




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     snake_case__							:
				"""simple docstring"""



				def __init__(      self				:     Optional[int]					,      __lowerCamelCase				:     List[str]					,      __lowerCamelCase				:     Tuple=13					,      __lowerCamelCase				:     Optional[Any]=7					,      __lowerCamelCase				:     Any=True					,      __lowerCamelCase				:     Optional[Any]=True					,      __lowerCamelCase				:     str=True					,      __lowerCamelCase				:     Union[str, Any]=True					,      __lowerCamelCase				:     Dict=99					,      __lowerCamelCase				:     Optional[Any]=32					,      __lowerCamelCase				:     Tuple=2					,      __lowerCamelCase				:     Union[str, Any]=4					,      __lowerCamelCase				:     str=37					,      __lowerCamelCase				:     Any="gelu"					,      __lowerCamelCase				:     List[Any]=0.1					,      __lowerCamelCase				:     str=0.1					,      __lowerCamelCase				:     Any=5_12					,      __lowerCamelCase				:     List[Any]=16					,      __lowerCamelCase				:     Optional[int]=2					,      __lowerCamelCase				:     List[str]=0.02					,      __lowerCamelCase				:     Optional[Any]=False					,      __lowerCamelCase				:     Optional[Any]=True					,      __lowerCamelCase				:     Optional[int]="None"					,      __lowerCamelCase				:     str=3					,      __lowerCamelCase				:     Union[str, Any]=4					,      __lowerCamelCase				:     Any=None					,      )					->     int:
							a    =			parent
							a    =			batch_size
							a    =			seq_length
							a    =			is_training
							a    =			use_input_mask
							a    =			use_token_type_ids
							a    =			use_labels
							a    =			vocab_size
							a    =			hidden_size
							a    =			num_hidden_layers
							a    =			num_attention_heads
							a    =			intermediate_size
							a    =			hidden_act
							a    =			hidden_dropout_prob
							a    =			attention_probs_dropout_prob
							a    =			max_position_embeddings
							a    =			type_vocab_size
							a    =			type_sequence_label_size
							a    =			initializer_range
							a    =			num_labels
							a    =			num_choices
							a    =			relative_attention
							a    =			position_biased_input
							a    =			pos_att_type
							a    =			scope





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     Optional[Any]:
							a    =			ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size							)

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

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

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

							a    =			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=__lowerCamelCase					,      )

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





				def 							__UpperCAmelCase							(      self				:     Optional[int]					,      __lowerCamelCase				:     List[str]					,      __lowerCamelCase				:     Tuple					,      __lowerCamelCase				:     int					,      __lowerCamelCase				:     List[str]					,      __lowerCamelCase				:     Dict					,      __lowerCamelCase				:     int					,      __lowerCamelCase				:     str							)					->     Union[str, Any]:
							a    =			TFDebertaVaModel(config=__lowerCamelCase							)
							a    =			{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

							a    =			[input_ids, input_mask]
							a    =			model(__lowerCamelCase							)

							a    =			model(__lowerCamelCase							)

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





				def 							__UpperCAmelCase							(      self				:     Optional[int]					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     Tuple					,      __lowerCamelCase				:     List[str]					,      __lowerCamelCase				:     int					,      __lowerCamelCase				:     List[str]					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     int							)					->     Optional[Any]:
							a    =			TFDebertaVaForMaskedLM(config=__lowerCamelCase							)
							a    =			{
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}
							a    =			model(__lowerCamelCase							)
							self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.vocab_size)							)





				def 							__UpperCAmelCase							(      self				:     List[Any]					,      __lowerCamelCase				:     List[Any]					,      __lowerCamelCase				:     Any					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     Dict					,      __lowerCamelCase				:     Optional[Any]					,      __lowerCamelCase				:     List[Any]							)					->     Optional[int]:
							a    =			self.num_labels
							a    =			TFDebertaVaForSequenceClassification(config=__lowerCamelCase							)
							a    =			{
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}

							a    =			model(__lowerCamelCase							)
							self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.num_labels)							)





				def 							__UpperCAmelCase							(      self				:     Optional[int]					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     List[str]					,      __lowerCamelCase				:     List[str]					,      __lowerCamelCase				:     int					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     Any					,      __lowerCamelCase				:     Union[str, Any]							)					->     Any:
							a    =			self.num_labels
							a    =			TFDebertaVaForTokenClassification(config=__lowerCamelCase							)
							a    =			{
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}
							a    =			model(__lowerCamelCase							)
							self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.num_labels)							)





				def 							__UpperCAmelCase							(      self				:     Tuple					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     Optional[Any]					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     Tuple					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     Union[str, Any]							)					->     List[Any]:
							a    =			TFDebertaVaForQuestionAnswering(config=__lowerCamelCase							)
							a    =			{
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}

							a    =			model(__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 							__UpperCAmelCase							(      self				:     Dict							)					->     int:
							a    =			self.prepare_config_and_inputs()
							(
							    (
							    a
							)    ,		(
							    a
							)    ,		(
							    a
							)    ,		(
							    a
							)    ,		(
							    a
							)    ,		(
							    a
							)    ,		(
							    a
							)    ,		
							)    =			config_and_inputs
							a    =			{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
							return config, inputs_dict




