






"""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 ViltImageProcessor






class 		snake_case    (    unittest.TestCase			):





    def __init__(				self      :   List[str]							,						A      :   Optional[int]							,						A      :   int=7							,						A      :   Tuple=3							,						A      :   int=1_8							,						A      :   Tuple=3_0							,						A      :   List[str]=4_0_0							,						A      :   List[Any]=True							,						A      :   List[Any]=None							,						A      :   List[str]=2							,						A      :   Optional[Any]=True							,						A      :   List[str]=[0.5, 0.5, 0.5]							,						A      :   Union[str, Any]=[0.5, 0.5, 0.5]							,						):


      '''simple docstring'''




      a	:     List[str]							=  size if size is not None else {'shortest_edge': 3_0}
      a	:     List[Any]							=  parent
      a	:     Optional[int]							=  batch_size
      a	:     Dict							=  num_channels
      a	:     int							=  image_size
      a	:     Optional[Any]							=  min_resolution
      a	:     Optional[Any]							=  max_resolution
      a	:     int							=  do_resize
      a	:     str							=  size
      a	:     Optional[Any]							=  size_divisor
      a	:     List[Any]							=  do_normalize
      a	:     Union[str, Any]							=  image_mean
      a	:     Optional[Any]							=  image_std





    def 							lowerCamelCase__ (				self      :   List[Any]     ):


      '''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,
          "size_divisor": self.size_divisor,
      }





    def 							lowerCamelCase__ (				self      :   Optional[Any]							,						A      :   Union[str, Any]							,						A      :   Any=False     ):


      '''simple docstring'''




      if not batched:
        a	:     Any							=  self.size['shortest_edge']
        a	:     int							=  image_inputs[0]
        if isinstance(SCREAMING_SNAKE_CASE_							,						Image.Image     ):
          a,	a	:     List[Any]							=  image.size
        else:
          a,	a	:     List[Any]							=  image.shape[1], image.shape[2]
        a	:     Optional[Any]							=  size / min(SCREAMING_SNAKE_CASE_							,						SCREAMING_SNAKE_CASE_     )
        if h < w:
          a,	a	:     Optional[int]							=  size, scale * w
        else:
          a,	a	:     Optional[int]							=  scale * h, size

        a	:     Dict							=  int((1_3_3_3 / 8_0_0) * size     )
        if max(SCREAMING_SNAKE_CASE_							,						SCREAMING_SNAKE_CASE_     ) > max_size:
          a	:     Optional[int]							=  max_size / max(SCREAMING_SNAKE_CASE_							,						SCREAMING_SNAKE_CASE_     )
          a	:     Optional[int]							=  newh * scale
          a	:     Union[str, Any]							=  neww * scale

        a,	a	:     int							=  int(newh + 0.5     ), int(neww + 0.5     )
        a,	a	:     Union[str, Any]							=  (
            newh // self.size_divisor * self.size_divisor,
            neww // self.size_divisor * self.size_divisor,
        )

      else:
        a	:     Any							=  []
        for image in image_inputs:
          a,	a	:     Optional[int]							=  self.get_expected_values([image]     )
          expected_values.append((expected_height, expected_width)     )
        a	:     Any							=  max(SCREAMING_SNAKE_CASE_							,						key=lambda A     : item[0]     )[0]
        a	:     Any							=  max(SCREAMING_SNAKE_CASE_							,						key=lambda A     : item[1]     )[1]

      return expected_height, expected_width






@require_torch
@require_vision
class 		snake_case    (    UpperCAmelCase  ,					unittest.TestCase			):
    __magic_name__       	=	ViltImageProcessor if is_vision_available() else None





    def 							lowerCamelCase__ (				self      :   List[str]     ):


      '''simple docstring'''




      a	:     Optional[Any]							=  ViltImageProcessingTester(self     )





    @property
    def 							lowerCamelCase__ (				self      :   Any     ):


      '''simple docstring'''




      return self.image_processor_tester.prepare_image_processor_dict()





    def 							lowerCamelCase__ (				self      :   Dict     ):


      '''simple docstring'''




      a	:     Dict							=  self.image_processing_class(**self.image_processor_dict     )
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,						'image_mean'     )     )
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,						'image_std'     )     )
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,						'do_normalize'     )     )
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,						'do_resize'     )     )
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,						'size'     )     )
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,						'size_divisor'     )     )





    def 							lowerCamelCase__ (				self      :   str     ):


      '''simple docstring'''




      a	:     Any							=  self.image_processing_class.from_dict(self.image_processor_dict     )
      self.assertEqual(image_processor.size							,						{'shortest_edge': 3_0}     )

      a	:     str							=  self.image_processing_class.from_dict(self.image_processor_dict							,						size=4_2     )
      self.assertEqual(image_processor.size							,						{'shortest_edge': 4_2}     )





    def 							lowerCamelCase__ (				self      :   Dict     ):


