



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  __magic_name__   :






		def __init__(						self      :     str				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Dict=2				,		UpperCamelCase__      :     int=8				,		UpperCamelCase__      :     Optional[Any]=True				,		UpperCamelCase__      :     Union[str, Any]=True				,		UpperCamelCase__      :     List[Any]=True				,		UpperCamelCase__      :     Union[str, Any]=True				,		UpperCamelCase__      :     Dict=99				,		UpperCamelCase__      :     str=16				,		UpperCamelCase__      :     Union[str, Any]=5				,		UpperCamelCase__      :     Optional[Any]=2				,		UpperCamelCase__      :     List[str]=36				,		UpperCamelCase__      :     List[str]="gelu"				,		UpperCamelCase__      :     List[Any]=0.0				,		UpperCamelCase__      :     int=0.0				,		UpperCamelCase__      :     Optional[Any]=5_12				,		UpperCamelCase__      :     Optional[Any]=16				,		UpperCamelCase__      :     Dict=2				,		UpperCamelCase__      :     List[str]=0.02				,		UpperCamelCase__      :     Union[str, Any]=3				,		UpperCamelCase__      :     List[str]=4				,		UpperCamelCase__      :     str=None				,		)				->					List[str]:
								'''simple docstring'''







								UpperCAmelCase         =     parent
								UpperCAmelCase         =     batch_size
								UpperCAmelCase         =     seq_length
								UpperCAmelCase         =     is_training
								UpperCAmelCase         =     use_input_mask
								UpperCAmelCase         =     use_token_type_ids
								UpperCAmelCase         =     use_labels
								UpperCAmelCase         =     vocab_size
								UpperCAmelCase         =     hidden_size
								UpperCAmelCase         =     num_hidden_layers
								UpperCAmelCase         =     num_attention_heads
								UpperCAmelCase         =     intermediate_size
								UpperCAmelCase         =     hidden_act
								UpperCAmelCase         =     hidden_dropout_prob
								UpperCAmelCase         =     attention_probs_dropout_prob
								UpperCAmelCase         =     max_position_embeddings
								UpperCAmelCase         =     type_vocab_size
								UpperCAmelCase         =     type_sequence_label_size
								UpperCAmelCase         =     initializer_range
								UpperCAmelCase         =     num_labels
								UpperCAmelCase         =     num_choices
								UpperCAmelCase         =     scope






		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     ids_tensor([self.batch_size, self.seq_length]				,		self.vocab_size      )

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

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

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

								UpperCAmelCase         =     self.get_config()

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






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					List[str]:
								'''simple docstring'''







								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=lowercase_				,		initializer_range=self.initializer_range				,		)






		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     self.get_config()
								UpperCAmelCase         =     3_00
								return config






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					int:
								'''simple docstring'''







								(
								    (
								    UpperCAmelCase	
								)       ,		(
								    UpperCAmelCase	
								)       ,		(
								    UpperCAmelCase	
								)       ,		(
								    UpperCAmelCase	
								)       ,		(
								    UpperCAmelCase	
								)       ,		(
								    UpperCAmelCase	
								)       ,		(
								    UpperCAmelCase	
								)       ,			
								)         =     self.prepare_config_and_inputs()

								UpperCAmelCase         =     True
								UpperCAmelCase         =     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
								UpperCAmelCase         =     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 SCREAMING_SNAKE_CASE_			(						self      :     Dict				,		UpperCamelCase__      :     List[str]				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     List[str]				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     Dict      )				->					Any:
								'''simple docstring'''







								UpperCAmelCase         =     MraModel(config=lowercase_      )
								model.to(lowercase_      )
								model.eval()
								UpperCAmelCase         =     model(lowercase_				,		attention_mask=lowercase_				,		token_type_ids=lowercase_      )
								UpperCAmelCase         =     model(lowercase_				,		token_type_ids=lowercase_      )
								UpperCAmelCase         =     model(lowercase_      )
								self.parent.assertEqual(result.last_hidden_state.shape				,		(self.batch_size, self.seq_length, self.hidden_size)      )






		def SCREAMING_SNAKE_CASE_			(						self      :     int				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     Optional[int]				,		UpperCamelCase__      :     List[Any]				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     Tuple				,		)				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     True
								UpperCAmelCase         =     MraModel(lowercase_      )
								model.to(lowercase_      )
								model.eval()
								UpperCAmelCase         =     model(
								    lowercase_				,		attention_mask=lowercase_				,		token_type_ids=lowercase_				,		encoder_hidden_states=lowercase_				,		encoder_attention_mask=lowercase_				,		)
								UpperCAmelCase         =     model(
								    lowercase_				,		attention_mask=lowercase_				,		token_type_ids=lowercase_				,		encoder_hidden_states=lowercase_				,		)
								UpperCAmelCase         =     model(lowercase_				,		attention_mask=lowercase_				,		token_type_ids=lowercase_      )
								self.parent.assertEqual(result.last_hidden_state.shape				,		(self.batch_size, self.seq_length, self.hidden_size)      )






		def SCREAMING_SNAKE_CASE_			(						self      :     int				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Optional[int]				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     Optional[int]      )				->					Tuple:
								'''simple docstring'''







