

import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


lowercase_			=  logging.get_logger(__name__)

lowercase_			=  OrderedDict(
    [
        ("align", "EfficientNetImageProcessor"),
        ("beit", "BeitImageProcessor"),
        ("bit", "BitImageProcessor"),
        ("blip", "BlipImageProcessor"),
        ("blip-2", "BlipImageProcessor"),
        ("bridgetower", "BridgeTowerImageProcessor"),
        ("chinese_clip", "ChineseCLIPImageProcessor"),
        ("clip", "CLIPImageProcessor"),
        ("clipseg", "ViTImageProcessor"),
        ("conditional_detr", "ConditionalDetrImageProcessor"),
        ("convnext", "ConvNextImageProcessor"),
        ("convnextv2", "ConvNextImageProcessor"),
        ("cvt", "ConvNextImageProcessor"),
        ("data2vec-vision", "BeitImageProcessor"),
        ("deformable_detr", "DeformableDetrImageProcessor"),
        ("deit", "DeiTImageProcessor"),
        ("deta", "DetaImageProcessor"),
        ("detr", "DetrImageProcessor"),
        ("dinat", "ViTImageProcessor"),
        ("donut-swin", "DonutImageProcessor"),
        ("dpt", "DPTImageProcessor"),
        ("efficientformer", "EfficientFormerImageProcessor"),
        ("efficientnet", "EfficientNetImageProcessor"),
        ("flava", "FlavaImageProcessor"),
        ("focalnet", "BitImageProcessor"),
        ("git", "CLIPImageProcessor"),
        ("glpn", "GLPNImageProcessor"),
        ("groupvit", "CLIPImageProcessor"),
        ("imagegpt", "ImageGPTImageProcessor"),
        ("instructblip", "BlipImageProcessor"),
        ("layoutlmv2", "LayoutLMv2ImageProcessor"),
        ("layoutlmv3", "LayoutLMv3ImageProcessor"),
        ("levit", "LevitImageProcessor"),
        ("mask2former", "Mask2FormerImageProcessor"),
        ("maskformer", "MaskFormerImageProcessor"),
        ("mgp-str", "ViTImageProcessor"),
        ("mobilenet_v1", "MobileNetV1ImageProcessor"),
        ("mobilenet_v2", "MobileNetV2ImageProcessor"),
        ("mobilevit", "MobileViTImageProcessor"),
        ("mobilevit", "MobileViTImageProcessor"),
        ("mobilevitv2", "MobileViTImageProcessor"),
        ("nat", "ViTImageProcessor"),
        ("oneformer", "OneFormerImageProcessor"),
        ("owlvit", "OwlViTImageProcessor"),
        ("perceiver", "PerceiverImageProcessor"),
        ("pix2struct", "Pix2StructImageProcessor"),
        ("poolformer", "PoolFormerImageProcessor"),
        ("regnet", "ConvNextImageProcessor"),
        ("resnet", "ConvNextImageProcessor"),
        ("sam", "SamImageProcessor"),
        ("segformer", "SegformerImageProcessor"),
        ("swiftformer", "ViTImageProcessor"),
        ("swin", "ViTImageProcessor"),
        ("swin2sr", "Swin2SRImageProcessor"),
        ("swinv2", "ViTImageProcessor"),
        ("table-transformer", "DetrImageProcessor"),
        ("timesformer", "VideoMAEImageProcessor"),
        ("tvlt", "TvltImageProcessor"),
        ("upernet", "SegformerImageProcessor"),
        ("van", "ConvNextImageProcessor"),
        ("videomae", "VideoMAEImageProcessor"),
        ("vilt", "ViltImageProcessor"),
        ("vit", "ViTImageProcessor"),
        ("vit_hybrid", "ViTHybridImageProcessor"),
        ("vit_mae", "ViTImageProcessor"),
        ("vit_msn", "ViTImageProcessor"),
        ("xclip", "CLIPImageProcessor"),
        ("yolos", "YolosImageProcessor"),
    ]
)

lowercase_			=  _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     str				)							->				Union[str, Any]:





