

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


A									=  logging.get_logger(__name__)

A									=  '▁'

A									=  {'vocab_file': 'sentencepiece.bpe.model'}

A									=  {
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

A									=  {
    'xlm-roberta-base': 512,
    'xlm-roberta-large': 512,
    'xlm-roberta-large-finetuned-conll02-dutch': 512,
    'xlm-roberta-large-finetuned-conll02-spanish': 512,
    'xlm-roberta-large-finetuned-conll03-english': 512,
    'xlm-roberta-large-finetuned-conll03-german': 512,
}


class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			__A    	=	VOCAB_FILES_NAMES
			__A    	=	PRETRAINED_VOCAB_FILES_MAP
			__A    	=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__A    	=	["""input_ids""", """attention_mask"""]


			def __init__(     self			,					__UpperCamelCase			,					__UpperCamelCase="<s>"			,					__UpperCamelCase="</s>"			,					__UpperCamelCase="</s>"			,					__UpperCamelCase="<s>"			,					__UpperCamelCase="<unk>"			,					__UpperCamelCase="<pad>"			,					__UpperCamelCase="<mask>"			,					__UpperCamelCase = None			,					**__UpperCamelCase			,					):

				"""simple docstring"""


				snake_case_   		=			AddedToken(__UpperCamelCase			,					lstrip=__UpperCamelCase			,					rstrip=__UpperCamelCase				) if isinstance(__UpperCamelCase			,					__UpperCamelCase				) else mask_token

				snake_case_   		=			{} if sp_model_kwargs is None else sp_model_kwargs

				super().__init__(
				    bos_token=__UpperCamelCase			,					eos_token=__UpperCamelCase			,					unk_token=__UpperCamelCase			,					sep_token=__UpperCamelCase			,					cls_token=__UpperCamelCase			,					pad_token=__UpperCamelCase			,					mask_token=__UpperCamelCase			,					sp_model_kwargs=self.sp_model_kwargs			,					**__UpperCamelCase			,					)

				snake_case_   		=			spm.SentencePieceProcessor(**self.sp_model_kwargs				)
				self.sp_model.Load(str(__UpperCamelCase				)				)
				snake_case_   		=			vocab_file

				# Original fairseq vocab and spm vocab must be "aligned":
				# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
				# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
				# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
				# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

				# Mimic fairseq token-to-id alignment for the first 4 token
				snake_case_   		=			{'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

				# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
				snake_case_   		=			1

				snake_case_   		=			len(self.sp_model				) + self.fairseq_offset
				snake_case_   		=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}


			def __getstate__(     self				):

				"""simple docstring"""


				snake_case_   		=			self.__dict__.copy()
				snake_case_   		=			None
				snake_case_   		=			self.sp_model.serialized_model_proto()
				return state


			def __setstate__(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			d

				# for backward compatibility
				if not hasattr(self			,					'sp_model_kwargs'				):
					snake_case_   		=			{}

				snake_case_   		=			spm.SentencePieceProcessor(**self.sp_model_kwargs				)
				self.sp_model.LoadFromSerializedProto(self.sp_model_proto				)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase = None				):

				"""simple docstring"""


				if token_ids_a is None:
					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				snake_case_   		=			[self.cls_token_id]
				snake_case_   		=			[self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase = None			,					__UpperCamelCase = False				):

				"""simple docstring"""


				if already_has_special_tokens:
					return super().get_special_tokens_mask(
					    token_ids_a=__UpperCamelCase			,					token_ids_a=__UpperCamelCase			,					already_has_special_tokens=__UpperCamelCase				)

				if token_ids_a is None:
					return [1] + ([0] * len(__UpperCamelCase				)) + [1]
				return [1] + ([0] * len(__UpperCamelCase				)) + [1, 1] + ([0] * len(__UpperCamelCase				)) + [1]


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase = None				):

				"""simple docstring"""


				snake_case_   		=			[self.sep_token_id]
				snake_case_   		=			[self.cls_token_id]

				if token_ids_a is None:
					return len(cls + token_ids_a + sep				) * [0]
				return len(cls + token_ids_a + sep + sep + token_ids_a + sep				) * [0]


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return len(self.sp_model				) + self.fairseq_offset + 1  # Add the <mask> token


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			{self.convert_ids_to_tokens(__UpperCamelCase				): i for i in range(self.vocab_size				)}
				vocab.update(self.added_tokens_encoder				)
				return vocab


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				return self.sp_model.encode(__UpperCamelCase			,					out_type=__UpperCamelCase				)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				if token in self.fairseq_tokens_to_ids:
					return self.fairseq_tokens_to_ids[token]
				snake_case_   		=			self.sp_model.PieceToId(__UpperCamelCase				)

				# Need to return unknown token if the SP model returned 0
				return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				if index in self.fairseq_ids_to_tokens:
					return self.fairseq_ids_to_tokens[index]
				return self.sp_model.IdToPiece(index - self.fairseq_offset				)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			''.join(__UpperCamelCase				).replace(__UpperCamelCase			,					' '				).strip()
				return out_string







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase = None				):

				"""simple docstring"""


				if not os.path.isdir(__UpperCamelCase				):
					logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
					return
				snake_case_   		=			os.path.join(
				    __UpperCamelCase			,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)

				if os.path.abspath(self.vocab_file				) != os.path.abspath(__UpperCamelCase				) and os.path.isfile(self.vocab_file				):
					copyfile(self.vocab_file			,					__UpperCamelCase				)
				elif not os.path.isfile(self.vocab_file				):
					with open(__UpperCamelCase			,					'wb'				) as fi:
						snake_case_   		=			self.sp_model.serialized_model_proto()
						fi.write(__UpperCamelCase				)

				return (out_vocab_file,)



from jiwer import compute_measures

import datasets


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

A									=  '\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe 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.\n\nThis 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.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n'

A									=  '\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = ["this is the prediction", "there is an other sample"]\n    >>> references = ["this is the reference", "there is another one"]\n    >>> wer = datasets.load_metric("wer")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n'


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






			"""simple docstring"""




			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				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       __lowerCAmelCase			(     self			,					__UpperCamelCase=None			,					__UpperCamelCase=None			,					__UpperCamelCase=False				):

				"""simple docstring"""


				if concatenate_texts:
					return compute_measures(__UpperCamelCase			,					__UpperCamelCase				)["wer"]
				else:
					snake_case_   		=			0
					snake_case_   		=			0
					for prediction, reference in zip(__UpperCamelCase			,					__UpperCamelCase				):
						snake_case_   		=			compute_measures(__UpperCamelCase			,					__UpperCamelCase				)
						incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
						total += measures["substitutions"] + measures["deletions"] + measures["hits"]
					return incorrect / total
