
import random
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():
   import PIL

   from transformers import FlavaImageProcessor
   from transformers.image_utils import PILImageResampling
   from transformers.models.flava.image_processing_flava import (
       FLAVA_CODEBOOK_MEAN,
       FLAVA_CODEBOOK_STD,
       FLAVA_IMAGE_MEAN,
       FLAVA_IMAGE_STD,
   )
else:
   __UpperCAmelCase   						=     __UpperCAmelCase   						=     __UpperCAmelCase   						=     __UpperCAmelCase   						=     None





class 				UpperCamelCase__		(	unittest.TestCase	):






       """simple docstring"""


       def __init__(							self ,     _A ,     _A=7 ,     _A=3 ,     _A=30 ,     _A=400 ,     _A=True ,     _A=None ,     _A=True ,     _A=None ,     _A=None ,     _A=True ,     _A=1 / 255 ,     _A=True ,     _A=FLAVA_IMAGE_MEAN ,     _A=FLAVA_IMAGE_STD ,     _A=14 ,     _A=75 ,     _A=None ,     _A=16 ,     _A=0.3 ,     _A=None ,     _A=True ,     _A=None ,     _A=None ,     _A=True ,     _A=None ,     _A=True ,     _A=True ,     _A=FLAVA_CODEBOOK_MEAN ,     _A=FLAVA_CODEBOOK_STD ,     )				->				List[Any]:
          SCREAMING_SNAKE_CASE_	=      size if size is not None else {'''height''': 224, '''width''': 224}
          SCREAMING_SNAKE_CASE_	=      crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
          SCREAMING_SNAKE_CASE_	=      codebook_size if codebook_size is not None else {'''height''': 112, '''width''': 112}
          SCREAMING_SNAKE_CASE_	=      codebook_crop_size if codebook_crop_size is not None else {'''height''': 112, '''width''': 112}

          SCREAMING_SNAKE_CASE_	=      parent
          SCREAMING_SNAKE_CASE_	=      batch_size
          SCREAMING_SNAKE_CASE_	=      num_channels
          SCREAMING_SNAKE_CASE_	=      do_resize
          SCREAMING_SNAKE_CASE_	=      do_rescale
          SCREAMING_SNAKE_CASE_	=      rescale_factor
          SCREAMING_SNAKE_CASE_	=      min_resolution
          SCREAMING_SNAKE_CASE_	=      max_resolution
          SCREAMING_SNAKE_CASE_	=      size
          SCREAMING_SNAKE_CASE_	=      resample if resample is not None else PILImageResampling.BICUBIC
          SCREAMING_SNAKE_CASE_	=      do_normalize
          SCREAMING_SNAKE_CASE_	=      image_mean
          SCREAMING_SNAKE_CASE_	=      image_std
          SCREAMING_SNAKE_CASE_	=      do_center_crop
          SCREAMING_SNAKE_CASE_	=      crop_size

          SCREAMING_SNAKE_CASE_	=      input_size_patches
          SCREAMING_SNAKE_CASE_	=      total_mask_patches
          SCREAMING_SNAKE_CASE_	=      mask_group_max_patches
          SCREAMING_SNAKE_CASE_	=      mask_group_min_patches
          SCREAMING_SNAKE_CASE_	=      mask_group_min_aspect_ratio
          SCREAMING_SNAKE_CASE_	=      mask_group_max_aspect_ratio

          SCREAMING_SNAKE_CASE_	=      codebook_do_resize
          SCREAMING_SNAKE_CASE_	=      codebook_size
          SCREAMING_SNAKE_CASE_	=      codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
          SCREAMING_SNAKE_CASE_	=      codebook_do_center_crop
          SCREAMING_SNAKE_CASE_	=      codebook_crop_size
          SCREAMING_SNAKE_CASE_	=      codebook_do_map_pixels
          SCREAMING_SNAKE_CASE_	=      codebook_do_normalize
          SCREAMING_SNAKE_CASE_	=      codebook_image_mean
          SCREAMING_SNAKE_CASE_	=      codebook_image_std



       def     _UpperCamelCase	(							self	)				->				Optional[Any]:
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
              "resample": self.resample,
              "do_rescale": self.do_rescale,
              "rescale_factor": self.rescale_factor,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
              "input_size_patches": self.input_size_patches,
              "total_mask_patches": self.total_mask_patches,
              "mask_group_max_patches": self.mask_group_max_patches,
              "mask_group_min_patches": self.mask_group_min_patches,
              "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
              "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
              "codebook_do_resize": self.codebook_do_resize,
              "codebook_size": self.codebook_size,
              "codebook_resample": self.codebook_resample,
              "codebook_do_center_crop": self.codebook_do_center_crop,
              "codebook_crop_size": self.codebook_crop_size,
              "codebook_do_map_pixels": self.codebook_do_map_pixels,
              "codebook_do_normalize": self.codebook_do_normalize,
              "codebook_image_mean": self.codebook_image_mean,
              "codebook_image_std": self.codebook_image_std,
          }



       def     _UpperCamelCase	(							self	)				->				List[Any]:
          return (self.size["height"], self.size["width"])



       def     _UpperCamelCase	(							self	)				->				Optional[Any]:
          return (
              (self.input_size_patches, self.input_size_patches)
              if not isinstance(self.input_size_patches ,     _A	)
              else self.input_size_patches
          )




       def     _UpperCamelCase	(							self	)				->				List[str]:
          return (self.codebook_size["height"], self.codebook_size["width"])







