



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,
)


A_		:     int        =     logging.get_logger(__name__)

A_		:     Union[str, 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'''),
    ]
)

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


def 	UpperCAmelCase__      (      UpperCAmelCase__ :str					):






 '''simple docstring'''

 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 	UpperCAmelCase__      (      UpperCAmelCase__ :Union[str, os.PathLike] ,					UpperCAmelCase__ :Optional[Union[str, os.PathLike]] = None ,					UpperCAmelCase__ :bool = False ,					UpperCAmelCase__ :bool = False ,					UpperCAmelCase__ :Optional[Dict[str, str]] = None ,					UpperCAmelCase__ :Optional[Union[bool, str]] = None ,					UpperCAmelCase__ :Optional[str] = None ,					UpperCAmelCase__ :bool = False ,					**UpperCAmelCase__ :Any ,					):






 '''simple docstring'''

 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 		_lowercase		:

   def __init__(      self			:       Tuple							)       ->				Dict:


    """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 		A							(      cls			:       Any							,   __lowerCAmelCase			:       str							,   **__lowerCAmelCase			:       Tuple							)       ->				Optional[Any]:


    """simple docstring"""






    a						=		kwargs.pop("config"							,   __lowerCAmelCase							)
    a						=		kwargs.pop("trust_remote_code"							,   __lowerCAmelCase							)
    a						=		True

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

    a						=		image_processor_auto_map is not None
    a						=		image_processor_class is not None or type(__lowerCAmelCase							) in IMAGE_PROCESSOR_MAPPING
    a						=		resolve_trust_remote_code(
        __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							)

    if has_remote_code and trust_remote_code:
     a						=		get_class_from_dynamic_module(
         __lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							)
     a						=		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:
     a						=		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 		A							(      __lowerCAmelCase			:       Dict							,   __lowerCAmelCase			:       str							)       ->				Optional[Any]:


    """simple docstring"""






    IMAGE_PROCESSOR_MAPPING.register(__lowerCAmelCase							,   __lowerCAmelCase							)






import re
from pathlib import Path
from unittest import TestCase

import pytest



@pytest.mark.integration
class 		_lowercase		(       UpperCAmelCase__	):

   def 		A							(      self			:       Optional[int]							,   __lowerCAmelCase			:       str							)       ->				Union[str, Any]:


    """simple docstring"""






    with open(__lowerCAmelCase							,   encoding="utf-8"							) as input_file:
     a						=		re.compile(R"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)"							)
     a						=		input_file.read()
     a						=		regexp.search(__lowerCAmelCase							)

    return match

   def 		A							(      self			:       List[Any]							,   __lowerCAmelCase			:       str							)       ->				Dict:


    """simple docstring"""






    with open(__lowerCAmelCase							,   encoding="utf-8"							) as input_file:
     a						=		re.compile(R"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()"							,   re.DOTALL							)
     a						=		input_file.read()
     # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
     a						=		regexp.finditer(__lowerCAmelCase							)

    a						=		[match for match in matches if match is not None and match.group(1							) is not None]
    return matches[0] if matches else None

   def 		A							(      self			:       List[str]							)       ->				List[Any]:


    """simple docstring"""






    a						=		Path("./datasets"							)
    a						=		list(dataset_paths.absolute().glob("**/*.py"							)							)

    for dataset in dataset_files:
     if self._no_encoding_on_file_open(str(__lowerCAmelCase							)							):
      raise AssertionError(f"""open(...) must use utf-8 encoding in {dataset}"""							)




   def 		A							(      self			:       Tuple							)       ->				Union[str, Any]:


    """simple docstring"""






    a						=		Path("./datasets"							)
    a						=		list(dataset_paths.absolute().glob("**/*.py"							)							)

    for dataset in dataset_files:
     if self._no_print_statements(str(__lowerCAmelCase							)							):
      raise AssertionError(f"""print statement found in {dataset}. Use datasets.logger/logging instead."""							)