      '''simple docstring'''




      pass





    def 							lowerCamelCase__ (				self      :   Union[str, Any]     ):


      '''simple docstring'''




      a	:     Optional[Any]							=  self.image_processing_class(**self.image_processor_dict     )
      # create random PIL images
      a	:     Any							=  prepare_image_inputs(self.image_processor_tester							,						equal_resolution=SCREAMING_SNAKE_CASE_     )
      for image in image_inputs:
        self.assertIsInstance(SCREAMING_SNAKE_CASE_							,						Image.Image     )

      # Test not batched input
      a	:     Optional[int]							=  image_processing(image_inputs[0]							,						return_tensors='pt'     ).pixel_values

      a,	a	:     int							=  self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_     )
      self.assertEqual(
          encoded_images.shape							,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,						)

      # Test batched
      a	:     Optional[Any]							=  image_processing(SCREAMING_SNAKE_CASE_							,						return_tensors='pt'     ).pixel_values

      a,	a	:     Any							=  self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_							,						batched=SCREAMING_SNAKE_CASE_     )
      self.assertEqual(
          encoded_images.shape							,						(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )							,						)





    def 							lowerCamelCase__ (				self      :   List[str]     ):


      '''simple docstring'''




      a	:     List[str]							=  self.image_processing_class(**self.image_processor_dict     )
      # create random numpy tensors
      a	:     str							=  prepare_image_inputs(self.image_processor_tester							,						equal_resolution=SCREAMING_SNAKE_CASE_							,						numpify=SCREAMING_SNAKE_CASE_     )
      for image in image_inputs:
        self.assertIsInstance(SCREAMING_SNAKE_CASE_							,						np.ndarray     )

      # Test not batched input
      a	:     int							=  image_processing(image_inputs[0]							,						return_tensors='pt'     ).pixel_values

      a,	a	:     Optional[Any]							=  self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_     )
      self.assertEqual(
          encoded_images.shape							,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,						)

      # Test batched
      a	:     List[str]							=  image_processing(SCREAMING_SNAKE_CASE_							,						return_tensors='pt'     ).pixel_values

      a,	a	:     Optional[Any]							=  self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_							,						batched=SCREAMING_SNAKE_CASE_     )
      self.assertEqual(
          encoded_images.shape							,						(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )							,						)





    def 							lowerCamelCase__ (				self      :   Dict     ):


      '''simple docstring'''




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

      # Test not batched input
      a	:     int							=  image_processing(image_inputs[0]							,						return_tensors='pt'     ).pixel_values

      a,	a	:     List[Any]							=  self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_     )
      self.assertEqual(
          encoded_images.shape							,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,						)

      # Test batched
      a	:     Union[str, Any]							=  image_processing(SCREAMING_SNAKE_CASE_							,						return_tensors='pt'     ).pixel_values

      a,	a	:     Any							=  self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_							,						batched=SCREAMING_SNAKE_CASE_     )
      self.assertEqual(
          encoded_images.shape							,						(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )							,						)






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


lowerCamelCase_				:							Any       =    logging.get_logger(__name__)

lowerCamelCase_				:							Optional[Any]       =    {
    "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  _lowerCamelCase			(lowerCamelCase					):
     lowercase__				  =			"""deberta"""

     def __init__(  self							,       SCREAMING_SNAKE_CASE_=50_265							,       SCREAMING_SNAKE_CASE_=768							,       SCREAMING_SNAKE_CASE_=12							,       SCREAMING_SNAKE_CASE_=12							,       SCREAMING_SNAKE_CASE_=3_072							,       SCREAMING_SNAKE_CASE_="gelu"							,       SCREAMING_SNAKE_CASE_=0.1							,       SCREAMING_SNAKE_CASE_=0.1							,       SCREAMING_SNAKE_CASE_=512							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_=0.0_2							,       SCREAMING_SNAKE_CASE_=1E-7							,       SCREAMING_SNAKE_CASE_=False							,       SCREAMING_SNAKE_CASE_=-1							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_=True							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_="gelu"							,       **SCREAMING_SNAKE_CASE_							,       ):
            super().__init__(**SCREAMING_SNAKE_CASE_    )

            __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						       =   initializer_range
            __snake_case						       =   relative_attention
            __snake_case						       =   max_relative_positions
            __snake_case						       =   pad_token_id
            __snake_case						       =   position_biased_input

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

            __snake_case						       =   pos_att_type
            __snake_case						       =   vocab_size
            __snake_case						       =   layer_norm_eps

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







class  _lowerCamelCase			(lowerCamelCase					):

     @property
     def 						__lowerCamelCase				(  self    ):
            if self.task == "multiple-choice":
                   __snake_case						       =   {0: 'batch', 1: 'choice', 2: 'sequence'}
            else:
                   __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 						__lowerCamelCase				(  self    ):
            return 12

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