


'''simple docstring'''

import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets

@contextmanager
def _SCREAMING_SNAKE_CASE   () -> str:






       """simple docstring"""







       def filter_log(A ):
              return False if "This IS expected if you are initializing" in record.msg else True

       lowercase__       =				datasets.utils.logging.get_logger('''transformers.modeling_utils''' )
       logger.addFilter(_A )
       try:
              yield
       finally:
              logger.removeFilter(_A )


lowerCamelCase							:     List[str]								=			'\\n@inproceedings{bert-score,\n  title={BERTScore: Evaluating Text Generation with BERT},\n  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},\n  booktitle={International Conference on Learning Representations},\n  year={2020},\n  url={https://openreview.net/forum?id=SkeHuCVFDr}\n}\n'

lowerCamelCase							:     List[str]								=			'\\nBERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference\nsentences by cosine similarity.\nIt has been shown to correlate with human judgment on sentence-level and system-level evaluation.\nMoreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language\ngeneration tasks.\n\nSee the project\'s README at https://github.com/Tiiiger/bert_score#readme for more information.\n'

lowerCamelCase							:     int								=			'\nBERTScore Metrics with the hashcode from a source against one or more references.\n\nArgs:\n    predictions (list of str): Prediction/candidate sentences.\n    references (list of str or list of list of str): Reference sentences.\n    lang (str): Language of the sentences; required (e.g. \'en\').\n    model_type (str): Bert specification, default using the suggested\n        model for the target language; has to specify at least one of\n        `model_type` or `lang`.\n    num_layers (int): The layer of representation to use,\n        default using the number of layers tuned on WMT16 correlation data.\n    verbose (bool): Turn on intermediate status update.\n    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.\n    device (str): On which the contextual embedding model will be allocated on.\n        If this argument is None, the model lives on cuda:0 if cuda is available.\n    nthreads (int): Number of threads.\n    batch_size (int): Bert score processing batch size,\n        at least one of `model_type` or `lang`. `lang` needs to be\n        specified when `rescale_with_baseline` is True.\n    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.\n    baseline_path (str): Customized baseline file.\n    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.\n\nReturns:\n    precision: Precision.\n    recall: Recall.\n    f1: F1 score.\n    hashcode: Hashcode of the library.\n\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bertscore = datasets.load_metric("bertscore")\n    >>> results = bertscore.compute(predictions=predictions, references=references, lang="en")\n    >>> print([round(v, 2) for v in results["f1"]])\n    [1.0, 1.0]\n'

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,  _KWARGS_DESCRIPTION  )
class 						__lowerCAmelCase		(datasets.Metric  ):





      '''simple docstring'''






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

             '''simple docstring'''







             return datasets.MetricInfo(
                 description=_DESCRIPTION     ,		citation=_CITATION     ,		homepage='''https://github.com/Tiiiger/bert_score'''     ,		inputs_description=_KWARGS_DESCRIPTION     ,		features=datasets.Features(
                     {
                         '''predictions''': datasets.Value('''string'''     ,		id='''sequence'''				),
                         '''references''': datasets.Sequence(datasets.Value('''string'''     ,		id='''sequence'''				)     ,		id='''references'''				),
                     }				)     ,		codebase_urls=['''https://github.com/Tiiiger/bert_score''']     ,		reference_urls=[
                     '''https://github.com/Tiiiger/bert_score''',
                     '''https://arxiv.org/abs/1904.09675''',
                 ]     ,		)



      def    UpperCamelCase__	(self					:					Optional[int]     ,		UpperCamelCase					:					Optional[int]     ,		UpperCamelCase					:					Tuple     ,		UpperCamelCase					:					str=None     ,		UpperCamelCase					:					Optional[Any]=None     ,		UpperCamelCase					:					Tuple=None     ,		UpperCamelCase					:					List[str]=False     ,		UpperCamelCase					:					List[str]=False     ,		UpperCamelCase					:					Optional[Any]=None     ,		UpperCamelCase					:					List[str]=64     ,		UpperCamelCase					:					Optional[int]=4     ,		UpperCamelCase					:					List[str]=False     ,		UpperCamelCase					:					Any=False     ,		UpperCamelCase					:					str=None     ,		UpperCamelCase					:					str=False     ,		):

