




'''simple docstring'''







import importlib
import inspect
import json
import os
from collections import OrderedDict

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...image_processing_utils import ImageProcessingMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
from ...utils import FEATURE_EXTRACTOR_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,
)
from .feature_extraction_auto import AutoFeatureExtractor
from .image_processing_auto import AutoImageProcessor
from .tokenization_auto import AutoTokenizer


lowercase			:      Optional[Any]			=					logging.get_logger(__name__)

lowercase			:      Optional[Any]			=					OrderedDict(
    [
        ("""align""", """AlignProcessor"""),
        ("""altclip""", """AltCLIPProcessor"""),
        ("""bark""", """BarkProcessor"""),
        ("""blip""", """BlipProcessor"""),
        ("""blip-2""", """Blip2Processor"""),
        ("""bridgetower""", """BridgeTowerProcessor"""),
        ("""chinese_clip""", """ChineseCLIPProcessor"""),
        ("""clap""", """ClapProcessor"""),
        ("""clip""", """CLIPProcessor"""),
        ("""clipseg""", """CLIPSegProcessor"""),
        ("""flava""", """FlavaProcessor"""),
        ("""git""", """GitProcessor"""),
        ("""groupvit""", """CLIPProcessor"""),
        ("""hubert""", """Wav2Vec2Processor"""),
        ("""instructblip""", """InstructBlipProcessor"""),
        ("""layoutlmv2""", """LayoutLMv2Processor"""),
        ("""layoutlmv3""", """LayoutLMv3Processor"""),
        ("""markuplm""", """MarkupLMProcessor"""),
        ("""mctct""", """MCTCTProcessor"""),
        ("""mgp-str""", """MgpstrProcessor"""),
        ("""oneformer""", """OneFormerProcessor"""),
        ("""owlvit""", """OwlViTProcessor"""),
        ("""pix2struct""", """Pix2StructProcessor"""),
        ("""sam""", """SamProcessor"""),
        ("""sew""", """Wav2Vec2Processor"""),
        ("""sew-d""", """Wav2Vec2Processor"""),
        ("""speech_to_text""", """Speech2TextProcessor"""),
        ("""speech_to_text_2""", """Speech2Text2Processor"""),
        ("""speecht5""", """SpeechT5Processor"""),
        ("""trocr""", """TrOCRProcessor"""),
        ("""tvlt""", """TvltProcessor"""),
        ("""unispeech""", """Wav2Vec2Processor"""),
        ("""unispeech-sat""", """Wav2Vec2Processor"""),
        ("""vilt""", """ViltProcessor"""),
        ("""vision-text-dual-encoder""", """VisionTextDualEncoderProcessor"""),
        ("""wav2vec2""", """Wav2Vec2Processor"""),
        ("""wav2vec2-conformer""", """Wav2Vec2Processor"""),
        ("""wavlm""", """Wav2Vec2Processor"""),
        ("""whisper""", """WhisperProcessor"""),
        ("""xclip""", """XCLIPProcessor"""),
    ]
)

lowercase			:      Optional[int]			=					_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)



def 					lowerCamelCase__							(			__lowercase							):
     for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
          if class_name in processors:
               snake_case :			Tuple					   = model_type_to_module_name(__lowercase							)

               snake_case :			Dict					   = importlib.import_module(F'''.{module_name}'''    ,      """transformers.models"""							)
               try:
                    return getattr(__lowercase    ,      __lowercase							)
               except AttributeError:
                    continue

     for processor in PROCESSOR_MAPPING._extra_content.values():
          if getattr(__lowercase    ,      """__name__"""    ,      __lowercase							) == class_name:
               return processor

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

     return None







class 				_a   :





     '''simple docstring'''



     def __init__(							self       )			->							str:
          raise EnvironmentError(
              """AutoProcessor is designed to be instantiated """
              """using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method."""       )







     @classmethod
     @replace_list_option_in_docstrings(__a       )
     def  snake_case_			(							cls       ,__a       ,**__a       )			->							Optional[Any]:
          snake_case :			Any					   = kwargs.pop("""config"""       ,__a       )
          snake_case :			Union[str, Any]					   = kwargs.pop("""trust_remote_code"""       ,__a       )
          snake_case :			List[str]					   = True

          snake_case :			List[str]					   = None
          snake_case :			List[str]					   = None

          # First, let's see if we have a preprocessor config.
          # Filter the kwargs for `get_file_from_repo`.
          snake_case :			List[str]					   = {
              key: kwargs[key] for key in inspect.signature(__a       ).parameters.keys() if key in kwargs
          }
          # Let's start by checking whether the processor class is saved in an image processor
          snake_case :			Any					   = get_file_from_repo(
              __a       ,__a       ,**__a       )
          if preprocessor_config_file is not None:
               snake_case   ,		snake_case :			List[str]					   = ImageProcessingMixin.get_image_processor_dict(__a       ,**__a       )
               snake_case :			Any					   = config_dict.get("""processor_class"""       ,__a       )
               if "AutoProcessor" in config_dict.get("""auto_map"""       ,{}       ):
                    snake_case :			Tuple					   = config_dict["""auto_map"""]["""AutoProcessor"""]

