

from jiwer import compute_measures

import datasets


_lowerCAmelCase  :      int			 ="""\
@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.}
}
"""

_lowerCAmelCase  :      Union[str, Any]			 ="""\
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.
"""

_lowerCAmelCase  :      List[str]			 ="""
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 __UpperCamelCase     (				datasets.Metric		):






					'''simple docstring'''



					def    _UpperCAmelCase    (				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    _UpperCAmelCase    (				self				,      lowerCamelCase__=None				,      lowerCamelCase__=None				,      lowerCamelCase__=False						):
						if concatenate_texts:
							return compute_measures(lowerCamelCase__				,      lowerCamelCase__						)["wer"]
						else:
							UpperCAmelCase__:	str									= 0
							UpperCAmelCase__:	Dict									= 0
							for prediction, reference in zip(lowerCamelCase__				,      lowerCamelCase__						):
								UpperCAmelCase__:	Any									= compute_measures(lowerCamelCase__				,      lowerCamelCase__						)
								incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
								total += measures["substitutions"] + measures["deletions"] + measures["hits"]
							return incorrect / total


import inspect
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_lowerCAmelCase  :      int			 ="""src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
_lowerCAmelCase  :      Dict			 =direct_transformers_import(PATH_TO_TRANSFORMERS)

_lowerCAmelCase  :      List[str]			 =transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
_lowerCAmelCase  :      int			 =re.compile(r"""\[(.+?)\]\((https://huggingface\.co/.+?)\)""")


_lowerCAmelCase  :      Union[str, Any]			 ={
    """DecisionTransformerConfig""",
    """EncoderDecoderConfig""",
    """MusicgenConfig""",
    """RagConfig""",
    """SpeechEncoderDecoderConfig""",
    """TimmBackboneConfig""",
    """VisionEncoderDecoderConfig""",
    """VisionTextDualEncoderConfig""",
    """LlamaConfig""",
}



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	str									= None

	# source code of `config_class`
	UpperCAmelCase__:	Optional[int]									= inspect.getsource(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	List[Any]									= _re_checkpoint.findall(SCREAMING_SNAKE_CASE      )

	# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
	# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
	for ckpt_name, ckpt_link in checkpoints:
		# allow the link to end with `/`
		if ckpt_link.endswith("/"      ):
			UpperCAmelCase__:	Any									= ckpt_link[:-1]

		# verify the checkpoint name corresponds to the checkpoint link
		UpperCAmelCase__:	int									= f"https://huggingface.co/{ckpt_name}"
		if ckpt_link == ckpt_link_from_name:
			UpperCAmelCase__:	Optional[int]									= ckpt_name
			break

	return checkpoint



def 			_A	(      ):
	UpperCAmelCase__:	List[str]									= []

	for config_class in list(CONFIG_MAPPING.values()      ):
		# Skip deprecated models
		if "models.deprecated" in config_class.__module__:
			continue
		UpperCAmelCase__:	Optional[Any]									= get_checkpoint_from_config_class(SCREAMING_SNAKE_CASE      )

		UpperCAmelCase__:	List[str]									= config_class.__name__
		if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
			configs_without_checkpoint.append(SCREAMING_SNAKE_CASE      )

	if len(SCREAMING_SNAKE_CASE      ) > 0:
		UpperCAmelCase__:	Dict									= "\n".join(sorted(SCREAMING_SNAKE_CASE      )      )
		raise ValueError(f"The following configurations don't contain any valid checkpoint:\n{message}"      )


if __name__ == "__main__":
					check_config_docstrings_have_checkpoints()