





'''simple docstring'''






# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

A__				:   Any      ='''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
A__				:   Optional[Any]      =[
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
A__				:   Dict      =dict(zip(vocab, range(len(vocab))))
A__				:   Tuple      =['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
      A__				:   str      =Path(tmpdirname)
      A__				:   Union[str, Any]      =build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
      A__				:   Union[str, Any]      =build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
      A__				:   str      =build_dir / VOCAB_FILES_NAMES['''merges_file''']
      with open(src_vocab_file, '''w''') as fp:
            fp.write(json.dumps(vocab_tokens))
      with open(tgt_vocab_file, '''w''') as fp:
            fp.write(json.dumps(vocab_tokens))
      with open(merges_file, '''w''') as fp:
            fp.write('''\n'''.join(merges))

      A__				:   Any      =FSMTTokenizer(
          langs=['''en''', '''ru'''],
          src_vocab_size=len(vocab),
          tgt_vocab_size=len(vocab),
          src_vocab_file=src_vocab_file,
          tgt_vocab_file=tgt_vocab_file,
          merges_file=merges_file,
      )

A__				:   Optional[Any]      =FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=10_00,
    tgt_vocab_size=10_00,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

A__				:   List[Any]      =FSMTForConditionalGeneration(config)
print(F"""num of params {tiny_model.num_parameters()}""")

# Test
A__				:   Union[str, Any]      =tokenizer(['''Making tiny model'''], return_tensors='''pt''')
A__				:   List[str]      =tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F"""Generated {mname_tiny}""")

# Upload
# transformers-cli upload tiny-wmt19-en-ru










"""simple docstring"""







from jiwer import compute_measures

import datasets


a__				:							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.}
}
'''

a__				:							List[str]						=     '''\
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__				:							Dict						=     '''
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  UpperCamelCase_	( datasets.Metric):

	"""simple docstring"""





	def   UpperCAmelCase_      ( self			:				List[Any]   )       ->	str:
						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			:				Tuple			,			UpperCAmelCase__			:				Tuple=None			,			UpperCAmelCase__			:				List[str]=None			,			UpperCAmelCase__			:				Any=False   )       ->	Optional[int]:
						if concatenate_texts:
											return compute_measures(UpperCAmelCase__			,			UpperCAmelCase__   )["wer"]
						else:
											__SCREAMING_SNAKE_CASE						   =				0
											__SCREAMING_SNAKE_CASE						   =				0
											for prediction, reference in zip(UpperCAmelCase__			,			UpperCAmelCase__   ):
																__SCREAMING_SNAKE_CASE						   =				compute_measures(UpperCAmelCase__			,			UpperCAmelCase__   )
																incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																total += measures["substitutions"] + measures["deletions"] + measures["hits"]
											return incorrect / total