@require_torch
@require_vision
class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE       ,    unittest.TestCase	):






       """simple docstring"""


       UpperCAmelCase_				       =FlavaImageProcessor if is_vision_available() else None
       UpperCAmelCase_				       =None



       def     _UpperCamelCase	(							self	)				->				Tuple:
          SCREAMING_SNAKE_CASE_	=      FlavaImageProcessingTester(self	)



       @property
       def     _UpperCamelCase	(							self	)				->				int:
          return self.image_processor_tester.prepare_image_processor_dict()



       def     _UpperCamelCase	(							self	)				->				Tuple:
          SCREAMING_SNAKE_CASE_	=      self.image_processing_class(**self.image_processor_dict	)
          self.assertTrue(hasattr(_A ,     '''image_mean'''	)	)
          self.assertTrue(hasattr(_A ,     '''image_std'''	)	)
          self.assertTrue(hasattr(_A ,     '''do_normalize'''	)	)
          self.assertTrue(hasattr(_A ,     '''do_resize'''	)	)
          self.assertTrue(hasattr(_A ,     '''resample'''	)	)
          self.assertTrue(hasattr(_A ,     '''crop_size'''	)	)
          self.assertTrue(hasattr(_A ,     '''do_center_crop'''	)	)
          self.assertTrue(hasattr(_A ,     '''do_rescale'''	)	)
          self.assertTrue(hasattr(_A ,     '''rescale_factor'''	)	)
          self.assertTrue(hasattr(_A ,     '''masking_generator'''	)	)
          self.assertTrue(hasattr(_A ,     '''codebook_do_resize'''	)	)
          self.assertTrue(hasattr(_A ,     '''codebook_size'''	)	)
          self.assertTrue(hasattr(_A ,     '''codebook_resample'''	)	)
          self.assertTrue(hasattr(_A ,     '''codebook_do_center_crop'''	)	)
          self.assertTrue(hasattr(_A ,     '''codebook_crop_size'''	)	)
          self.assertTrue(hasattr(_A ,     '''codebook_do_map_pixels'''	)	)
          self.assertTrue(hasattr(_A ,     '''codebook_do_normalize'''	)	)
          self.assertTrue(hasattr(_A ,     '''codebook_image_mean'''	)	)
          self.assertTrue(hasattr(_A ,     '''codebook_image_std'''	)	)



       def     _UpperCamelCase	(							self	)				->				Any:
          SCREAMING_SNAKE_CASE_	=      self.image_processing_class.from_dict(self.image_processor_dict	)
          self.assertEqual(image_processor.size ,     {'''height''': 224, '''width''': 224}	)
          self.assertEqual(image_processor.crop_size ,     {'''height''': 224, '''width''': 224}	)
          self.assertEqual(image_processor.codebook_size ,     {'''height''': 112, '''width''': 112}	)
          self.assertEqual(image_processor.codebook_crop_size ,     {'''height''': 112, '''width''': 112}	)

          SCREAMING_SNAKE_CASE_	=      self.image_processing_class.from_dict(
              self.image_processor_dict ,     size=42 ,     crop_size=84 ,     codebook_size=33 ,     codebook_crop_size=66	)
          self.assertEqual(image_processor.size ,     {'''height''': 42, '''width''': 42}	)
          self.assertEqual(image_processor.crop_size ,     {'''height''': 84, '''width''': 84}	)
          self.assertEqual(image_processor.codebook_size ,     {'''height''': 33, '''width''': 33}	)
          self.assertEqual(image_processor.codebook_crop_size ,     {'''height''': 66, '''width''': 66}	)



       def     _UpperCamelCase	(							self	)				->				int:
          pass



       def     _UpperCamelCase	(							self	)				->				List[str]:
          # Initialize image_processing
          SCREAMING_SNAKE_CASE_	=      self.image_processing_class(**self.image_processor_dict	)
          # create random PIL images
          SCREAMING_SNAKE_CASE_	=      prepare_image_inputs(self.image_processor_tester ,     equal_resolution=_A	)
          for image in image_inputs:
             self.assertIsInstance(_A ,     PIL.Image.Image	)

          # Test not batched input
          SCREAMING_SNAKE_CASE_	=      image_processing(image_inputs[0] ,     return_tensors='''pt'''	)

