






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 	__lowerCamelCase      (		snake_case__		)							->  Optional[Any]:






      """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(__SCREAMING_SNAKE_CASE		)

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

      for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
            if getattr(__SCREAMING_SNAKE_CASE	,"""__name__"""	,__SCREAMING_SNAKE_CASE		) == 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(__SCREAMING_SNAKE_CASE	,__SCREAMING_SNAKE_CASE		):
            return getattr(__SCREAMING_SNAKE_CASE	,__SCREAMING_SNAKE_CASE		)

      return None

def 	__lowerCamelCase      (		snake_case__	,snake_case__ = None	,snake_case__ = False	,snake_case__ = False	,snake_case__ = None	,snake_case__ = None	,snake_case__ = None	,snake_case__ = False	,**snake_case__	,)							->  Dict:






      """simple docstring"""





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




class 				__UpperCAmelCase		:







   def __init__(     self:  str					):







         '''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(_lowerCAmelCase					)
   def 							UpperCamelCase				(     cls:  Optional[Any]				,     UpperCAmelCase_:  List[Any]				,     **UpperCAmelCase_:  str					):







         '''simple docstring'''




         _SCREAMING_SNAKE_CASE     				=  kwargs.pop("""config"""				,     _lowerCAmelCase					)
         _SCREAMING_SNAKE_CASE     				=  kwargs.pop("""trust_remote_code"""				,     _lowerCAmelCase					)
         _SCREAMING_SNAKE_CASE     				=  True

         _SCREAMING_SNAKE_CASE     				=  ImageProcessingMixin.get_image_processor_dict(_lowerCAmelCase				,     **_lowerCAmelCase					)
         _SCREAMING_SNAKE_CASE     				=  config_dict.get("""image_processor_type"""				,     _lowerCAmelCase					)
         _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"""				,     _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."""					)
                     _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(_lowerCAmelCase				,     _lowerCAmelCase					):
                     _SCREAMING_SNAKE_CASE     				=  AutoConfig.from_pretrained(_lowerCAmelCase				,     **_lowerCAmelCase					)
               # It could be in `config.image_processor_type``
               _SCREAMING_SNAKE_CASE     				=  getattr(_lowerCAmelCase				,     """image_processor_type"""				,     _lowerCAmelCase					)
               if hasattr(_lowerCAmelCase				,     """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(_lowerCAmelCase					)

         _SCREAMING_SNAKE_CASE     				=  image_processor_auto_map is not None
         _SCREAMING_SNAKE_CASE     				=  image_processor_class is not None or type(_lowerCAmelCase					) in IMAGE_PROCESSOR_MAPPING
         _SCREAMING_SNAKE_CASE     				=  resolve_trust_remote_code(
             _lowerCAmelCase				,     _lowerCAmelCase				,     _lowerCAmelCase				,     _lowerCAmelCase					)

         if has_remote_code and trust_remote_code:
               _SCREAMING_SNAKE_CASE     				=  get_class_from_dynamic_module(
                   _lowerCAmelCase				,     _lowerCAmelCase				,     **_lowerCAmelCase					)
               _SCREAMING_SNAKE_CASE     				=  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:
               _SCREAMING_SNAKE_CASE     				=  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 							UpperCamelCase				(     UpperCAmelCase_:  Tuple				,     UpperCAmelCase_:  Tuple					):







         '''simple docstring'''




         IMAGE_PROCESSOR_MAPPING.register(_lowerCAmelCase				,     _lowerCAmelCase					)









import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				Optional[Any]									=							(UnCLIPScheduler,)







			def 							UpperCamelCase				(     self:  int				,     **UpperCAmelCase_:  Dict					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  {
									    """num_train_timesteps""": 1_000,
									    """variance_type""": """fixed_small_log""",
									    """clip_sample""": True,
									    """clip_sample_range""": 1.0,
									    """prediction_type""": """epsilon""",
									}

									config.update(**UpperCAmelCase_					)
									return config







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									for timesteps in [1, 5, 100, 1_000]:
															self.check_over_configs(num_train_timesteps=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									for variance in ["fixed_small_log", "learned_range"]:
															self.check_over_configs(variance_type=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									for clip_sample in [True, False]:
															self.check_over_configs(clip_sample=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[int]					):







