


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case             =	{
    """configuration_deberta_v2""": ["""DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaV2Config""", """DebertaV2OnnxConfig"""],
    """tokenization_deberta_v2""": ["""DebertaV2Tokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case             =	["""DebertaV2TokenizerFast"""]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case             =	[
        """TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFDebertaV2ForMaskedLM""",
        """TFDebertaV2ForQuestionAnswering""",
        """TFDebertaV2ForSequenceClassification""",
        """TFDebertaV2ForTokenClassification""",
        """TFDebertaV2Model""",
        """TFDebertaV2PreTrainedModel""",
    ]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case             =	[
        """DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """DebertaV2ForMaskedLM""",
        """DebertaV2ForMultipleChoice""",
        """DebertaV2ForQuestionAnswering""",
        """DebertaV2ForSequenceClassification""",
        """DebertaV2ForTokenClassification""",
        """DebertaV2Model""",
        """DebertaV2PreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_deberta_va import (
        DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
        DebertaVaConfig,
        DebertaVaOnnxConfig,
    )
    from .tokenization_deberta_va import DebertaVaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_deberta_va import (
            TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFDebertaVaForMaskedLM,
            TFDebertaVaForQuestionAnswering,
            TFDebertaVaForSequenceClassification,
            TFDebertaVaForTokenClassification,
            TFDebertaVaModel,
            TFDebertaVaPreTrainedModel,
        )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_deberta_va import (
            DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
            DebertaVaForMaskedLM,
            DebertaVaForMultipleChoice,
            DebertaVaForQuestionAnswering,
            DebertaVaForSequenceClassification,
            DebertaVaForTokenClassification,
            DebertaVaModel,
            DebertaVaPreTrainedModel,
        )

else:
    import sys

    __snake_case             =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






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 DeiTImageProcessor



class   __lowerCamelCase (unittest.TestCase	):







       def __init__( self:   List[str],A_:   List[Any],A_:   List[Any]=7,A_:   str=3,A_:   Tuple=18,A_:   Union[str, Any]=30,A_:   Union[str, Any]=400,A_:   List[str]=True,A_:   List[Any]=None,A_:   Tuple=True,A_:   Union[str, Any]=None,A_:   int=True,A_:   Any=[0.5, 0.5, 0.5],A_:   Optional[Any]=[0.5, 0.5, 0.5],):






            '''simple docstring'''




            __UpperCamelCase   = size if size is not None else {'height': 20, 'width': 20}
            __UpperCamelCase   = crop_size if crop_size is not None else {'height': 18, 'width': 18}

            __UpperCamelCase   = parent
            __UpperCamelCase   = batch_size
            __UpperCamelCase   = num_channels
            __UpperCamelCase   = image_size
            __UpperCamelCase   = min_resolution
            __UpperCamelCase   = max_resolution
            __UpperCamelCase   = do_resize
            __UpperCamelCase   = size
            __UpperCamelCase   = do_center_crop
            __UpperCamelCase   = crop_size
            __UpperCamelCase   = do_normalize
            __UpperCamelCase   = image_mean
            __UpperCamelCase   = image_std







       def 		snake_case_  ( self:   List[str]				):






            '''simple docstring'''




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



@require_torch
@require_vision
class   __lowerCamelCase (_a							,			unittest.TestCase	):
       _lowercase       							=    DeiTImageProcessor if is_vision_available() else None
       _lowercase       							=    True







       def 		snake_case_  ( self:   List[Any]				):






            '''simple docstring'''




            __UpperCamelCase   = DeiTImageProcessingTester(self				)







       @property
       def 		snake_case_  ( self:   Optional[int]				):






            '''simple docstring'''




            return self.image_processor_tester.prepare_image_processor_dict()







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






            '''simple docstring'''




            __UpperCamelCase   = self.image_processing_class(**self.image_processor_dict				)
            self.assertTrue(hasattr(A_,'do_resize'				)				)
            self.assertTrue(hasattr(A_,'size'				)				)
            self.assertTrue(hasattr(A_,'do_center_crop'				)				)
            self.assertTrue(hasattr(A_,'center_crop'				)				)
            self.assertTrue(hasattr(A_,'do_normalize'				)				)
            self.assertTrue(hasattr(A_,'image_mean'				)				)
            self.assertTrue(hasattr(A_,'image_std'				)				)







       def 		snake_case_  ( self:   Optional[int]				):






            '''simple docstring'''




            __UpperCamelCase   = self.image_processing_class.from_dict(self.image_processor_dict				)
            self.assertEqual(image_processor.size,{'height': 20, 'width': 20}				)
            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,{'height': 42, 'width': 42}				)
            self.assertEqual(image_processor.crop_size,{'height': 84, 'width': 84}				)







       def 		snake_case_  ( self:   Tuple				):






            '''simple docstring'''




            pass







       def 		snake_case_  ( self:   Optional[int]				):






            '''simple docstring'''




            __UpperCamelCase   = self.image_processing_class(**self.image_processor_dict				)
            # create random PIL images
            __UpperCamelCase   = prepare_image_inputs(self.image_processor_tester,equal_resolution=A_				)
            for image in image_inputs:
                 self.assertIsInstance(A_,Image.Image				)

            # Test not batched input
            __UpperCamelCase   = 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.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ),)

            # Test batched
            __UpperCamelCase   = image_processing(A_,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.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ),)







       def 		snake_case_  ( self:   List[str]				):






            '''simple docstring'''




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

            # Test not batched input
            __UpperCamelCase   = 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.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ),)

            # Test batched
            __UpperCamelCase   = image_processing(A_,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.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ),)







       def 		snake_case_  ( self:   Any				):






            '''simple docstring'''




            __UpperCamelCase   = self.image_processing_class(**self.image_processor_dict				)
            # create random PyTorch tensors
            __UpperCamelCase   = prepare_image_inputs(self.image_processor_tester,equal_resolution=A_,torchify=A_				)
            for image in image_inputs:
                 self.assertIsInstance(A_,torch.Tensor				)

            # Test not batched input
            __UpperCamelCase   = 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.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ),)

            # Test batched
            __UpperCamelCase   = image_processing(A_,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.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ),)