             '''simple docstring'''







             lowercase__       =				bert_score.utils.get_hash
             lowercase__       =				bert_score.BERTScorer

             if version.parse(bert_score.__version__				) >= version.parse('''0.3.10'''				):
                    lowercase__       =				functools.partial(UpperCamelCase     ,		use_fast_tokenizer=UpperCamelCase				)
                    lowercase__       =				functools.partial(UpperCamelCase     ,		use_fast_tokenizer=UpperCamelCase				)
             elif use_fast_tokenizer:
                    raise ImportWarning(
                        '''To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn\'t match this condition.\n'''
                        '''You can install it with `pip install \"bert-score>=0.3.10\"`.'''				)

             if model_type is None:
                    assert lang is not None, "either lang or model_type should be specified"
                    lowercase__       =				bert_score.utils.langamodel[lang.lower()]

             if num_layers is None:
                    lowercase__       =				bert_score.utils.modelalayers[model_type]

             lowercase__       =				get_hash(
                 model=UpperCamelCase     ,		num_layers=UpperCamelCase     ,		idf=UpperCamelCase     ,		rescale_with_baseline=UpperCamelCase     ,		use_custom_baseline=baseline_path is not None     ,		)

             with filter_logging_context():
                    if not hasattr(self     ,		'''cached_bertscorer'''				) or self.cached_bertscorer.hash != hashcode:
                           lowercase__       =				scorer(
                               model_type=UpperCamelCase     ,		num_layers=UpperCamelCase     ,		batch_size=UpperCamelCase     ,		nthreads=UpperCamelCase     ,		all_layers=UpperCamelCase     ,		idf=UpperCamelCase     ,		device=UpperCamelCase     ,		lang=UpperCamelCase     ,		rescale_with_baseline=UpperCamelCase     ,		baseline_path=UpperCamelCase     ,		)

             ((lowercase__)    ,(lowercase__)    ,(lowercase__))       =				self.cached_bertscorer.score(
                 cands=UpperCamelCase     ,		refs=UpperCamelCase     ,		verbose=UpperCamelCase     ,		batch_size=UpperCamelCase     ,		)
             lowercase__       =				{
                 '''precision''': P.tolist(),
                 '''recall''': R.tolist(),
                 '''f1''': F.tolist(),
                 '''hashcode''': hashcode,
             }
             return output_dict



      def    UpperCamelCase__	(self					:					List[str]     ,		UpperCamelCase					:					Any=None     ,		UpperCamelCase					:					int=None     ,		**UpperCamelCase					:					Tuple				):

             '''simple docstring'''







             if references is not None:
                    lowercase__       =				[[ref] if isinstance(UpperCamelCase     ,		UpperCamelCase				) else ref for ref in references]
             super().add_batch(predictions=UpperCamelCase     ,		references=UpperCamelCase     ,		**UpperCamelCase				)






      def    UpperCamelCase__	(self					:					Union[str, Any]     ,		UpperCamelCase					:					Any=None     ,		UpperCamelCase					:					int=None     ,		**UpperCamelCase					:					Any				):

             '''simple docstring'''







             if isinstance(UpperCamelCase     ,		UpperCamelCase				):
                    lowercase__       =				[reference]
             super().add(prediction=UpperCamelCase     ,		reference=UpperCamelCase     ,		**UpperCamelCase				)








'''simple docstring'''

import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np
import regex as re

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
 if is_torch_available():
  import torch
 if is_tf_available():
  import tensorflow as tf

from ...tokenization_utils import AddedToken, PreTrainedTokenizer


lowerCamelCase							:     Union[str, Any]								=			logging.get_logger(__name__)

lowerCamelCase							:     Tuple								=			{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

lowerCamelCase							:     Tuple								=			{
    'vocab_file': {
        'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json',
    },
    'merges_file': {
        'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt',
    },
}

lowerCamelCase							:     Optional[int]								=			{
    'Salesforce/codegen-350M-mono': 2_048,
}

