from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse('''3.8'''):
      import importlib_metadata
else:
      import importlib.metadata as importlib_metadata


UpperCamelCase  							=						''''''


if version.parse(importlib_metadata.version('''jiwer''')) < version.parse('''2.3.0'''):



      class   snake_case_       (		tr.AbstractTransform       ):






         def __init__(      self				:     int	,						lowercase_				:     str = " "  )					->						Optional[Any]:
             lowercase__ :   Optional[int]    					=				sentence_delimiter






         def       __UpperCamelCase     (      self				:     Any	,						lowercase_				:     str  )					->						List[str]:
             return list(lowercase_  )







         def       __UpperCamelCase     (      self				:     Optional[int]	,						lowercase_				:     List[str]  )					->						Union[str, Any]:
             lowercase__ :   str    					=				[]
             for sent_idx, sentence in enumerate(lowercase_  ):
                 chars.extend(self.process_string(lowercase_  )  )
                 if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(lowercase_  ) - 1:
                     chars.append(self.sentence_delimiter  )
             return chars



      UpperCamelCase  							=						tr.Compose(
          [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
      )
else:
      UpperCamelCase  							=						tr.Compose(
          [
              tr.RemoveMultipleSpaces(),
              tr.Strip(),
              tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
              tr.ReduceToListOfListOfChars(),
          ]
      )


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

CER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.

Character error rate can be computed as:

CER = (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 characters,
N is the number of characters in the reference (N=S+D+C).

CER\'s output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the
performance of the ASR system with a CER of 0 being a perfect score.
'''

UpperCamelCase  							=						'''
Computes CER score of transcribed segments against references.
Args:
    references: list of references for each speech input.
    predictions: list of transcribtions to score.
    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.
Returns:
    (float): the character error rate

Examples:

    >>> predictions = ["this is the prediction", "there is an other sample"]
    >>> references = ["this is the reference", "there is another one"]
    >>> cer = datasets.load_metric("cer")
    >>> cer_score = cer.compute(predictions=predictions, references=references)
    >>> print(cer_score)
    0.34146341463414637
'''



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






   def       __UpperCamelCase     (      self				:     Tuple  )					->						Any:
       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",
               "https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates",
           ]	,						)







   def       __UpperCamelCase     (      self				:     List[str]	,						lowercase_				:     Any	,						lowercase_				:     List[str]	,						lowercase_				:     Union[str, Any]=False  )					->						Union[str, Any]:
       if concatenate_texts:
           return jiwer.compute_measures(
               lowercase_	,						lowercase_	,						truth_transform=lowercase_	,						hypothesis_transform=lowercase_	,						)["wer"]

       lowercase__ :   List[str]    					=				0
       lowercase__ :   Dict    					=				0
       for prediction, reference in zip(lowercase_	,						lowercase_  ):
           lowercase__ :   Union[str, Any]    					=				jiwer.compute_measures(
               lowercase_	,						lowercase_	,						truth_transform=lowercase_	,						hypothesis_transform=lowercase_	,						)
           incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
           total += measures["substitutions"] + measures["deletions"] + measures["hits"]

       return incorrect / total







"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_			     =  logging.get_logger(__name__)

lowercase_			     =  {
    "microsoft/beit-base-patch16-224-pt22k": (
        "https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			List[str]										=				'beit'

		def __init__(  self							, _a=8_192							, _a=768							, _a=12							, _a=12							, _a=3_072							, _a="gelu"							, _a=0.0							, _a=0.0							, _a=0.02							, _a=1E-12							, _a=224							, _a=16							, _a=3							, _a=False							, _a=False							, _a=False							, _a=False							, _a=0.1							, _a=0.1							, _a=True							, _a=[3, 5, 7, 11]							, _a=[1, 2, 3, 6]							, _a=True							, _a=0.4							, _a=256							, _a=1							, _a=False							, _a=255							, **_a							, ):
				super().__init__(**_a      )

				__a           =			vocab_size
				__a           =			hidden_size
				__a           =			num_hidden_layers
				__a           =			num_attention_heads
				__a           =			intermediate_size
				__a           =			hidden_act
				__a           =			hidden_dropout_prob
				__a           =			attention_probs_dropout_prob
				__a           =			initializer_range
				__a           =			layer_norm_eps

				__a           =			image_size
				__a           =			patch_size
				__a           =			num_channels
				__a           =			use_mask_token
				__a           =			use_absolute_position_embeddings
				__a           =			use_relative_position_bias
				__a           =			use_shared_relative_position_bias
				__a           =			layer_scale_init_value
				__a           =			drop_path_rate
				__a           =			use_mean_pooling
				# decode head attributes (semantic segmentation)
				__a           =			out_indices
				__a           =			pool_scales
				# auxiliary head attributes (semantic segmentation)
				__a           =			use_auxiliary_head
				__a           =			auxiliary_loss_weight
				__a           =			auxiliary_channels
				__a           =			auxiliary_num_convs
				__a           =			auxiliary_concat_input
				__a           =			semantic_loss_ignore_index







class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			List[Any]										=				version.parse('1.11'       )

		@property
		def 			__UpperCAmelCase       (  self      ):
				return OrderedDict(
				    [
				        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
				    ]      )





		@property
		def 			__UpperCAmelCase       (  self      ):
				return 1E-4