								UpperCAmelCase         =     MraForMaskedLM(config=lowercase_      )
								model.to(lowercase_      )
								model.eval()
								UpperCAmelCase         =     model(lowercase_				,		attention_mask=lowercase_				,		token_type_ids=lowercase_				,		labels=lowercase_      )
								self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.seq_length, self.vocab_size)      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     Optional[int]				,		UpperCamelCase__      :     List[str]				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     Any				,		UpperCamelCase__      :     Optional[int]				,		UpperCamelCase__      :     int      )				->					int:
								'''simple docstring'''







								UpperCAmelCase         =     MraForQuestionAnswering(config=lowercase_      )
								model.to(lowercase_      )
								model.eval()
								UpperCAmelCase         =     model(
								    lowercase_				,		attention_mask=lowercase_				,		token_type_ids=lowercase_				,		start_positions=lowercase_				,		end_positions=lowercase_				,		)
								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 SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]				,		UpperCamelCase__      :     List[Any]				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     Any				,		UpperCamelCase__      :     int      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     self.num_labels
								UpperCAmelCase         =     MraForSequenceClassification(lowercase_      )
								model.to(lowercase_      )
								model.eval()
								UpperCAmelCase         =     model(lowercase_				,		attention_mask=lowercase_				,		token_type_ids=lowercase_				,		labels=lowercase_      )
								self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.num_labels)      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     Optional[int]				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     Optional[int]				,		UpperCamelCase__      :     Optional[int]				,		UpperCamelCase__      :     Dict      )				->					Any:
								'''simple docstring'''







								UpperCAmelCase         =     self.num_labels
								UpperCAmelCase         =     MraForTokenClassification(config=lowercase_      )
								model.to(lowercase_      )
								model.eval()
								UpperCAmelCase         =     model(lowercase_				,		attention_mask=lowercase_				,		token_type_ids=lowercase_				,		labels=lowercase_      )
								self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.seq_length, self.num_labels)      )






		def SCREAMING_SNAKE_CASE_			(						self      :     str				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     Optional[Any]				,		UpperCamelCase__      :     Optional[Any]				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     List[Any]				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     int      )				->					List[str]:
								'''simple docstring'''







								UpperCAmelCase         =     self.num_choices
								UpperCAmelCase         =     MraForMultipleChoice(config=lowercase_      )
								model.to(lowercase_      )
								model.eval()
								UpperCAmelCase         =     input_ids.unsqueeze(1      ).expand(-1				,		self.num_choices				,		-1      ).contiguous()
								UpperCAmelCase         =     token_type_ids.unsqueeze(1      ).expand(-1				,		self.num_choices				,		-1      ).contiguous()
								UpperCAmelCase         =     input_mask.unsqueeze(1      ).expand(-1				,		self.num_choices				,		-1      ).contiguous()
								UpperCAmelCase         =     model(
								    lowercase_				,		attention_mask=lowercase_				,		token_type_ids=lowercase_				,		labels=lowercase_				,		)
								self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.num_choices)      )







		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     self.prepare_config_and_inputs()
								(
								    (
								    UpperCAmelCase	
								)       ,		(
								    UpperCAmelCase	
								)       ,		(
								    UpperCAmelCase	
								)       ,		(
								    UpperCAmelCase	
								)       ,		(
								    UpperCAmelCase	
								)       ,		(
								    UpperCAmelCase	
								)       ,		(
								    UpperCAmelCase	
								)       ,			
								)         =     config_and_inputs
								UpperCAmelCase         =     {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
								return config, inputs_dict





