


from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import (
	    TFDebertaVaForMaskedLM,
	    TFDebertaVaForQuestionAnswering,
	    TFDebertaVaForSequenceClassification,
	    TFDebertaVaForTokenClassification,
	    TFDebertaVaModel,
	)






class      UpperCamelCase:


					def __init__(    self						:		Optional[int]  ,     SCREAMING_SNAKE_CASE						:		Tuple  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]=1_3  ,     SCREAMING_SNAKE_CASE						:		List[Any]=7  ,     SCREAMING_SNAKE_CASE						:		Tuple=True  ,     SCREAMING_SNAKE_CASE						:		List[Any]=True  ,     SCREAMING_SNAKE_CASE						:		Tuple=True  ,     SCREAMING_SNAKE_CASE						:		Tuple=True  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]=9_9  ,     SCREAMING_SNAKE_CASE						:		Tuple=3_2  ,     SCREAMING_SNAKE_CASE						:		int=2  ,     SCREAMING_SNAKE_CASE						:		Tuple=4  ,     SCREAMING_SNAKE_CASE						:		Tuple=3_7  ,     SCREAMING_SNAKE_CASE						:		Tuple="gelu"  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]=0.1  ,     SCREAMING_SNAKE_CASE						:		int=0.1  ,     SCREAMING_SNAKE_CASE						:		Dict=5_1_2  ,     SCREAMING_SNAKE_CASE						:		Tuple=1_6  ,     SCREAMING_SNAKE_CASE						:		Optional[int]=2  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]=0.02  ,     SCREAMING_SNAKE_CASE						:		List[str]=False  ,     SCREAMING_SNAKE_CASE						:		Tuple=True  ,     SCREAMING_SNAKE_CASE						:		Tuple="None"  ,     SCREAMING_SNAKE_CASE						:		List[str]=3  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]=4  ,     SCREAMING_SNAKE_CASE						:		Tuple=None  ,     )      ->						Union[str, Any]:




											'''simple docstring'''

											__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 				SCREAMING_SNAKE_CASE_	(    self						:		Any    )      ->						str:




											'''simple docstring'''

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

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

											__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				=		DebertaVaConfig(
											    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  ,     relative_attention=self.relative_attention  ,     position_biased_input=self.position_biased_input  ,     initializer_range=self.initializer_range  ,     return_dict=SCREAMING_SNAKE_CASE  ,     )

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple  ,     SCREAMING_SNAKE_CASE						:		Dict  ,     SCREAMING_SNAKE_CASE						:		Any  ,     SCREAMING_SNAKE_CASE						:		Tuple  ,     SCREAMING_SNAKE_CASE						:		int  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]  ,     SCREAMING_SNAKE_CASE						:		Any  ,     SCREAMING_SNAKE_CASE						:		Dict    )      ->						Optional[int]:




											'''simple docstring'''

											__snake_case				=		TFDebertaVaModel(config=SCREAMING_SNAKE_CASE    )
											__snake_case				=		{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

											__snake_case				=		[input_ids, input_mask]
											__snake_case				=		model(SCREAMING_SNAKE_CASE    )

											__snake_case				=		model(SCREAMING_SNAKE_CASE    )

											self.parent.assertEqual(result.last_hidden_state.shape  ,     (self.batch_size, self.seq_length, self.hidden_size)    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]  ,     SCREAMING_SNAKE_CASE						:		str  ,     SCREAMING_SNAKE_CASE						:		Tuple  ,     SCREAMING_SNAKE_CASE						:		Any  ,     SCREAMING_SNAKE_CASE						:		str  ,     SCREAMING_SNAKE_CASE						:		Tuple  ,     SCREAMING_SNAKE_CASE						:		Tuple    )      ->						Any:




											'''simple docstring'''

											__snake_case				=		TFDebertaVaForMaskedLM(config=SCREAMING_SNAKE_CASE    )
											__snake_case				=		{
											    "input_ids": input_ids,
											    "attention_mask": input_mask,
											    "token_type_ids": token_type_ids,
											}
											__snake_case				=		model(SCREAMING_SNAKE_CASE    )
											self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.seq_length, self.vocab_size)    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple  ,     SCREAMING_SNAKE_CASE						:		int  ,     SCREAMING_SNAKE_CASE						:		Tuple  ,     SCREAMING_SNAKE_CASE						:		Optional[int]  ,     SCREAMING_SNAKE_CASE						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		Optional[int]  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]  ,     SCREAMING_SNAKE_CASE						:		Optional[int]    )      ->						List[Any]:




											'''simple docstring'''

											__snake_case				=		self.num_labels
											__snake_case				=		TFDebertaVaForSequenceClassification(config=SCREAMING_SNAKE_CASE    )
											__snake_case				=		{
											    "input_ids": input_ids,
											    "attention_mask": input_mask,
											    "token_type_ids": token_type_ids,
											}

