





"""simple docstring"""




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,
    get_resize_output_image_size,
    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, logging


__lowerCAmelCase    :							List[Any]													=						logging.get_logger(__name__)

class       a_      (     __lowerCAmelCase			):
					UpperCamelCase_ :	Tuple			=    ["pixel_values"]




					def __init__(	self						:     Tuple  ,						snake_case__						:     bool = True  ,						snake_case__						:     Optional[Dict[str, int]] = None  ,						snake_case__						:     PILImageResampling = PILImageResampling.BILINEAR  ,						snake_case__						:     bool = True  ,						snake_case__						:     Dict[str, int] = None  ,						snake_case__						:     bool = True  ,						snake_case__						:     Union[int, float] = 1 / 255  ,						snake_case__						:     bool = True  ,						snake_case__						:     Optional[Union[float, List[float]]] = None  ,						snake_case__						:     Optional[Union[float, List[float]]] = None  ,						**snake_case__						:     Tuple  ,						):
							super().__init__(**lowerCamelCase__     )
							lowerCAmelCase__        =   size if size is not None else {'''shortest_edge''': 256}
							lowerCAmelCase__        =   get_size_dict(lowerCamelCase__  ,						default_to_square=lowerCamelCase__     )
							lowerCAmelCase__        =   crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
							lowerCAmelCase__        =   get_size_dict(lowerCamelCase__     )
							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 IMAGENET_STANDARD_MEAN
							lowerCAmelCase__        =   image_std if image_std is not None else IMAGENET_STANDARD_STD




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any  ,						snake_case__						:     np.ndarray  ,						snake_case__						:     Dict[str, int]  ,						snake_case__						:     PILImageResampling = PILImageResampling.BICUBIC  ,						snake_case__						:     Optional[Union[str, ChannelDimension]] = None  ,						**snake_case__						:     Optional[int]  ,						):
							lowerCAmelCase__        =   get_size_dict(lowerCamelCase__  ,						default_to_square=lowerCamelCase__     )
							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(lowerCamelCase__  ,						size=size["""shortest_edge"""]  ,						default_to_square=lowerCamelCase__     )
							return resize(lowerCamelCase__  ,						size=lowerCamelCase__  ,						resample=lowerCamelCase__  ,						data_format=lowerCamelCase__  ,						**lowerCamelCase__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any  ,						snake_case__						:     np.ndarray  ,						snake_case__						:     Dict[str, int]  ,						snake_case__						:     Optional[Union[str, ChannelDimension]] = None  ,						**snake_case__						:     List[str]  ,						):
							lowerCAmelCase__        =   get_size_dict(lowerCamelCase__     )
							return center_crop(lowerCamelCase__  ,						size=(size["""height"""], size["""width"""])  ,						data_format=lowerCamelCase__  ,						**lowerCamelCase__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]  ,						snake_case__						:     np.ndarray  ,						snake_case__						:     float  ,						snake_case__						:     Optional[Union[str, ChannelDimension]] = None  ,						**snake_case__						:     Dict     ):
							return rescale(lowerCamelCase__  ,						scale=lowerCamelCase__  ,						data_format=lowerCamelCase__  ,						**lowerCamelCase__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]  ,						snake_case__						:     np.ndarray  ,						snake_case__						:     Union[float, List[float]]  ,						snake_case__						:     Union[float, List[float]]  ,						snake_case__						:     Optional[Union[str, ChannelDimension]] = None  ,						**snake_case__						:     Union[str, Any]  ,						):
							return normalize(lowerCamelCase__  ,						mean=lowerCamelCase__  ,						std=lowerCamelCase__  ,						data_format=lowerCamelCase__  ,						**lowerCamelCase__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     ImageInput  ,						snake_case__						:     Optional[bool] = None  ,						snake_case__						:     Dict[str, int] = None  ,						snake_case__						:     PILImageResampling = None  ,						snake_case__						:     bool = None  ,						snake_case__						:     Dict[str, int] = None  ,						snake_case__						:     Optional[bool] = None  ,						snake_case__						:     Optional[float] = None  ,						snake_case__						:     Optional[bool] = None  ,						snake_case__						:     Optional[Union[float, List[float]]] = None  ,						snake_case__						:     Optional[Union[float, List[float]]] = None  ,						snake_case__						:     Optional[Union[str, TensorType]] = None  ,						snake_case__						:     Union[str, ChannelDimension] = ChannelDimension.FIRST  ,						**snake_case__						:     List[str]  ,						):
							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(lowerCamelCase__  ,						default_to_square=lowerCamelCase__     )
							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(lowerCamelCase__     )
							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__        =   make_list_of_images(lowerCamelCase__     )

