__lowercase			:  Union[str, Any]								=       input_mask.unsqueeze(1       ).expand(-1				,						self.num_choices				,						-1       ).contiguous()
					__lowercase			:  List[str]								=       model(
					    UpperCamelCase_				,						attention_mask=UpperCamelCase_				,						token_type_ids=UpperCamelCase_				,						labels=UpperCamelCase_				,						)
					self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.num_choices)       )
		def 	_lowerCamelCase							(						self       )						->       Any:
					__lowercase			:  List[str]								=       self.prepare_config_and_inputs()
					(
					    (
					    __lowercase					
					)			,(
					    __lowercase					
					)			,(
					    __lowercase					
					)			,(
					    __lowercase					
					)			,(
					    __lowercase					
					)			,(
					    __lowercase					
					)			,(
					    __lowercase					
					)			,					
					)			:  int								=       config_and_inputs
					__lowercase			:  Any								=       {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
					return config, inputs_dict


@require_torch
class   UpperCAmelCase_    (			snake_case   ,					unittest.TestCase		):
		UpperCamelCase							=(
		    (
		        MraModel,
		        MraForMaskedLM,
		        MraForMultipleChoice,
		        MraForQuestionAnswering,
		        MraForSequenceClassification,
		        MraForTokenClassification,
		    )
		    if is_torch_available()
		    else ()
		)
		UpperCamelCase							=False
		UpperCamelCase							=False
		UpperCamelCase							=False
		UpperCamelCase							=False

		UpperCamelCase							=()
		def 	_lowerCamelCase							(						self       )						->       List[str]:
					__lowercase			:  List[Any]								=       MraModelTester(self       )
					__lowercase			:  str								=       ConfigTester(self				,						config_class=UpperCamelCase_				,						hidden_size=37       )
		def 	_lowerCamelCase							(						self       )						->       Any:
					self.config_tester.run_common_tests()
		def 	_lowerCamelCase							(						self       )						->       int:
					__lowercase			:  Dict								=       self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*UpperCamelCase_       )
		def 	_lowerCamelCase							(						self       )						->       str:
					__lowercase			:  Optional[int]								=       self.model_tester.prepare_config_and_inputs()
					for type in ["absolute", "relative_key", "relative_key_query"]:
								__lowercase			:  str								=       type
								self.model_tester.create_and_check_model(*UpperCamelCase_       )
		def 	_lowerCamelCase							(						self       )						->       List[Any]:
					__lowercase			:  int								=       self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase_       )
		def 	_lowerCamelCase							(						self       )						->       Union[str, Any]:
					__lowercase			:  List[str]								=       self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_multiple_choice(*UpperCamelCase_       )
		def 	_lowerCamelCase							(						self       )						->       Union[str, Any]:
					__lowercase			:  str								=       self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*UpperCamelCase_       )
		def 	_lowerCamelCase							(						self       )						->       Optional[Any]:
					__lowercase			:  Tuple								=       self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase_       )
		def 	_lowerCamelCase							(						self       )						->       Optional[Any]:
					__lowercase			:  Union[str, Any]								=       self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*UpperCamelCase_       )
		@slow
		def 	_lowerCamelCase							(						self       )						->       Optional[Any]:
					for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								__lowercase			:  Any								=       MraModel.from_pretrained(UpperCamelCase_       )
								self.assertIsNotNone(UpperCamelCase_       )
		@unittest.skip(reason='''MRA does not output attentions'''       )
		def 	_lowerCamelCase							(						self       )						->       str:
					return


@require_torch
class   UpperCAmelCase_    (			unittest.TestCase		):
		@slow
		def 	_lowerCamelCase							(						self       )						->       Tuple:
					__lowercase			:  str								=       MraModel.from_pretrained('''uw-madison/mra-base-512-4'''       )
					__lowercase			:  Optional[int]								=       torch.arange(2_56       ).unsqueeze(0       )

					with torch.no_grad():
								__lowercase			:  str								=       model(UpperCamelCase_       )[0]

					__lowercase			:  str								=       torch.Size((1, 2_56, 7_68)       )
					self.assertEqual(output.shape				,						UpperCamelCase_       )