@require_tf
class     snake_case__							(_UpperCamelCase    ,       _UpperCamelCase    ,       unittest.TestCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Optional[int]							 =					(
				    (
				        TFDebertaVaModel,
				        TFDebertaVaForMaskedLM,
				        TFDebertaVaForQuestionAnswering,
				        TFDebertaVaForSequenceClassification,
				        TFDebertaVaForTokenClassification,
				    )
				    if is_tf_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE_       :							Tuple							 =					(
				    {
				        """feature-extraction""": TFDebertaVaModel,
				        """fill-mask""": TFDebertaVaForMaskedLM,
				        """question-answering""": TFDebertaVaForQuestionAnswering,
				        """text-classification""": TFDebertaVaForSequenceClassification,
				        """token-classification""": TFDebertaVaForTokenClassification,
				        """zero-shot""": TFDebertaVaForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)

				SCREAMING_SNAKE_CASE_       :							Optional[Any]							 =					False
				SCREAMING_SNAKE_CASE_       :							Tuple							 =					False





				def 							__UpperCAmelCase							(      self				:     int							)					->     int:
							a    =			TFDebertaVaModelTester(self							)
							a    =			ConfigTester(self					,      config_class=__lowerCamelCase					,      hidden_size=37							)





				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     List[Any]:
							self.config_tester.run_common_tests()





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Dict:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Optional[Any]:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     int							)					->     Tuple:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     Optional[Any]:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     Any:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase							)





				@slow
				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     Dict:
							a    =			TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"							)
							self.assertIsNotNone(__lowerCamelCase							)




@require_tf
class     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



				@unittest.skip(reason="Model not available yet"							)
				def 							__UpperCAmelCase							(      self				:     Any							)					->     Tuple:
							pass





				@slow
				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     Tuple:
							a    =			TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"							)
							a    =			tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]							)
							a    =			tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
							a    =			model(__lowerCamelCase					,      attention_mask=__lowerCamelCase							)[0]

							a    =			tf.constant(
							    [[[0.2_356, 0.1_948, 0.0_369], [-0.1_063, 0.3_586, -0.5_152], [-0.6_399, -0.0_259, -0.2_525]]]							)
							tf.debugging.assert_near(output[:, 1:4, 1:4]					,      __lowerCamelCase					,      atol=1e-4							)












from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


UpperCAmelCase_									=	logging.get_logger(__name__)





class     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	Tuple    		=   ['pixel_values']
      def __init__(				self: Any    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: Union[int, float] = 1 / 2_55    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: int = 8    ,       **UpperCamelCase_: Tuple    ,       ):
             super().__init__(**UpperCamelCase_   )

             __lowerCamelCase   =						do_rescale
             __lowerCamelCase   =						rescale_factor
             __lowerCamelCase   =						do_pad
             __lowerCamelCase   =						pad_size
      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: np.ndarray    ,       UpperCamelCase_: float    ,       UpperCamelCase_: Optional[Union[str, ChannelDimension]] = None    ,       **UpperCamelCase_: Tuple   ):
             return rescale(UpperCamelCase_    ,       scale=UpperCamelCase_    ,       data_format=UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Union[str, Any]    ,       UpperCamelCase_: np.ndarray    ,       UpperCamelCase_: int    ,       UpperCamelCase_: Optional[Union[str, ChannelDimension]] = None   ):
             __lowerCamelCase,   __lowerCamelCase   =						get_image_size(UpperCamelCase_   )
             __lowerCamelCase   =						(old_height // size + 1) * size - old_height
             __lowerCamelCase   =						(old_width // size + 1) * size - old_width

             return pad(UpperCamelCase_    ,       ((0, pad_height), (0, pad_width))    ,       mode="""symmetric"""    ,       data_format=UpperCamelCase_   )




      def 			lowerCAmelCase__						(				self: str    ,       UpperCamelCase_: ImageInput    ,       UpperCamelCase_: Optional[bool] = None    ,       UpperCamelCase_: Optional[float] = None    ,       UpperCamelCase_: Optional[bool] = None    ,       UpperCamelCase_: Optional[int] = None    ,       UpperCamelCase_: Optional[Union[str, TensorType]] = None    ,       UpperCamelCase_: Union[str, ChannelDimension] = ChannelDimension.FIRST    ,       **UpperCamelCase_: Any    ,       ):
             __lowerCamelCase   =						do_rescale if do_rescale is not None else self.do_rescale
             __lowerCamelCase   =						rescale_factor if rescale_factor is not None else self.rescale_factor
             __lowerCamelCase   =						do_pad if do_pad is not None else self.do_pad
             __lowerCamelCase   =						pad_size if pad_size is not None else self.pad_size

             __lowerCamelCase   =						make_list_of_images(UpperCamelCase_   )

             if not valid_images(UpperCamelCase_   ):
                    raise ValueError(
                        """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                        """torch.Tensor, tf.Tensor or jax.ndarray."""   )

             if do_rescale and rescale_factor is None:
                    raise ValueError("""Rescale factor must be specified if do_rescale is True."""   )

             # All transformations expect numpy arrays.
             __lowerCamelCase   =						[to_numpy_array(UpperCamelCase_   ) for image in images]

             if do_rescale:
                    __lowerCamelCase   =						[self.rescale(image=UpperCamelCase_    ,       scale=UpperCamelCase_   ) for image in images]

             if do_pad:
                    __lowerCamelCase   =						[self.pad(UpperCamelCase_    ,       size=UpperCamelCase_   ) for image in images]

             __lowerCamelCase   =						[to_channel_dimension_format(UpperCamelCase_    ,       UpperCamelCase_   ) for image in images]

             __lowerCamelCase   =						{"""pixel_values""": images}
             return BatchFeature(data=UpperCamelCase_    ,       tensor_type=UpperCamelCase_   )

