





import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / """utils"""))

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


_UpperCAmelCase				:       Union[str, Any]	   =				get_tests_dir("""fixtures""")

class    lowerCAmelCase							(					unittest.TestCase			):
 def      A_	(      self  :      Dict	)			->	Tuple:
     # A mock response for an HTTP head request to emulate server down
     lowerCamelCase__    :      Optional[Any]					 =		mock.Mock()
     lowerCamelCase__    :      Dict					 =		500
     lowerCamelCase__    :      Any					 =		{}
     lowerCamelCase__    :      Any					 =		HTTPError
     lowerCamelCase__    :      int					 =		{}

     # Download this model to make sure it's in the cache.
     lowerCamelCase__    :      Any					 =		ViTImageProcessor.from_pretrained('hf-internal-testing/tiny-random-vit'	)
     # Under the mock environment we get a 500 error when trying to reach the model.
     with mock.patch('requests.Session.request'			,     return_value=UpperCAmelCase	) as mock_head:
         lowerCamelCase__    :      Tuple					 =		ViTImageProcessor.from_pretrained('hf-internal-testing/tiny-random-vit'	)
         # This check we did call the fake head request
         mock_head.assert_called()
 def      A_	(      self  :      Optional[int]	)			->	Tuple:
     # This test is for deprecated behavior and can be removed in v5
     lowerCamelCase__    :      Optional[Any]					 =		ViTImageProcessor.from_pretrained(
         'https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json'	)







 def      A_	(      self  :      str	)			->	Tuple:
     with self.assertRaises(UpperCAmelCase	):
         # config is in subfolder, the following should not work without specifying the subfolder
         lowerCamelCase__    :      List[Any]					 =		AutoImageProcessor.from_pretrained('hf-internal-testing/stable-diffusion-all-variants'	)

     lowerCamelCase__    :      List[str]					 =		AutoImageProcessor.from_pretrained(
         'hf-internal-testing/stable-diffusion-all-variants'			,     subfolder='feature_extractor'	)

     self.assertIsNotNone(UpperCAmelCase	)


@is_staging_test
class    lowerCAmelCase							(					unittest.TestCase			):
 @classmethod
 def      A_	(      cls  :      str	)			->	int:
     lowerCamelCase__    :      Union[str, Any]					 =		TOKEN
     HfFolder.save_token(UpperCAmelCase	)
 @classmethod
 def      A_	(      cls  :      Tuple	)			->	Dict:
     try:
         delete_repo(token=cls._token			,     repo_id='test-image-processor'	)
     except HTTPError:
         pass

     try:
         delete_repo(token=cls._token			,     repo_id='valid_org/test-image-processor-org'	)
     except HTTPError:
         pass

     try:
         delete_repo(token=cls._token			,     repo_id='test-dynamic-image-processor'	)
     except HTTPError:
         pass
 def      A_	(      self  :      Any	)			->	Dict:
     lowerCamelCase__    :      Dict					 =		ViTImageProcessor.from_pretrained(UpperCAmelCase	)
     image_processor.push_to_hub('test-image-processor'			,     use_auth_token=self._token	)

     lowerCamelCase__    :      Any					 =		ViTImageProcessor.from_pretrained(F"""{USER}/test-image-processor"""	)
     for k, v in image_processor.__dict__.items():
         self.assertEqual(UpperCAmelCase			,     getattr(UpperCAmelCase			,     UpperCAmelCase	)	)

     # Reset repo
     delete_repo(token=self._token			,     repo_id='test-image-processor'	)

     # Push to hub via save_pretrained
     with tempfile.TemporaryDirectory() as tmp_dir:
         image_processor.save_pretrained(
             UpperCAmelCase			,     repo_id='test-image-processor'			,     push_to_hub=UpperCAmelCase			,     use_auth_token=self._token	)

     lowerCamelCase__    :      Tuple					 =		ViTImageProcessor.from_pretrained(F"""{USER}/test-image-processor"""	)
     for k, v in image_processor.__dict__.items():
         self.assertEqual(UpperCAmelCase			,     getattr(UpperCAmelCase			,     UpperCAmelCase	)	)
 def      A_	(      self  :      List[Any]	)			->	Tuple:
     lowerCamelCase__    :      List[str]					 =		ViTImageProcessor.from_pretrained(UpperCAmelCase	)
     image_processor.push_to_hub('valid_org/test-image-processor'			,     use_auth_token=self._token	)

     lowerCamelCase__    :      List[str]					 =		ViTImageProcessor.from_pretrained('valid_org/test-image-processor'	)
     for k, v in image_processor.__dict__.items():
         self.assertEqual(UpperCAmelCase			,     getattr(UpperCAmelCase			,     UpperCAmelCase	)	)

