'''simple docstring'''




from jiwer import compute_measures

import datasets


__lowercase          =  '''\
@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.}
}
'''

__lowercase          =  '''\
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.
'''

__lowercase          =  '''
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 				a__(					datasets.Metric	):


			'''simple docstring'''






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




					"""simple docstring"""


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





'''simple docstring'''




import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors






@require_tokenizers
class 				a__(					lowerCAmelCase__ ,						unittest.TestCase	):


			'''simple docstring'''






			UpperCAmelCase_			:     Dict      =       MvpTokenizer
			UpperCAmelCase_			:     Optional[Any]      =       MvpTokenizerFast
			UpperCAmelCase_			:     str      =       True
			UpperCAmelCase_			:     List[Any]      =       filter_roberta_detectors



			def 							a_       (   self):




					"""simple docstring"""


					super().setUp()
					lowerCAmelCase   					=  [
					    """l""",
					    """o""",
					    """w""",
					    """e""",
					    """r""",
					    """s""",
					    """t""",
					    """i""",
					    """d""",
					    """n""",
					    """\u0120""",
					    """\u0120l""",
					    """\u0120n""",
					    """\u0120lo""",
					    """\u0120low""",
					    """er""",
					    """\u0120lowest""",
					    """\u0120newer""",
					    """\u0120wider""",
					    """<unk>""",
					]
					lowerCAmelCase   					=  dict(zip(__lowerCAmelCase       ,    range(len(__lowerCAmelCase))))
					lowerCAmelCase   					=  ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
					lowerCAmelCase   					=  {"""unk_token""": """<unk>"""}

					lowerCAmelCase   					=  os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES["""vocab_file"""])
					lowerCAmelCase   					=  os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES["""merges_file"""])
					with open(self.vocab_file       ,    """w"""       ,    encoding="""utf-8""") as fp:
							fp.write(json.dumps(__lowerCAmelCase) + """\n""")
					with open(self.merges_file       ,    """w"""       ,    encoding="""utf-8""") as fp:
							fp.write("""\n""".join(__lowerCAmelCase))



			def 							a_       (   self       ,    **__lowerCAmelCase):




					"""simple docstring"""


					kwargs.update(self.special_tokens_map)
					return self.tokenizer_class.from_pretrained(self.tmpdirname       ,    **__lowerCAmelCase)



			def 							a_       (   self       ,    **__lowerCAmelCase):




					"""simple docstring"""


					kwargs.update(self.special_tokens_map)
					return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,    **__lowerCAmelCase)



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					return "lower newer", "lower newer"



			@cached_property
			def 							a_       (   self):




					"""simple docstring"""


					return MvpTokenizer.from_pretrained("""RUCAIBox/mvp""")



			@cached_property
			def 							a_       (   self):




					"""simple docstring"""


					return MvpTokenizerFast.from_pretrained("""RUCAIBox/mvp""")



			@require_torch
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
					lowerCAmelCase   					=  [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							lowerCAmelCase   					=  tokenizer(__lowerCAmelCase       ,    max_length=len(__lowerCAmelCase)       ,    padding=__lowerCAmelCase       ,    return_tensors="""pt""")
							self.assertIsInstance(__lowerCAmelCase       ,    __lowerCAmelCase)

							self.assertEqual((2, 9)       ,    batch.input_ids.shape)
							self.assertEqual((2, 9)       ,    batch.attention_mask.shape)
							lowerCAmelCase   					=  batch.input_ids.tolist()[0]
							self.assertListEqual(__lowerCAmelCase       ,    __lowerCAmelCase)
							# Test that special tokens are reset



			@require_torch
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							lowerCAmelCase   					=  tokenizer(__lowerCAmelCase       ,    padding=__lowerCAmelCase       ,    return_tensors="""pt""")
							# check if input_ids are returned and no labels
							self.assertIn("""input_ids"""       ,    __lowerCAmelCase)
							self.assertIn("""attention_mask"""       ,    __lowerCAmelCase)
							self.assertNotIn("""labels"""       ,    __lowerCAmelCase)
							self.assertNotIn("""decoder_attention_mask"""       ,    __lowerCAmelCase)



			@require_torch
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  [
					    """Summary of the text.""",
					    """Another summary.""",
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							lowerCAmelCase   					=  tokenizer(text_target=__lowerCAmelCase       ,    max_length=32       ,    padding="""max_length"""       ,    return_tensors="""pt""")
							self.assertEqual(32       ,    targets["""input_ids"""].shape[1])



			@require_torch
			def 							a_       (   self):




					"""simple docstring"""


					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							lowerCAmelCase   					=  tokenizer(
							    ["""I am a small frog""" * 1024, """I am a small frog"""]       ,    padding=__lowerCAmelCase       ,    truncation=__lowerCAmelCase       ,    return_tensors="""pt""")
							self.assertIsInstance(__lowerCAmelCase       ,    __lowerCAmelCase)
							self.assertEqual(batch.input_ids.shape       ,    (2, 1024))



			@require_torch
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  ["""A long paragraph for summarization."""]
					lowerCAmelCase   					=  [
					    """Summary of the text.""",
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							lowerCAmelCase   					=  tokenizer(__lowerCAmelCase       ,    text_target=__lowerCAmelCase       ,    return_tensors="""pt""")
							lowerCAmelCase   					=  inputs["""input_ids"""]
							lowerCAmelCase   					=  inputs["""labels"""]
							self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
							self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
							self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
							self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())



			def 							a_       (   self):




					"""simple docstring"""


					pass







			def 							a_       (   self):




					"""simple docstring"""


					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
									lowerCAmelCase   					=  self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase       ,    **__lowerCAmelCase)
									lowerCAmelCase   					=  self.tokenizer_class.from_pretrained(__lowerCAmelCase       ,    **__lowerCAmelCase)
									lowerCAmelCase   					=  """A, <mask> AllenNLP sentence."""
									lowerCAmelCase   					=  tokenizer_r.encode_plus(__lowerCAmelCase       ,    add_special_tokens=__lowerCAmelCase       ,    return_token_type_ids=__lowerCAmelCase)
									lowerCAmelCase   					=  tokenizer_p.encode_plus(__lowerCAmelCase       ,    add_special_tokens=__lowerCAmelCase       ,    return_token_type_ids=__lowerCAmelCase)

									# token_type_ids should put 0 everywhere
									self.assertEqual(sum(tokens_r["""token_type_ids"""])       ,    sum(tokens_p["""token_type_ids"""]))

									# attention_mask should put 1 everywhere, so sum over length should be 1
									self.assertEqual(
									    sum(tokens_r["""attention_mask"""]) / len(tokens_r["""attention_mask"""])       ,    sum(tokens_p["""attention_mask"""]) / len(tokens_p["""attention_mask"""])       ,    )

									lowerCAmelCase   					=  tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""])
									lowerCAmelCase   					=  tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""])

									# Rust correctly handles the space before the mask while python doesnt
									self.assertSequenceEqual(tokens_p["""input_ids"""]       ,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])
									self.assertSequenceEqual(tokens_r["""input_ids"""]       ,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])

									self.assertSequenceEqual(
									    __lowerCAmelCase       ,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""])
									self.assertSequenceEqual(
									    __lowerCAmelCase       ,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""])




