



import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__snake_case		:Dict   	=  get_tests_dir('''fixtures/test_sentencepiece_no_bos.model''')

@require_sentencepiece
@require_tokenizers
class 					_A (			_UpperCAmelCase   ,unittest.TestCase					):
     UpperCamelCase__						:					Optional[int]		      =		PegasusTokenizer
     UpperCamelCase__						:					str		      =		PegasusTokenizerFast
     UpperCamelCase__						:					Union[str, Any]		      =		True
     UpperCamelCase__						:					Union[str, Any]		      =		True



     def 	_lowerCamelCase    (	self							:  Any):


         '''simple docstring'''

         super().setUp()

         # We have a SentencePiece fixture for testing
         __a		 =       PegasusTokenizer(lowerCamelCase_)
         tokenizer.save_pretrained(self.tmpdirname)



     @cached_property
     def 	_lowerCamelCase    (	self							:  Tuple):


         '''simple docstring'''

         return PegasusTokenizer.from_pretrained('''google/pegasus-large''')



     def 	_lowerCamelCase    (	self							:  int  ,	**__SCREAMING_SNAKE_CASE							:  Tuple):


         '''simple docstring'''

         return PegasusTokenizer.from_pretrained(self.tmpdirname  ,	**lowerCamelCase_)



     def 	_lowerCamelCase    (	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]):


         '''simple docstring'''

         return ("This is a test", "This is a test")



     def 	_lowerCamelCase    (	self							:  Optional[int]):


         '''simple docstring'''

         __a		 =       '''</s>'''
         __a		 =       1

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase_)  ,	lowerCamelCase_)
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase_)  ,	lowerCamelCase_)



     def 	_lowerCamelCase    (	self							:  Any):


         '''simple docstring'''

         __a		 =       list(self.get_tokenizer().get_vocab().keys())

         self.assertEqual(vocab_keys[0]  ,	'''<pad>''')
         self.assertEqual(vocab_keys[1]  ,	'''</s>''')
         self.assertEqual(vocab_keys[-1]  ,	'''v''')
         self.assertEqual(len(lowerCamelCase_)  ,	1_103)



     def 	_lowerCamelCase    (	self							:  Dict):


         '''simple docstring'''

         self.assertEqual(self.get_tokenizer().vocab_size  ,	1_103)



     def 	_lowerCamelCase    (	self							:  Any):


         '''simple docstring'''

         __a		 =       self.rust_tokenizer_class.from_pretrained(self.tmpdirname)
         __a		 =       self.tokenizer_class.from_pretrained(self.tmpdirname)
         __a		 =       (
             '''Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'''
             ''' </s> <pad> <pad> <pad>'''
         )
         __a		 =       rust_tokenizer([raw_input_str]  ,	return_tensors=lowerCamelCase_  ,	add_special_tokens=lowerCamelCase_).input_ids[0]
         __a		 =       py_tokenizer([raw_input_str]  ,	return_tensors=lowerCamelCase_  ,	add_special_tokens=lowerCamelCase_).input_ids[0]
         self.assertListEqual(lowerCamelCase_  ,	lowerCamelCase_)



     def 	_lowerCamelCase    (	self							:  Dict):


         '''simple docstring'''

         __a		 =       self._large_tokenizer
         # <mask_1> masks whole sentence while <mask_2> masks single word
         __a		 =       '''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
         __a		 =       [2, 413, 615, 114, 3, 1_971, 113, 1_679, 10_710, 107, 1]
         __a		 =       tokenizer([raw_input_str]  ,	return_tensors=lowerCamelCase_).input_ids[0]
         self.assertListEqual(lowerCamelCase_  ,	lowerCamelCase_)



     def 	_lowerCamelCase    (	self							:  List[str]):


         '''simple docstring'''

         __a		 =       self._large_tokenizer
         # The tracebacks for the following asserts are **better** without messages or self.assertEqual
         assert tokenizer.vocab_size == 96_103
         assert tokenizer.pad_token_id == 0
         assert tokenizer.eos_token_id == 1
         assert tokenizer.offset == 103
         assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
         assert tokenizer.unk_token == "<unk>"
         assert tokenizer.model_max_length == 1_024
         __a		 =       '''To ensure a smooth flow of bank resolutions.'''
         __a		 =       [413, 615, 114, 2_291, 1_971, 113, 1_679, 10_710, 107, 1]
         __a		 =       tokenizer([raw_input_str]  ,	return_tensors=lowerCamelCase_).input_ids[0]
         self.assertListEqual(lowerCamelCase_  ,	lowerCamelCase_)
         assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]



