




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,
)


lowerCAmelCase_					    = logging.get_logger(__name__)

lowerCAmelCase_					    = 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"""),
    ]
)

lowerCAmelCase_					    = _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	Any:
  for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
    if class_name in extractors:
      lowerCAmelCase__				:  Tuple		       =     model_type_to_module_name(UpperCamelCase      )

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

  for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
    if getattr(UpperCamelCase	,      '''__name__'''	,      UpperCamelCase      ) == 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__				:  Optional[int]		       =     importlib.import_module('''transformers'''      )
  if hasattr(UpperCamelCase	,      UpperCamelCase      ):
    return getattr(UpperCamelCase	,      UpperCamelCase      )

  return None



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase = None	,      UpperCamelCase = False	,      UpperCamelCase = False	,      UpperCamelCase = None	,      UpperCamelCase = None	,      UpperCamelCase = None	,      UpperCamelCase = False	,      **UpperCamelCase	,      )     ->	Optional[int]:
  lowerCAmelCase__				:  Dict		       =     get_file_from_repo(
      UpperCamelCase	,      UpperCamelCase	,      cache_dir=UpperCamelCase	,      force_download=UpperCamelCase	,      resume_download=UpperCamelCase	,      proxies=UpperCamelCase	,      use_auth_token=UpperCamelCase	,      revision=UpperCamelCase	,      local_files_only=UpperCamelCase	,      )
  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(UpperCamelCase	,      encoding='''utf-8'''      ) as reader:
    return json.load(UpperCamelCase      )






class  _lowerCAmelCase     :

      def __init__(   self		):
        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(__UpperCAmelCase		)
      def 	__magic_name__(   cls      ,				__UpperCAmelCase      ,				**__UpperCAmelCase		):
        lowerCAmelCase__				:  Union[str, Any]		       =     kwargs.pop('''config'''      ,				__UpperCAmelCase		)
        lowerCAmelCase__				:  List[str]		       =     kwargs.pop('''trust_remote_code'''      ,				__UpperCAmelCase		)
        lowerCAmelCase__				:  List[str]		       =     True

        lowerCAmelCase__				:  Dict		       =     ImageProcessingMixin.get_image_processor_dict(__UpperCAmelCase      ,				**__UpperCAmelCase		)
        lowerCAmelCase__				:  int		       =     config_dict.get('''image_processor_type'''      ,				__UpperCAmelCase		)
        lowerCAmelCase__				:  str		       =     None
        if "AutoImageProcessor" in config_dict.get('''auto_map'''      ,				{}		):
          lowerCAmelCase__				:  Dict		       =     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__				:  int		       =     config_dict.pop('''feature_extractor_type'''      ,				__UpperCAmelCase		)
          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__				:  Optional[int]		       =     feature_extractor_class.replace('''FeatureExtractor'''      ,				'''ImageProcessor'''		)
          if "AutoFeatureExtractor" in config_dict.get('''auto_map'''      ,				{}		):
            lowerCAmelCase__				:  Dict		       =     config_dict['''auto_map''']['''AutoFeatureExtractor''']
            lowerCAmelCase__				:  Optional[int]		       =     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(__UpperCAmelCase      ,				__UpperCAmelCase		):
            lowerCAmelCase__				:  Optional[int]		       =     AutoConfig.from_pretrained(__UpperCAmelCase      ,				**__UpperCAmelCase		)
          # It could be in `config.image_processor_type``
          lowerCAmelCase__				:  List[str]		       =     getattr(__UpperCAmelCase      ,				'''image_processor_type'''      ,				__UpperCAmelCase		)
          if hasattr(__UpperCAmelCase      ,				'''auto_map'''		) and "AutoImageProcessor" in config.auto_map:
            lowerCAmelCase__				:  Tuple		       =     config.auto_map['''AutoImageProcessor''']

        if image_processor_class is not None:
          lowerCAmelCase__				:  Union[str, Any]		       =     image_processor_class_from_name(__UpperCAmelCase		)

        lowerCAmelCase__				:  Union[str, Any]		       =     image_processor_auto_map is not None
        lowerCAmelCase__				:  Dict		       =     image_processor_class is not None or type(__UpperCAmelCase		) in IMAGE_PROCESSOR_MAPPING
        lowerCAmelCase__				:  Tuple		       =     resolve_trust_remote_code(
            __UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		)

