



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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


_snake_case		=  logging.get_logger(__name__)



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		['pixel_values']







							def __init__(   self,			__a = True,			__a = None,			__a = PIL.Image.BICUBIC,			__a = True,			__a = None,			__a = 1 / 255,			__a = True,			__a = True,			__a = None,			__a = None,			**__a,			):

														'''simple docstring'''

														super().__init__(**__a)
														_lowerCAmelCase     :							Any    =    size if size is not None else {"height": 256, "width": 256}
														_lowerCAmelCase     :							List[Any]    =    get_size_dict(__a)
														_lowerCAmelCase     :							Any    =    crop_size if crop_size is not None else {"height": 224, "width": 224}
														_lowerCAmelCase     :							List[Any]    =    get_size_dict(__a,			param_name="crop_size")

														_lowerCAmelCase     :							int    =    do_resize
														_lowerCAmelCase     :							List[str]    =    size
														_lowerCAmelCase     :							str    =    resample
														_lowerCAmelCase     :							Optional[Any]    =    do_center_crop
														_lowerCAmelCase     :							Tuple    =    crop_size
														_lowerCAmelCase     :							List[str]    =    do_rescale
														_lowerCAmelCase     :							str    =    rescale_factor
														_lowerCAmelCase     :							Optional[Any]    =    do_normalize
														_lowerCAmelCase     :							Tuple    =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
														_lowerCAmelCase     :							Optional[int]    =    image_std if image_std is not None else IMAGENET_STANDARD_STD







							def    snake_case__      (   self,			__a,			__a,			__a = PIL.Image.BICUBIC,			__a = None,			**__a,			):

														'''simple docstring'''

														_lowerCAmelCase     :							Any    =    get_size_dict(__a)
														if "height" not in size or "width" not in size:
																					raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}")
														return resize(
														    __a,			size=(size["height"], size["width"]),			resample=__a,			data_format=__a,			**__a)







							def    snake_case__      (   self,			__a,			__a,			__a = None,			**__a,			):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    get_size_dict(__a)
														if "height" not in size or "width" not in size:
																					raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}")
														return center_crop(__a,			size=(size["height"], size["width"]),			data_format=__a,			**__a)







							def    snake_case__      (   self,			__a,			__a,			__a = None,			**__a,			):

														'''simple docstring'''

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







							def    snake_case__      (   self,			__a,			__a,			__a,			__a = None,			**__a,			):

														'''simple docstring'''

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







							def    snake_case__      (   self,			__a,			__a = None,			__a = None,			__a=None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = ChannelDimension.FIRST,			**__a,			):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    do_resize if do_resize is not None else self.do_resize
														_lowerCAmelCase     :							int    =    resample if resample is not None else self.resample
														_lowerCAmelCase     :							Dict    =    do_center_crop if do_center_crop is not None else self.do_center_crop
														_lowerCAmelCase     :							Tuple    =    do_rescale if do_rescale is not None else self.do_rescale
														_lowerCAmelCase     :							Union[str, Any]    =    rescale_factor if rescale_factor is not None else self.rescale_factor
														_lowerCAmelCase     :							Any    =    do_normalize if do_normalize is not None else self.do_normalize
														_lowerCAmelCase     :							Optional[int]    =    image_mean if image_mean is not None else self.image_mean
														_lowerCAmelCase     :							Dict    =    image_std if image_std is not None else self.image_std

														_lowerCAmelCase     :							Any    =    size if size is not None else self.size
														_lowerCAmelCase     :							int    =    get_size_dict(__a)
														_lowerCAmelCase     :							Optional[int]    =    crop_size if crop_size is not None else self.crop_size
														_lowerCAmelCase     :							Any    =    get_size_dict(__a,			param_name="crop_size")

														_lowerCAmelCase     :							int    =    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 or resample is None:
																					raise ValueError("Size and resample 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.")

