"""simple docstring"""





import json
import os
import unittest

from transformers.models.roc_bert.tokenization_roc_bert import (
    VOCAB_FILES_NAMES,
    RoCBertBasicTokenizer,
    RoCBertTokenizer,
    RoCBertWordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english




@require_tokenizers
class 			_snake_case		(     __snake_case ,							unittest.TestCase			):







				"""simple docstring"""






				a      =    RoCBertTokenizer
				a      =    None
				a      =    False
				a      =    True
				a      =    filter_non_english





				def 		_lowerCAmelCase		(   self				:				str):



											"""simple docstring"""







											super().setUp()

											_SCREAMING_SNAKE_CASE		:    List[str]      	=     ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """你""", """好""", """是""", """谁""", """a""", """b""", """c""", """d"""]
											_SCREAMING_SNAKE_CASE		:    List[Any]      	=     {}
											_SCREAMING_SNAKE_CASE		:    str      	=     {}
											for i, value in enumerate(_A):
																		_SCREAMING_SNAKE_CASE		:    List[str]      	=     i
																		_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     i
											_SCREAMING_SNAKE_CASE		:    List[Any]      	=     os.path.join(self.tmpdirname   ,     VOCAB_FILES_NAMES["""vocab_file"""])
											_SCREAMING_SNAKE_CASE		:    str      	=     os.path.join(self.tmpdirname   ,     VOCAB_FILES_NAMES["""word_shape_file"""])
											_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     os.path.join(self.tmpdirname   ,     VOCAB_FILES_NAMES["""word_pronunciation_file"""])
											with open(self.vocab_file   ,     """w"""   ,     encoding="""utf-8""") as vocab_writer:
																		vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]))
											with open(self.word_shape_file   ,     """w"""   ,     encoding="""utf-8""") as word_shape_writer:
																		json.dump(_A   ,     _A   ,     ensure_ascii=_A)
											with open(self.word_pronunciation_file   ,     """w"""   ,     encoding="""utf-8""") as word_pronunciation_writer:
																		json.dump(_A   ,     _A   ,     ensure_ascii=_A)





				def 		_lowerCAmelCase		(   self				:				Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    Any      	=     self.tokenizer_class(self.vocab_file   ,     self.word_shape_file   ,     self.word_pronunciation_file)

											_SCREAMING_SNAKE_CASE		:    int      	=     tokenizer.tokenize("""你好[SEP]你是谁""")
											self.assertListEqual(_A   ,     ["""你""", """好""", """[SEP]""", """你""", """是""", """谁"""])
											self.assertListEqual(tokenizer.convert_tokens_to_ids(_A)   ,     [5, 6, 2, 5, 7, 8])
											self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(_A)   ,     [5, 6, 2, 5, 7, 8])
											self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(_A)   ,     [5, 6, 2, 5, 7, 8])





				def 		_lowerCAmelCase		(   self				:				List[str]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    Dict      	=     RoCBertBasicTokenizer()

											self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz""")   ,     ["""ah""", """\u535A""", """\u63A8""", """zz"""])





				def 		_lowerCAmelCase		(   self				:				Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    Optional[Any]      	=     RoCBertBasicTokenizer(do_lower_case=_A)

											self.assertListEqual(
											    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """)   ,     ["""hello""", """!""", """how""", """are""", """you""", """?"""])
											self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""")   ,     ["""hello"""])





				def 		_lowerCAmelCase		(   self				:				int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    Dict      	=     RoCBertBasicTokenizer(do_lower_case=_A   ,     strip_accents=_A)

											self.assertListEqual(
											    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """)   ,     ["""hällo""", """!""", """how""", """are""", """you""", """?"""])
											self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""")   ,     ["""h\u00E9llo"""])





				def 		_lowerCAmelCase		(   self				:				Optional[int]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    List[str]      	=     RoCBertBasicTokenizer(do_lower_case=_A   ,     strip_accents=_A)

