






'''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,
)


lowerCamelCase_												=					logging.get_logger(__name__)

lowerCamelCase_												=					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'''),
    ]
)

lowerCamelCase_												=					_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)

def 					__lowercase						(			__lowercase       )       ->						Optional[int]:

      '''simple docstring'''


      for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
            if class_name in extractors:
                  _A			     =	model_type_to_module_name(__lowercase       )

                  _A			     =	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.
      _A			     =	importlib.import_module("transformers"       )
      if hasattr(__lowercase   ,  __lowercase       ):
            return getattr(__lowercase   ,  __lowercase       )

      return None

def 					__lowercase						(			__lowercase   ,  __lowercase = None   ,  __lowercase = False   ,  __lowercase = False   ,  __lowercase = None   ,  __lowercase = None   ,  __lowercase = None   ,  __lowercase = False   ,  **__lowercase   ,  )       ->						Tuple:

      '''simple docstring'''


      _A			     =	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    _UpperCAmelCase			:




   """simple docstring"""

   def __init__(		self :	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(__UpperCAmelCase		)
   def 						lowerCAmelCase (		cls :	List[str]      ,      __UpperCAmelCase :	str      ,      **__UpperCAmelCase :	Optional[Any]		):





         '''simple docstring'''





         _A			     =	kwargs.pop("config"      ,      __UpperCAmelCase		)
         _A			     =	kwargs.pop("trust_remote_code"      ,      __UpperCAmelCase		)
         _A			     =	True

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

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

         if has_remote_code and trust_remote_code:
               _A			     =	get_class_from_dynamic_module(
                   __UpperCAmelCase      ,      __UpperCAmelCase      ,      **__UpperCAmelCase		)
               _A			     =	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:
               _A			     =	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 						lowerCAmelCase (		__UpperCAmelCase :	Tuple      ,      __UpperCAmelCase :	Union[str, Any]		):





         '''simple docstring'''





         IMAGE_PROCESSOR_MAPPING.register(__UpperCAmelCase      ,      __UpperCAmelCase		)









'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A					:  List[Any]											=      logging.get_logger(__name__)

__A					:  Optional[Any]											=      {
    "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}






class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase    =					'gpt_neox'

    def __init__(   self    :  Optional[int]    ,						lowerCamelCase    :  Tuple=5_04_32    ,						lowerCamelCase    :  Optional[int]=61_44    ,						lowerCamelCase    :  Tuple=44    ,						lowerCamelCase    :  Any=64    ,						lowerCamelCase    :  List[Any]=2_45_76    ,						lowerCamelCase    :  List[Any]="gelu"    ,						lowerCamelCase    :  Optional[Any]=0.25    ,						lowerCamelCase    :  Any=1_00_00    ,						lowerCamelCase    :  Any=0.0    ,						lowerCamelCase    :  str=0.0    ,						lowerCamelCase    :  Optional[int]=0.1    ,						lowerCamelCase    :  List[Any]=20_48    ,						lowerCamelCase    :  List[Any]=0.02    ,						lowerCamelCase    :  Any=1E-5    ,						lowerCamelCase    :  Dict=True    ,						lowerCamelCase    :  Optional[int]=0    ,						lowerCamelCase    :  List[str]=2    ,						lowerCamelCase    :  Dict=False    ,						lowerCamelCase    :  Tuple=True    ,						lowerCamelCase    :  Optional[int]=None    ,						**lowerCamelCase    :  int    ,						)   ->  Optional[Any]:
      super().__init__(bos_token_id=lowerCamelCase    ,						eos_token_id=lowerCamelCase    ,						**lowerCamelCase		)
      lowerCAmelCase_			:      Tuple			=   vocab_size
      lowerCAmelCase_			:      Union[str, Any]			=   max_position_embeddings
      lowerCAmelCase_			:      Any			=   hidden_size
      lowerCAmelCase_			:      List[Any]			=   num_hidden_layers
      lowerCAmelCase_			:      Optional[int]			=   num_attention_heads
      lowerCAmelCase_			:      str			=   intermediate_size
      lowerCAmelCase_			:      int			=   hidden_act
      lowerCAmelCase_			:      List[Any]			=   rotary_pct
      lowerCAmelCase_			:      Any			=   rotary_emb_base
      lowerCAmelCase_			:      List[str]			=   attention_dropout
      lowerCAmelCase_			:      Union[str, Any]			=   hidden_dropout
      lowerCAmelCase_			:      Tuple			=   classifier_dropout
      lowerCAmelCase_			:      Union[str, Any]			=   initializer_range
      lowerCAmelCase_			:      Any			=   layer_norm_eps
      lowerCAmelCase_			:      str			=   use_cache
      lowerCAmelCase_			:      str			=   tie_word_embeddings
      lowerCAmelCase_			:      str			=   use_parallel_residual
      lowerCAmelCase_			:      Any			=   rope_scaling
      self._rope_scaling_validation()

      if self.hidden_size % self.num_attention_heads != 0:
        raise ValueError(
            """The hidden size is not divisble by the number of attention heads! Make sure to update them!"""		)





    def __lowercase   (   self    :  List[str]		)   ->  List[str]:
      if self.rope_scaling is None:
        return

      if not isinstance(self.rope_scaling    ,						lowerCamelCase		) or len(self.rope_scaling		) != 2:
        raise ValueError(
            """`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
            F'got {self.rope_scaling}'		)
      lowerCAmelCase_			:      Optional[Any]			=   self.rope_scaling.get("""type"""    ,						lowerCamelCase		)
      lowerCAmelCase_			:      int			=   self.rope_scaling.get("""factor"""    ,						lowerCamelCase		)
      if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
        raise ValueError(
            F'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'		)
      if rope_scaling_factor is None or not isinstance(lowerCamelCase    ,						lowerCamelCase		) or rope_scaling_factor <= 1.0:
        raise ValueError(F'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'		)



