


"""simple docstring"""






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import ConvNextImageProcessor


class 			SCREAMING_SNAKE_CASE_  (   unittest.TestCase):




							'''simple docstring'''





							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__=7	,					lowerCamelCase__=3	,					lowerCamelCase__=18	,					lowerCamelCase__=30	,					lowerCamelCase__=400	,					lowerCamelCase__=True	,					lowerCamelCase__=None	,					lowerCamelCase__=0.8_75	,					lowerCamelCase__=True	,					lowerCamelCase__=[0.5, 0.5, 0.5]	,					lowerCamelCase__=[0.5, 0.5, 0.5]	,					)   ->	Dict:







								'''simple docstring'''




								snake_case__  :						Optional[Any]		=      size if size is not None else {"shortest_edge": 20}
								snake_case__  :						List[Any]		=      parent
								snake_case__  :						Dict		=      batch_size
								snake_case__  :						Any		=      num_channels
								snake_case__  :						Optional[int]		=      image_size
								snake_case__  :						int		=      min_resolution
								snake_case__  :						Tuple		=      max_resolution
								snake_case__  :						int		=      do_resize
								snake_case__  :						Union[str, Any]		=      size
								snake_case__  :						Tuple		=      crop_pct
								snake_case__  :						int		=      do_normalize
								snake_case__  :						str		=      image_mean
								snake_case__  :						Union[str, Any]		=      image_std







							def        UpperCAmelCase (						self)   ->	Dict:







								'''simple docstring'''




								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "crop_pct": self.crop_pct,
								}


@require_torch
@require_vision
class 			SCREAMING_SNAKE_CASE_  (   _lowercase	,      unittest.TestCase):




							'''simple docstring'''





							__magic_name__					:     Optional[Any]				  =  ConvNextImageProcessor if is_vision_available() else None




							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								snake_case__  :						Tuple		=      ConvNextImageProcessingTester(self)




							@property
							def        UpperCAmelCase (						self)   ->	Dict:







								'''simple docstring'''




								return self.image_processor_tester.prepare_image_processor_dict()




							def        UpperCAmelCase (						self)   ->	List[Any]:







								'''simple docstring'''




								snake_case__  :						Optional[Any]		=      self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(lowerCamelCase__	,					"do_resize"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"size"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"crop_pct"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"do_normalize"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"image_mean"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"image_std"))




							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								snake_case__  :						Union[str, Any]		=      self.image_processing_class.from_dict(self.image_processor_dict)
								self.assertEqual(image_processor.size	,					{"shortest_edge": 20})

								snake_case__  :						List[Any]		=      self.image_processing_class.from_dict(self.image_processor_dict	,					size=42)
								self.assertEqual(image_processor.size	,					{"shortest_edge": 42})




							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								pass




							def        UpperCAmelCase (						self)   ->	Any:







								'''simple docstring'''




								snake_case__  :						Union[str, Any]		=      self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								snake_case__  :						Union[str, Any]		=      prepare_image_inputs(self.image_processor_tester	,					equal_resolution=lowerCamelCase__)
								for image in image_inputs:
									self.assertIsInstance(lowerCamelCase__	,					Image.Image)

								# Test not batched input
								snake_case__  :						List[Any]		=      image_processing(image_inputs[0]	,					return_tensors="pt").pixel_values
								self.assertEqual(
								    encoded_images.shape	,					(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )	,					)

								# Test batched
								snake_case__  :						Union[str, Any]		=      image_processing(lowerCamelCase__	,					return_tensors="pt").pixel_values
								self.assertEqual(
								    encoded_images.shape	,					(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )	,					)




							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								snake_case__  :						Tuple		=      self.image_processing_class(**self.image_processor_dict)
								# create random numpy tensors
								snake_case__  :						int		=      prepare_image_inputs(self.image_processor_tester	,					equal_resolution=lowerCamelCase__	,					numpify=lowerCamelCase__)
								for image in image_inputs:
									self.assertIsInstance(lowerCamelCase__	,					np.ndarray)