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

        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 	__magic_name__(   __UpperCAmelCase      ,				__UpperCAmelCase		):
        IMAGE_PROCESSOR_MAPPING.register(__UpperCAmelCase      ,				__UpperCAmelCase		)







import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
     import onnxruntime as ort






class  _lowerCAmelCase     (  _lowercase    ,			unittest.TestCase	):
      # FIXME: add fast tests
      pass






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

      @property
      def 	__magic_name__(   self		):
        return (
            "CUDAExecutionProvider",
            {
                "gpu_mem_limit": "15000000000",  # 15GB
                "arena_extend_strategy": "kSameAsRequested",
            },
        )

      @property
      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  str		       =     ort.SessionOptions()
        lowerCAmelCase__				:  List[Any]		       =     False
        return options

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Tuple		       =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo.png'''		)
        lowerCAmelCase__				:  Optional[int]		       =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''		)
        lowerCAmelCase__				:  List[Any]		       =     OnnxStableDiffusionInpaintPipeline.from_pretrained(
            '''runwayml/stable-diffusion-inpainting'''      ,				revision='''onnx'''      ,				safety_checker=__UpperCAmelCase      ,				feature_extractor=__UpperCAmelCase      ,				provider=self.gpu_provider      ,				sess_options=self.gpu_options      ,				)
        pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

        lowerCAmelCase__				:  Optional[int]		       =     '''A red cat sitting on a park bench'''

        lowerCAmelCase__				:  List[Any]		       =     np.random.RandomState(0		)
        lowerCAmelCase__				:  int		       =     pipe(
            prompt=__UpperCAmelCase      ,				image=__UpperCAmelCase      ,				mask_image=__UpperCAmelCase      ,				guidance_scale=7.5      ,				num_inference_steps=10      ,				generator=__UpperCAmelCase      ,				output_type='''np'''      ,				)
        lowerCAmelCase__				:  Any		       =     output.images
        lowerCAmelCase__				:  Any		       =     images[0, 255:258, 255:258, -1]

        assert images.shape == (1, 512, 512, 3)
        lowerCAmelCase__				:  Any		       =     np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]		)

        assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3







      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  List[str]		       =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo.png'''		)
        lowerCAmelCase__				:  str		       =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''		)
        lowerCAmelCase__				:  Union[str, Any]		       =     LMSDiscreteScheduler.from_pretrained(
            '''runwayml/stable-diffusion-inpainting'''      ,				subfolder='''scheduler'''      ,				revision='''onnx'''		)
        lowerCAmelCase__				:  Union[str, Any]		       =     OnnxStableDiffusionInpaintPipeline.from_pretrained(
            '''runwayml/stable-diffusion-inpainting'''      ,				revision='''onnx'''      ,				scheduler=__UpperCAmelCase      ,				safety_checker=__UpperCAmelCase      ,				feature_extractor=__UpperCAmelCase      ,				provider=self.gpu_provider      ,				sess_options=self.gpu_options      ,				)
        pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

        lowerCAmelCase__				:  str		       =     '''A red cat sitting on a park bench'''

        lowerCAmelCase__				:  Union[str, Any]		       =     np.random.RandomState(0		)
        lowerCAmelCase__				:  Optional[Any]		       =     pipe(
            prompt=__UpperCAmelCase      ,				image=__UpperCAmelCase      ,				mask_image=__UpperCAmelCase      ,				guidance_scale=7.5      ,				num_inference_steps=20      ,				generator=__UpperCAmelCase      ,				output_type='''np'''      ,				)
        lowerCAmelCase__				:  Tuple		       =     output.images
        lowerCAmelCase__				:  Optional[Any]		       =     images[0, 255:258, 255:258, -1]

        assert images.shape == (1, 512, 512, 3)
        lowerCAmelCase__				:  Optional[int]		       =     np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]		)

        assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3