    '''simple docstring'''






    for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
        if class_name in extractors:
            A__   				=       model_type_to_module_name(SCREAMING_SNAKE_CASE__				)

            A__   				=       importlib.import_module(f'.{module_name}'   ,      'transformers.models'				)
            try:
                return getattr(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
            except AttributeError:
                continue

    for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
        if getattr(SCREAMING_SNAKE_CASE__   ,      '__name__'   ,      SCREAMING_SNAKE_CASE__				) == class_name:
            return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
    A__   				=       importlib.import_module('transformers'				)
    if hasattr(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				):
        return getattr(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)

    return None
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Union[str, os.PathLike]   ,      SCREAMING_SNAKE_CASE__      :     Optional[Union[str, os.PathLike]] = None   ,      SCREAMING_SNAKE_CASE__      :     bool = False   ,      SCREAMING_SNAKE_CASE__      :     bool = False   ,      SCREAMING_SNAKE_CASE__      :     Optional[Dict[str, str]] = None   ,      SCREAMING_SNAKE_CASE__      :     Optional[Union[bool, str]] = None   ,      SCREAMING_SNAKE_CASE__      :     Optional[str] = None   ,      SCREAMING_SNAKE_CASE__      :     bool = False   ,      **SCREAMING_SNAKE_CASE__      :     List[Any]   ,      )							->				int:





    '''simple docstring'''






    A__   				=       get_file_from_repo(
        SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      cache_dir=SCREAMING_SNAKE_CASE__   ,      force_download=SCREAMING_SNAKE_CASE__   ,      resume_download=SCREAMING_SNAKE_CASE__   ,      proxies=SCREAMING_SNAKE_CASE__   ,      use_auth_token=SCREAMING_SNAKE_CASE__   ,      revision=SCREAMING_SNAKE_CASE__   ,      local_files_only=SCREAMING_SNAKE_CASE__   ,      )
    if resolved_config_file is None:
        logger.info(
            'Could not locate the image processor configuration file, will try to use the model config instead.'				)
        return {}

    with open(SCREAMING_SNAKE_CASE__   ,      encoding='utf-8'				) as reader:
        return json.load(SCREAMING_SNAKE_CASE__				)


class 			A						:


    def __init__(				self			:	Any		)->   Dict:





        '''simple docstring'''







        raise EnvironmentError(
            'AutoImageProcessor is designed to be instantiated '
            'using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.'		)


    @classmethod
    @replace_list_option_in_docstrings(lowercase_		)
    def 						snake_case__					(				cls			:	Tuple,lowercase_			:	str,**lowercase_			:	Union[str, Any]		)->   List[Any]:





        '''simple docstring'''







        A__   				=       kwargs.pop('config',lowercase_		)
        A__   				=       kwargs.pop('trust_remote_code',lowercase_		)
        A__   				=       True

        A__				,						A__   				=       ImageProcessingMixin.get_image_processor_dict(lowercase_,**lowercase_		)
        A__   				=       config_dict.get('image_processor_type',lowercase_		)
        A__   				=       None
        if "AutoImageProcessor" in config_dict.get('auto_map',{}		):
            A__   				=       config_dict['auto_map']['AutoImageProcessor']

        # If we still don't have the image processor class, check if we're loading from a previous feature extractor config
        # and if so, infer the image processor class from there.
        if image_processor_class is None and image_processor_auto_map is None:
            A__   				=       config_dict.pop('feature_extractor_type',lowercase_		)
            if feature_extractor_class is not None:
                logger.warning(
                    'Could not find image processor class in the image processor config or the model config. Loading'
                    ' based on pattern matching with the model\'s feature extractor configuration.'		)
                A__   				=       feature_extractor_class.replace('FeatureExtractor','ImageProcessor'		)
            if "AutoFeatureExtractor" in config_dict.get('auto_map',{}		):
                A__   				=       config_dict['auto_map']['AutoFeatureExtractor']
                A__   				=       feature_extractor_auto_map.replace('FeatureExtractor','ImageProcessor'		)
                logger.warning(
                    'Could not find image processor auto map in the image processor config or the model config.'
                    ' Loading based on pattern matching with the model\'s feature extractor configuration.'		)