								# Test not batched input
								snake_case__  :						Optional[int]		=      image_processing(image_inputs[0]	,					return_tensors="pt").pixel_values
								self.assertEqual(
								    encoded_images.shape	,					(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )	,					)

								# Test batched
								snake_case__  :						Dict		=      image_processing(lowerCamelCase__	,					return_tensors="pt").pixel_values
								self.assertEqual(
								    encoded_images.shape	,					(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )	,					)







							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								snake_case__  :						List[Any]		=      self.image_processing_class(**self.image_processor_dict)
								# create random PyTorch tensors
								snake_case__  :						Any		=      prepare_image_inputs(self.image_processor_tester	,					equal_resolution=lowerCamelCase__	,					torchify=lowerCamelCase__)
								for image in image_inputs:
									self.assertIsInstance(lowerCamelCase__	,					torch.Tensor)

								# Test not batched input
								snake_case__  :						Union[str, Any]		=      image_processing(image_inputs[0]	,					return_tensors="pt").pixel_values
								self.assertEqual(
								    encoded_images.shape	,					(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )	,					)

								# Test batched
								snake_case__  :						List[Any]		=      image_processing(lowerCamelCase__	,					return_tensors="pt").pixel_values
								self.assertEqual(
								    encoded_images.shape	,					(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )	,					)







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
   from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__UpperCAmelCase   						=     logging.get_logger(__name__)

__UpperCAmelCase   						=     {
    "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/config.json",
    "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/config.json",
    "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json",
    "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json",
    "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json",
    "microsoft/deberta-xlarge-mnli": "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json",
}





class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       UpperCAmelCase_				       ="deberta"



       def __init__(							self ,     _A=50265 ,     _A=768 ,     _A=12 ,     _A=12 ,     _A=3072 ,     _A="gelu" ,     _A=0.1 ,     _A=0.1 ,     _A=512 ,     _A=0 ,     _A=0.02 ,     _A=1E-7 ,     _A=False ,     _A=-1 ,     _A=0 ,     _A=True ,     _A=None ,     _A=0 ,     _A="gelu" ,     **_A ,     )				->				Optional[int]:
          super().__init__(**_A	)

          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_	=      initializer_range
          SCREAMING_SNAKE_CASE_	=      relative_attention
          SCREAMING_SNAKE_CASE_	=      max_relative_positions
          SCREAMING_SNAKE_CASE_	=      pad_token_id
          SCREAMING_SNAKE_CASE_	=      position_biased_input

          # Backwards compatibility
          if type(_A	) == str:
             SCREAMING_SNAKE_CASE_	=      [x.strip() for x in pos_att_type.lower().split('''|'''	)]

          SCREAMING_SNAKE_CASE_	=      pos_att_type
          SCREAMING_SNAKE_CASE_	=      vocab_size
          SCREAMING_SNAKE_CASE_	=      layer_norm_eps

          SCREAMING_SNAKE_CASE_	=      kwargs.get('''pooler_hidden_size''' ,     _A	)
          SCREAMING_SNAKE_CASE_	=      pooler_dropout
          SCREAMING_SNAKE_CASE_	=      pooler_hidden_act







class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       @property
       def     _UpperCamelCase	(							self	)				->				Mapping[str, Mapping[int, str]]:
          if self.task == "multiple-choice":
             SCREAMING_SNAKE_CASE_	=      {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
          else:
             SCREAMING_SNAKE_CASE_	=      {0: '''batch''', 1: '''sequence'''}
          if self._config.type_vocab_size > 0:
             return OrderedDict(
                 [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)]	)
          else:
             return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)]	)



       @property
       def     _UpperCamelCase	(							self	)				->				int:
          return 12




       def     _UpperCamelCase	(							self ,     _A ,     _A = -1 ,     _A = -1 ,     _A = -1 ,     _A = False ,     _A = None ,     _A = 3 ,     _A = 40 ,     _A = 40 ,     _A = None ,     )				->				Mapping[str, Any]:
          SCREAMING_SNAKE_CASE_	=      super().generate_dummy_inputs(preprocessor=_A ,     framework=_A	)
          if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
             del dummy_inputs["token_type_ids"]
          return dummy_inputs