											self.assertListEqual(
											    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """)   ,     ["""hallo""", """!""", """how""", """are""", """you""", """?"""])
											self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""")   ,     ["""hello"""])





				def 		_lowerCAmelCase		(   self				:				Tuple):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    int      	=     RoCBertBasicTokenizer(do_lower_case=_A)

											self.assertListEqual(
											    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """)   ,     ["""hallo""", """!""", """how""", """are""", """you""", """?"""])
											self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""")   ,     ["""hello"""])





				def 		_lowerCAmelCase		(   self				:				List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     RoCBertBasicTokenizer(do_lower_case=_A)

											self.assertListEqual(
											    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """)   ,     ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""])





				def 		_lowerCAmelCase		(   self				:				List[str]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    int      	=     RoCBertBasicTokenizer(do_lower_case=_A   ,     strip_accents=_A)

											self.assertListEqual(
											    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """)   ,     ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""])





				def 		_lowerCAmelCase		(   self				:				int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    List[Any]      	=     RoCBertBasicTokenizer(do_lower_case=_A   ,     strip_accents=_A)

											self.assertListEqual(
											    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """)   ,     ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""])





				def 		_lowerCAmelCase		(   self				:				Optional[int]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    List[Any]      	=     RoCBertBasicTokenizer(do_lower_case=_A   ,     never_split=["""[UNK]"""])

											self.assertListEqual(
											    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU? [UNK]""")   ,     ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""])





				def 		_lowerCAmelCase		(   self				:				str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    Optional[Any]      	=     ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

											_SCREAMING_SNAKE_CASE		:    List[str]      	=     {}
											for i, token in enumerate(_A):
																		_SCREAMING_SNAKE_CASE		:    List[str]      	=     i
											_SCREAMING_SNAKE_CASE		:    List[Any]      	=     RoCBertWordpieceTokenizer(vocab=_A   ,     unk_token="""[UNK]""")

											self.assertListEqual(tokenizer.tokenize("""""")   ,     [])

											self.assertListEqual(tokenizer.tokenize("""unwanted running""")   ,     ["""un""", """##want""", """##ed""", """runn""", """##ing"""])

											self.assertListEqual(tokenizer.tokenize("""unwantedX running""")   ,     ["""[UNK]""", """runn""", """##ing"""])





				def 		_lowerCAmelCase		(   self				:				int):



											"""simple docstring"""







											self.assertTrue(_is_whitespace(""" """))
											self.assertTrue(_is_whitespace("""\t"""))
											self.assertTrue(_is_whitespace("""\r"""))
											self.assertTrue(_is_whitespace("""\n"""))
											self.assertTrue(_is_whitespace("""\u00A0"""))

											self.assertFalse(_is_whitespace("""A"""))
											self.assertFalse(_is_whitespace("""-"""))





				def 		_lowerCAmelCase		(   self				:				Dict):



											"""simple docstring"""







											self.assertTrue(_is_control("""\u0005"""))

											self.assertFalse(_is_control("""A"""))
											self.assertFalse(_is_control(""" """))
											self.assertFalse(_is_control("""\t"""))
											self.assertFalse(_is_control("""\r"""))





				def 		_lowerCAmelCase		(   self				:				Optional[Any]):



											"""simple docstring"""







											self.assertTrue(_is_punctuation("""-"""))
											self.assertTrue(_is_punctuation("""$"""))
											self.assertTrue(_is_punctuation("""`"""))
											self.assertTrue(_is_punctuation("""."""))

											self.assertFalse(_is_punctuation("""A"""))
											self.assertFalse(_is_punctuation(""" """))





				def 		_lowerCAmelCase		(   self				:				Dict):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    List[Any]      	=     self.get_tokenizer()

											# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
											self.assertListEqual([tokenizer.tokenize(_A) for t in ["""Test""", """\xad""", """test"""]]   ,     [["""[UNK]"""], [], ["""[UNK]"""]])

											if self.test_rust_tokenizer:
																		_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     self.get_rust_tokenizer()
																		self.assertListEqual(
																		    [rust_tokenizer.tokenize(_A) for t in ["""Test""", """\xad""", """test"""]]   ,     [["""[UNK]"""], [], ["""[UNK]"""]])





				def 		_lowerCAmelCase		(   self				:				Union[str, Any]):



											"""simple docstring"""







											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
																									_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     self.rust_tokenizer_class.from_pretrained(_A   ,     **_A)

																									_SCREAMING_SNAKE_CASE		:    str      	=     f"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
																									_SCREAMING_SNAKE_CASE		:    int      	=     tokenizer_r.encode_plus(
																									    _A   ,     return_attention_mask=_A   ,     return_token_type_ids=_A   ,     return_offsets_mapping=_A   ,     add_special_tokens=_A   ,     )

																									_SCREAMING_SNAKE_CASE		:    List[Any]      	=     tokenizer_r.do_lower_case if hasattr(_A   ,     """do_lower_case""") else False
																									_SCREAMING_SNAKE_CASE		:    Dict      	=     (
																									    [
																									        ((0, 0), tokenizer_r.cls_token),
																									        ((0, 1), """A"""),
																									        ((1, 2), ""","""),
																									        ((3, 5), """na"""),
																									        ((5, 6), """##ï"""),
																									        ((6, 8), """##ve"""),
																									        ((9, 1_5), tokenizer_r.mask_token),
																									        ((1_6, 2_1), """Allen"""),
																									        ((2_1, 2_3), """##NL"""),
																									        ((2_3, 2_4), """##P"""),
																									        ((2_5, 3_3), """sentence"""),
																									        ((3_3, 3_4), """."""),
																									        ((0, 0), tokenizer_r.sep_token),
																									    ]
																									    if not do_lower_case
																									    else [
																									        ((0, 0), tokenizer_r.cls_token),
																									        ((0, 1), """a"""),
																									        ((1, 2), ""","""),
																									        ((3, 8), """naive"""),
																									        ((9, 1_5), tokenizer_r.mask_token),
																									        ((1_6, 2_1), """allen"""),
																									        ((2_1, 2_3), """##nl"""),
																									        ((2_3, 2_4), """##p"""),
																									        ((2_5, 3_3), """sentence"""),
																									        ((3_3, 3_4), """."""),
																									        ((0, 0), tokenizer_r.sep_token),
																									    ]
																									)

																									self.assertEqual(
																									    [e[1] for e in expected_results]   ,     tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""]))
																									self.assertEqual([e[0] for e in expected_results]   ,     tokens["""offset_mapping"""])





				def 		_lowerCAmelCase		(   self				:				str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    int      	=     ["""的""", """人""", """有"""]
											_SCREAMING_SNAKE_CASE		:    Any      	=     """""".join(_A)
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
																									_SCREAMING_SNAKE_CASE		:    Dict      	=     True
																									_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     self.tokenizer_class.from_pretrained(_A   ,     **_A)
																									_SCREAMING_SNAKE_CASE		:    int      	=     self.rust_tokenizer_class.from_pretrained(_A   ,     **_A)

																									_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     tokenizer_p.encode(_A   ,     add_special_tokens=_A)
																									_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     tokenizer_r.encode(_A   ,     add_special_tokens=_A)

																									_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     tokenizer_r.convert_ids_to_tokens(_A)
																									_SCREAMING_SNAKE_CASE		:    Optional[Any]      	=     tokenizer_p.convert_ids_to_tokens(_A)

																									# it is expected that each Chinese character is not preceded by "##"
																									self.assertListEqual(_A   ,     _A)
																									self.assertListEqual(_A   ,     _A)

																									_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     False
																									_SCREAMING_SNAKE_CASE		:    Optional[Any]      	=     self.rust_tokenizer_class.from_pretrained(_A   ,     **_A)
																									_SCREAMING_SNAKE_CASE		:    Optional[Any]      	=     self.tokenizer_class.from_pretrained(_A   ,     **_A)

