





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,
)


_snake_case  :   Optional[int]						   =    logging.get_logger(__name__)

_snake_case  :   List[str]						   =    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'),
    ]
)

_snake_case  :   Union[str, Any]						   =    _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)

def   a_   (			lowerCAmelCase_				:      str     ):





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

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

   for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
      if getattr(A__,     '__name__',     A__     ) == 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.
   __lowerCAmelCase							     =     importlib.import_module('transformers'     )
   if hasattr(A__,     A__     ):
      return getattr(A__,     A__     )

   return None

def   a_   (			lowerCAmelCase_				:      Union[str, os.PathLike],     lowerCAmelCase_				:      Optional[Union[str, os.PathLike]] = None,     lowerCAmelCase_				:      bool = False,     lowerCAmelCase_				:      bool = False,     lowerCAmelCase_				:      Optional[Dict[str, str]] = None,     lowerCAmelCase_				:      Optional[Union[bool, str]] = None,     lowerCAmelCase_				:      Optional[str] = None,     lowerCAmelCase_				:      bool = False,     **lowerCAmelCase_				:      Tuple,     ):
   __lowerCAmelCase							     =     get_file_from_repo(
       A__,     A__,     cache_dir=A__,     force_download=A__,     resume_download=A__,     proxies=A__,     use_auth_token=A__,     revision=A__,     local_files_only=A__,     )
   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(A__,     encoding='utf-8'     ) as reader:
      return json.load(A__     )




class     _UpperCAmelCase   :

  """simple docstring"""


  def __init__(    self			:		Union[str, Any]					)							->						str:
     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(lowerCAmelCase_					)
  def     lowercase		(    cls			:		Tuple ,  lowerCAmelCase_			:		Dict ,  **lowerCAmelCase_			:		Optional[Any]					)							->						Dict:
     __lowerCAmelCase							     =     kwargs.pop('config' ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     kwargs.pop('trust_remote_code' ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     True

     __lowerCAmelCase							     =     ImageProcessingMixin.get_image_processor_dict(lowerCAmelCase_ ,  **lowerCAmelCase_					)
     __lowerCAmelCase							     =     config_dict.get('image_processor_type' ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     None
     if "AutoImageProcessor" in config_dict.get('auto_map' ,  {}					):
        __lowerCAmelCase							     =     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:
        __lowerCAmelCase							     =     config_dict.pop('feature_extractor_type' ,  lowerCAmelCase_					)
        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.'					)
           __lowerCAmelCase							     =     feature_extractor_class.replace('FeatureExtractor' ,  'ImageProcessor'					)
        if "AutoFeatureExtractor" in config_dict.get('auto_map' ,  {}					):
           __lowerCAmelCase							     =     config_dict["""auto_map"""]["""AutoFeatureExtractor"""]
           __lowerCAmelCase							     =     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(lowerCAmelCase_ ,  lowerCAmelCase_					):
           __lowerCAmelCase							     =     AutoConfig.from_pretrained(lowerCAmelCase_ ,  **lowerCAmelCase_					)
        # It could be in `config.image_processor_type``
        __lowerCAmelCase							     =     getattr(lowerCAmelCase_ ,  'image_processor_type' ,  lowerCAmelCase_					)
        if hasattr(lowerCAmelCase_ ,  'auto_map'					) and "AutoImageProcessor" in config.auto_map:
           __lowerCAmelCase							     =     config.auto_map["""AutoImageProcessor"""]

     if image_processor_class is not None:
        __lowerCAmelCase							     =     image_processor_class_from_name(lowerCAmelCase_					)

     __lowerCAmelCase							     =     image_processor_auto_map is not None
     __lowerCAmelCase							     =     image_processor_class is not None or type(lowerCAmelCase_					) in IMAGE_PROCESSOR_MAPPING
     __lowerCAmelCase							     =     resolve_trust_remote_code(
         lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)

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

     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     lowercase		(    lowerCAmelCase_			:		Optional[Any] ,  lowerCAmelCase_			:		Optional[Any]					)							->						List[Any]:
     IMAGE_PROCESSOR_MAPPING.register(lowerCAmelCase_ ,  lowerCAmelCase_					)








import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
     import onnxruntime as ort



@nightly
@require_onnxruntime
@require_torch_gpu
class     _UpperCAmelCase   (   unittest.TestCase   ):

  """simple docstring"""


  @property
  def     lowercase		(    self			:		Union[str, Any]					)							->						Tuple:
     return (
         "CUDAExecutionProvider",
         {
             "gpu_mem_limit": "15000000000",  # 15GB
             "arena_extend_strategy": "kSameAsRequested",
         },
     )






  @property
  def     lowercase		(    self			:		List[Any]					)							->						int:
     __lowerCAmelCase							     =     ort.SessionOptions()
     __lowerCAmelCase							     =     False
     return options






  def     lowercase		(    self			:		Tuple					)							->						List[Any]:
     __lowerCAmelCase							     =     load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo.png'					)
     __lowerCAmelCase							     =     load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'					)
     __lowerCAmelCase							     =     load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'					)

     # using the PNDM scheduler by default
     __lowerCAmelCase							     =     OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
         'CompVis/stable-diffusion-v1-4' ,  revision='onnx' ,  safety_checker=lowerCAmelCase_ ,  feature_extractor=lowerCAmelCase_ ,  provider=self.gpu_provider ,  sess_options=self.gpu_options ,  )
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     __lowerCAmelCase							     =     'A red cat sitting on a park bench'

     __lowerCAmelCase							     =     np.random.RandomState(0					)
     __lowerCAmelCase							     =     pipe(
         prompt=lowerCAmelCase_ ,  image=lowerCAmelCase_ ,  mask_image=lowerCAmelCase_ ,  strength=0.75 ,  guidance_scale=7.5 ,  num_inference_steps=1_5 ,  generator=lowerCAmelCase_ ,  output_type='np' ,  )

     __lowerCAmelCase							     =     output.images[0]

     assert image.shape == (5_1_2, 5_1_2, 3)
     assert np.abs(expected_image - image					).max() < 1e-2

