



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 EfficientNetImageProcessor







class 							a_  (		unittest.TestCase				):

						def __init__(    self	:int   ,						_lowercase	:Dict   ,						_lowercase	:List[str]=13   ,						_lowercase	:Any=3   ,						_lowercase	:Optional[int]=18   ,						_lowercase	:Union[str, Any]=30   ,						_lowercase	:Tuple=400   ,						_lowercase	:List[str]=True   ,						_lowercase	:Dict=None   ,						_lowercase	:List[Any]=True   ,						_lowercase	:str=[0.5, 0.5, 0.5]   ,						_lowercase	:Optional[int]=[0.5, 0.5, 0.5]   ,						)		->					Any:
												UpperCAmelCase_     							=     size if 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_normalize
												UpperCAmelCase_     							=     image_mean
												UpperCAmelCase_     							=     image_std




						def 	__a       (    self	:List[Any])		->					List[str]:
												return {
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_normalize": self.do_normalize,
												    "do_resize": self.do_resize,
												    "size": self.size,
												}







@require_torch
@require_vision
class 							a_  (		_snake_case					, unittest.TestCase				):
						UpperCamelCase__      :						List[str]				       =EfficientNetImageProcessor if is_vision_available() else None

						def 	__a       (    self	:int)		->					int:
												UpperCAmelCase_     							=     EfficientNetImageProcessorTester(self)

						@property
						def 	__a       (    self	:Any)		->					Dict:
												return self.image_processor_tester.prepare_image_processor_dict()

						def 	__a       (    self	:str)		->					List[Any]:
												UpperCAmelCase_     							=     self.image_processing_class(**self.image_processor_dict)
												self.assertTrue(hasattr(_lowercase   ,						'''image_mean'''))
												self.assertTrue(hasattr(_lowercase   ,						'''image_std'''))
												self.assertTrue(hasattr(_lowercase   ,						'''do_normalize'''))
												self.assertTrue(hasattr(_lowercase   ,						'''do_resize'''))
												self.assertTrue(hasattr(_lowercase   ,						'''size'''))

						def 	__a       (    self	:str)		->					Union[str, Any]:
												UpperCAmelCase_     							=     self.image_processing_class.from_dict(self.image_processor_dict)
												self.assertEqual(image_processor.size   ,						{'''height''': 18, '''width''': 18})

												UpperCAmelCase_     							=     self.image_processing_class.from_dict(self.image_processor_dict   ,						size=42)
												self.assertEqual(image_processor.size   ,						{'''height''': 42, '''width''': 42})

						def 	__a       (    self	:str)		->					Dict:
												# Initialize image_processing
												UpperCAmelCase_     							=     self.image_processing_class(**self.image_processor_dict)
												# create random PIL images
												UpperCAmelCase_     							=     prepare_image_inputs(self.image_processor_tester   ,						equal_resolution=_lowercase)
												for image in image_inputs:
																		self.assertIsInstance(_lowercase   ,						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.size['''height'''],
												        self.image_processor_tester.size['''width'''],
												    )   ,						)

												# Test batched
												UpperCAmelCase_     							=     image_processing(_lowercase   ,						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['''height'''],
												        self.image_processor_tester.size['''width'''],
												    )   ,						)

						def 	__a       (    self	:Union[str, Any])		->					List[Any]:
												# Initialize image_processing
												UpperCAmelCase_     							=     self.image_processing_class(**self.image_processor_dict)
												# create random numpy tensors
												UpperCAmelCase_     							=     prepare_image_inputs(self.image_processor_tester   ,						equal_resolution=_lowercase   ,						numpify=_lowercase)
												for image in image_inputs:
																		self.assertIsInstance(_lowercase   ,						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.size['''height'''],
												        self.image_processor_tester.size['''width'''],
												    )   ,						)

												# Test batched
												UpperCAmelCase_     							=     image_processing(_lowercase   ,						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['''height'''],
												        self.image_processor_tester.size['''width'''],
												    )   ,						)




						def 	__a       (    self	:Union[str, Any])		->					Dict:
												# Initialize image_processing
												UpperCAmelCase_     							=     self.image_processing_class(**self.image_processor_dict)
												# create random PyTorch tensors
												UpperCAmelCase_     							=     prepare_image_inputs(self.image_processor_tester   ,						equal_resolution=_lowercase   ,						torchify=_lowercase)
												for image in image_inputs:
																		self.assertIsInstance(_lowercase   ,						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.size['''height'''],
												        self.image_processor_tester.size['''width'''],
												    )   ,						)

												# Test batched
												UpperCAmelCase_     							=     image_processing(_lowercase   ,						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['''height'''],
												        self.image_processor_tester.size['''width'''],
												    )   ,						)






from typing import TYPE_CHECKING

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


lowerCAmelCase__							= {
    '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:
    lowerCAmelCase__							= ['DebertaV2TokenizerFast']

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__							= [
        '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:
    lowerCAmelCase__							= [
        '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

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