




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_								=							logging.get_logger(__name__)

UpperCAmelCase_								=							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_								=							_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						List[Any]:




       '''simple docstring'''






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

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

       for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
              if getattr(lowerCAmelCase_       ,					'__name__'       ,					lowerCAmelCase_   ) == 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(lowerCAmelCase_       ,					lowerCAmelCase_   ):
              return getattr(lowerCAmelCase_       ,					lowerCAmelCase_   )

       return None

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_ = None       ,					lowerCAmelCase_ = False       ,					lowerCAmelCase_ = False       ,					lowerCAmelCase_ = None       ,					lowerCAmelCase_ = None       ,					lowerCAmelCase_ = None       ,					lowerCAmelCase_ = False       ,					**lowerCAmelCase_       ,					)		->						List[Any]:




       '''simple docstring'''






       _A=	get_file_from_repo(
           lowerCAmelCase_       ,					lowerCAmelCase_       ,					cache_dir=lowerCAmelCase_       ,					force_download=lowerCAmelCase_       ,					resume_download=lowerCAmelCase_       ,					proxies=lowerCAmelCase_       ,					use_auth_token=lowerCAmelCase_       ,					revision=lowerCAmelCase_       ,					local_files_only=lowerCAmelCase_       ,					)
       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(lowerCAmelCase_       ,					encoding='utf-8'   ) as reader:
              return json.load(lowerCAmelCase_   )





class      lowerCAmelCase						:



     def __init__(					self       ):
            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				,      lowerCAmelCase__				,      **lowerCAmelCase__       ):
            _A=	kwargs.pop('config'				,      lowerCAmelCase__       )
            _A=	kwargs.pop('trust_remote_code'				,      lowerCAmelCase__       )
            _A=	True

            _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__				,      lowerCAmelCase__       ):
            IMAGE_PROCESSOR_MAPPING.register(lowerCAmelCase__				,      lowerCAmelCase__       )
from jiwer import compute_measures

import datasets


UpperCAmelCase_								=							'''\
@inproceedings{inproceedings,
    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
    year = {2004},
    month = {01},
    pages = {},
    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
'''

UpperCAmelCase_								=							'''\
Word error rate (WER) is a common metric of the performance of an automatic speech recognition system.

The general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.

This problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.

Word error rate can then be computed as:

WER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct words,
N is the number of words in the reference (N=S+D+C).

This value indicates the average number of errors per reference word. The lower the value, the better the
performance of the ASR system with a WER of 0 being a perfect score.
'''

UpperCAmelCase_								=							'''
Compute WER score of transcribed segments against references.

Args:
    references: List of references for each speech input.
    predictions: List of transcriptions to score.
    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.

Returns:
    (float): the word error rate

Examples:

    >>> predictions = ["this is the prediction", "there is an other sample"]
    >>> references = ["this is the reference", "there is another one"]
    >>> wer = datasets.load_metric("wer")
    >>> wer_score = wer.compute(predictions=predictions, references=references)
    >>> print(wer_score)
    0.5
'''





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



     def 			a__  (					self       ):
            return datasets.MetricInfo(
                description=_DESCRIPTION				,      citation=_CITATION				,      inputs_description=_KWARGS_DESCRIPTION				,      features=datasets.Features(
                    {
                        'predictions': datasets.Value('string'				,      id='sequence'       ),
                        'references': datasets.Value('string'				,      id='sequence'       ),
                    }       )				,      codebase_urls=['https://github.com/jitsi/jiwer/']				,      reference_urls=[
                    'https://en.wikipedia.org/wiki/Word_error_rate',
                ]				,      )





     def 			a__  (					self				,      lowerCAmelCase__=None				,      lowerCAmelCase__=None				,      lowerCAmelCase__=False       ):
            if concatenate_texts:
                   return compute_measures(lowerCAmelCase__				,      lowerCAmelCase__       )["wer"]
            else:
                   _A=	0
                   _A=	0
                   for prediction, reference in zip(lowerCAmelCase__				,      lowerCAmelCase__       ):
                          _A=	compute_measures(lowerCAmelCase__				,      lowerCAmelCase__       )
                          incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
                          total += measures["substitutions"] + measures["deletions"] + measures["hits"]
                   return incorrect / total