


from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


__lowerCamelCase        =     logging.get_logger(__name__)


class   a__	(						_snake_case    ):
       lowerCamelCase__:     Optional[int]							    =     ["""pixel_values"""]

       def __init__(   self      :     Optional[Any]      ,							lowerCamelCase_      :     bool = True      ,							lowerCamelCase_      :     Union[int, float] = 1 / 2_5_5      ,							lowerCamelCase_      :     bool = True      ,							lowerCamelCase_      :     int = 8      ,							**lowerCamelCase_      :     Optional[Any]      ,							):
            super().__init__(**lowerCAmelCase__    )

            a_    :     Dict										=    do_rescale
            a_    :     Union[str, Any]										=    rescale_factor
            a_    :     List[Any]										=    do_pad
            a_    :     List[str]										=    pad_size

       def 						UpperCAmelCase(   self      :     Dict      ,							lowerCamelCase_      :     np.ndarray      ,							lowerCamelCase_      :     float      ,							lowerCamelCase_      :     Optional[Union[str, ChannelDimension]] = None      ,							**lowerCamelCase_      :     int    ):
            return rescale(lowerCAmelCase__      ,							scale=lowerCAmelCase__      ,							data_format=lowerCAmelCase__      ,							**lowerCAmelCase__    )

       def 						UpperCAmelCase(   self      :     List[str]      ,							lowerCamelCase_      :     np.ndarray      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     Optional[Union[str, ChannelDimension]] = None    ):
            a_			,			a_    :     str										=    get_image_size(lowerCAmelCase__    )
            a_    :     int										=    (old_height // size + 1) * size - old_height
            a_    :     str										=    (old_width // size + 1) * size - old_width

            return pad(lowerCAmelCase__      ,							((0, pad_height), (0, pad_width))      ,							mode="""symmetric"""      ,							data_format=lowerCAmelCase__    )

       def 						UpperCAmelCase(   self      :     List[str]      ,							lowerCamelCase_      :     ImageInput      ,							lowerCamelCase_      :     Optional[bool] = None      ,							lowerCamelCase_      :     Optional[float] = None      ,							lowerCamelCase_      :     Optional[bool] = None      ,							lowerCamelCase_      :     Optional[int] = None      ,							lowerCamelCase_      :     Optional[Union[str, TensorType]] = None      ,							lowerCamelCase_      :     Union[str, ChannelDimension] = ChannelDimension.FIRST      ,							**lowerCamelCase_      :     Union[str, Any]      ,							):
            a_    :     Tuple										=    do_rescale if do_rescale is not None else self.do_rescale
            a_    :     int										=    rescale_factor if rescale_factor is not None else self.rescale_factor
            a_    :     Optional[int]										=    do_pad if do_pad is not None else self.do_pad
            a_    :     Optional[int]										=    pad_size if pad_size is not None else self.pad_size

            a_    :     str										=    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_rescale and rescale_factor is None:
                 raise ValueError("""Rescale factor must be specified if do_rescale is True."""    )

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

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

            if do_pad:
                 a_    :     Optional[int]										=    [self.pad(lowerCAmelCase__      ,							size=lowerCAmelCase__    ) for image in images]

            a_    :     Optional[int]										=    [to_channel_dimension_format(lowerCAmelCase__      ,							lowerCAmelCase__    ) for image in images]

            a_    :     List[str]										=    {"""pixel_values""": images}
            return BatchFeature(data=lowerCAmelCase__      ,							tensor_type=lowerCAmelCase__    )







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 						UpperCAmelCase(   self      :     int    ):
            a_    :     Any										=    0

       def 						UpperCAmelCase(   self      :     Dict    ):
            a_    :     List[str]										=    AutoImageProcessor.from_pretrained("""openai/clip-vit-base-patch32"""    )
            self.assertIsInstance(lowerCamelCase_      ,							lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     int    ):
            with tempfile.TemporaryDirectory() as tmpdirname:
                 a_    :     Any										=    Path(lowerCamelCase_    ) / """preprocessor_config.json"""
                 a_    :     Tuple										=    Path(lowerCamelCase_    ) / """config.json"""
                 json.dump(
                     {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}      ,							open(lowerCamelCase_      ,							"""w"""    )      ,							)
                 json.dump({"""model_type""": """clip"""}      ,							open(lowerCamelCase_      ,							"""w"""    )    )

                 a_    :     Union[str, Any]										=    AutoImageProcessor.from_pretrained(lowerCamelCase_    )
                 self.assertIsInstance(lowerCamelCase_      ,							lowerCamelCase_    )