					__lowercase			:  List[str]								=       torch.tensor(
					    [[[-0.0_1_4_0, 0.0_8_3_0, -0.0_3_8_1], [0.1_5_4_6, 0.1_4_0_2, 0.0_2_2_0], [0.1_1_6_2, 0.0_8_5_1, 0.0_1_6_5]]]       )

					self.assertTrue(torch.allclose(output[:, :3, :3]				,						UpperCamelCase_				,						atol=1E-4       )       )
		@slow
		def 	_lowerCamelCase							(						self       )						->       Tuple:
					__lowercase			:  Optional[int]								=       MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4'''       )
					__lowercase			:  Tuple								=       torch.arange(2_56       ).unsqueeze(0       )

					with torch.no_grad():
								__lowercase			:  List[str]								=       model(UpperCamelCase_       )[0]

					__lowercase			:  Dict								=       5_02_65

					__lowercase			:  Union[str, Any]								=       torch.Size((1, 2_56, vocab_size)       )
					self.assertEqual(output.shape				,						UpperCamelCase_       )

					__lowercase			:  Dict								=       torch.tensor(
					    [[[9.2_5_9_5, -3.6_0_3_8, 11.88_19], [9.3_8_6_9, -3.2_6_9_3, 11.09_56], [11.85_24, -3.4_9_3_8, 13.12_10]]]       )

					self.assertTrue(torch.allclose(output[:, :3, :3]				,						UpperCamelCase_				,						atol=1E-4       )       )
		@slow
		def 	_lowerCamelCase							(						self       )						->       Any:
					__lowercase			:  Optional[Any]								=       MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3'''       )
					__lowercase			:  int								=       torch.arange(40_96       ).unsqueeze(0       )

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

					__lowercase			:  Any								=       5_02_65

					__lowercase			:  Union[str, Any]								=       torch.Size((1, 40_96, vocab_size)       )
					self.assertEqual(output.shape				,						UpperCamelCase_       )

					__lowercase			:  str								=       torch.tensor(
					    [[[5.4_7_8_9, -2.3_5_6_4, 7.5_0_6_4], [7.9_0_6_7, -1.3_3_6_9, 9.9_6_6_8], [9.0_7_1_2, -1.8_1_0_6, 7.0_3_8_0]]]       )

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







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
							from ..ta.tokenization_ta import TaTokenizer
else:
							from ...utils.dummy_sentencepiece_objects import TaTokenizer

__SCREAMING_SNAKE_CASE					:				str									=		TaTokenizer

if is_tokenizers_available():
							from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
							from ...utils.dummy_tokenizers_objects import TaTokenizerFast

__SCREAMING_SNAKE_CASE					:				Any									=		TaTokenizerFast

__SCREAMING_SNAKE_CASE					:				Tuple									=		{'''configuration_mt5''': ['''MT5Config''', '''MT5OnnxConfig''']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE					:				List[Any]									=		[
							    '''MT5EncoderModel''',
							    '''MT5ForConditionalGeneration''',
							    '''MT5ForQuestionAnswering''',
							    '''MT5Model''',
							    '''MT5PreTrainedModel''',
							    '''MT5Stack''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE					:				str									=		['''TFMT5EncoderModel''', '''TFMT5ForConditionalGeneration''', '''TFMT5Model''']

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE					:				List[str]									=		['''FlaxMT5EncoderModel''', '''FlaxMT5ForConditionalGeneration''', '''FlaxMT5Model''']


if TYPE_CHECKING:
							from .configuration_mta import MTaConfig, MTaOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mta import (
														    MTaEncoderModel,
														    MTaForConditionalGeneration,
														    MTaForQuestionAnswering,
														    MTaModel,
														    MTaPreTrainedModel,
														    MTaStack,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
							import sys

							__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		_LazyModule(
							    __name__,
							    globals()['''__file__'''],
							    _import_structure,
							    extra_objects={'''MT5Tokenizer''': MTaTokenizer, '''MT5TokenizerFast''': MTaTokenizerFast},
							    module_spec=__spec__,
							)