											__snake_case				=		model(SCREAMING_SNAKE_CASE    )
											self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.num_labels)    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		int  ,     SCREAMING_SNAKE_CASE						:		Tuple  ,     SCREAMING_SNAKE_CASE						:		str  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]  ,     SCREAMING_SNAKE_CASE						:		str  ,     SCREAMING_SNAKE_CASE						:		List[Any]  ,     SCREAMING_SNAKE_CASE						:		Any  ,     SCREAMING_SNAKE_CASE						:		Dict    )      ->						List[Any]:




											'''simple docstring'''

											__snake_case				=		self.num_labels
											__snake_case				=		TFDebertaVaForTokenClassification(config=SCREAMING_SNAKE_CASE    )
											__snake_case				=		{
											    "input_ids": input_ids,
											    "attention_mask": input_mask,
											    "token_type_ids": token_type_ids,
											}
											__snake_case				=		model(SCREAMING_SNAKE_CASE    )
											self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.seq_length, self.num_labels)    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Any  ,     SCREAMING_SNAKE_CASE						:		int  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]  ,     SCREAMING_SNAKE_CASE						:		Dict  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]  ,     SCREAMING_SNAKE_CASE						:		List[Any]  ,     SCREAMING_SNAKE_CASE						:		str  ,     SCREAMING_SNAKE_CASE						:		Optional[int]    )      ->						List[Any]:




											'''simple docstring'''

											__snake_case				=		TFDebertaVaForQuestionAnswering(config=SCREAMING_SNAKE_CASE    )
											__snake_case				=		{
											    "input_ids": input_ids,
											    "attention_mask": input_mask,
											    "token_type_ids": token_type_ids,
											}

											__snake_case				=		model(SCREAMING_SNAKE_CASE    )
											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]    )      ->						Dict:




											'''simple docstring'''

											__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_tf
class      UpperCamelCase(	_a  ,				_a  ,				unittest.TestCase ):
					snake_case_		:					Any    					=						(
					    (
					        TFDebertaVaModel,
					        TFDebertaVaForMaskedLM,
					        TFDebertaVaForQuestionAnswering,
					        TFDebertaVaForSequenceClassification,
					        TFDebertaVaForTokenClassification,
					    )
					    if is_tf_available()
					    else ()
					)
					snake_case_		:					Any    					=						(
					    {
					        """feature-extraction""": TFDebertaVaModel,
					        """fill-mask""": TFDebertaVaForMaskedLM,
					        """question-answering""": TFDebertaVaForQuestionAnswering,
					        """text-classification""": TFDebertaVaForSequenceClassification,
					        """token-classification""": TFDebertaVaForTokenClassification,
					        """zero-shot""": TFDebertaVaForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)

					snake_case_		:					int    					=						False
					snake_case_		:					Optional[int]    					=						False


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




											'''simple docstring'''

											__snake_case				=		TFDebertaVaModelTester(self    )
											__snake_case				=		ConfigTester(self  ,     config_class=SCREAMING_SNAKE_CASE  ,     hidden_size=3_7    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]    )      ->						Dict:




											'''simple docstring'''

											self.config_tester.run_common_tests()


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Dict    )      ->						Optional[int]:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]    )      ->						Tuple:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]    )      ->						Dict:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		int    )      ->						Dict:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE    )


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




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE    )






					@slow
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Any    )      ->						str:




											'''simple docstring'''

											__snake_case				=		TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"    )
											self.assertIsNotNone(SCREAMING_SNAKE_CASE    )






@require_tf
class      UpperCamelCase(	unittest.TestCase ):


					@unittest.skip(reason="Model not available yet"    )
					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]    )      ->						Union[str, Any]:




											'''simple docstring'''

											pass






					@slow
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple    )      ->						Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"    )
											__snake_case				=		tf.constant([[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				=		tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]    )
											__snake_case				=		model(SCREAMING_SNAKE_CASE  ,     attention_mask=SCREAMING_SNAKE_CASE    )[0]

											__snake_case				=		tf.constant(
											    [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]    )
											tf.debugging.assert_near(output[:, 1:4, 1:4]  ,     SCREAMING_SNAKE_CASE  ,     atol=1e-4    )









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A				:       List[str]      =  {
    'configuration_time_series_transformer': [
        'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TimeSeriesTransformerConfig',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A				:       str      =  [
	    'TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TimeSeriesTransformerForPrediction',
	    'TimeSeriesTransformerModel',
	    'TimeSeriesTransformerPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_time_series_transformer import (
	    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    TimeSeriesTransformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_time_series_transformer import (
		    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimeSeriesTransformerForPrediction,
		    TimeSeriesTransformerModel,
		    TimeSeriesTransformerPreTrainedModel,
		)

else:
	import sys

	A				:       str      =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





