




'''simple docstring'''


import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    DebertaForMaskedLM,
	    DebertaForQuestionAnswering,
	    DebertaForSequenceClassification,
	    DebertaForTokenClassification,
	    DebertaModel,
	)
	from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST
class 						a_				(			UpperCAmelCase__						):




	def __init__(       self     :  Optional[Any]       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  List[str]=1_3       ,		__lowerCAmelCase     :  Union[str, Any]=7       ,		__lowerCAmelCase     :  Union[str, Any]=True       ,		__lowerCAmelCase     :  str=True       ,		__lowerCAmelCase     :  Tuple=True       ,		__lowerCAmelCase     :  Optional[Any]=True       ,		__lowerCAmelCase     :  Tuple=9_9       ,		__lowerCAmelCase     :  Optional[Any]=3_2       ,		__lowerCAmelCase     :  Optional[int]=5       ,		__lowerCAmelCase     :  Any=4       ,		__lowerCAmelCase     :  Any=3_7       ,		__lowerCAmelCase     :  Union[str, Any]="gelu"       ,		__lowerCAmelCase     :  Optional[int]=0.1       ,		__lowerCAmelCase     :  List[str]=0.1       ,		__lowerCAmelCase     :  Dict=5_1_2       ,		__lowerCAmelCase     :  Union[str, Any]=1_6       ,		__lowerCAmelCase     :  Optional[int]=2       ,		__lowerCAmelCase     :  str=0.02       ,		__lowerCAmelCase     :  Dict=False       ,		__lowerCAmelCase     :  str=True       ,		__lowerCAmelCase     :  Tuple="None"       ,		__lowerCAmelCase     :  Any=3       ,		__lowerCAmelCase     :  str=4       ,		__lowerCAmelCase     :  int=None       ,		):
							__snake_case						 =   parent
							__snake_case						 =   batch_size
							__snake_case						 =   seq_length
							__snake_case						 =   is_training
							__snake_case						 =   use_input_mask
							__snake_case						 =   use_token_type_ids
							__snake_case						 =   use_labels
							__snake_case						 =   vocab_size
							__snake_case						 =   hidden_size
							__snake_case						 =   num_hidden_layers
							__snake_case						 =   num_attention_heads
							__snake_case						 =   intermediate_size
							__snake_case						 =   hidden_act
							__snake_case						 =   hidden_dropout_prob
							__snake_case						 =   attention_probs_dropout_prob
							__snake_case						 =   max_position_embeddings
							__snake_case						 =   type_vocab_size
							__snake_case						 =   type_sequence_label_size
							__snake_case						 =   initializer_range
							__snake_case						 =   num_labels
							__snake_case						 =   num_choices
							__snake_case						 =   relative_attention
							__snake_case						 =   position_biased_input
							__snake_case						 =   pos_att_type
							__snake_case						 =   scope




	def   lowercase__   (       self     :  List[Any]      ):
							__snake_case						 =   ids_tensor([self.batch_size, self.seq_length]       ,		self.vocab_size      )

							__snake_case						 =   None
							if self.use_input_mask:
													__snake_case						 =   ids_tensor([self.batch_size, self.seq_length]       ,		vocab_size=2      )

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

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

							__snake_case						 =   self.get_config()

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




	def   lowercase__   (       self     :  Tuple      ):
							return DebertaConfig(
							    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       ,		initializer_range=self.initializer_range       ,		relative_attention=self.relative_attention       ,		position_biased_input=self.position_biased_input       ,		pos_att_type=self.pos_att_type       ,		)




	def   lowercase__   (       self     :  List[str]      ):
							__snake_case						 =   self.get_config()
							__snake_case						 =   3_0_0
							return config




	def   lowercase__   (       self     :  Dict       ,		__lowerCAmelCase     :  str      ):
							self.parent.assertListEqual(list(result.loss.size()      )       ,		[]      )




	def   lowercase__   (       self     :  Union[str, Any]       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  Optional[int]       ,		__lowerCAmelCase     :  Any       ,		__lowerCAmelCase     :  Any       ,		__lowerCAmelCase     :  List[Any]       ,		__lowerCAmelCase     :  Dict      ):
							__snake_case						 =   DebertaModel(config=__lowerCAmelCase      )
							model.to(__lowerCAmelCase      )
							model.eval()
							__snake_case						 =   model(__lowerCAmelCase       ,		attention_mask=__lowerCAmelCase       ,		token_type_ids=__lowerCAmelCase      )[0]
							__snake_case						 =   model(__lowerCAmelCase       ,		token_type_ids=__lowerCAmelCase      )[0]
							__snake_case						 =   model(__lowerCAmelCase      )[0]

							self.parent.assertListEqual(list(sequence_output.size()      )       ,		[self.batch_size, self.seq_length, self.hidden_size]      )




	def   lowercase__   (       self     :  Any       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  List[Any]       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  Dict       ,		__lowerCAmelCase     :  List[str]       ,		__lowerCAmelCase     :  Dict       ,		__lowerCAmelCase     :  Any      ):
							__snake_case						 =   DebertaForMaskedLM(config=__lowerCAmelCase      )
							model.to(__lowerCAmelCase      )
							model.eval()
							__snake_case						 =   model(__lowerCAmelCase       ,		attention_mask=__lowerCAmelCase       ,		token_type_ids=__lowerCAmelCase       ,		labels=__lowerCAmelCase      )