							if not valid_images(lowerCamelCase__     ):
									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."""     )

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

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

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

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

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

							lowerCAmelCase__        =   [to_channel_dimension_format(lowerCamelCase__  ,						lowerCamelCase__     ) for image in images]

							lowerCAmelCase__        =   {'''pixel_values''': images}
							return BatchFeature(data=lowerCamelCase__  ,						tensor_type=lowerCamelCase__     )





"""simple docstring"""




import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402

class       a_      (     unittest.TestCase			):




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							lowerCAmelCase__        =   0




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__        =   AutoImageProcessor.from_pretrained("""openai/clip-vit-base-patch32"""     )
							self.assertIsInstance(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							with tempfile.TemporaryDirectory() as tmpdirname:
									lowerCAmelCase__        =   Path(snake_case__     ) / """preprocessor_config.json"""
									lowerCAmelCase__        =   Path(snake_case__     ) / """config.json"""
									json.dump(
									    {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}  ,						open(snake_case__  ,						"""w"""     )  ,						)
									json.dump({"""model_type""": """clip"""}  ,						open(snake_case__  ,						"""w"""     )     )

									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__     )
									self.assertIsInstance(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							# Ensure we can load the image processor from the feature extractor config
							with tempfile.TemporaryDirectory() as tmpdirname:
									lowerCAmelCase__        =   Path(snake_case__     ) / """preprocessor_config.json"""
									lowerCAmelCase__        =   Path(snake_case__     ) / """config.json"""
									json.dump(
									    {"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""}  ,						open(snake_case__  ,						"""w"""     )  ,						)
									json.dump({"""model_type""": """clip"""}  ,						open(snake_case__  ,						"""w"""     )     )

									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__     )
									self.assertIsInstance(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							with tempfile.TemporaryDirectory() as tmpdirname:
									lowerCAmelCase__        =   CLIPConfig()

									# Create a dummy config file with image_proceesor_type
									lowerCAmelCase__        =   Path(snake_case__     ) / """preprocessor_config.json"""
									lowerCAmelCase__        =   Path(snake_case__     ) / """config.json"""
									json.dump(
									    {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}  ,						open(snake_case__  ,						"""w"""     )  ,						)
									json.dump({"""model_type""": """clip"""}  ,						open(snake_case__  ,						"""w"""     )     )

									# remove image_processor_type to make sure config.json alone is enough to load image processor locally
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__     ).to_dict()

									config_dict.pop("""image_processor_type"""     )
									lowerCAmelCase__        =   CLIPImageProcessor(**snake_case__     )

									# save in new folder
									model_config.save_pretrained(snake_case__     )
									config.save_pretrained(snake_case__     )

									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__     )

									# make sure private variable is not incorrectly saved
									lowerCAmelCase__        =   json.loads(config.to_json_string()     )
									self.assertTrue("""_processor_class""" not in dict_as_saved     )

							self.assertIsInstance(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							with tempfile.TemporaryDirectory() as tmpdirname:
									lowerCAmelCase__        =   Path(snake_case__     ) / """preprocessor_config.json"""
									json.dump(
									    {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}  ,						open(snake_case__  ,						"""w"""     )  ,						)