     @require_torch
     def 	_lowerCamelCase    (	self							:  Union[str, Any]):


         '''simple docstring'''

         __a		 =       ['''This is going to be way too long.''' * 150, '''short example''']
         __a		 =       ['''not super long but more than 5 tokens''', '''tiny''']
         __a		 =       self._large_tokenizer(lowerCamelCase_  ,	padding=lowerCamelCase_  ,	truncation=lowerCamelCase_  ,	return_tensors='''pt''')
         __a		 =       self._large_tokenizer(
             text_target=lowerCamelCase_  ,	max_length=5  ,	padding=lowerCamelCase_  ,	truncation=lowerCamelCase_  ,	return_tensors='''pt''')

         assert batch.input_ids.shape == (2, 1_024)
         assert batch.attention_mask.shape == (2, 1_024)
         assert targets["input_ids"].shape == (2, 5)
         assert len(lowerCamelCase_) == 2  # input_ids, attention_mask.





     @slow
     def 	_lowerCamelCase    (	self							:  Optional[Any]):


         '''simple docstring'''

         __a		 =       {'''input_ids''': [[38_979, 143, 18_485, 606, 130, 26_669, 87_686, 121, 54_189, 1_129, 111, 26_669, 87_686, 121, 9_114, 14_787, 121, 13_249, 158, 592, 956, 121, 14_621, 31_576, 143, 62_613, 108, 9_688, 930, 43_430, 11_562, 62_613, 304, 108, 11_443, 897, 108, 9_314, 17_415, 63_399, 108, 11_443, 7_614, 18_316, 118, 4_284, 7_148, 12_430, 143, 1_400, 25_703, 158, 111, 4_284, 7_148, 11_772, 143, 21_297, 1_064, 158, 122, 204, 3_506, 1_754, 1_133, 14_787, 1_581, 115, 33_224, 4_482, 111, 1_355, 110, 29_173, 317, 50_833, 108, 20_147, 94_665, 111, 77_198, 107, 1], [110, 62_613, 117, 638, 112, 1_133, 121, 20_098, 1_355, 79_050, 13_872, 135, 1_596, 53_541, 1_352, 141, 13_039, 5_542, 124, 302, 518, 111, 268, 2_956, 115, 149, 4_427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1_235, 2_799, 18_289, 17_780, 204, 109, 9_474, 1_296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=lowerCamelCase_  ,	model_name='''google/bigbird-pegasus-large-arxiv'''  ,	revision='''ba85d0851d708441f91440d509690f1ab6353415'''  ,	)


@require_sentencepiece
@require_tokenizers
class 					_A (			_UpperCAmelCase   ,unittest.TestCase					):
     UpperCamelCase__						:					List[Any]		      =		PegasusTokenizer
     UpperCamelCase__						:					List[str]		      =		PegasusTokenizerFast
     UpperCamelCase__						:					Any		      =		True
     UpperCamelCase__						:					Optional[Any]		      =		True



     def 	_lowerCamelCase    (	self							:  List[Any]):


         '''simple docstring'''

         super().setUp()

         # We have a SentencePiece fixture for testing
         __a		 =       PegasusTokenizer(lowerCamelCase_  ,	offset=0  ,	mask_token_sent=lowerCamelCase_  ,	mask_token='''[MASK]''')
         tokenizer.save_pretrained(self.tmpdirname)



     @cached_property
     def 	_lowerCamelCase    (	self							:  Dict):


         '''simple docstring'''

         return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv''')



     def 	_lowerCamelCase    (	self							:  Any  ,	**__SCREAMING_SNAKE_CASE							:  int):


         '''simple docstring'''

         return PegasusTokenizer.from_pretrained(self.tmpdirname  ,	**lowerCamelCase_)



     def 	_lowerCamelCase    (	self							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  str):


         '''simple docstring'''

         return ("This is a test", "This is a test")



     def 	_lowerCamelCase    (	self							:  Any):


         '''simple docstring'''

