




"""simple docstring"""

from __future__ import annotations

import unittest

from transformers import DebertaConfig, 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 (
							    TFDebertaForMaskedLM,
							    TFDebertaForQuestionAnswering,
							    TFDebertaForSequenceClassification,
							    TFDebertaForTokenClassification,
							    TFDebertaModel,
							)
class      lowerCamelCase__      :


			def __init__(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE=13			,	SCREAMING_SNAKE_CASE=7			,	SCREAMING_SNAKE_CASE=True			,	SCREAMING_SNAKE_CASE=True			,	SCREAMING_SNAKE_CASE=True			,	SCREAMING_SNAKE_CASE=True			,	SCREAMING_SNAKE_CASE=99			,	SCREAMING_SNAKE_CASE=32			,	SCREAMING_SNAKE_CASE=2			,	SCREAMING_SNAKE_CASE=4			,	SCREAMING_SNAKE_CASE=37			,	SCREAMING_SNAKE_CASE="gelu"			,	SCREAMING_SNAKE_CASE=0.1			,	SCREAMING_SNAKE_CASE=0.1			,	SCREAMING_SNAKE_CASE=512			,	SCREAMING_SNAKE_CASE=16			,	SCREAMING_SNAKE_CASE=2			,	SCREAMING_SNAKE_CASE=0.02			,	SCREAMING_SNAKE_CASE=3			,	SCREAMING_SNAKE_CASE=4			,	SCREAMING_SNAKE_CASE=None			,	):



					"""simple docstring"""







					snake_case						:  Any        =			parent
					snake_case						:  Union[str, Any]        =			13
					snake_case						:  List[str]        =			7
					snake_case						:  Optional[int]        =			True
					snake_case						:  str        =			True
					snake_case						:  List[str]        =			True
					snake_case						:  Tuple        =			True
					snake_case						:  List[str]        =			99
					snake_case						:  List[Any]        =			32
					snake_case						:  Tuple        =			2
					snake_case						:  Dict        =			4
					snake_case						:  str        =			37
					snake_case						:  List[str]        =			'gelu'
					snake_case						:  Tuple        =			0.1
					snake_case						:  List[Any]        =			0.1
					snake_case						:  Dict        =			512
					snake_case						:  Any        =			16
					snake_case						:  Dict        =			False
					snake_case						:  str        =			-1
					snake_case						:  str        =			True
					snake_case						:  List[str]        =			2
					snake_case						:  Dict        =			0.02
					snake_case						:  List[str]        =			3
					snake_case						:  int        =			4
					snake_case						:  Any        =			None


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  List[str]        =			ids_tensor([self.batch_size, self.seq_length]			,	self.vocab_size							)

					snake_case						:  Optional[Any]        =			None
					if self.use_input_mask:
							snake_case						:  List[str]        =			random_attention_mask([self.batch_size, self.seq_length]							)

					snake_case						:  Optional[int]        =			None
					if self.use_token_type_ids:
							snake_case						:  List[str]        =			ids_tensor([self.batch_size, self.seq_length]			,	self.type_vocab_size							)

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

					snake_case						:  Union[str, Any]        =			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			,	relative_attention=self.relative_attention			,	max_relative_positions=self.max_relative_positions			,	position_biased_input=self.position_biased_input			,	initializer_range=self.initializer_range			,	return_dict=snake_case_			,	)

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


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  Optional[int]        =			TFDebertaModel(config=snake_case_							)
					snake_case						:  str        =			{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

					snake_case						:  Union[str, Any]        =			[input_ids, input_mask]
					snake_case						:  Dict        =			model(snake_case_							)

					snake_case						:  str        =			model(snake_case_							)

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


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  Optional[Any]        =			TFDebertaForMaskedLM(config=snake_case_							)
					snake_case						:  Dict        =			{
					    'input_ids': input_ids,
					    'attention_mask': input_mask,
					    'token_type_ids': token_type_ids,
					}
					snake_case						:  Union[str, Any]        =			model(snake_case_							)
					self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.seq_length, self.vocab_size)							)


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  Tuple        =			self.num_labels
					snake_case						:  int        =			TFDebertaForSequenceClassification(config=snake_case_							)
					snake_case						:  List[Any]        =			{
					    'input_ids': input_ids,
					    'attention_mask': input_mask,
					    'token_type_ids': token_type_ids,
					}

					snake_case						:  List[str]        =			model(snake_case_							)
					self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.num_labels)							)


