

'''simple docstring'''

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 ConvNextImageProcessor


class 					lowerCAmelCase       ( unittest.TestCase				):







       def __init__(							self     :	Tuple  , __lowercase     :	Optional[Any]  , __lowercase     :	Optional[int]=7  , __lowercase     :	Optional[Any]=3  , __lowercase     :	Union[str, Any]=18  , __lowercase     :	Any=30  , __lowercase     :	str=400  , __lowercase     :	Any=True  , __lowercase     :	Optional[int]=None  , __lowercase     :	List[Any]=0.8_7_5  , __lowercase     :	Any=True  , __lowercase     :	Optional[int]=[0.5, 0.5, 0.5]  , __lowercase     :	Optional[Any]=[0.5, 0.5, 0.5]  , ):







            """simple docstring"""







            __lowercase     						=size if size is not None else {'shortest_edge': 20}
            __lowercase     						=parent
            __lowercase     						=batch_size
            __lowercase     						=num_channels
            __lowercase     						=image_size
            __lowercase     						=min_resolution
            __lowercase     						=max_resolution
            __lowercase     						=do_resize
            __lowercase     						=size
            __lowercase     						=crop_pct
            __lowercase     						=do_normalize
            __lowercase     						=image_mean
            __lowercase     						=image_std







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            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_pct": self.crop_pct,
            }






@require_torch
@require_vision
class 					lowerCAmelCase       ( A				,     unittest.TestCase				):
       lowerCAmelCase_				=	ConvNextImageProcessor if is_vision_available() else None







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            __lowercase     						=ConvNextImageProcessingTester(self )







       @property
       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            return self.image_processor_tester.prepare_image_processor_dict()







       def      snake_case				(							self     :	Optional[int] ):







            """simple docstring"""







            __lowercase     						=self.image_processing_class(**self.image_processor_dict )
            self.assertTrue(hasattr(__lowercase  , 'do_resize' ) )
            self.assertTrue(hasattr(__lowercase  , 'size' ) )
            self.assertTrue(hasattr(__lowercase  , 'crop_pct' ) )
            self.assertTrue(hasattr(__lowercase  , 'do_normalize' ) )
            self.assertTrue(hasattr(__lowercase  , 'image_mean' ) )
            self.assertTrue(hasattr(__lowercase  , 'image_std' ) )







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            __lowercase     						=self.image_processing_class.from_dict(self.image_processor_dict )
            self.assertEqual(image_processor.size  , {'shortest_edge': 20} )

            __lowercase     						=self.image_processing_class.from_dict(self.image_processor_dict  , size=42 )
            self.assertEqual(image_processor.size  , {'shortest_edge': 42} )







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            pass







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







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

            # Test not batched input
            __lowercase     						=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['shortest_edge'],
                    self.image_processor_tester.size['shortest_edge'],
                )  , )

            # Test batched
            __lowercase     						=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['shortest_edge'],
                    self.image_processor_tester.size['shortest_edge'],
                )  , )







       def      snake_case				(							self     :	List[Any] ):







            """simple docstring"""







            __lowercase     						=self.image_processing_class(**self.image_processor_dict )
            # create random numpy tensors
            __lowercase     						=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
            __lowercase     						=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['shortest_edge'],
                    self.image_processor_tester.size['shortest_edge'],
                )  , )

            # Test batched
            __lowercase     						=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['shortest_edge'],
                    self.image_processor_tester.size['shortest_edge'],
                )  , )







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            __lowercase     						=self.image_processing_class(**self.image_processor_dict )
            # create random PyTorch tensors
            __lowercase     						=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
            __lowercase     						=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['shortest_edge'],
                    self.image_processor_tester.size['shortest_edge'],
                )  , )

            # Test batched
            __lowercase     						=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['shortest_edge'],
                    self.image_processor_tester.size['shortest_edge'],
                )  , )





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase							=					{'''configuration_vit_hybrid''': ['''VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTHybridConfig''']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase							=					[
           '''VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''ViTHybridForImageClassification''',
           '''ViTHybridModel''',
           '''ViTHybridPreTrainedModel''',
       ]

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase							=					['''ViTHybridImageProcessor''']


if TYPE_CHECKING:
       from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vit_hybrid import (
                  VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ViTHybridForImageClassification,
                  ViTHybridModel,
                  ViTHybridPreTrainedModel,
              )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
       import sys

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


