


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,
)


SCREAMING_SNAKE_CASE						:List[str]			=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						:Optional[Any]			=  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'''),
    ]
)

SCREAMING_SNAKE_CASE						:List[Any]			=  _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)



def       _lowerCAmelCase    (				lowerCAmelCase_				:str				)->Any:
   '''simple docstring'''






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

         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.
   snake_case_            =       importlib.import_module("transformers"				)
   if hasattr(lowerCAmelCase_		,    lowerCAmelCase_				):
      return getattr(lowerCAmelCase_		,    lowerCAmelCase_				)

   return None



def       _lowerCAmelCase    (				lowerCAmelCase_				:Union[str, os.PathLike]		,    lowerCAmelCase_				:Optional[Union[str, os.PathLike]] = None		,    lowerCAmelCase_				:bool = False		,    lowerCAmelCase_				:bool = False		,    lowerCAmelCase_				:Optional[Dict[str, str]] = None		,    lowerCAmelCase_				:Optional[Union[bool, str]] = None		,    lowerCAmelCase_				:Optional[str] = None		,    lowerCAmelCase_				:bool = False		,    **lowerCAmelCase_				:List[Any]		,    )->int:
   '''simple docstring'''






   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      __lowerCAmelCase  :






  """simple docstring"""





  def __init__(  self							:							List[str]	) ->			int:




     """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        lowerCAmelCase__	(  cls							:							int     ,					_lowerCAmelCase							:							Optional[int]     ,					**_lowerCAmelCase							:							Optional[Any]	) ->			Any:




     """simple docstring"""
     snake_case_            =       kwargs.pop("config"     ,					_lowerCAmelCase	)
     snake_case_            =       kwargs.pop("trust_remote_code"     ,					_lowerCAmelCase	)
     snake_case_            =       True

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

     if image_processor_class is not None:
        snake_case_            =       image_processor_class_from_name(_lowerCAmelCase	)

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

     if has_remote_code and trust_remote_code:
        snake_case_            =       get_class_from_dynamic_module(
            _lowerCAmelCase     ,					_lowerCAmelCase     ,					**_lowerCAmelCase	)
        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:
        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        lowerCAmelCase__	(  _lowerCAmelCase							:							Union[str, Any]     ,					_lowerCAmelCase							:							Optional[Any]	) ->			Tuple:




     """simple docstring"""
     IMAGE_PROCESSOR_MAPPING.register(_lowerCAmelCase     ,					_lowerCAmelCase	)








def       _lowerCAmelCase    (				lowerCAmelCase_				:int		,    lowerCAmelCase_				:int				)->str:
   '''simple docstring'''






   if not isinstance(lowerCAmelCase_		,    lowerCAmelCase_				):
      raise ValueError("iterations must be defined as integers"				)
   if not isinstance(lowerCAmelCase_		,    lowerCAmelCase_				) or not number >= 1:
      raise ValueError(
          "starting number must be\n                         and integer and be more than 0"				)
   if not iterations >= 1:
      raise ValueError("Iterations must be done more than 0 times to play FizzBuzz"				)

   snake_case_            =       ""
   while number <= iterations:
      if number % 3 == 0:
         out += "Fizz"
      if number % 5 == 0:
         out += "Buzz"
      if 0 not in (number % 3, number % 5):
         out += str(lowerCAmelCase_				)

      # print(out)
      number += 1
      out += " "
   return out


if __name__ == "__main__":
   import doctest

   doctest.testmod()




