





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_video_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import VideoMAEImageProcessor




class  __UpperCamelCase						(       unittest.TestCase						):





     def __init__(    self							,       _UpperCamelCase							,       _UpperCamelCase=7							,       _UpperCamelCase=3							,       _UpperCamelCase=10							,       _UpperCamelCase=18							,       _UpperCamelCase=30							,       _UpperCamelCase=400							,       _UpperCamelCase=True							,       _UpperCamelCase=None							,       _UpperCamelCase=True							,       _UpperCamelCase=[0.5, 0.5, 0.5]							,       _UpperCamelCase=[0.5, 0.5, 0.5]							,       _UpperCamelCase=None							,       ):
        _UpperCAmelCase											=      size if size is not None else {'''shortest_edge''': 18}
        _UpperCAmelCase											=      crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

        _UpperCAmelCase											=      parent
        _UpperCAmelCase											=      batch_size
        _UpperCAmelCase											=      num_channels
        _UpperCAmelCase											=      num_frames
        _UpperCAmelCase											=      image_size
        _UpperCAmelCase											=      min_resolution
        _UpperCAmelCase											=      max_resolution
        _UpperCAmelCase											=      do_resize
        _UpperCAmelCase											=      size
        _UpperCAmelCase											=      do_normalize
        _UpperCAmelCase											=      image_mean
        _UpperCAmelCase											=      image_std
        _UpperCAmelCase											=      crop_size





     def 		UpperCamelCase(    self				):
        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_size": self.crop_size,
        }




@require_torch
@require_vision
class  __UpperCamelCase						(       A__  ,   unittest.TestCase						):
     __A  :     Optional[int]            =      VideoMAEImageProcessor if is_vision_available() else None





     def 		UpperCamelCase(    self				):
        _UpperCAmelCase											=      VideoMAEImageProcessingTester(self				)





     @property
     def 		UpperCamelCase(    self				):
        return self.image_processor_tester.prepare_image_processor_dict()





     def 		UpperCamelCase(    self				):
        _UpperCAmelCase											=      self.image_processing_class(**self.image_processor_dict				)
        self.assertTrue(hasattr(_UpperCamelCase							,       '''image_mean'''				)				)
        self.assertTrue(hasattr(_UpperCamelCase							,       '''image_std'''				)				)
        self.assertTrue(hasattr(_UpperCamelCase							,       '''do_normalize'''				)				)
        self.assertTrue(hasattr(_UpperCamelCase							,       '''do_resize'''				)				)
        self.assertTrue(hasattr(_UpperCamelCase							,       '''do_center_crop'''				)				)
        self.assertTrue(hasattr(_UpperCamelCase							,       '''size'''				)				)





     def 		UpperCamelCase(    self				):
        _UpperCAmelCase											=      self.image_processing_class.from_dict(self.image_processor_dict				)
        self.assertEqual(image_processor.size							,       {'''shortest_edge''': 18}				)
        self.assertEqual(image_processor.crop_size							,       {'''height''': 18, '''width''': 18}				)

        _UpperCAmelCase											=      self.image_processing_class.from_dict(self.image_processor_dict							,       size=42							,       crop_size=84				)
        self.assertEqual(image_processor.size							,       {'''shortest_edge''': 42}				)
        self.assertEqual(image_processor.crop_size							,       {'''height''': 84, '''width''': 84}				)





     def 		UpperCamelCase(    self				):
        pass





     def 		UpperCamelCase(    self				):
        # Initialize image_processing
        _UpperCAmelCase											=      self.image_processing_class(**self.image_processor_dict				)
        # create random PIL videos
        _UpperCAmelCase											=      prepare_video_inputs(self.image_processor_tester							,       equal_resolution=_UpperCamelCase				)
        for video in video_inputs:
           self.assertIsInstance(_UpperCamelCase							,       _UpperCamelCase				)
           self.assertIsInstance(video[0]							,       Image.Image				)

        # Test not batched input
        _UpperCAmelCase											=      image_processing(video_inputs[0]							,       return_tensors='''pt'''				).pixel_values
        self.assertEqual(
            encoded_videos.shape							,       (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )							,       )