@lru_cache()
def _SCREAMING_SNAKE_CASE   () -> Optional[int]:






       """simple docstring"""







       lowercase__       =				(
           list(range(ord('''!''' )						,				ord('''~''' ) + 1 ) ) + list(range(ord('''¡''' )						,				ord('''¬''' ) + 1 ) ) + list(range(ord('''®''' )						,				ord('''ÿ''' ) + 1 ) )
       )
       lowercase__       =				bs[:]
       lowercase__       =				0
       for b in range(2**8 ):
              if b not in bs:
                     bs.append(A )
                     cs.append(2**8 + n )
                     n += 1
       lowercase__       =				[chr(A ) for n in cs]
       return dict(zip(A						,				A ) )

def _SCREAMING_SNAKE_CASE   (A ) -> Tuple:






       """simple docstring"""







       lowercase__       =				set()
       lowercase__       =				word[0]
       for char in word[1:]:
              pairs.add((prev_char, char) )
              lowercase__       =				char
       return pairs

class 						__lowerCAmelCase		(lowercase_  ):





      '''simple docstring'''






      lowerCAmelCase__      :  int						      =							VOCAB_FILES_NAMES
      lowerCAmelCase__      :  Union[str, Any]						      =							PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__      :  Optional[int]						      =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__      :  Tuple						      =							["""input_ids""", """attention_mask"""]



      def __init__(self					:					List[Any]     ,		UpperCamelCase					:					List[str]     ,		UpperCamelCase					:					List[Any]     ,		UpperCamelCase					:					Optional[int]="replace"     ,		UpperCamelCase					:					Optional[Any]="<|endoftext|>"     ,		UpperCamelCase					:					str="<|endoftext|>"     ,		UpperCamelCase					:					Optional[int]="<|endoftext|>"     ,		UpperCamelCase					:					Optional[int]=None     ,		UpperCamelCase					:					str=False     ,		UpperCamelCase					:					Any=False     ,		**UpperCamelCase					:					Dict     ,		):

             '''simple docstring'''







             lowercase__       =				AddedToken(UpperCamelCase     ,		lstrip=UpperCamelCase     ,		rstrip=UpperCamelCase				) if isinstance(UpperCamelCase     ,		UpperCamelCase				) else bos_token
             lowercase__       =				AddedToken(UpperCamelCase     ,		lstrip=UpperCamelCase     ,		rstrip=UpperCamelCase				) if isinstance(UpperCamelCase     ,		UpperCamelCase				) else eos_token
             lowercase__       =				AddedToken(UpperCamelCase     ,		lstrip=UpperCamelCase     ,		rstrip=UpperCamelCase				) if isinstance(UpperCamelCase     ,		UpperCamelCase				) else unk_token
             lowercase__       =				AddedToken(UpperCamelCase     ,		lstrip=UpperCamelCase     ,		rstrip=UpperCamelCase				) if isinstance(UpperCamelCase     ,		UpperCamelCase				) else pad_token
             super().__init__(
                 errors=UpperCamelCase     ,		unk_token=UpperCamelCase     ,		bos_token=UpperCamelCase     ,		eos_token=UpperCamelCase     ,		pad_token=UpperCamelCase     ,		add_prefix_space=UpperCamelCase     ,		add_bos_token=UpperCamelCase     ,		**UpperCamelCase     ,		)
             lowercase__       =				add_bos_token

             with open(UpperCamelCase     ,		encoding='''utf-8'''				) as vocab_handle:
                    lowercase__       =				json.load(UpperCamelCase				)
             lowercase__       =				{v: k for k, v in self.encoder.items()}
             lowercase__       =				errors  # how to handle errors in decoding
             lowercase__       =				bytes_to_unicode()
             lowercase__       =				{v: k for k, v in self.byte_encoder.items()}
             with open(UpperCamelCase     ,		encoding='''utf-8'''				) as merges_handle:
                    lowercase__       =				merges_handle.read().split('''\n'''				)[1:-1]
             lowercase__       =				[tuple(merge.split()				) for merge in bpe_merges]
             lowercase__       =				dict(zip(UpperCamelCase     ,		range(len(UpperCamelCase				)				)				)				)
             lowercase__       =				{}
             lowercase__       =				add_prefix_space

             # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
             lowercase__       =				re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''				)



      @property
      def    UpperCamelCase__	(self					:					Any				):

             '''simple docstring'''







             return len(self.encoder				)



      def    UpperCamelCase__	(self					:					Optional[Any]				):

             '''simple docstring'''







             return dict(self.encoder     ,		**self.added_tokens_encoder				)



      def    UpperCamelCase__	(self					:					Union[str, Any]     ,		UpperCamelCase					:					Optional[int]				):

             '''simple docstring'''







             if token in self.cache:
                    return self.cache[token]
             lowercase__       =				tuple(UpperCamelCase				)
             lowercase__       =				get_pairs(UpperCamelCase				)

             if not pairs:
                    return token

             while True:
                    lowercase__       =				min(UpperCamelCase     ,		key=lambda UpperCamelCase				: self.bpe_ranks.get(UpperCamelCase     ,		float('''inf'''				)				)				)
                    if bigram not in self.bpe_ranks:
                           break
                    lowercase__    ,lowercase__       =				bigram
                    lowercase__       =				[]
                    lowercase__       =				0
                    while i < len(UpperCamelCase				):
                           try:
                                  lowercase__       =				word.index(UpperCamelCase     ,		UpperCamelCase				)
                           except ValueError:
                                  new_word.extend(word[i:]				)
                                  break
                           else:
                                  new_word.extend(word[i:j]				)
                                  lowercase__       =				j

                           if word[i] == first and i < len(UpperCamelCase				) - 1 and word[i + 1] == second:
                                  new_word.append(first + second				)
                                  i += 2
                           else:
                                  new_word.append(word[i]				)
                                  i += 1
                    lowercase__       =				tuple(UpperCamelCase				)
                    lowercase__       =				new_word
                    if len(UpperCamelCase				) == 1:
                           break
                    else:
                           lowercase__       =				get_pairs(UpperCamelCase				)
             lowercase__       =				''' '''.join(UpperCamelCase				)
             lowercase__       =				word
             return word



      def    UpperCamelCase__	(self					:					Any     ,		UpperCamelCase					:					str     ,		UpperCamelCase					:					str=None				):

             '''simple docstring'''







             if self.add_bos_token:
                    lowercase__       =				[self.bos_token_id]
             else:
                    lowercase__       =				[]

             lowercase__       =				bos_token_ids + token_ids_a

             if token_ids_a is None:
                    return output

             return output + bos_token_ids + token_ids_a



      def    UpperCamelCase__	(self					:					Optional[Any]     ,		UpperCamelCase					:					str				):

             '''simple docstring'''







             lowercase__       =				[]
             for token in re.findall(self.pat     ,		UpperCamelCase				):
                    lowercase__       =				''''''.join(
                        self.byte_encoder[b] for b in token.encode('''utf-8'''				)				)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
                    bpe_tokens.extend(bpe_token for bpe_token in self.bpe(UpperCamelCase				).split(''' '''				)				)
             return bpe_tokens



      def    UpperCamelCase__	(self					:					str     ,		UpperCamelCase					:					str				):

             '''simple docstring'''







             return self.encoder.get(UpperCamelCase     ,		self.encoder.get(self.unk_token				)				)



      def    UpperCamelCase__	(self					:					Any     ,		UpperCamelCase					:					Optional[int]				):

             '''simple docstring'''







             return self.decoder.get(UpperCamelCase				)



      def    UpperCamelCase__	(self					:					List[Any]     ,		UpperCamelCase					:					Optional[int]				):