									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__     )
									self.assertIsInstance(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							with self.assertRaisesRegex(
							    snake_case__  ,						"""clip-base is not a local folder and is not a valid model identifier"""     ):
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained("""clip-base"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							with self.assertRaisesRegex(
							    snake_case__  ,						R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""     ):
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__  ,						revision="""aaaaaa"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							with self.assertRaisesRegex(
							    snake_case__  ,						"""hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."""  ,						):
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained("""hf-internal-testing/config-no-model"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							# If remote code is not set, we will time out when asking whether to load the model.
							with self.assertRaises(snake_case__     ):
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor"""     )
							# If remote code is disabled, we can't load this config.
							with self.assertRaises(snake_case__     ):
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(
									    """hf-internal-testing/test_dynamic_image_processor"""  ,						trust_remote_code=snake_case__     )

							lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(
							    """hf-internal-testing/test_dynamic_image_processor"""  ,						trust_remote_code=snake_case__     )
							self.assertEqual(image_processor.__class__.__name__  ,						"""NewImageProcessor"""     )

							# Test image processor can be reloaded.
							with tempfile.TemporaryDirectory() as tmp_dir:
									image_processor.save_pretrained(snake_case__     )
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__  ,						trust_remote_code=snake_case__     )
							self.assertEqual(reloaded_image_processor.__class__.__name__  ,						"""NewImageProcessor"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							try:
									AutoConfig.register("""custom"""  ,						snake_case__     )
									AutoImageProcessor.register(snake_case__  ,						snake_case__     )
									# Trying to register something existing in the Transformers library will raise an error
									with self.assertRaises(snake_case__     ):
											AutoImageProcessor.register(snake_case__  ,						snake_case__     )

									with tempfile.TemporaryDirectory() as tmpdirname:
											lowerCAmelCase__        =   Path(snake_case__     ) / """preprocessor_config.json"""
											lowerCAmelCase__        =   Path(snake_case__     ) / """config.json"""
											json.dump(
											    {"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""}  ,						open(snake_case__  ,						"""w"""     )  ,						)
											json.dump({"""model_type""": """clip"""}  ,						open(snake_case__  ,						"""w"""     )     )

											lowerCAmelCase__        =   CustomImageProcessor.from_pretrained(snake_case__     )

									# Now that the config is registered, it can be used as any other config with the auto-API
									with tempfile.TemporaryDirectory() as tmp_dir:
											image_processor.save_pretrained(snake_case__     )
											lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__     )
											self.assertIsInstance(snake_case__  ,						snake_case__     )

							finally:
									if "custom" in CONFIG_MAPPING._extra_content:
											del CONFIG_MAPPING._extra_content["custom"]
									if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
											del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):

							class       a_      (     __UpperCamelCase			):
												UpperCamelCase_ :	Tuple			=    True

							try:
									AutoConfig.register("""custom"""  ,						snake_case__     )
									AutoImageProcessor.register(snake_case__  ,						snake_case__     )
									# If remote code is not set, the default is to use local
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor"""     )
									self.assertEqual(image_processor.__class__.__name__  ,						"""NewImageProcessor"""     )
									self.assertTrue(image_processor.is_local     )

									# If remote code is disabled, we load the local one.
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(
									    """hf-internal-testing/test_dynamic_image_processor"""  ,						trust_remote_code=snake_case__     )
									self.assertEqual(image_processor.__class__.__name__  ,						"""NewImageProcessor"""     )
									self.assertTrue(image_processor.is_local     )

									# If remote is enabled, we load from the Hub
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(
									    """hf-internal-testing/test_dynamic_image_processor"""  ,						trust_remote_code=snake_case__     )
									self.assertEqual(image_processor.__class__.__name__  ,						"""NewImageProcessor"""     )
									self.assertTrue(not hasattr(snake_case__  ,						"""is_local"""     )     )

							finally:
									if "custom" in CONFIG_MAPPING._extra_content:
											del CONFIG_MAPPING._extra_content["custom"]
									if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
											del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]




