




"""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_     :

	"""simple docstring"""





	def __init__(			self      :    Any      ,      UpperCAmelCase__      :    List[str]      ,      UpperCAmelCase__      :    Optional[int]=2      ,      UpperCAmelCase__      :    Optional[int]=8      ,      UpperCAmelCase__      :    Optional[Any]=True      ,      UpperCAmelCase__      :    List[str]=True      ,      UpperCAmelCase__      :    List[Any]=True      ,      UpperCAmelCase__      :    List[str]=True      ,      UpperCAmelCase__      :    str=9_9      ,      UpperCAmelCase__      :    List[str]=1_6      ,      UpperCAmelCase__      :    int=5      ,      UpperCAmelCase__      :    List[Any]=2      ,      UpperCAmelCase__      :    Union[str, Any]=3_6      ,      UpperCAmelCase__      :    Dict="gelu"      ,      UpperCAmelCase__      :    List[str]=0.0      ,      UpperCAmelCase__      :    List[str]=0.0      ,      UpperCAmelCase__      :    List[Any]=5_1_2      ,      UpperCAmelCase__      :    Dict=1_6      ,      UpperCAmelCase__      :    Tuple=2      ,      UpperCAmelCase__      :    int=0.02      ,      UpperCAmelCase__      :    str=3      ,      UpperCAmelCase__      :    Union[str, Any]=4      ,      UpperCAmelCase__      :    Any=None      ,      )	->     Tuple:
						__SCREAMING_SNAKE_CASE						       =    parent
						__SCREAMING_SNAKE_CASE						       =    batch_size
						__SCREAMING_SNAKE_CASE						       =    seq_length
						__SCREAMING_SNAKE_CASE						       =    is_training
						__SCREAMING_SNAKE_CASE						       =    use_input_mask
						__SCREAMING_SNAKE_CASE						       =    use_token_type_ids
						__SCREAMING_SNAKE_CASE						       =    use_labels
						__SCREAMING_SNAKE_CASE						       =    vocab_size
						__SCREAMING_SNAKE_CASE						       =    hidden_size
						__SCREAMING_SNAKE_CASE						       =    num_hidden_layers
						__SCREAMING_SNAKE_CASE						       =    num_attention_heads
						__SCREAMING_SNAKE_CASE						       =    intermediate_size
						__SCREAMING_SNAKE_CASE						       =    hidden_act
						__SCREAMING_SNAKE_CASE						       =    hidden_dropout_prob
						__SCREAMING_SNAKE_CASE						       =    attention_probs_dropout_prob
						__SCREAMING_SNAKE_CASE						       =    max_position_embeddings
						__SCREAMING_SNAKE_CASE						       =    type_vocab_size
						__SCREAMING_SNAKE_CASE						       =    type_sequence_label_size
						__SCREAMING_SNAKE_CASE						       =    initializer_range
						__SCREAMING_SNAKE_CASE						       =    num_labels
						__SCREAMING_SNAKE_CASE						       =    num_choices
						__SCREAMING_SNAKE_CASE						       =    scope




	def   UpperCAmelCase_				(			self      :    List[str]       )	->     Optional[int]:
						__SCREAMING_SNAKE_CASE						       =    ids_tensor([self.batch_size, self.seq_length]      ,      self.vocab_size       )

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

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

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

						__SCREAMING_SNAKE_CASE						       =    self.get_config()

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




	def   UpperCAmelCase_				(			self      :    str       )	->     Optional[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      :    List[Any]       )	->     str:
						__SCREAMING_SNAKE_CASE						       =    self.get_config()
						__SCREAMING_SNAKE_CASE						       =    3_0_0
						return config




	def   UpperCAmelCase_				(			self      :    Tuple       )	->     Optional[Any]:
						(
						    __SCREAMING_SNAKE_CASE						
						)						       =    self.prepare_config_and_inputs()