									'''simple docstring'''




									for clip_sample_range in [1, 5, 10, 20]:
															self.check_over_configs(clip_sample_range=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




									for prediction_type in ["epsilon", "sample"]:
															self.check_over_configs(prediction_type=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									for time_step in [0, 500, 999]:
															for prev_timestep in [None, 5, 100, 250, 500, 750]:
																					if prev_timestep is not None and prev_timestep >= time_step:
																											continue

																					self.check_over_forward(time_step=UpperCAmelCase_				,     prev_timestep=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.scheduler_classes[0]
									_SCREAMING_SNAKE_CASE     				=  self.get_scheduler_config(variance_type="""fixed_small_log"""					)
									_SCREAMING_SNAKE_CASE     				=  scheduler_class(**UpperCAmelCase_					)

									assert torch.sum(torch.abs(scheduler._get_variance(0					) - 1.0_000E-10					)					) < 1E-5
									assert torch.sum(torch.abs(scheduler._get_variance(487					) - 0.0_54_96_25					)					) < 1E-5
									assert torch.sum(torch.abs(scheduler._get_variance(999					) - 0.9_99_49_87					)					) < 1E-5







			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.scheduler_classes[0]
									_SCREAMING_SNAKE_CASE     				=  self.get_scheduler_config(variance_type="""learned_range"""					)
									_SCREAMING_SNAKE_CASE     				=  scheduler_class(**UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  0.5

									assert scheduler._get_variance(1				,     predicted_variance=UpperCAmelCase_					) - -10.1_71_27_90 < 1E-5
									assert scheduler._get_variance(487				,     predicted_variance=UpperCAmelCase_					) - -5.7_99_80_52 < 1E-5
									assert scheduler._get_variance(999				,     predicted_variance=UpperCAmelCase_					) - -0.0_01_00_11 < 1E-5







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.scheduler_classes[0]
									_SCREAMING_SNAKE_CASE     				=  self.get_scheduler_config()
									_SCREAMING_SNAKE_CASE     				=  scheduler_class(**UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  scheduler.timesteps

									_SCREAMING_SNAKE_CASE     				=  self.dummy_model()
									_SCREAMING_SNAKE_CASE     				=  self.dummy_sample_deter
									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(0					)

									for i, t in enumerate(UpperCAmelCase_					):
															# 1. predict noise residual
															_SCREAMING_SNAKE_CASE     				=  model(UpperCAmelCase_				,     UpperCAmelCase_					)

															# 2. predict previous mean of sample x_t-1
															_SCREAMING_SNAKE_CASE     				=  scheduler.step(UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     generator=UpperCAmelCase_					).prev_sample

															_SCREAMING_SNAKE_CASE     				=  pred_prev_sample

									_SCREAMING_SNAKE_CASE     				=  torch.sum(torch.abs(UpperCAmelCase_					)					)
									_SCREAMING_SNAKE_CASE     				=  torch.mean(torch.abs(UpperCAmelCase_					)					)

									assert abs(result_sum.item() - 2_52.2_68_24_95					) < 1E-2
									assert abs(result_mean.item() - 0.3_28_47_43					) < 1E-3







			def 							UpperCamelCase				(     self:  List[str]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.scheduler_classes[0]
									_SCREAMING_SNAKE_CASE     				=  self.get_scheduler_config()
									_SCREAMING_SNAKE_CASE     				=  scheduler_class(**UpperCAmelCase_					)

									scheduler.set_timesteps(25					)

									_SCREAMING_SNAKE_CASE     				=  scheduler.timesteps

									_SCREAMING_SNAKE_CASE     				=  self.dummy_model()
									_SCREAMING_SNAKE_CASE     				=  self.dummy_sample_deter
									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(0					)

									for i, t in enumerate(UpperCAmelCase_					):
															# 1. predict noise residual
															_SCREAMING_SNAKE_CASE     				=  model(UpperCAmelCase_				,     UpperCAmelCase_					)

															if i + 1 == timesteps.shape[0]:
																					_SCREAMING_SNAKE_CASE     				=  None
															else:
																					_SCREAMING_SNAKE_CASE     				=  timesteps[i + 1]

															# 2. predict previous mean of sample x_t-1
															_SCREAMING_SNAKE_CASE     				=  scheduler.step(
															    UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     prev_timestep=UpperCAmelCase_				,     generator=UpperCAmelCase_					).prev_sample

															_SCREAMING_SNAKE_CASE     				=  pred_prev_sample

									_SCREAMING_SNAKE_CASE     				=  torch.sum(torch.abs(UpperCAmelCase_					)					)
									_SCREAMING_SNAKE_CASE     				=  torch.mean(torch.abs(UpperCAmelCase_					)					)

									assert abs(result_sum.item() - 2_58.2_04_49_83					) < 1E-2
									assert abs(result_mean.item() - 0.3_36_20_38					) < 1E-3







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									pass







			def 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									pass