        # Test batched
        _UpperCAmelCase											=      image_processing(_UpperCamelCase							,       return_tensors='''pt'''				).pixel_values
        self.assertEqual(
            encoded_videos.shape							,       (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )							,       )





     def 		UpperCamelCase(    self				):
        # Initialize image_processing
        _UpperCAmelCase											=      self.image_processing_class(**self.image_processor_dict				)
        # create random numpy tensors
        _UpperCAmelCase											=      prepare_video_inputs(self.image_processor_tester							,       equal_resolution=_UpperCamelCase							,       numpify=_UpperCamelCase				)
        for video in video_inputs:
           self.assertIsInstance(_UpperCamelCase							,       _UpperCamelCase				)
           self.assertIsInstance(video[0]							,       np.ndarray				)

        # Test not batched input
        _UpperCAmelCase											=      image_processing(video_inputs[0]							,       return_tensors='''pt'''				).pixel_values
        self.assertEqual(
            encoded_videos.shape							,       (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )							,       )

        # Test batched
        _UpperCAmelCase											=      image_processing(_UpperCamelCase							,       return_tensors='''pt'''				).pixel_values
        self.assertEqual(
            encoded_videos.shape							,       (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )							,       )





     def 		UpperCamelCase(    self				):
        # Initialize image_processing
        _UpperCAmelCase											=      self.image_processing_class(**self.image_processor_dict				)
        # create random PyTorch tensors
        _UpperCAmelCase											=      prepare_video_inputs(self.image_processor_tester							,       equal_resolution=_UpperCamelCase							,       torchify=_UpperCamelCase				)
        for video in video_inputs:
           self.assertIsInstance(_UpperCamelCase							,       _UpperCamelCase				)
           self.assertIsInstance(video[0]							,       torch.Tensor				)

        # Test not batched input
        _UpperCAmelCase											=      image_processing(video_inputs[0]							,       return_tensors='''pt'''				).pixel_values
        self.assertEqual(
            encoded_videos.shape							,       (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )							,       )

        # Test batched
        _UpperCAmelCase											=      image_processing(_UpperCamelCase							,       return_tensors='''pt'''				).pixel_values
        self.assertEqual(
            encoded_videos.shape							,       (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''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						(       A__						):
     __A  :     Dict            =      """deberta"""





     def __init__(    self							,       _UpperCamelCase=50265							,       _UpperCamelCase=768							,       _UpperCamelCase=12							,       _UpperCamelCase=12							,       _UpperCamelCase=3072							,       _UpperCamelCase="gelu"							,       _UpperCamelCase=0.1							,       _UpperCamelCase=0.1							,       _UpperCamelCase=512							,       _UpperCamelCase=0							,       _UpperCamelCase=0.02							,       _UpperCamelCase=1e-7							,       _UpperCamelCase=False							,       _UpperCamelCase=-1							,       _UpperCamelCase=0							,       _UpperCamelCase=True							,       _UpperCamelCase=None							,       _UpperCamelCase=0							,       _UpperCamelCase="gelu"							,       **_UpperCamelCase							,       ):
        super().__init__(**_UpperCamelCase				)

        _UpperCAmelCase											=      hidden_size
        _UpperCAmelCase											=      num_hidden_layers
        _UpperCAmelCase											=      num_attention_heads
        _UpperCAmelCase											=      intermediate_size
        _UpperCAmelCase											=      hidden_act
        _UpperCAmelCase											=      hidden_dropout_prob
        _UpperCAmelCase											=      attention_probs_dropout_prob
        _UpperCAmelCase											=      max_position_embeddings
        _UpperCAmelCase											=      type_vocab_size
        _UpperCAmelCase											=      initializer_range
        _UpperCAmelCase											=      relative_attention
        _UpperCAmelCase											=      max_relative_positions
        _UpperCAmelCase											=      pad_token_id
        _UpperCAmelCase											=      position_biased_input

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

        _UpperCAmelCase											=      pos_att_type
        _UpperCAmelCase											=      vocab_size
        _UpperCAmelCase											=      layer_norm_eps

        _UpperCAmelCase											=      kwargs.get('''pooler_hidden_size'''							,       _UpperCamelCase				)
        _UpperCAmelCase											=      pooler_dropout
        _UpperCAmelCase											=      pooler_hidden_act




class  __UpperCamelCase						(       A__						):





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





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