





"""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_			)					->		Dict:




      """simple docstring"""

      for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
            if class_name in extractors:
                  _UpperCamelCase      :	Tuple						=       model_type_to_module_name(lowercase_			)

                  _UpperCamelCase      :	Dict						=       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.
      _UpperCamelCase      :	Tuple						=       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"""

      _UpperCamelCase      :	Tuple						=       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      __SCREAMING_SNAKE_CASE    :







  '''simple docstring'''


  def __init__(		self   :       int	)		->				List[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(__a	)
  def    __SCREAMING_SNAKE_CASE				(		cls   :       str					,   __a   :       Tuple					,   **__a   :       Optional[int]	)		->				Optional[Any]:
        _UpperCamelCase      :	Tuple						=       kwargs.pop("config"					,   __a	)
        _UpperCamelCase      :	List[str]						=       kwargs.pop("trust_remote_code"					,   __a	)
        _UpperCamelCase      :	Dict						=       True

        _UpperCamelCase,  _UpperCamelCase      :	Union[str, Any]						=       ImageProcessingMixin.get_image_processor_dict(__a					,   **__a	)
        _UpperCamelCase      :	Optional[int]						=       config_dict.get("image_processor_type"					,   __a	)
        _UpperCamelCase      :	List[Any]						=       None
        if "AutoImageProcessor" in config_dict.get("auto_map"					,   {}	):
              _UpperCamelCase      :	List[str]						=       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:
              _UpperCamelCase      :	Dict						=       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."	)
                    _UpperCamelCase      :	List[str]						=       feature_extractor_class.replace("FeatureExtractor"					,   "ImageProcessor"	)
              if "AutoFeatureExtractor" in config_dict.get("auto_map"					,   {}	):
                    _UpperCamelCase      :	Tuple						=       config_dict["auto_map"]["AutoFeatureExtractor"]
                    _UpperCamelCase      :	Optional[Any]						=       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	):
                    _UpperCamelCase      :	Optional[int]						=       AutoConfig.from_pretrained(__a					,   **__a	)
              # It could be in `config.image_processor_type``
              _UpperCamelCase      :	Dict						=       getattr(__a					,   "image_processor_type"					,   __a	)
              if hasattr(__a					,   "auto_map"	) and "AutoImageProcessor" in config.auto_map:
                    _UpperCamelCase      :	Tuple						=       config.auto_map["AutoImageProcessor"]

        if image_processor_class is not None:
              _UpperCamelCase      :	Optional[int]						=       image_processor_class_from_name(__a	)

        _UpperCamelCase      :	Any						=       image_processor_auto_map is not None
        _UpperCamelCase      :	str						=       image_processor_class is not None or type(__a	) in IMAGE_PROCESSOR_MAPPING
        _UpperCamelCase      :	int						=       resolve_trust_remote_code(
            __a					,   __a					,   __a					,   __a	)

        if has_remote_code and trust_remote_code:
              _UpperCamelCase      :	Dict						=       get_class_from_dynamic_module(
                  __a					,   __a					,   **__a	)
              _UpperCamelCase      :	int						=       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:
              _UpperCamelCase      :	Optional[int]						=       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    __SCREAMING_SNAKE_CASE				(		__a   :       List[str]					,   __a   :       Optional[int]	)		->				str:
        IMAGE_PROCESSOR_MAPPING.register(__a					,   __a	)







"""simple docstring"""


import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
lowerCamelCase__				  =   "src/transformers"
lowerCamelCase__				  =   "docs/source/en"
lowerCamelCase__				  =   "."




def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_			)					->		List[str]:




      """simple docstring"""

      with open(lowercase_		,"r"		,encoding="utf-8"		,newline="\n"			) as f:
            _UpperCamelCase      :	Union[str, Any]						=       f.readlines()
      # Find the start prompt.
      _UpperCamelCase      :	Dict						=       0
      while not lines[start_index].startswith(lowercase_			):
            start_index += 1
      start_index += 1

      _UpperCamelCase      :	Optional[int]						=       start_index
      while not lines[end_index].startswith(lowercase_			):
            end_index += 1
      end_index -= 1

      while len(lines[start_index]			) <= 1:
            start_index += 1
      while len(lines[end_index]			) <= 1:
            end_index -= 1
      end_index += 1
      return "".join(lines[start_index:end_index]			), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
lowerCamelCase__				  =   "Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
lowerCamelCase__				  =   re.compile(R"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
lowerCamelCase__				  =   re.compile(R"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
lowerCamelCase__				  =   re.compile(R"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# This is to make sure the transformers module imported is the one in the repo.
lowerCamelCase__				  =   direct_transformers_import(TRANSFORMERS_PATH)




def 					lowercase__    (					lowercase_			)					->		Any:




      """simple docstring"""

      _UpperCamelCase      :	Tuple						=       re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"		,lowercase_			)
      return [m.group(0			) for m in matches]




def 					lowercase__    (					lowercase_		,lowercase_			)					->		Union[str, Any]:




      """simple docstring"""