             '''simple docstring'''







             lowercase__       =				''''''.join(UpperCamelCase				)
             lowercase__       =				bytearray([self.byte_decoder[c] for c in text]				).decode('''utf-8'''     ,		errors=self.errors				)
             return text



      def    UpperCamelCase__	(self					:					int     ,		UpperCamelCase					:					str     ,		UpperCamelCase					:					Optional[str] = None				):

             '''simple docstring'''







             if not os.path.isdir(UpperCamelCase				):
                    logger.error(f"Vocabulary path ({save_directory}) should be a directory"				)
                    return
             lowercase__       =				os.path.join(
                 UpperCamelCase     ,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)
             lowercase__       =				os.path.join(
                 UpperCamelCase     ,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']				)

             with open(UpperCamelCase     ,		'''w'''     ,		encoding='''utf-8'''				) as f:
                    f.write(json.dumps(self.encoder     ,		indent=2     ,		sort_keys=UpperCamelCase     ,		ensure_ascii=UpperCamelCase				) + '''\n'''				)

             lowercase__       =				0
             with open(UpperCamelCase     ,		'''w'''     ,		encoding='''utf-8'''				) as writer:
                    writer.write('''#version: 0.2\n'''				)
                    for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,		key=lambda UpperCamelCase				: kv[1]				):
                           if index != token_index:
                                  logger.warning(
                                      f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
                                      ''' Please check that the tokenizer is not corrupted!'''				)
                                  lowercase__       =				token_index
                           writer.write(''' '''.join(UpperCamelCase				) + '''\n'''				)
                           index += 1

             return vocab_file, merge_file



      def    UpperCamelCase__	(self					:					str     ,		UpperCamelCase					:					List[Any]     ,		UpperCamelCase					:					int=False     ,		**UpperCamelCase					:					Optional[int]				):

             '''simple docstring'''







             lowercase__       =				kwargs.pop('''add_prefix_space'''     ,		self.add_prefix_space				)
             if is_split_into_words or add_prefix_space:
                    lowercase__       =				''' ''' + text
             return (text, kwargs)



      def    UpperCamelCase__	(self					:					Tuple     ,		UpperCamelCase					:					Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]     ,		UpperCamelCase					:					bool = False     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					Optional[List[str]] = None     ,		**UpperCamelCase					:					Optional[Any]     ,		):

             '''simple docstring'''







             lowercase__       =				super()._decode(
                 token_ids=UpperCamelCase     ,		skip_special_tokens=UpperCamelCase     ,		clean_up_tokenization_spaces=UpperCamelCase     ,		**UpperCamelCase     ,		)

             if truncate_before_pattern is not None and len(UpperCamelCase				) > 0:
                    lowercase__       =				self.truncate(UpperCamelCase     ,		UpperCamelCase				)

             return decoded_text






      def    UpperCamelCase__	(self					:					str     ,		UpperCamelCase					:					List[str]     ,		UpperCamelCase					:					Union[str, Any]				):

             '''simple docstring'''







             def find_re(UpperCamelCase					:					Dict     ,		UpperCamelCase					:					Dict     ,		UpperCamelCase					:					Optional[Any]				):
                    lowercase__       =				pattern.search(UpperCamelCase     ,		UpperCamelCase				)
                    return m.start() if m else -1

             lowercase__       =				[re.compile(UpperCamelCase     ,		re.MULTILINE				) for pattern in truncate_before_pattern]

             lowercase__       =				list(re.finditer('''^print'''     ,		UpperCamelCase     ,		re.MULTILINE				)				)

             if len(UpperCamelCase				) > 1:
                    lowercase__       =				completion[: prints[1].start()]

             lowercase__       =				list(re.finditer('''^def'''     ,		UpperCamelCase     ,		re.MULTILINE				)				)

             if len(UpperCamelCase				) > 1:
                    lowercase__       =				completion[: defs[1].start()]

             lowercase__       =				0

             lowercase__       =				[
                 pos for pos in [find_re(UpperCamelCase     ,		UpperCamelCase     ,		UpperCamelCase				) for terminal in terminals] if pos != -1
             ]

             if len(UpperCamelCase				) > 0:
                    return completion[: min(UpperCamelCase				)]
             else:
                    return completion




