



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,
)


UpperCamelCase__ =						logging.get_logger(__name__)

UpperCamelCase__ =						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"),
    ]
)

UpperCamelCase__ =						_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)






def  UpperCAmelCase__    (    lowercase__   ) ->      str:
      for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
            if class_name in extractors:
                  __lowercase					=				model_type_to_module_name(_lowercase   )

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

      for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
            if getattr(_lowercase						,       """__name__"""						,       _lowercase   ) == 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.
      __lowercase					=				importlib.import_module("""transformers"""   )
      if hasattr(_lowercase						,       _lowercase   ):
            return getattr(_lowercase						,       _lowercase   )

      return None






def  UpperCAmelCase__    (    lowercase__						,       lowercase__ = None						,       lowercase__ = False						,       lowercase__ = False						,       lowercase__ = None						,       lowercase__ = None						,       lowercase__ = None						,       lowercase__ = False						,       **lowercase__						,       ) ->      List[Any]:
      __lowercase					=				get_file_from_repo(
          _lowercase						,       _lowercase						,       cache_dir=_lowercase						,       force_download=_lowercase						,       resume_download=_lowercase						,       proxies=_lowercase						,       use_auth_token=_lowercase						,       revision=_lowercase						,       local_files_only=_lowercase						,       )
      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(_lowercase						,       encoding="""utf-8"""   ) as reader:
            return json.load(_lowercase   )







class 			_lowerCAmelCase   :






   """simple docstring"""





   def __init__(				self	:       Union[str, Any]   )						->					Optional[Any]:
         """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(UpperCamelCase_   )
   def 		snake_case__			(				cls	:       Tuple			,     lowercase	:       str			,     **lowercase	:       List[Any]   )						->					List[str]:
         """simple docstring"""







         __lowercase					=				kwargs.pop("""config"""			,     UpperCamelCase_   )
         __lowercase					=				kwargs.pop("""trust_remote_code"""			,     UpperCamelCase_   )
         __lowercase					=				True

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

         if image_processor_class is not None:
               __lowercase					=				image_processor_class_from_name(UpperCamelCase_   )

         __lowercase					=				image_processor_auto_map is not None
         __lowercase					=				image_processor_class is not None or type(UpperCamelCase_   ) in IMAGE_PROCESSOR_MAPPING
         __lowercase					=				resolve_trust_remote_code(
             UpperCamelCase_			,     UpperCamelCase_			,     UpperCamelCase_			,     UpperCamelCase_   )

         if has_remote_code and trust_remote_code:
               __lowercase					=				get_class_from_dynamic_module(
                   UpperCamelCase_			,     UpperCamelCase_			,     **UpperCamelCase_   )
               __lowercase					=				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:
               __lowercase					=				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 		snake_case__			(				lowercase	:       Dict			,     lowercase	:       int   )						->					List[str]:
         """simple docstring"""







         IMAGE_PROCESSOR_MAPPING.register(UpperCamelCase_			,     UpperCamelCase_   )










import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class 			_lowerCAmelCase   ( _UpperCAmelCase    ,					_UpperCAmelCase    ,					unittest.TestCase			):






			"""simple docstring"""





			lowercase__      :  int      =			IFImgaImgSuperResolutionPipeline
			lowercase__      :  Optional[int]      =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
			lowercase__      :  Optional[int]      =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""original_image"""}			)
			lowercase__      :  Tuple      =			PipelineTesterMixin.required_optional_params - {"""latents"""}





			def 		snake_case__			(				self	:       Tuple   )						->					Any:
									"""simple docstring"""







									return self._get_superresolution_dummy_components()





			def 		snake_case__			(				self	:       List[str]			,     lowercase	:       Optional[int]			,     lowercase	:       Optional[Any]=0   )						->					Union[str, Any]:
									"""simple docstring"""







									if str(lowercase   ).startswith("""mps"""   ):
															__lowercase					=				torch.manual_seed(lowercase   )
									else:
															__lowercase					=				torch.Generator(device=lowercase   ).manual_seed(lowercase   )

									__lowercase					=				floats_tensor((1, 3, 32, 32)			,     rng=random.Random(lowercase   )   ).to(lowercase   )
									__lowercase					=				floats_tensor((1, 3, 16, 16)			,     rng=random.Random(lowercase   )   ).to(lowercase   )

									__lowercase					=				{
									    """prompt""": """A painting of a squirrel eating a burger""",
									    """image""": image,
									    """original_image""": original_image,
									    """generator""": generator,
									    """num_inference_steps""": 2,
									    """output_type""": """numpy""",
									}

									return inputs





			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()			,     reason="""XFormers attention is only available with CUDA and `xformers` installed"""			,     )
			def 		snake_case__			(				self	:       Optional[Any]   )						->					Tuple:
									"""simple docstring"""







									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3   )





			def 		snake_case__			(				self	:       List[Any]   )						->					Union[str, Any]:
									"""simple docstring"""







									self._test_save_load_optional_components()





			@unittest.skipIf(torch_device != """cuda"""			,     reason="""float16 requires CUDA"""   )
			def 		snake_case__			(				self	:       List[Any]   )						->					Dict:
									"""simple docstring"""







									super().test_save_load_floataa(expected_max_diff=1E-1   )





			def 		snake_case__			(				self	:       Dict   )						->					int:
									"""simple docstring"""







									self._test_attention_slicing_forward_pass(expected_max_diff=1E-2   )





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







									self._test_save_load_local()







			def 		snake_case__			(				self	:       Optional[int]   )						->					Optional[int]:
									"""simple docstring"""







									self._test_inference_batch_single_identical(
									    expected_max_diff=1E-2			,     )