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




	def   UpperCAmelCase_				(			self      :    List[Any]      ,      UpperCAmelCase__      :    int      ,      UpperCAmelCase__      :    Optional[Any]      ,      UpperCAmelCase__      :    List[str]      ,      UpperCAmelCase__      :    int      ,      UpperCAmelCase__      :    Dict      ,      UpperCAmelCase__      :    Any      ,      UpperCAmelCase__      :    List[str]      ,      UpperCAmelCase__      :    str      ,      UpperCAmelCase__      :    Tuple      ,      )	->     Tuple:
						__SCREAMING_SNAKE_CASE						       =    True
						__SCREAMING_SNAKE_CASE						       =    MraModel(UpperCAmelCase__       )
						model.to(UpperCAmelCase__       )
						model.eval()
						__SCREAMING_SNAKE_CASE						       =    model(
						    UpperCAmelCase__      ,      attention_mask=UpperCAmelCase__      ,      token_type_ids=UpperCAmelCase__      ,      encoder_hidden_states=UpperCAmelCase__      ,      encoder_attention_mask=UpperCAmelCase__      ,      )
						__SCREAMING_SNAKE_CASE						       =    model(
						    UpperCAmelCase__      ,      attention_mask=UpperCAmelCase__      ,      token_type_ids=UpperCAmelCase__      ,      encoder_hidden_states=UpperCAmelCase__      ,      )
						__SCREAMING_SNAKE_CASE						       =    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      :    Union[str, Any]      ,      UpperCAmelCase__      :    List[Any]      ,      UpperCAmelCase__      :    Tuple      ,      UpperCAmelCase__      :    Optional[int]      ,      UpperCAmelCase__      :    List[str]      ,      UpperCAmelCase__      :    Optional[Any]      ,      UpperCAmelCase__      :    int      ,      UpperCAmelCase__      :    Tuple       )	->     int:
						__SCREAMING_SNAKE_CASE						       =    MraForMaskedLM(config=UpperCAmelCase__       )
						model.to(UpperCAmelCase__       )
						model.eval()
						__SCREAMING_SNAKE_CASE						       =    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      :    Union[str, Any]      ,      UpperCAmelCase__      :    Any      ,      UpperCAmelCase__      :    Tuple      ,      UpperCAmelCase__      :    Dict      ,      UpperCAmelCase__      :    Union[str, Any]      ,      UpperCAmelCase__      :    List[str]      ,      UpperCAmelCase__      :    List[str]      ,      UpperCAmelCase__      :    Tuple       )	->     str:
						__SCREAMING_SNAKE_CASE						       =    MraForQuestionAnswering(config=UpperCAmelCase__       )
						model.to(UpperCAmelCase__       )
						model.eval()
						__SCREAMING_SNAKE_CASE						       =    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      :    Tuple      ,      UpperCAmelCase__      :    List[str]      ,      UpperCAmelCase__      :    int      ,      UpperCAmelCase__      :    Optional[int]      ,      UpperCAmelCase__      :    Any      ,      UpperCAmelCase__      :    str      ,      UpperCAmelCase__      :    Any      ,      UpperCAmelCase__      :    Tuple       )	->     Dict:
						__SCREAMING_SNAKE_CASE						       =    self.num_labels
						__SCREAMING_SNAKE_CASE						       =    MraForSequenceClassification(UpperCAmelCase__       )
						model.to(UpperCAmelCase__       )
						model.eval()
						__SCREAMING_SNAKE_CASE						       =    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      :    Any      ,      UpperCAmelCase__      :    int      ,      UpperCAmelCase__      :    List[Any]      ,      UpperCAmelCase__      :    List[Any]      ,      UpperCAmelCase__      :    str      ,      UpperCAmelCase__      :    Optional[int]      ,      UpperCAmelCase__      :    Optional[Any]      ,      UpperCAmelCase__      :    Optional[int]       )	->     List[str]:
						__SCREAMING_SNAKE_CASE						       =    self.num_labels
						__SCREAMING_SNAKE_CASE						       =    MraForTokenClassification(config=UpperCAmelCase__       )
						model.to(UpperCAmelCase__       )
						model.eval()
						__SCREAMING_SNAKE_CASE						       =    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      :    Union[str, Any]      ,      UpperCAmelCase__      :    Optional[int]      ,      UpperCAmelCase__      :    Optional[Any]      ,      UpperCAmelCase__      :    Dict      ,      UpperCAmelCase__      :    Tuple      ,      UpperCAmelCase__      :    str      ,      UpperCAmelCase__      :    Tuple      ,      UpperCAmelCase__      :    List[str]       )	->     int:
						__SCREAMING_SNAKE_CASE						       =    self.num_choices
						__SCREAMING_SNAKE_CASE						       =    MraForMultipleChoice(config=UpperCAmelCase__       )
						model.to(UpperCAmelCase__       )
						model.eval()
						__SCREAMING_SNAKE_CASE						       =    input_ids.unsqueeze(1       ).expand(-1      ,      self.num_choices      ,      -1       ).contiguous()
						__SCREAMING_SNAKE_CASE						       =    token_type_ids.unsqueeze(1       ).expand(-1      ,      self.num_choices      ,      -1       ).contiguous()
						__SCREAMING_SNAKE_CASE						       =    input_mask.unsqueeze(1       ).expand(-1      ,      self.num_choices      ,      -1       ).contiguous()
						__SCREAMING_SNAKE_CASE						       =    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      :    str       )	->     Dict:
						__SCREAMING_SNAKE_CASE						       =    self.prepare_config_and_inputs()
						(
						    __SCREAMING_SNAKE_CASE						
						)						       =    config_and_inputs
						__SCREAMING_SNAKE_CASE						       =    {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
						return config, inputs_dict




