


"""simple docstring"""







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,
)


a__    :							str            =					logging.get_logger(__name__)

a__    :							Dict            =					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'''),
    ]
)

a__    :							Any            =					_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
										if class_name in extractors:
															__SCREAMING_SNAKE_CASE						       =    model_type_to_module_name(lowerCAmelCase_       )

															__SCREAMING_SNAKE_CASE						       =    importlib.import_module(f""".{module_name}"""  ,						"transformers.models"       )
															try:
																				return getattr(lowerCAmelCase_  ,						lowerCAmelCase_       )
															except AttributeError:
																				continue

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

					return None




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_ = False  ,						lowerCAmelCase_ = False  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_ = False  ,						**lowerCAmelCase_  ,						):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    get_file_from_repo(
					    lowerCAmelCase_  ,						lowerCAmelCase_  ,						cache_dir=lowerCAmelCase_  ,						force_download=lowerCAmelCase_  ,						resume_download=lowerCAmelCase_  ,						proxies=lowerCAmelCase_  ,						use_auth_token=lowerCAmelCase_  ,						revision=lowerCAmelCase_  ,						local_files_only=lowerCAmelCase_  ,						)
					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(lowerCAmelCase_  ,						encoding="utf-8"       ) as reader:
										return json.load(lowerCAmelCase_       )




class 			UpperCamelCase_     :

	"""simple docstring"""





	def __init__(			self      :    Tuple       )	->     Union[str, Any]:
						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   UpperCAmelCase_				(			cls      :    str      ,      UpperCAmelCase__      :    List[str]      ,      **UpperCAmelCase__      :    List[Any]       )	->     List[Any]:
						__SCREAMING_SNAKE_CASE						       =    kwargs.pop("config"      ,      UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    kwargs.pop("trust_remote_code"      ,      UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    True

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

						if image_processor_class is not None:
											__SCREAMING_SNAKE_CASE						       =    image_processor_class_from_name(UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    image_processor_auto_map is not None
						__SCREAMING_SNAKE_CASE						       =    image_processor_class is not None or type(UpperCAmelCase__       ) in IMAGE_PROCESSOR_MAPPING
						__SCREAMING_SNAKE_CASE						       =    resolve_trust_remote_code(
						    UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__       )

						if has_remote_code and trust_remote_code:
											__SCREAMING_SNAKE_CASE						       =    get_class_from_dynamic_module(
											    UpperCAmelCase__      ,      UpperCAmelCase__      ,      **UpperCAmelCase__       )
											__SCREAMING_SNAKE_CASE						       =    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:
											__SCREAMING_SNAKE_CASE						       =    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   UpperCAmelCase_				(			UpperCAmelCase__      :    List[str]      ,      UpperCAmelCase__      :    Any       )	->     Dict:
						IMAGE_PROCESSOR_MAPPING.register(UpperCAmelCase__      ,      UpperCAmelCase__       )




"""simple docstring"""







from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
		if not (is_transformers_available() and is_torch_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
		from .pipeline_kandinsky import KandinskyPipeline
		from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
		from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
		from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
		from .text_encoder import MultilingualCLIP