							self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.seq_length, self.vocab_size)      )




	def   lowercase__   (       self     :  List[Any]       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  List[str]       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  Dict       ,		__lowerCAmelCase     :  Dict       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  str      ):
							__snake_case						 =   self.num_labels
							__snake_case						 =   DebertaForSequenceClassification(__lowerCAmelCase      )
							model.to(__lowerCAmelCase      )
							model.eval()
							__snake_case						 =   model(__lowerCAmelCase       ,		attention_mask=__lowerCAmelCase       ,		token_type_ids=__lowerCAmelCase       ,		labels=__lowerCAmelCase      )
							self.parent.assertListEqual(list(result.logits.size()      )       ,		[self.batch_size, self.num_labels]      )
							self.check_loss_output(__lowerCAmelCase      )




	def   lowercase__   (       self     :  Dict       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  Dict       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  Tuple      ):
							__snake_case						 =   self.num_labels
							__snake_case						 =   DebertaForTokenClassification(config=__lowerCAmelCase      )
							model.to(__lowerCAmelCase      )
							model.eval()
							__snake_case						 =   model(__lowerCAmelCase       ,		attention_mask=__lowerCAmelCase       ,		token_type_ids=__lowerCAmelCase       ,		labels=__lowerCAmelCase      )
							self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.seq_length, self.num_labels)      )




	def   lowercase__   (       self     :  List[str]       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  Optional[int]       ,		__lowerCAmelCase     :  Any       ,		__lowerCAmelCase     :  Optional[int]       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  Tuple      ):
							__snake_case						 =   DebertaForQuestionAnswering(config=__lowerCAmelCase      )
							model.to(__lowerCAmelCase      )
							model.eval()
							__snake_case						 =   model(
							    __lowerCAmelCase       ,		attention_mask=__lowerCAmelCase       ,		token_type_ids=__lowerCAmelCase       ,		start_positions=__lowerCAmelCase       ,		end_positions=__lowerCAmelCase       ,		)
							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   lowercase__   (       self     :  Optional[Any]      ):
							__snake_case						 =   self.prepare_config_and_inputs()
							(
							    (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       
							)						 =   config_and_inputs
							__snake_case						 =   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
							return config, inputs_dict





@require_torch
class 						a_				(			UpperCAmelCase__     ,						UpperCAmelCase__     ,						unittest.TestCase						):
	lowercase_       :     Optional[int]			  =      (
	    (
	        DebertaModel,
	        DebertaForMaskedLM,
	        DebertaForSequenceClassification,
	        DebertaForTokenClassification,
	        DebertaForQuestionAnswering,
	    )
	    if is_torch_available()
	    else ()
	)
	lowercase_       :     Dict			  =      (
	    {
	        '''feature-extraction''': DebertaModel,
	        '''fill-mask''': DebertaForMaskedLM,
	        '''question-answering''': DebertaForQuestionAnswering,
	        '''text-classification''': DebertaForSequenceClassification,
	        '''token-classification''': DebertaForTokenClassification,
	        '''zero-shot''': DebertaForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)

	lowercase_       :     Any			  =      True
	lowercase_       :     str			  =      False
	lowercase_       :     Optional[Any]			  =      False
	lowercase_       :     Dict			  =      False
	lowercase_       :     int			  =      False




	def   lowercase__   (       self     :  Union[str, Any]      ):
							__snake_case						 =   DebertaModelTester(self      )
							__snake_case						 =   ConfigTester(self       ,		config_class=__lowerCAmelCase       ,		hidden_size=3_7      )




	def   lowercase__   (       self     :  List[str]      ):
							self.config_tester.run_common_tests()




	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_model(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  Dict      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_sequence_classification(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  List[str]      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_masked_lm(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  Dict      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_question_answering(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  str      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_token_classification(*__lowerCAmelCase      )




	@slow
	def   lowercase__   (       self     :  Optional[Any]      ):
							for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__snake_case						 =   DebertaModel.from_pretrained(__lowerCAmelCase      )
													self.assertIsNotNone(__lowerCAmelCase      )





@require_torch
@require_sentencepiece
@require_tokenizers
class 						a_				(			unittest.TestCase						):




	@unittest.skip(reason='Model not available yet'      )
	def   lowercase__   (       self     :  Dict      ):
							pass




	@slow
	def   lowercase__   (       self     :  str      ):
							__snake_case						 =   DebertaModel.from_pretrained('microsoft/deberta-base'      )

							__snake_case						 =   torch.tensor([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]      )
							__snake_case						 =   torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]      )
							with torch.no_grad():
													__snake_case						 =   model(__lowerCAmelCase       ,		attention_mask=__lowerCAmelCase      )[0]
							# compare the actual values for a slice.
							__snake_case						 =   torch.tensor(
							    [[[-0.5986, -0.8055, -0.8462], [1.4484, -0.9348, -0.8059], [0.3123, 0.0032, -1.4131]]]      )
							self.assertTrue(torch.allclose(output[:, 1:4, 1:4]       ,		__lowerCAmelCase       ,		atol=1E-4      )       ,		F'{output[:, 1:4, 1:4]}'      )







'''simple docstring'''


from typing import TYPE_CHECKING

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


_lowercase  =			{
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase  =			[
	    """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

	_lowercase  =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