@require_torch
class  __magic_name__   (							A__,  unittest.TestCase						):
		lowercase      :       str          =(
		    (
		        MraModel,
		        MraForMaskedLM,
		        MraForMultipleChoice,
		        MraForQuestionAnswering,
		        MraForSequenceClassification,
		        MraForTokenClassification,
		    )
		    if is_torch_available()
		    else ()
		)
		lowercase      :       List[Any]          =False
		lowercase      :       str          =False
		lowercase      :       Dict          =False
		lowercase      :       List[Any]          =False

		lowercase      :       Any          =()






		def SCREAMING_SNAKE_CASE_			(						self      :     Any      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     MraModelTester(self      )
								UpperCAmelCase         =     ConfigTester(self				,		config_class=lowercase_				,		hidden_size=37      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Dict      )				->					Any:
								'''simple docstring'''







								self.config_tester.run_common_tests()






		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Dict:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowercase_      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Dict      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
														UpperCAmelCase         =     type
														self.model_tester.create_and_check_model(*lowercase_      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*lowercase_      )






		def SCREAMING_SNAKE_CASE_			(						self      :     int      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_multiple_choice(*lowercase_      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*lowercase_      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					List[str]:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*lowercase_      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*lowercase_      )






		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					Optional[int]:
								'''simple docstring'''







								for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														UpperCAmelCase         =     MraModel.from_pretrained(lowercase_      )
														self.assertIsNotNone(lowercase_      )







		@unittest.skip(reason="MRA does not output attentions"      )
		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					List[Any]:
								'''simple docstring'''







								return





@require_torch
class  __magic_name__   (							unittest.TestCase						):






		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     Dict      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     MraModel.from_pretrained("uw-madison/mra-base-512-4"      )
								UpperCAmelCase         =     torch.arange(2_56      ).unsqueeze(0      )

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

								UpperCAmelCase         =     torch.Size((1, 2_56, 7_68)      )
								self.assertEqual(output.shape				,		lowercase_      )

								UpperCAmelCase         =     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]				,		lowercase_				,		atol=1e-4      )      )






		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     Dict      )				->					Dict:
								'''simple docstring'''







								UpperCAmelCase         =     MraForMaskedLM.from_pretrained("uw-madison/mra-base-512-4"      )
								UpperCAmelCase         =     torch.arange(2_56      ).unsqueeze(0      )

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

								UpperCAmelCase         =     5_02_65

								UpperCAmelCase         =     torch.Size((1, 2_56, vocab_size)      )
								self.assertEqual(output.shape				,		lowercase_      )

								UpperCAmelCase         =     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]				,		lowercase_				,		atol=1e-4      )      )







		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					Tuple:
								'''simple docstring'''







								UpperCAmelCase         =     MraForMaskedLM.from_pretrained("uw-madison/mra-base-4096-8-d3"      )
								UpperCAmelCase         =     torch.arange(40_96      ).unsqueeze(0      )

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

								UpperCAmelCase         =     5_02_65

								UpperCAmelCase         =     torch.Size((1, 40_96, vocab_size)      )
								self.assertEqual(output.shape				,		lowercase_      )

								UpperCAmelCase         =     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]				,		lowercase_				,		atol=1e-4      )      )








import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
		import torch





@require_torch
@require_optimum
@slow
class  __magic_name__   (							unittest.TestCase						):






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     "hf-internal-testing/tiny-random-t5"
								UpperCAmelCase         =     AutoTokenizer.from_pretrained(UpperCamelCase__      )
								UpperCAmelCase         =     AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__      )

								UpperCAmelCase         =     tokenizer("This is me"				,		return_tensors="pt"      )

								UpperCAmelCase         =     model.to_bettertransformer()

								self.assertTrue(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()      )      )

								UpperCAmelCase         =     model.generate(**UpperCamelCase__      )

								UpperCAmelCase         =     model.reverse_bettertransformer()

								self.assertFalse(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()      )      )

								with tempfile.TemporaryDirectory() as tmpdirname:
														model.save_pretrained(UpperCamelCase__      )

														UpperCAmelCase         =     AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__      )

														self.assertFalse(
														    any("BetterTransformer" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()      )      )

														UpperCAmelCase         =     model_reloaded.generate(**UpperCamelCase__      )
														self.assertTrue(torch.allclose(UpperCamelCase__				,		UpperCamelCase__      )      )







		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     "hf-internal-testing/tiny-random-t5"
								UpperCAmelCase         =     AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__      )

								UpperCAmelCase         =     model.to_bettertransformer()

								with tempfile.TemporaryDirectory() as tmpdirname:
														with self.assertRaises(UpperCamelCase__      ):
																				model.save_pretrained(UpperCamelCase__      )

														UpperCAmelCase         =     model.reverse_bettertransformer()
														model.save_pretrained(UpperCamelCase__      )