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  List[Any]        =			self.num_labels
					snake_case						:  List[Any]        =			TFDebertaForTokenClassification(config=snake_case_							)
					snake_case						:  Any        =			{
					    'input_ids': input_ids,
					    'attention_mask': input_mask,
					    'token_type_ids': token_type_ids,
					}
					snake_case						:  Tuple        =			model(snake_case_							)
					self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.seq_length, self.num_labels)							)


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  Union[str, Any]        =			TFDebertaForQuestionAnswering(config=snake_case_							)
					snake_case						:  str        =			{
					    'input_ids': input_ids,
					    'attention_mask': input_mask,
					    'token_type_ids': token_type_ids,
					}

					snake_case						:  Optional[Any]        =			model(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 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Optional[int]        =			self.prepare_config_and_inputs()
					(
					    snake_case	
					)						:  int        =			config_and_inputs
					snake_case						:  Dict        =			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
					return config, inputs_dict







@require_tf
class      lowerCamelCase__      (					UpperCAmelCase__			,					UpperCAmelCase__			,					unittest.TestCase     ):
			a__	: int        =       (
			    (
			        TFDebertaModel,
			        TFDebertaForMaskedLM,
			        TFDebertaForQuestionAnswering,
			        TFDebertaForSequenceClassification,
			        TFDebertaForTokenClassification,
			    )
			    if is_tf_available()
			    else ()
			)
			a__	: Any        =       (
			    {
			        """feature-extraction""": TFDebertaModel,
			        """fill-mask""": TFDebertaForMaskedLM,
			        """question-answering""": TFDebertaForQuestionAnswering,
			        """text-classification""": TFDebertaForSequenceClassification,
			        """token-classification""": TFDebertaForTokenClassification,
			        """zero-shot""": TFDebertaForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)

			a__	: List[Any]        =       False
			a__	: Union[str, Any]        =       False


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Dict        =			TFDebertaModelTester(self							)
					snake_case						:  int        =			ConfigTester(self			,	config_class=snake_case_			,	hidden_size=37							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					self.config_tester.run_common_tests()


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  str        =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*snake_case_							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  List[str]        =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_lm(*snake_case_							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  str        =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*snake_case_							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  int        =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(*snake_case_							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  List[str]        =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*snake_case_							)



			@slow
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Tuple        =			TFDebertaModel.from_pretrained("kamalkraj/deberta-base"							)
					self.assertIsNotNone(snake_case_							)







@require_tf
class      lowerCamelCase__      (					unittest.TestCase     ):


			@unittest.skip(reason="Model not available yet"							)
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					pass



			@slow
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Dict        =			TFDebertaModel.from_pretrained("kamalkraj/deberta-base"							)
					snake_case						:  List[Any]        =			tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]							)
					snake_case						:  Optional[Any]        =			tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
					snake_case						:  str        =			model(snake_case_			,	attention_mask=snake_case_							)[0]

					snake_case						:  Optional[Any]        =			tf.constant(
					    [
					        [
					            [-0.59_85_58_96, -0.80_55_25_66, -0.8_46_21_35],
					            [1.4_48_40_25, -0.93_48_37_94, -0.80_59_30_85],
					            [0.3_12_27_41, 0.00_31_60_59, -1.4_13_13_77],
					        ]
					    ]							)
					tf.debugging.assert_near(output[:, 1:4, 1:4]			,	snake_case_			,	atol=1E-4							)







"""simple docstring"""

from typing import TYPE_CHECKING

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


__A							=     {"configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     [
							    "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "GPTJForCausalLM",
							    "GPTJForQuestionAnswering",
							    "GPTJForSequenceClassification",
							    "GPTJModel",
							    "GPTJPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     [
							    "TFGPTJForCausalLM",
							    "TFGPTJForQuestionAnswering",
							    "TFGPTJForSequenceClassification",
							    "TFGPTJModel",
							    "TFGPTJPreTrainedModel",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     [
							    "FlaxGPTJForCausalLM",
							    "FlaxGPTJModel",
							    "FlaxGPTJPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gptj import (
														    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTJForCausalLM,
														    GPTJForQuestionAnswering,
														    GPTJForSequenceClassification,
														    GPTJModel,
														    GPTJPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_gptj import (
														    TFGPTJForCausalLM,
														    TFGPTJForQuestionAnswering,
														    TFGPTJForSequenceClassification,
														    TFGPTJModel,
														    TFGPTJPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
							import sys

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