          # Test no bool masked pos
          self.assertFalse('''bool_masked_pos''' in encoded_images	)

          SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	=      self.image_processor_tester.get_expected_image_size()

          self.assertEqual(
              encoded_images.pixel_values.shape ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width) ,     )

          # Test batched
          SCREAMING_SNAKE_CASE_	=      image_processing(_A ,     return_tensors='''pt'''	)
          SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	=      self.image_processor_tester.get_expected_image_size()

          # Test no bool masked pos
          self.assertFalse('''bool_masked_pos''' in encoded_images	)

          self.assertEqual(
              encoded_images.pixel_values.shape ,     (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              ) ,     )



       def     _UpperCamelCase	(							self ,     _A ,     _A	)				->				Optional[Any]:
          # Initialize image_processing
          SCREAMING_SNAKE_CASE_	=      self.image_processing_class(**self.image_processor_dict	)
          # create random tensors
          SCREAMING_SNAKE_CASE_	=      prepare_image_inputs(self.image_processor_tester ,     equal_resolution=_A ,     **_A	)
          for image in image_inputs:
             self.assertIsInstance(_A ,     _A	)

          # Test not batched input
          SCREAMING_SNAKE_CASE_	=      image_processing(image_inputs[0] ,     return_tensors='''pt'''	)

          SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	=      self.image_processor_tester.get_expected_image_size()
          self.assertEqual(
              encoded_images.pixel_values.shape ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width) ,     )

          SCREAMING_SNAKE_CASE_	=      image_processing(_A ,     return_image_mask=_A ,     return_tensors='''pt'''	)

          SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	=      self.image_processor_tester.get_expected_image_size()
          self.assertEqual(
              encoded_images.pixel_values.shape ,     (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              ) ,     )

          SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	=      self.image_processor_tester.get_expected_mask_size()
          self.assertEqual(
              encoded_images.bool_masked_pos.shape ,     (
                  self.image_processor_tester.batch_size,
                  expected_height,
                  expected_width,
              ) ,     )

          # Test batched
          SCREAMING_SNAKE_CASE_	=      image_processing(_A ,     return_tensors='''pt'''	).pixel_values

          SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	=      self.image_processor_tester.get_expected_image_size()
          self.assertEqual(
              encoded_images.shape ,     (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              ) ,     )

          # Test masking
          SCREAMING_SNAKE_CASE_	=      image_processing(_A ,     return_image_mask=_A ,     return_tensors='''pt'''	)

          SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	=      self.image_processor_tester.get_expected_image_size()
          self.assertEqual(
              encoded_images.pixel_values.shape ,     (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              ) ,     )

          SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	=      self.image_processor_tester.get_expected_mask_size()
          self.assertEqual(
              encoded_images.bool_masked_pos.shape ,     (
                  self.image_processor_tester.batch_size,
                  expected_height,
                  expected_width,
              ) ,     )



       def     _UpperCamelCase	(							self	)				->				List[str]:
          self._test_call_framework(np.ndarray ,     prepare_kwargs={'''numpify''': True}	)



       def     _UpperCamelCase	(							self	)				->				Optional[Any]:
          self._test_call_framework(torch.Tensor ,     prepare_kwargs={'''torchify''': True}	)



       def     _UpperCamelCase	(							self	)				->				Optional[int]:
          # Initialize image_processing
          random.seed(1234	)
          SCREAMING_SNAKE_CASE_	=      self.image_processing_class(**self.image_processor_dict	)
          SCREAMING_SNAKE_CASE_	=      prepare_image_inputs(self.image_processor_tester ,     equal_resolution=_A ,     torchify=_A	)

          # Test not batched input
          SCREAMING_SNAKE_CASE_	=      image_processing(image_inputs[0] ,     return_image_mask=_A ,     return_tensors='''pt'''	)
          self.assertEqual(encoded_images.bool_masked_pos.sum().item() ,     75	)




       def     _UpperCamelCase	(							self	)				->				str:
          # Initialize image_processing
          SCREAMING_SNAKE_CASE_	=      self.image_processing_class(**self.image_processor_dict	)
          # create random PIL images
          SCREAMING_SNAKE_CASE_	=      prepare_image_inputs(self.image_processor_tester ,     equal_resolution=_A	)
          for image in image_inputs:
             self.assertIsInstance(_A ,     PIL.Image.Image	)

          # Test not batched input
          SCREAMING_SNAKE_CASE_	=      image_processing(image_inputs[0] ,     return_codebook_pixels=_A ,     return_tensors='''pt'''	)
          SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	=      self.image_processor_tester.get_expected_codebook_image_size()
          self.assertEqual(
              encoded_images.codebook_pixel_values.shape ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width) ,     )

          # Test batched
          SCREAMING_SNAKE_CASE_	=      image_processing(_A ,     return_codebook_pixels=_A ,     return_tensors='''pt'''	)
          SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	=      self.image_processor_tester.get_expected_codebook_image_size()
          self.assertEqual(
              encoded_images.codebook_pixel_values.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


__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


