





"""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,
)


__UpperCamelCase     :						Any						       =  logging.get_logger(__name__)

__UpperCamelCase     :						List[str]						       =  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     :						Optional[int]						       =  _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str			):
 for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
  if class_name in extractors:
   lowerCAmelCase    				= model_type_to_module_name(_UpperCAmelCase			)

   lowerCAmelCase    				= 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.
 lowerCAmelCase    				= importlib.import_module('transformers'			)
 if hasattr(_UpperCAmelCase  ,     _UpperCAmelCase			):
  return getattr(_UpperCAmelCase  ,     _UpperCAmelCase			)

 return None

def 		_SCREAMING_SNAKE_CASE			(_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					:    List[Any]  ,     ):
 lowerCAmelCase    				= 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       a							:



 def __init__(     self     ):







  """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(_snake_case     )
 def    UpperCamelCase__  (     cls				,       _snake_case				,       **_snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= kwargs.pop('config'				,       _snake_case     )
  lowerCAmelCase    				= kwargs.pop('trust_remote_code'				,       _snake_case     )
  lowerCAmelCase    				= True

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

  if image_processor_class is not None:
   lowerCAmelCase    				= image_processor_class_from_name(_snake_case     )

  lowerCAmelCase    				= image_processor_auto_map is not None
  lowerCAmelCase    				= image_processor_class is not None or type(_snake_case     ) in IMAGE_PROCESSOR_MAPPING
  lowerCAmelCase    				= resolve_trust_remote_code(
      _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     )

  if has_remote_code and trust_remote_code:
   lowerCAmelCase    				= get_class_from_dynamic_module(
       _snake_case				,       _snake_case				,       **_snake_case     )
   lowerCAmelCase    				= kwargs.pop('code_revision'				,       _snake_case     )
   if os.path.isdir(_snake_case     ):
    image_processor_class.register_for_auto_class()
   return image_processor_class.from_dict(_snake_case				,       **_snake_case     )
  elif image_processor_class is not None:
   return image_processor_class.from_dict(_snake_case				,       **_snake_case     )
  # Last try: we use the IMAGE_PROCESSOR_MAPPING.
  elif type(_snake_case     ) in IMAGE_PROCESSOR_MAPPING:
   lowerCAmelCase    				= IMAGE_PROCESSOR_MAPPING[type(_snake_case     )]
   return image_processor_class.from_dict(_snake_case				,       **_snake_case     )

  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__  (     _snake_case				,       _snake_case     ):







  """simple docstring"""






  IMAGE_PROCESSOR_MAPPING.register(_snake_case				,       _snake_case     )











from sklearn.metrics import matthews_corrcoef

import datasets


UpperCAmelCase_		:					Dict          =  "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction.  The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n"

UpperCAmelCase_		:					Any          =  "\nArgs:\n    predictions (list of int): Predicted labels, as returned by a model.\n    references (list of int): Ground truth labels.\n    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n    matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n    Example 1, a basic example with only predictions and references as inputs:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3])\n        >>> print(round(results['matthews_correlation'], 2))\n        0.54\n\n    Example 2, the same example as above, but also including sample weights:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])\n        >>> print(round(results['matthews_correlation'], 2))\n        0.1\n\n    Example 3, the same example as above, but with sample weights that cause a negative correlation:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])\n        >>> print(round(results['matthews_correlation'], 2))\n        -0.25\n"

UpperCAmelCase_		:					Dict          =  "\\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n"




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,    _KWARGS_DESCRIPTION      )
class 		__A   (      datasets.Metric      ):

						def      A__    (		self :List[str]			):
										'''simple docstring'''

										return datasets.MetricInfo(
										    description=_DESCRIPTION     ,			citation=_CITATION     ,			inputs_description=_KWARGS_DESCRIPTION     ,			features=datasets.Features(
										        {
										            """predictions""": datasets.Value("""int32"""			),
										            """references""": datasets.Value("""int32"""			),
										        }			)     ,			reference_urls=[
										        """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
										    ]     ,			)


						def      A__    (		self :Tuple     ,			__snake_case :str     ,			__snake_case :Tuple     ,			__snake_case :List[str]=None			):
										'''simple docstring'''

										return {
										    "matthews_correlation": float(matthews_corrcoef(__snake_case     ,			__snake_case     ,			sample_weight=__snake_case			)			),
										}