     # Reset repo
     delete_repo(token=self._token			,     repo_id='valid_org/test-image-processor'	)

     # Push to hub via save_pretrained
     with tempfile.TemporaryDirectory() as tmp_dir:
         image_processor.save_pretrained(
             UpperCAmelCase			,     repo_id='valid_org/test-image-processor-org'			,     push_to_hub=UpperCAmelCase			,     use_auth_token=self._token	)

     lowerCamelCase__    :      Tuple					 =		ViTImageProcessor.from_pretrained('valid_org/test-image-processor-org'	)
     for k, v in image_processor.__dict__.items():
         self.assertEqual(UpperCAmelCase			,     getattr(UpperCAmelCase			,     UpperCAmelCase	)	)







 def      A_	(      self  :      List[str]	)			->	List[Any]:
     CustomImageProcessor.register_for_auto_class()
     lowerCamelCase__    :      Union[str, Any]					 =		CustomImageProcessor.from_pretrained(UpperCAmelCase	)

     image_processor.push_to_hub('test-dynamic-image-processor'			,     use_auth_token=self._token	)

     # This has added the proper auto_map field to the config
     self.assertDictEqual(
         image_processor.auto_map			,     {'AutoImageProcessor': 'custom_image_processing.CustomImageProcessor'}			,     )

     lowerCamelCase__    :      int					 =		AutoImageProcessor.from_pretrained(
         F"""{USER}/test-dynamic-image-processor"""			,     trust_remote_code=UpperCAmelCase	)
     # Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
     self.assertEqual(new_image_processor.__class__.__name__			,     'CustomImageProcessor'	)











from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCamelCase				:		Optional[Any]     =   logging.get_logger(__name__)


if is_vision_available():
 import PIL







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						['''pixel_values''']

 def __init__(				self					:  List[Any]						,							A_					:  bool = True						,							A_					:  Dict[str, int] = None						,							A_					:  PILImageResampling = PILImageResampling.BICUBIC						,							A_					:  bool = True						,							A_					:  Dict[str, int] = None						,							A_					:  bool = True						,							A_					:  Union[int, float] = 1 / 255						,							A_					:  bool = True						,							A_					:  Optional[Union[float, List[float]]] = None						,							A_					:  Optional[Union[float, List[float]]] = None						,							A_					:  bool = True						,							**A_					:  Dict						,							)   ->	None:

       """simple docstring"""






       super().__init__(**A_  )
       lowerCamelCase_		     =    size if size is not None else {'shortest_edge': 224}
       lowerCamelCase_		     =    get_size_dict(A_						,							default_to_square=A_  )
       lowerCamelCase_		     =    crop_size if crop_size is not None else {'height': 224, 'width': 224}
       lowerCamelCase_		     =    get_size_dict(A_						,							default_to_square=A_						,							param_name='crop_size'  )

       lowerCamelCase_		     =    do_resize
       lowerCamelCase_		     =    size
       lowerCamelCase_		     =    resample
       lowerCamelCase_		     =    do_center_crop
       lowerCamelCase_		     =    crop_size
       lowerCamelCase_		     =    do_rescale
       lowerCamelCase_		     =    rescale_factor
       lowerCamelCase_		     =    do_normalize
       lowerCamelCase_		     =    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       lowerCamelCase_		     =    image_std if image_std is not None else OPENAI_CLIP_STD
       lowerCamelCase_		     =    do_convert_rgb

 def 				a__ (				self					:  Dict						,							A_					:  np.ndarray						,							A_					:  Dict[str, int]						,							A_					:  PILImageResampling = PILImageResampling.BICUBIC						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  Union[str, Any]						,							)   ->	np.ndarray:

       """simple docstring"""






       lowerCamelCase_		     =    get_size_dict(A_						,							default_to_square=A_  )
       if "shortest_edge" not in size:
             raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""  )
       lowerCamelCase_		     =    get_resize_output_image_size(A_						,							size=size['shortest_edge']						,							default_to_square=A_  )
       return resize(A_						,							size=A_						,							resample=A_						,							data_format=A_						,							**A_  )

 def 				a__ (				self					:  Tuple						,							A_					:  np.ndarray						,							A_					:  Dict[str, int]						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  Dict						,							)   ->	np.ndarray:

       """simple docstring"""






       lowerCamelCase_		     =    get_size_dict(A_  )
       if "height" not in size or "width" not in size:
             raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""  )
       return center_crop(A_						,							size=(size['height'], size['width'])						,							data_format=A_						,							**A_  )

 def 				a__ (				self					:  str						,							A_					:  np.ndarray						,							A_					:  Union[int, float]						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  Union[str, Any]						,							)   ->	str:

       """simple docstring"""






       return rescale(A_						,							scale=A_						,							data_format=A_						,							**A_  )

 def 				a__ (				self					:  Optional[Any]						,							A_					:  np.ndarray						,							A_					:  Union[float, List[float]]						,							A_					:  Union[float, List[float]]						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  str						,							)   ->	np.ndarray:

       """simple docstring"""






       return normalize(A_						,							mean=A_						,							std=A_						,							data_format=A_						,							**A_  )





 def 				a__ (				self					:  Any						,							A_					:  ImageInput						,							A_					:  bool = None						,							A_					:  Dict[str, int] = None						,							A_					:  PILImageResampling = None						,							A_					:  bool = None						,							A_					:  int = None						,							A_					:  bool = None						,							A_					:  float = None						,							A_					:  bool = None						,							A_					:  Optional[Union[float, List[float]]] = None						,							A_					:  Optional[Union[float, List[float]]] = None						,							A_					:  bool = None						,							A_					:  Optional[Union[str, TensorType]] = None						,							A_					:  Optional[ChannelDimension] = ChannelDimension.FIRST						,							**A_					:  Dict						,							)   ->	PIL.Image.Image:

       """simple docstring"""






       lowerCamelCase_		     =    do_resize if do_resize is not None else self.do_resize
       lowerCamelCase_		     =    size if size is not None else self.size
       lowerCamelCase_		     =    get_size_dict(A_						,							param_name='size'						,							default_to_square=A_  )
       lowerCamelCase_		     =    resample if resample is not None else self.resample
       lowerCamelCase_		     =    do_center_crop if do_center_crop is not None else self.do_center_crop
       lowerCamelCase_		     =    crop_size if crop_size is not None else self.crop_size
       lowerCamelCase_		     =    get_size_dict(A_						,							param_name='crop_size'						,							default_to_square=A_  )
       lowerCamelCase_		     =    do_rescale if do_rescale is not None else self.do_rescale
       lowerCamelCase_		     =    rescale_factor if rescale_factor is not None else self.rescale_factor
       lowerCamelCase_		     =    do_normalize if do_normalize is not None else self.do_normalize
       lowerCamelCase_		     =    image_mean if image_mean is not None else self.image_mean
       lowerCamelCase_		     =    image_std if image_std is not None else self.image_std
       lowerCamelCase_		     =    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       lowerCamelCase_		     =    make_list_of_images(A_  )

       if not valid_images(A_  ):
             raise ValueError(
                 'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                 'torch.Tensor, tf.Tensor or jax.ndarray.'  )

       if do_resize and size is None:
             raise ValueError('Size must be specified if do_resize is True.'  )

       if do_center_crop and crop_size is None:
             raise ValueError('Crop size must be specified if do_center_crop is True.'  )

       if do_rescale and rescale_factor is None:
             raise ValueError('Rescale factor must be specified if do_rescale is True.'  )

       if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('Image mean and std must be specified if do_normalize is True.'  )

       # PIL RGBA images are converted to RGB
       if do_convert_rgb:
             lowerCamelCase_		     =    [convert_to_rgb(A_  ) for image in images]

       # All transformations expect numpy arrays.
       lowerCamelCase_		     =    [to_numpy_array(A_  ) for image in images]

       if do_resize:
             lowerCamelCase_		     =    [self.resize(image=A_						,							size=A_						,							resample=A_  ) for image in images]

       if do_center_crop:
             lowerCamelCase_		     =    [self.center_crop(image=A_						,							size=A_  ) for image in images]

       if do_rescale:
             lowerCamelCase_		     =    [self.rescale(image=A_						,							scale=A_  ) for image in images]

       if do_normalize:
             lowerCamelCase_		     =    [self.normalize(image=A_						,							mean=A_						,							std=A_  ) for image in images]

       lowerCamelCase_		     =    [to_channel_dimension_format(A_						,							A_  ) for image in images]

       lowerCamelCase_		     =    {'pixel_values': images}
       return BatchFeature(data=A_						,							tensor_type=A_  )



