
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


lowercase_ 				=			get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")





@require_sentencepiece
@require_tokenizers
class     __UpperCamelCase (							lowerCAmelCase__ ,						unittest.TestCase       ):






     """simple docstring"""







     lowerCAmelCase_							   =			PegasusTokenizer
     lowerCAmelCase_							   =			PegasusTokenizerFast
     lowerCAmelCase_							   =			True
     lowerCAmelCase_							   =			True



     def  UpperCAmelCase__							(				self							:							Any ):







       """simple docstring"""




       super().setUp()

       # We have a SentencePiece fixture for testing
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		PegasusTokenizer(_A )
       tokenizer.save_pretrained(self.tmpdirname )



     @cached_property
     def  UpperCAmelCase__							(				self							:							Optional[Any] ):







       """simple docstring"""




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



     def  UpperCAmelCase__							(				self							:							Tuple     ,						**_A							:							Any ):







       """simple docstring"""




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



     def  UpperCAmelCase__							(				self							:							Optional[Any]     ,						_A							:							Tuple ):







       """simple docstring"""




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



     def  UpperCAmelCase__							(				self							:							str ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Dict					=		'''</s>'''
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		1

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



     def  UpperCAmelCase__							(				self							:							List[Any] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		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(_A )     ,						1103 )



     def  UpperCAmelCase__							(				self							:							Any ):







       """simple docstring"""




       self.assertEqual(self.get_tokenizer().vocab_size     ,						1103 )



     def  UpperCAmelCase__							(				self							:							Any ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Tuple					=		self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		self.tokenizer_class.from_pretrained(self.tmpdirname )
       __SCREAMING_SNAKE_CASE      :	Tuple					=		(
           '''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>'''
       )
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		rust_tokenizer([raw_input_str]     ,						return_tensors=_A     ,						add_special_tokens=_A ).input_ids[0]
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		py_tokenizer([raw_input_str]     ,						return_tensors=_A     ,						add_special_tokens=_A ).input_ids[0]
       self.assertListEqual(_A     ,						_A )



     def  UpperCAmelCase__							(				self							:							int ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	str					=		self._large_tokenizer
       # <mask_1> masks whole sentence while <mask_2> masks single word
       __SCREAMING_SNAKE_CASE      :	Dict					=		'''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		[2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
       __SCREAMING_SNAKE_CASE      :	Any					=		tokenizer([raw_input_str]     ,						return_tensors=_A ).input_ids[0]
       self.assertListEqual(_A     ,						_A )



     def  UpperCAmelCase__							(				self							:							Optional[Any] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	List[str]					=		self._large_tokenizer
       # The tracebacks for the following asserts are **better** without messages or self.assertEqual
       assert tokenizer.vocab_size == 9_6103
       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 == 1024
       __SCREAMING_SNAKE_CASE      :	List[str]					=		'''To ensure a smooth flow of bank resolutions.'''
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		[413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		tokenizer([raw_input_str]     ,						return_tensors=_A ).input_ids[0]
       self.assertListEqual(_A     ,						_A )
       assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]



     @require_torch
     def  UpperCAmelCase__							(				self							:							Any ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		['''This is going to be way too long.''' * 150, '''short example''']
       __SCREAMING_SNAKE_CASE      :	Dict					=		['''not super long but more than 5 tokens''', '''tiny''']
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		self._large_tokenizer(_A     ,						padding=_A     ,						truncation=_A     ,						return_tensors='''pt''' )
       __SCREAMING_SNAKE_CASE      :	Dict					=		self._large_tokenizer(
           text_target=_A     ,						max_length=5     ,						padding=_A     ,						truncation=_A     ,						return_tensors='''pt''' )

       assert batch.input_ids.shape == (2, 1024)
       assert batch.attention_mask.shape == (2, 1024)
       assert targets["input_ids"].shape == (2, 5)
       assert len(_A ) == 2  # input_ids, attention_mask.




     @slow
     def  UpperCAmelCase__							(				self							:							List[str] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	List[str]					=		{'''input_ids''': [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 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, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 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=_A     ,						model_name='''google/bigbird-pegasus-large-arxiv'''     ,						revision='''ba85d0851d708441f91440d509690f1ab6353415'''     ,						)






@require_sentencepiece
@require_tokenizers
class     __UpperCamelCase (							lowerCAmelCase__ ,						unittest.TestCase       ):






     """simple docstring"""







     lowerCAmelCase_							   =			PegasusTokenizer
     lowerCAmelCase_							   =			PegasusTokenizerFast
     lowerCAmelCase_							   =			True
     lowerCAmelCase_							   =			True



     def  UpperCAmelCase__							(				self							:							Any ):







       """simple docstring"""




       super().setUp()

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



     @cached_property
     def  UpperCAmelCase__							(				self							:							Optional[Any] ):







       """simple docstring"""




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



     def  UpperCAmelCase__							(				self							:							Dict     ,						**_A							:							Tuple ):







       """simple docstring"""




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



     def  UpperCAmelCase__							(				self							:							Optional[Any]     ,						_A							:							Union[str, Any] ):







       """simple docstring"""




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



     def  UpperCAmelCase__							(				self							:							Dict ):







       """simple docstring"""




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



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







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	int					=		['''This is going to be way too long.''' * 1000, '''short example''']
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		['''not super long but more than 5 tokens''', '''tiny''']
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		self._large_tokenizer(_A     ,						padding=_A     ,						truncation=_A     ,						return_tensors='''pt''' )
       __SCREAMING_SNAKE_CASE      :	Dict					=		self._large_tokenizer(
           text_target=_A     ,						max_length=5     ,						padding=_A     ,						truncation=_A     ,						return_tensors='''pt''' )

       assert batch.input_ids.shape == (2, 4096)
       assert batch.attention_mask.shape == (2, 4096)
       assert targets["input_ids"].shape == (2, 5)
       assert len(_A ) == 2  # input_ids, attention_mask.




     def  UpperCAmelCase__							(				self							:							Any ):







       """simple docstring"""




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

       __SCREAMING_SNAKE_CASE      :	Dict					=		self._large_tokenizer(_A ).input_ids

       self.assertListEqual(
           _A     ,						[182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1]     ,						)



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