																									_SCREAMING_SNAKE_CASE		:    int      	=     tokenizer_r.encode(_A   ,     add_special_tokens=_A)
																									_SCREAMING_SNAKE_CASE		:    Optional[Any]      	=     tokenizer_p.encode(_A   ,     add_special_tokens=_A)

																									_SCREAMING_SNAKE_CASE		:    Tuple      	=     tokenizer_r.convert_ids_to_tokens(_A)
																									_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     tokenizer_p.convert_ids_to_tokens(_A)

																									# it is expected that only the first Chinese character is not preceded by "##".
																									_SCREAMING_SNAKE_CASE		:    List[str]      	=     [
																									    f"""##{token}""" if idx != 0 else token for idx, token in enumerate(_A)
																									]
																									self.assertListEqual(_A   ,     _A)
																									self.assertListEqual(_A   ,     _A)





				@slow
				def 		_lowerCAmelCase		(   self				:				Dict):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    List[str]      	=     self.tokenizer_class(self.vocab_file   ,     self.word_shape_file   ,     self.word_pronunciation_file)

											_SCREAMING_SNAKE_CASE		:    List[str]      	=     tokenizer.encode("""你好"""   ,     add_special_tokens=_A)
											_SCREAMING_SNAKE_CASE		:    Tuple      	=     tokenizer.encode("""你是谁"""   ,     add_special_tokens=_A)

											_SCREAMING_SNAKE_CASE		:    int      	=     tokenizer.build_inputs_with_special_tokens(_A)
											_SCREAMING_SNAKE_CASE		:    List[Any]      	=     tokenizer.build_inputs_with_special_tokens(_A   ,     _A)

											assert encoded_sentence == [1] + text + [2]
											assert encoded_pair == [1] + text + [2] + text_a + [2]





				def 		_lowerCAmelCase		(   self				:				List[str]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    str      	=     self.get_tokenizers(do_lower_case=_A)
											for tokenizer in tokenizers:
																		with self.subTest(f"""{tokenizer.__class__.__name__}"""):
																									_SCREAMING_SNAKE_CASE		:    List[Any]      	=     """你好，你是谁"""
																									_SCREAMING_SNAKE_CASE		:    Optional[Any]      	=     tokenizer.tokenize(_A)
																									_SCREAMING_SNAKE_CASE		:    List[Any]      	=     tokenizer.convert_tokens_to_ids(_A)
																									_SCREAMING_SNAKE_CASE		:    Any      	=     tokenizer.convert_tokens_to_shape_ids(_A)
																									_SCREAMING_SNAKE_CASE		:    int      	=     tokenizer.convert_tokens_to_pronunciation_ids(_A)
																									_SCREAMING_SNAKE_CASE		:    Dict      	=     tokenizer.prepare_for_model(
																									    _A   ,     _A   ,     _A   ,     add_special_tokens=_A)

																									_SCREAMING_SNAKE_CASE		:    List[str]      	=     tokenizer.encode_plus(_A   ,     add_special_tokens=_A)

																									self.assertEqual(_A   ,     _A)




"""simple docstring"""


from jiwer import compute_measures

import datasets


A_    							=					"""\
@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.}
}
"""

A_    							=					"""\
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.
"""

A_    							=					"""
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        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							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=False   ):
							if concatenate_texts:
										return compute_measures(UpperCAmelCase							,						UpperCAmelCase   )["wer"]
							else:
										lowerCamelCase_					=  0
										lowerCamelCase_					=  0
										for prediction, reference in zip(UpperCAmelCase							,						UpperCAmelCase   ):
													lowerCamelCase_					=  compute_measures(UpperCAmelCase							,						UpperCAmelCase   )
													incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
													total += measures["substitutions"] + measures["deletions"] + measures["hits"]
										return incorrect / total



