from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE										=						{
    'configuration_owlvit': [
        'OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'OwlViTConfig',
        'OwlViTOnnxConfig',
        'OwlViTTextConfig',
        'OwlViTVisionConfig',
    ],
    'processing_owlvit': ['OwlViTProcessor'],
}


try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE										=						['OwlViTFeatureExtractor']
    _SCREAMING_SNAKE_CASE										=						['OwlViTImageProcessor']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE										=						[
        'OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'OwlViTModel',
        'OwlViTPreTrainedModel',
        'OwlViTTextModel',
        'OwlViTVisionModel',
        'OwlViTForObjectDetection',
    ]

if TYPE_CHECKING:
    from .configuration_owlvit import (
        OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        OwlViTConfig,
        OwlViTOnnxConfig,
        OwlViTTextConfig,
        OwlViTVisionConfig,
    )
    from .processing_owlvit import OwlViTProcessor

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_owlvit import OwlViTFeatureExtractor
        from .image_processing_owlvit import OwlViTImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_owlvit import (
            OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            OwlViTForObjectDetection,
            OwlViTModel,
            OwlViTPreTrainedModel,
            OwlViTTextModel,
            OwlViTVisionModel,
        )

else:
    import sys

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





import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor



class 				a							(			unittest.TestCase       ):







 """simple docstring"""






 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_=7		,				lowerCAmelCase_=3		,				lowerCAmelCase_=18		,				lowerCAmelCase_=30		,				lowerCAmelCase_=4_00		,				lowerCAmelCase_=True		,				lowerCAmelCase_=None		,				lowerCAmelCase_=True		,				lowerCAmelCase_=False		,				lowerCAmelCase_=True		,				lowerCAmelCase_=True		,				lowerCAmelCase_=[0.5, 0.5, 0.5]		,				lowerCAmelCase_=[0.5, 0.5, 0.5]		,				)  ->					Optional[Any]:
        _A        =  parent
        _A        =  batch_size
        _A        =  num_channels
        _A        =  image_size
        _A        =  min_resolution
        _A        =  max_resolution
        _A        =  do_resize
        _A        =  size if size is not None else {"""height""": 18, """width""": 20}
        _A        =  do_thumbnail
        _A        =  do_align_axis
        _A        =  do_pad
        _A        =  do_normalize
        _A        =  image_mean
        _A        =  image_std



 def      UpperCAmelCase							(					self							)  ->					str:
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_thumbnail": self.do_thumbnail,
            "do_align_long_axis": self.do_align_axis,
            "do_pad": self.do_pad,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
        }



@require_torch
@require_vision
class 				a							(			__lowerCAmelCase       ,			unittest.TestCase       ):







 """simple docstring"""






 lowerCamelCase	:int    			=		DonutImageProcessor if is_vision_available() else None



 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  DonutImageProcessingTester(self							)



 @property
 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        return self.image_processor_tester.prepare_image_processor_dict()



 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        _A        =  self.image_processing_class(**self.image_processor_dict							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"""do_resize"""							)							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"""size"""							)							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"""do_thumbnail"""							)							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"""do_align_long_axis"""							)							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"""do_pad"""							)							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"""do_normalize"""							)							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"""image_mean"""							)							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"""image_std"""							)							)



 def      UpperCAmelCase							(					self							)  ->					Tuple:
        _A        =  self.image_processing_class.from_dict(self.image_processor_dict							)
        self.assertEqual(image_processor.size		,				{"""height""": 18, """width""": 20}							)

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

        # Previous config had dimensions in (width, height) order
        _A        =  self.image_processing_class.from_dict(self.image_processor_dict		,				size=(42, 84)							)
        self.assertEqual(image_processor.size		,				{"""height""": 84, """width""": 42}							)



 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        pass



 @is_flaky()
 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        # Initialize image_processing
        _A        =  self.image_processing_class(**self.image_processor_dict							)
        # create random PIL images
        _A        =  prepare_image_inputs(self.image_processor_tester		,				equal_resolution=lowerCAmelCase_							)
        for image in image_inputs:
               self.assertIsInstance(lowerCAmelCase_		,				Image.Image							)

        # Test not batched input
        _A        =  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
        _A        =  image_processing(lowerCAmelCase_		,				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"""],
            )		,				)



 @is_flaky()
 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        # Initialize image_processing
        _A        =  self.image_processing_class(**self.image_processor_dict							)
        # create random numpy tensors
        _A        =  prepare_image_inputs(self.image_processor_tester		,				equal_resolution=lowerCAmelCase_		,				numpify=lowerCAmelCase_							)
        for image in image_inputs:
               self.assertIsInstance(lowerCAmelCase_		,				np.ndarray							)

        # Test not batched input
        _A        =  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
        _A        =  image_processing(lowerCAmelCase_		,				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"""],
            )		,				)



 @is_flaky()
 def      UpperCAmelCase							(					self							)  ->					int:
        # Initialize image_processing
        _A        =  self.image_processing_class(**self.image_processor_dict							)
        # create random PyTorch tensors
        _A        =  prepare_image_inputs(self.image_processor_tester		,				equal_resolution=lowerCAmelCase_		,				torchify=lowerCAmelCase_							)
        for image in image_inputs:
               self.assertIsInstance(lowerCAmelCase_		,				torch.Tensor							)

        # Test not batched input
        _A        =  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
        _A        =  image_processing(lowerCAmelCase_		,				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"""],
            )		,				)