        # If we don't find the image processor class in the image processor config, let's try the model config.
        if image_processor_class is None and image_processor_auto_map is None:
            if not isinstance(lowercase_,lowercase_		):
                A__   				=       AutoConfig.from_pretrained(lowercase_,**lowercase_		)
            # It could be in `config.image_processor_type``
            A__   				=       getattr(lowercase_,'image_processor_type',lowercase_		)
            if hasattr(lowercase_,'auto_map'		) and "AutoImageProcessor" in config.auto_map:
                A__   				=       config.auto_map['AutoImageProcessor']

        if image_processor_class is not None:
            A__   				=       image_processor_class_from_name(lowercase_		)

        A__   				=       image_processor_auto_map is not None
        A__   				=       image_processor_class is not None or type(lowercase_		) in IMAGE_PROCESSOR_MAPPING
        A__   				=       resolve_trust_remote_code(
            lowercase_,lowercase_,lowercase_,lowercase_		)

        if has_remote_code and trust_remote_code:
            A__   				=       get_class_from_dynamic_module(
                lowercase_,lowercase_,**lowercase_		)
            A__   				=       kwargs.pop('code_revision',lowercase_		)
            if os.path.isdir(lowercase_		):
                image_processor_class.register_for_auto_class()
            return image_processor_class.from_dict(lowercase_,**lowercase_		)
        elif image_processor_class is not None:
            return image_processor_class.from_dict(lowercase_,**lowercase_		)
        # Last try: we use the IMAGE_PROCESSOR_MAPPING.
        elif type(lowercase_		) in IMAGE_PROCESSOR_MAPPING:
            A__   				=       IMAGE_PROCESSOR_MAPPING[type(lowercase_		)]
            return image_processor_class.from_dict(lowercase_,**lowercase_		)

        raise ValueError(
            F'Unrecognized image processor in {pretrained_model_name_or_path}. Should have a '
            F'`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following '
            F'`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()		)}'		)


    @staticmethod
    def 						snake_case__					(				lowercase_			:	List[str],lowercase_			:	Tuple		)->   str:





        '''simple docstring'''







        IMAGE_PROCESSOR_MAPPING.register(lowercase_,lowercase_		)




import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor


class 			A						(							unittest.TestCase			):







    """simple docstring"""





    def __init__(				self			:	List[str],lowercase_			:	List[str],lowercase_			:	bool = True,lowercase_			:	Dict[str, int] = None,lowercase_			:	int = 3_2,lowercase_			:	bool = True,lowercase_			:	Union[int, float] = 1 / 2_5_5,lowercase_			:	bool = True,lowercase_			:	bool = True,lowercase_			:	Optional[Union[float, List[float]]] = [0.48_145_466, 0.4_578_275, 0.40_821_073],lowercase_			:	Optional[Union[float, List[float]]] = [0.26_862_954, 0.26_130_258, 0.27_577_711],lowercase_			:	bool = True,lowercase_			:	Tuple=7,lowercase_			:	str=3_0,lowercase_			:	Union[str, Any]=4_0_0,lowercase_			:	Dict=3,)->   List[Any]:





        '''simple docstring'''







        A__   				=       parent
        A__   				=       do_resize
        A__   				=       size if size is not None else {'shortest_edge': 2_8_8}
        A__   				=       size_divisor
        A__   				=       do_rescale
        A__   				=       rescale_factor
        A__   				=       do_normalize
        A__   				=       do_center_crop
        A__   				=       image_mean
        A__   				=       image_std
        A__   				=       do_pad
        A__   				=       batch_size
        A__   				=       num_channels
        A__   				=       min_resolution
        A__   				=       max_resolution


    def 						snake_case__					(				self			:	Optional[Any]		)->   Optional[int]:





        '''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,
            "size_divisor": self.size_divisor,
        }


    def 						snake_case__					(				self			:	int,lowercase_			:	Optional[int],lowercase_			:	List[str]=False		)->   Any:





        '''simple docstring'''







        if not batched:
            A__   				=       self.size['shortest_edge']
            A__   				=       image_inputs[0]
            if isinstance(lowercase_,Image.Image		):
                A__				,						A__   				=       image.size
            else:
                A__				,						A__   				=       image.shape[1], image.shape[2]
            A__   				=       size / min(lowercase_,lowercase_		)
            if h < w:
                A__				,						A__   				=       size, scale * w
            else:
                A__				,						A__   				=       scale * h, size

            A__   				=       int((1_3_3_3 / 8_0_0) * size		)
            if max(lowercase_,lowercase_		) > max_size:
                A__   				=       max_size / max(lowercase_,lowercase_		)
                A__   				=       newh * scale
                A__   				=       neww * scale

            A__				,						A__   				=       int(newh + 0.5		), int(neww + 0.5		)
            A__				,						A__   				=       (
                newh // self.size_divisor * self.size_divisor,
                neww // self.size_divisor * self.size_divisor,
            )

        else:
            A__   				=       []
            for image in image_inputs:
                A__				,						A__   				=       self.get_expected_values([image]		)
                expected_values.append((expected_height, expected_width)		)
            A__   				=       max(lowercase_,key=lambda lowercase_		: item[0]		)[0]
            A__   				=       max(lowercase_,key=lambda lowercase_		: item[1]		)[1]

        return expected_height, expected_width




@require_torch
@require_vision
class 			A						(							_UpperCAmelCase   ,							unittest.TestCase			):







    """simple docstring"""





    lowerCamelCase		     =							BridgeTowerImageProcessor if is_vision_available() else None


    def 						snake_case__					(				self			:	str		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       BridgeTowerImageProcessingTester(self		)


    @property
    def 						snake_case__					(				self			:	Dict		)->   List[str]:





        '''simple docstring'''







        return self.image_processor_tester.prepare_image_processor_dict()


    def 						snake_case__					(				self			:	Optional[Any]		)->   Tuple:





        '''simple docstring'''







        A__   				=       self.image_processing_class(**self.image_processor_dict		)
        self.assertTrue(hasattr(lowercase_,'image_mean'		)		)
        self.assertTrue(hasattr(lowercase_,'image_std'		)		)
        self.assertTrue(hasattr(lowercase_,'do_normalize'		)		)
        self.assertTrue(hasattr(lowercase_,'do_resize'		)		)
        self.assertTrue(hasattr(lowercase_,'size'		)		)
        self.assertTrue(hasattr(lowercase_,'size_divisor'		)		)


    def 						snake_case__					(				self			:	Any		)->   List[str]:





        '''simple docstring'''







        pass


    def 						snake_case__					(				self			:	int		)->   Any:





        '''simple docstring'''







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

        # Test not batched input
        A__   				=       image_processing(image_inputs[0],return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_		)
        self.assertEqual(
            encoded_images.shape,(1, self.image_processor_tester.num_channels, expected_height, expected_width),)

        # Test batched
        A__   				=       image_processing(lowercase_,return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_,batched=lowercase_		)
        self.assertEqual(
            encoded_images.shape,(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),)


    def 						snake_case__					(				self			:	List[str]		)->   Tuple:





        '''simple docstring'''







        A__   				=       self.image_processing_class(**self.image_processor_dict		)
        # create random numpy tensors
        A__   				=       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
        A__   				=       image_processing(image_inputs[0],return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_		)
        self.assertEqual(
            encoded_images.shape,(1, self.image_processor_tester.num_channels, expected_height, expected_width),)

        # Test batched
        A__   				=       image_processing(lowercase_,return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_,batched=lowercase_		)
        self.assertEqual(
            encoded_images.shape,(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),)


    def 						snake_case__					(				self			:	Optional[Any]		)->   List[Any]:





        '''simple docstring'''







        A__   				=       self.image_processing_class(**self.image_processor_dict		)
        # create random PyTorch tensors
        A__   				=       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
        A__   				=       image_processing(image_inputs[0],return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_		)
        self.assertEqual(
            encoded_images.shape,(1, self.image_processor_tester.num_channels, expected_height, expected_width),)

        # Test batched
        A__   				=       image_processing(lowercase_,return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_,batched=lowercase_		)
        self.assertEqual(
            encoded_images.shape,(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),)