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

                 a_    :     int										=    AutoImageProcessor.from_pretrained(lowerCamelCase_    )
                 self.assertIsInstance(lowerCamelCase_      ,							lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
            with tempfile.TemporaryDirectory() as tmpdirname:
                 a_    :     int										=    CLIPConfig()

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

                 # remove image_processor_type to make sure config.json alone is enough to load image processor locally
                 a_    :     Optional[int]										=    AutoImageProcessor.from_pretrained(lowerCamelCase_    ).to_dict()

                 config_dict.pop("""image_processor_type"""    )
                 a_    :     Tuple										=    CLIPImageProcessor(**lowerCamelCase_    )

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

                 a_    :     Optional[int]										=    AutoImageProcessor.from_pretrained(lowerCamelCase_    )

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

            self.assertIsInstance(lowerCamelCase_      ,							lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Dict    ):
            with tempfile.TemporaryDirectory() as tmpdirname:
                 a_    :     Union[str, Any]										=    Path(lowerCamelCase_    ) / """preprocessor_config.json"""
                 json.dump(
                     {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}      ,							open(lowerCamelCase_      ,							"""w"""    )      ,							)

                 a_    :     Optional[int]										=    AutoImageProcessor.from_pretrained(lowerCamelCase_    )
                 self.assertIsInstance(lowerCamelCase_      ,							lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     int    ):
            with self.assertRaisesRegex(
                lowerCamelCase_      ,							"""clip-base is not a local folder and is not a valid model identifier"""    ):
                 a_    :     Any										=    AutoImageProcessor.from_pretrained("""clip-base"""    )

       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
            with self.assertRaisesRegex(
                lowerCamelCase_      ,							R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""    ):
                 a_    :     Dict										=    AutoImageProcessor.from_pretrained(lowerCamelCase_      ,							revision="""aaaaaa"""    )

       def 						UpperCAmelCase(   self      :     Dict    ):
            with self.assertRaisesRegex(
                lowerCamelCase_      ,							"""hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."""      ,							):
                 a_    :     Any										=    AutoImageProcessor.from_pretrained("""hf-internal-testing/config-no-model"""    )

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

            a_    :     int										=    AutoImageProcessor.from_pretrained(
                """hf-internal-testing/test_dynamic_image_processor"""      ,							trust_remote_code=lowerCamelCase_    )
            self.assertEqual(image_processor.__class__.__name__      ,							"""NewImageProcessor"""    )

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

       def 						UpperCAmelCase(   self      :     List[str]    ):
            try:
                 AutoConfig.register("""custom"""      ,							lowerCamelCase_    )
                 AutoImageProcessor.register(lowerCamelCase_      ,							lowerCamelCase_    )
                 # Trying to register something existing in the Transformers library will raise an error
                 with self.assertRaises(lowerCamelCase_    ):
                      AutoImageProcessor.register(lowerCamelCase_      ,							lowerCamelCase_    )

                 with tempfile.TemporaryDirectory() as tmpdirname:
                      a_    :     Dict										=    Path(lowerCamelCase_    ) / """preprocessor_config.json"""
                      a_    :     Union[str, Any]										=    Path(lowerCamelCase_    ) / """config.json"""
                      json.dump(
                          {"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""}      ,							open(lowerCamelCase_      ,							"""w"""    )      ,							)
                      json.dump({"""model_type""": """clip"""}      ,							open(lowerCamelCase_      ,							"""w"""    )    )

                      a_    :     str										=    CustomImageProcessor.from_pretrained(lowerCamelCase_    )

                 # 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(lowerCamelCase_    )
                      a_    :     Any										=    AutoImageProcessor.from_pretrained(lowerCamelCase_    )
                      self.assertIsInstance(lowerCamelCase_      ,							lowerCamelCase_    )

            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 						UpperCAmelCase(   self      :     Union[str, Any]    ):


            class   a__	(						lowerCAmelCase_    ):
                   lowerCamelCase__:     List[Any]							    =     True

            try:
                 AutoConfig.register("""custom"""      ,							lowerCamelCase_    )
                 AutoImageProcessor.register(lowerCamelCase_      ,							lowerCamelCase_    )
                 # If remote code is not set, the default is to use local
                 a_    :     Dict										=    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.
                 a_    :     Optional[Any]										=    AutoImageProcessor.from_pretrained(
                     """hf-internal-testing/test_dynamic_image_processor"""      ,							trust_remote_code=lowerCamelCase_    )
                 self.assertEqual(image_processor.__class__.__name__      ,							"""NewImageProcessor"""    )
                 self.assertTrue(image_processor.is_local    )

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



