


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[Any]     			=      logging.get_logger(__name__)

snake_case							:       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'''),
    ]
)

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






def 							__lowercase   ( __lowerCAmelCase							:				Dict   ):

							for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
														if class_name in extractors:
																					a__             =     model_type_to_module_name(UpperCamelCase__   )

																					a__             =     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.
							a__             =     importlib.import_module('transformers'   )
							if hasattr(UpperCamelCase__					,    UpperCamelCase__   ):
														return getattr(UpperCamelCase__					,    UpperCamelCase__   )

							return None






def 							__lowercase   ( __lowerCAmelCase							:				List[str]					,    __lowerCAmelCase							:				Dict = None					,    __lowerCAmelCase							:				List[str] = False					,    __lowerCAmelCase							:				List[Any] = False					,    __lowerCAmelCase							:				List[Any] = None					,    __lowerCAmelCase							:				Any = None					,    __lowerCAmelCase							:				Any = None					,    __lowerCAmelCase							:				Tuple = False					,    **__lowerCAmelCase							:				int					,    ):
							a__             =     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        snake_case_   :
					def __init__(      self   :List[str]      )					->    Tuple:
												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(_A      )
					def  lowerCamelCase__(      cls   :Any			,__snake_case   :List[Any]			,**__snake_case   :Tuple      )					->    Tuple:
												a__             =     kwargs.pop('config'			,_A      )
												a__             =     kwargs.pop('trust_remote_code'			,_A      )
												a__             =     True

												a__      ,					a__             =     ImageProcessingMixin.get_image_processor_dict(_A			,**_A      )
												a__             =     config_dict.get('image_processor_type'			,_A      )
												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'			,_A      )
																			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(_A			,_A      ):
																										a__             =     AutoConfig.from_pretrained(_A			,**_A      )
																			# It could be in `config.image_processor_type``
																			a__             =     getattr(_A			,'image_processor_type'			,_A      )
																			if hasattr(_A			,'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(_A      )

												a__             =     image_processor_auto_map is not None
												a__             =     image_processor_class is not None or type(_A      ) in IMAGE_PROCESSOR_MAPPING
												a__             =     resolve_trust_remote_code(
												    _A			,_A			,_A			,_A      )

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

												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  lowerCamelCase__(      __snake_case   :int			,__snake_case   :List[str]      )					->    List[str]:
												IMAGE_PROCESSOR_MAPPING.register(_A			,_A      )











from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 	A_       (a_			):
	UpperCAmelCase__					=			42
	UpperCAmelCase__					=			42
	def __init__(    self							,					_A							,					_A		):






				'''simple docstring'''







				super().__init__()
				self.register_modules(unet=_A							,					scheduler=_A		)







	@torch.no_grad()
	def __call__(    self							,					_A = 1							,					_A = 5_0							,					_A = None							,					_A = "pil"							,					_A = True							,					**_A							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				self.unet.config.sample_size
				UpperCAmelCase   		=				(batch_size, 3, img_size, img_size)

				UpperCAmelCase   		=				self.unet

				# sample x_0 ~ N(0, sigma_0^2 * I)
				UpperCAmelCase   		=				randn_tensor(_A							,					generator=_A							,					device=self.device		) * self.scheduler.init_noise_sigma

				self.scheduler.set_timesteps(_A		)

				for t in self.progress_bar(self.scheduler.timesteps		):
							# here sigma_t == t_i from the paper
							UpperCAmelCase   		=				self.scheduler.schedule[t]
							UpperCAmelCase   		=				self.scheduler.schedule[t - 1] if t > 0 else 0

							# 1. Select temporarily increased noise level sigma_hat
							# 2. Add new noise to move from sample_i to sample_hat
							UpperCAmelCase		,			UpperCAmelCase   		=				self.scheduler.add_noise_to_input(_A							,					_A							,					generator=_A		)

							# 3. Predict the noise residual given the noise magnitude `sigma_hat`
							# The model inputs and output are adjusted by following eq. (213) in [1].
							UpperCAmelCase   		=				(sigma_hat / 2) * model((sample_hat + 1) / 2							,					sigma_hat / 2		).sample

							# 4. Evaluate dx/dt at sigma_hat
							# 5. Take Euler step from sigma to sigma_prev
							UpperCAmelCase   		=				self.scheduler.step(_A							,					_A							,					_A							,					_A		)

							if sigma_prev != 0:
										# 6. Apply 2nd order correction
										# The model inputs and output are adjusted by following eq. (213) in [1].
										UpperCAmelCase   		=				(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2							,					sigma_prev / 2		).sample
										UpperCAmelCase   		=				self.scheduler.step_correct(
										    _A							,					_A							,					_A							,					_A							,					step_output.prev_sample							,					step_output['''derivative''']							,					)
							UpperCAmelCase   		=				step_output.prev_sample

				UpperCAmelCase   		=				(sample / 2 + 0.5).clamp(0							,					1		)
				UpperCAmelCase   		=				sample.cpu().permute(0							,					2							,					3							,					1		).numpy()
				if output_type == "pil":
							UpperCAmelCase   		=				self.numpy_to_pil(_A		)

				if not return_dict:
							return (image,)

				return ImagePipelineOutput(images=_A		)