														# All transformations expect numpy arrays.
														_lowerCAmelCase     :							List[Any]    =    [to_numpy_array(__a) for image in images]

														if do_resize:
																					_lowerCAmelCase     :							str    =    [self.resize(image=__a,			size=__a,			resample=__a) for image in images]

														if do_center_crop:
																					_lowerCAmelCase     :							int    =    [self.center_crop(image=__a,			size=__a) for image in images]

														if do_rescale:
																					_lowerCAmelCase     :							Optional[int]    =    [self.rescale(image=__a,			scale=__a) for image in images]

														if do_normalize:
																					_lowerCAmelCase     :							List[Any]    =    [self.normalize(image=__a,			mean=__a,			std=__a) for image in images]

														_lowerCAmelCase     :							List[Any]    =    [to_channel_dimension_format(__a,			__a) for image in images]

														_lowerCAmelCase     :							str    =    {"pixel_values": images}
														return BatchFeature(data=__a,			tensor_type=__a)





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


_snake_case		=  get_tests_dir("fixtures")



class   UpperCAmelCase_    (       unittest.TestCase):







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    mock.Mock()
														_lowerCAmelCase     :							int    =    500
														_lowerCAmelCase     :							Tuple    =    {}
														_lowerCAmelCase     :							str    =    HTTPError
														_lowerCAmelCase     :							Union[str, Any]    =    {}

														# Download this model to make sure it's in the cache.
														_lowerCAmelCase     :							Tuple    =    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=__a) as mock_head:
																					_lowerCAmelCase     :							Optional[int]    =    ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit")
																					# This check we did call the fake head request
																					mock_head.assert_called()







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    ViTImageProcessor.from_pretrained(
														    "https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json")







							def    snake_case__      (   self):

														'''simple docstring'''

														with self.assertRaises(__a):
																					# config is in subfolder, the following should not work without specifying the subfolder
																					_lowerCAmelCase     :							int    =    AutoImageProcessor.from_pretrained("hf-internal-testing/stable-diffusion-all-variants")

														_lowerCAmelCase     :							Optional[Any]    =    AutoImageProcessor.from_pretrained(
														    "hf-internal-testing/stable-diffusion-all-variants",			subfolder="feature_extractor")

														self.assertIsNotNone(__a)



@is_staging_test
class   UpperCAmelCase_    (       unittest.TestCase):







							@classmethod
							def    snake_case__      (   cls):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    TOKEN
														HfFolder.save_token(__a)







							@classmethod
							def    snake_case__      (   cls):

														'''simple docstring'''

														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    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    ViTImageProcessor.from_pretrained(__a)
														image_processor.push_to_hub("test-image-processor",			use_auth_token=self._token)

														_lowerCAmelCase     :							str    =    ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor")
														for k, v in image_processor.__dict__.items():
																					self.assertEqual(__a,			getattr(__a,			__a))

														# 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(
																					    __a,			repo_id="test-image-processor",			push_to_hub=__a,			use_auth_token=self._token)

														_lowerCAmelCase     :							Optional[int]    =    ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor")
														for k, v in image_processor.__dict__.items():
																					self.assertEqual(__a,			getattr(__a,			__a))







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Any    =    ViTImageProcessor.from_pretrained(__a)
														image_processor.push_to_hub("valid_org/test-image-processor",			use_auth_token=self._token)

														_lowerCAmelCase     :							Tuple    =    ViTImageProcessor.from_pretrained("valid_org/test-image-processor")
														for k, v in image_processor.__dict__.items():
																					self.assertEqual(__a,			getattr(__a,			__a))

														# 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(
																					    __a,			repo_id="valid_org/test-image-processor-org",			push_to_hub=__a,			use_auth_token=self._token)

														_lowerCAmelCase     :							Optional[int]    =    ViTImageProcessor.from_pretrained("valid_org/test-image-processor-org")
														for k, v in image_processor.__dict__.items():
																					self.assertEqual(__a,			getattr(__a,			__a))







							def    snake_case__      (   self):

														'''simple docstring'''

														CustomImageProcessor.register_for_auto_class()
														_lowerCAmelCase     :							List[str]    =    CustomImageProcessor.from_pretrained(__a)

														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     :							Tuple    =    AutoImageProcessor.from_pretrained(
														    f"{USER}/test-dynamic-image-processor",			trust_remote_code=__a)
														# 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")