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

	"""simple docstring"""





	snake_case__    :  Optional[Any]    =      (
	    (
	        MraModel,
	        MraForMaskedLM,
	        MraForMultipleChoice,
	        MraForQuestionAnswering,
	        MraForSequenceClassification,
	        MraForTokenClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	snake_case__    :  List[Any]    =      False
	snake_case__    :  List[Any]    =      False
	snake_case__    :  Optional[Any]    =      False
	snake_case__    :  Union[str, Any]    =      False

	snake_case__    :  Optional[Any]    =      ()




	def   UpperCAmelCase_				(			self      :    Tuple       )	->     Dict:
						__SCREAMING_SNAKE_CASE						       =    MraModelTester(self       )
						__SCREAMING_SNAKE_CASE						       =    ConfigTester(self      ,      config_class=UpperCAmelCase__      ,      hidden_size=3_7       )




	def   UpperCAmelCase_				(			self      :    int       )	->     Union[str, Any]:
						self.config_tester.run_common_tests()




	def   UpperCAmelCase_				(			self      :    Tuple       )	->     Optional[Any]:
						__SCREAMING_SNAKE_CASE						       =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    Optional[int]       )	->     Dict:
						__SCREAMING_SNAKE_CASE						       =    self.model_tester.prepare_config_and_inputs()
						for type in ["absolute", "relative_key", "relative_key_query"]:
											__SCREAMING_SNAKE_CASE						       =    type
											self.model_tester.create_and_check_model(*UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    Optional[Any]       )	->     Any:
						__SCREAMING_SNAKE_CASE						       =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    List[str]       )	->     str:
						__SCREAMING_SNAKE_CASE						       =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_multiple_choice(*UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    Dict       )	->     Any:
						__SCREAMING_SNAKE_CASE						       =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    Optional[Any]       )	->     Union[str, Any]:
						__SCREAMING_SNAKE_CASE						       =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    Tuple       )	->     List[Any]:
						__SCREAMING_SNAKE_CASE						       =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase__       )




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




	@unittest.skip(reason="MRA does not output attentions"       )
	def   UpperCAmelCase_				(			self      :    Any       )	->     Dict:
						return




@require_torch
class 			UpperCamelCase_     (			unittest.TestCase):

	"""simple docstring"""





	@slow
	def   UpperCAmelCase_				(			self      :    Any       )	->     Optional[Any]:
						__SCREAMING_SNAKE_CASE						       =    MraModel.from_pretrained("uw-madison/mra-base-512-4"       )
						__SCREAMING_SNAKE_CASE						       =    torch.arange(2_5_6       ).unsqueeze(0       )

						with torch.no_grad():
											__SCREAMING_SNAKE_CASE						       =    model(UpperCAmelCase__       )[0]

						__SCREAMING_SNAKE_CASE						       =    torch.Size((1, 2_5_6, 7_6_8)       )
						self.assertEqual(output.shape      ,      UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    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      :    Optional[Any]       )	->     Tuple:
						__SCREAMING_SNAKE_CASE						       =    MraForMaskedLM.from_pretrained("uw-madison/mra-base-512-4"       )
						__SCREAMING_SNAKE_CASE						       =    torch.arange(2_5_6       ).unsqueeze(0       )

						with torch.no_grad():
											__SCREAMING_SNAKE_CASE						       =    model(UpperCAmelCase__       )[0]

						__SCREAMING_SNAKE_CASE						       =    5_0_2_6_5

						__SCREAMING_SNAKE_CASE						       =    torch.Size((1, 2_5_6, vocab_size)       )
						self.assertEqual(output.shape      ,      UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    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[int]       )	->     Dict:
						__SCREAMING_SNAKE_CASE						       =    MraForMaskedLM.from_pretrained("uw-madison/mra-base-4096-8-d3"       )
						__SCREAMING_SNAKE_CASE						       =    torch.arange(4_0_9_6       ).unsqueeze(0       )

						with torch.no_grad():
											__SCREAMING_SNAKE_CASE						       =    model(UpperCAmelCase__       )[0]

						__SCREAMING_SNAKE_CASE						       =    5_0_2_6_5

						__SCREAMING_SNAKE_CASE						       =    torch.Size((1, 4_0_9_6, vocab_size)       )
						self.assertEqual(output.shape      ,      UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    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"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a__    :							List[str]            =					{
    '''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
    '''tokenization_biogpt''': ['''BioGptTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__    :							Union[str, Any]            =					[
		    '''BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BioGptForCausalLM''',
		    '''BioGptForTokenClassification''',
		    '''BioGptForSequenceClassification''',
		    '''BioGptModel''',
		    '''BioGptPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
		from .tokenization_biogpt import BioGptTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_biogpt import (
				    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BioGptForCausalLM,
				    BioGptForSequenceClassification,
				    BioGptForTokenClassification,
				    BioGptModel,
				    BioGptPreTrainedModel,
				)


else:
		import sys

		a__    :							Optional[int]            =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