        # If not found, let's check whether the processor class is saved in a feature extractor config
          if preprocessor_config_file is not None and processor_class is None:
               snake_case   ,		snake_case :			str					   = FeatureExtractionMixin.get_feature_extractor_dict(__a       ,**__a       )
               snake_case :			str					   = config_dict.get("""processor_class"""       ,__a       )
               if "AutoProcessor" in config_dict.get("""auto_map"""       ,{}       ):
                    snake_case :			int					   = config_dict["""auto_map"""]["""AutoProcessor"""]

          if processor_class is None:
               # Next, let's check whether the processor class is saved in a tokenizer
               snake_case :			List[Any]					   = get_file_from_repo(
                   __a       ,__a       ,**__a       )
               if tokenizer_config_file is not None:
                    with open(__a       ,encoding="""utf-8"""       ) as reader:
                         snake_case :			Dict					   = json.load(__a       )

                    snake_case :			Any					   = config_dict.get("""processor_class"""       ,__a       )
                    if "AutoProcessor" in config_dict.get("""auto_map"""       ,{}       ):
                         snake_case :			Optional[int]					   = config_dict["""auto_map"""]["""AutoProcessor"""]

          if processor_class is None:
               # Otherwise, load config, if it can be loaded.
               if not isinstance(__a       ,__a       ):
                    snake_case :			Tuple					   = AutoConfig.from_pretrained(
                        __a       ,trust_remote_code=__a       ,**__a       )

               # And check if the config contains the processor class.
               snake_case :			List[Any]					   = getattr(__a       ,"""processor_class"""       ,__a       )
               if hasattr(__a       ,"""auto_map"""       ) and "AutoProcessor" in config.auto_map:
                    snake_case :			Optional[Any]					   = config.auto_map["""AutoProcessor"""]

          if processor_class is not None:
               snake_case :			List[str]					   = processor_class_from_name(__a       )

          snake_case :			int					   = processor_auto_map is not None
          snake_case :			int					   = processor_class is not None or type(__a       ) in PROCESSOR_MAPPING
          snake_case :			List[str]					   = resolve_trust_remote_code(
              __a       ,__a       ,__a       ,__a       )

          if has_remote_code and trust_remote_code:
               snake_case :			List[str]					   = get_class_from_dynamic_module(
                   __a       ,__a       ,**__a       )
               snake_case :			List[str]					   = kwargs.pop("""code_revision"""       ,__a       )
               if os.path.isdir(__a       ):
                    processor_class.register_for_auto_class()
               return processor_class.from_pretrained(
                   __a       ,trust_remote_code=__a       ,**__a       )
          elif processor_class is not None:
               return processor_class.from_pretrained(
                   __a       ,trust_remote_code=__a       ,**__a       )
          # Last try: we use the PROCESSOR_MAPPING.
          elif type(__a       ) in PROCESSOR_MAPPING:
               return PROCESSOR_MAPPING[type(__a       )].from_pretrained(__a       ,**__a       )

          # At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
          # tokenizer.
          try:
               return AutoTokenizer.from_pretrained(
                   __a       ,trust_remote_code=__a       ,**__a       )
          except Exception:
               try:
                    return AutoImageProcessor.from_pretrained(
                        __a       ,trust_remote_code=__a       ,**__a       )
               except Exception:
                    pass

               try:
                    return AutoFeatureExtractor.from_pretrained(
                        __a       ,trust_remote_code=__a       ,**__a       )
               except Exception:
                    pass

          raise ValueError(
              F'''Unrecognized processing class in {pretrained_model_name_or_path}. Can\'t instantiate a processor, a '''
              """tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains"""
              """the files of at least one of those processing classes."""       )







     @staticmethod
     def  snake_case_			(							__a       ,__a       )			->							Optional[int]:
          PROCESSOR_MAPPING.register(__a       ,__a       )









'''simple docstring'''







from sklearn.metrics import mean_absolute_error

import datasets


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

lowercase			:      Optional[Any]			=					"""\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
"""


lowercase			:      List[Any]			=					"""
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

                 \"raw_values\" : Returns a full set of errors in case of multioutput input.

                 \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

Returns:
    mae : mean absolute error.
        If multioutput is \"raw_values\", then mean absolute error is returned for each output separately. If multioutput is \"uniform_average\" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric(\"mae\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.5}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results)
    {'mae': array([0.5, 1. ])}
"""







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





     '''simple docstring'''



     def  snake_case_			(							self       )			->							int:
          return datasets.MetricInfo(
              description=_DESCRIPTION       ,citation=_CITATION       ,inputs_description=_KWARGS_DESCRIPTION       ,features=datasets.Features(self._get_feature_types()       )       ,reference_urls=[
                  """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html"""
              ]       ,)







     def  snake_case_			(							self       )			->							Any:
          if self.config_name == "multilist":
               return {
                   "predictions": datasets.Sequence(datasets.Value("""float"""       )       ),
                   "references": datasets.Sequence(datasets.Value("""float"""       )       ),
               }
          else:
               return {
                   "predictions": datasets.Value("""float"""       ),
                   "references": datasets.Value("""float"""       ),
               }







     def  snake_case_			(							self       ,__a       ,__a       ,__a=None       ,__a="uniform_average"       )			->							str:
          snake_case :			int					   = mean_absolute_error(__a       ,__a       ,sample_weight=__a       ,multioutput=__a       )

          return {"mae": mae_score}