         __a		 =       self.rust_tokenizer_class.from_pretrained(self.tmpdirname)
         __a		 =       self.tokenizer_class.from_pretrained(self.tmpdirname)
         __a		 =       (
             '''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
             ''' <pad> <pad> <pad>'''
         )
         __a		 =       rust_tokenizer([raw_input_str]  ,	return_tensors=lowerCamelCase_  ,	add_special_tokens=lowerCamelCase_).input_ids[0]
         __a		 =       py_tokenizer([raw_input_str]  ,	return_tensors=lowerCamelCase_  ,	add_special_tokens=lowerCamelCase_).input_ids[0]
         self.assertListEqual(lowerCamelCase_  ,	lowerCamelCase_)



     @require_torch
     def 	_lowerCamelCase    (	self							:  List[str]):


         '''simple docstring'''

         __a		 =       ['''This is going to be way too long.''' * 1_000, '''short example''']
         __a		 =       ['''not super long but more than 5 tokens''', '''tiny''']
         __a		 =       self._large_tokenizer(lowerCamelCase_  ,	padding=lowerCamelCase_  ,	truncation=lowerCamelCase_  ,	return_tensors='''pt''')
         __a		 =       self._large_tokenizer(
             text_target=lowerCamelCase_  ,	max_length=5  ,	padding=lowerCamelCase_  ,	truncation=lowerCamelCase_  ,	return_tensors='''pt''')

         assert batch.input_ids.shape == (2, 4_096)
         assert batch.attention_mask.shape == (2, 4_096)
         assert targets["input_ids"].shape == (2, 5)
         assert len(lowerCamelCase_) == 2  # input_ids, attention_mask.





     def 	_lowerCamelCase    (	self							:  Optional[int]):


         '''simple docstring'''

         __a		 =       (
             '''This is an example string that is used to test the original TF implementation against the HF'''
             ''' implementation'''
         )

         __a		 =       self._large_tokenizer(lowerCamelCase_).input_ids

         self.assertListEqual(
             lowerCamelCase_  ,	[182, 117, 142, 587, 4_211, 120, 117, 263, 112, 804, 109, 856, 25_016, 3_137, 464, 109, 26_955, 3_137, 1]  ,	)










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


__snake_case		:int   	=  ''''''


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

 class 					_A (			tr.AbstractTransform					):



        def __init__(	self							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  str = " "):


            '''simple docstring'''

            __a		 =       sentence_delimiter



        def 	_lowerCamelCase    (	self							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  str):


            '''simple docstring'''

            return list(__SCREAMING_SNAKE_CASE)





        def 	_lowerCamelCase    (	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  List[str]):


            '''simple docstring'''

            __a		 =       []
            for sent_idx, sentence in enumerate(__SCREAMING_SNAKE_CASE):
                chars.extend(self.process_string(__SCREAMING_SNAKE_CASE))
                if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(__SCREAMING_SNAKE_CASE) - 1:
                    chars.append(self.sentence_delimiter)
            return chars


 __snake_case		:Any   	=  tr.Compose(
     [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
 )
else:
 __snake_case		:Optional[int]   	=  tr.Compose(
     [
         tr.RemoveMultipleSpaces(),
         tr.Strip(),
         tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
         tr.ReduceToListOfListOfChars(),
     ]
 )


__snake_case		:Optional[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.}
}
'''

__snake_case		:Tuple   	=  '''\
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.
'''

__snake_case		:Tuple   	=  '''
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 					_A (			datasets.Metric					):



       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''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''',
                   '''https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates''',
               ]  ,	)





       def 	_lowerCamelCase    (	self							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  Dict=False):


           '''simple docstring'''

           if concatenate_texts:
               return jiwer.compute_measures(
                   __SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	truth_transform=__SCREAMING_SNAKE_CASE  ,	hypothesis_transform=__SCREAMING_SNAKE_CASE  ,	)["wer"]

           __a		 =       0
           __a		 =       0
           for prediction, reference in zip(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE):
               __a		 =       jiwer.compute_measures(
                   __SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	truth_transform=__SCREAMING_SNAKE_CASE  ,	hypothesis_transform=__SCREAMING_SNAKE_CASE  ,	)
               incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
               total += measures["substitutions"] + measures["deletions"] + measures["hits"]

           return incorrect / total