      _UpperCamelCase      :	str						=       2 if text == "✅" or text == "❌" else len(lowercase_			)
      _UpperCamelCase      :	Union[str, Any]						=       (width - text_length) // 2
      _UpperCamelCase      :	Dict						=       width - text_length - left_indent
      return " " * left_indent + text + " " * right_indent




def 					lowercase__    (					)					->		str:




      """simple docstring"""

      _UpperCamelCase      :	Optional[Any]						=       transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
      _UpperCamelCase      :	str						=       {
          name: config_maping_names[code]
          for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
          if code in config_maping_names
      }
      _UpperCamelCase      :	Dict						=       {name: config.replace("Config"		,""			) for name, config in model_name_to_config.items()}

      # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
      _UpperCamelCase      :	int						=       collections.defaultdict(lowercase_			)
      _UpperCamelCase      :	Dict						=       collections.defaultdict(lowercase_			)
      _UpperCamelCase      :	Dict						=       collections.defaultdict(lowercase_			)
      _UpperCamelCase      :	int						=       collections.defaultdict(lowercase_			)
      _UpperCamelCase      :	str						=       collections.defaultdict(lowercase_			)

      # Let's lookup through all transformers object (once).
      for attr_name in dir(lowercase_			):
            _UpperCamelCase      :	List[str]						=       None
            if attr_name.endswith("Tokenizer"			):
                  _UpperCamelCase      :	Tuple						=       slow_tokenizers
                  _UpperCamelCase      :	Any						=       attr_name[:-9]
            elif attr_name.endswith("TokenizerFast"			):
                  _UpperCamelCase      :	Optional[Any]						=       fast_tokenizers
                  _UpperCamelCase      :	List[str]						=       attr_name[:-13]
            elif _re_tf_models.match(lowercase_			) is not None:
                  _UpperCamelCase      :	List[Any]						=       tf_models
                  _UpperCamelCase      :	Dict						=       _re_tf_models.match(lowercase_			).groups()[0]
            elif _re_flax_models.match(lowercase_			) is not None:
                  _UpperCamelCase      :	Dict						=       flax_models
                  _UpperCamelCase      :	Union[str, Any]						=       _re_flax_models.match(lowercase_			).groups()[0]
            elif _re_pt_models.match(lowercase_			) is not None:
                  _UpperCamelCase      :	Optional[int]						=       pt_models
                  _UpperCamelCase      :	Any						=       _re_pt_models.match(lowercase_			).groups()[0]

            if lookup_dict is not None:
                  while len(lowercase_			) > 0:
                        if attr_name in model_name_to_prefix.values():
                              _UpperCamelCase      :	Dict						=       True
                              break
                        # Try again after removing the last word in the name
                        _UpperCamelCase      :	List[str]						=       "".join(camel_case_split(lowercase_			)[:-1]			)

    # Let's build that table!
      _UpperCamelCase      :	Any						=       list(model_name_to_config.keys()			)
      model_names.sort(key=str.lower			)
      _UpperCamelCase      :	List[str]						=       ["Model", "Tokenizer slow", "Tokenizer fast", "PyTorch support", "TensorFlow support", "Flax Support"]
      # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
      _UpperCamelCase      :	Union[str, Any]						=       [len(lowercase_			) + 2 for c in columns]
      _UpperCamelCase      :	Any						=       max([len(lowercase_			) for name in model_names]			) + 2

      # Build the table per se
      _UpperCamelCase      :	Tuple						=       "|" + "|".join([_center_text(lowercase_		,lowercase_			) for c, w in zip(lowercase_		,lowercase_			)]			) + "|\n"
      # Use ":-----:" format to center-aligned table cell texts
      table += "|" + "|".join([":" + "-" * (w - 2) + ":" for w in widths]			) + "|\n"

      _UpperCamelCase      :	Union[str, Any]						=       {True: "✅", False: "❌"}
      for name in model_names:
            _UpperCamelCase      :	Optional[int]						=       model_name_to_prefix[name]
            _UpperCamelCase      :	Tuple						=       [
                name,
                check[slow_tokenizers[prefix]],
                check[fast_tokenizers[prefix]],
                check[pt_models[prefix]],
                check[tf_models[prefix]],
                check[flax_models[prefix]],
            ]
            table += "|" + "|".join([_center_text(lowercase_		,lowercase_			) for l, w in zip(lowercase_		,lowercase_			)]			) + "|\n"
      return table




def 					lowercase__    (					lowercase_=False			)					->		List[Any]:




      """simple docstring"""

      _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase      :	str						=       _find_text_in_file(
          filename=os.path.join(lowercase_		,"index.md"			)		,start_prompt="<!--This table is updated automatically from the auto modules"		,end_prompt="<!-- End table-->"		,)
      _UpperCamelCase      :	Any						=       get_model_table_from_auto_modules()

      if current_table != new_table:
            if overwrite:
                  with open(os.path.join(lowercase_		,"index.md"			)		,"w"		,encoding="utf-8"		,newline="\n"			) as f:
                        f.writelines(lines[:start_index] + [new_table] + lines[end_index:]			)
            else:
                  raise ValueError(
                      "The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this."			)


if __name__ == "__main__":
  lowerCamelCase__				  =   argparse.ArgumentParser()
  parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
  lowerCamelCase__				  =   parser.parse_args()

  check_model_table(args.fix_and_overwrite)
