
import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


UpperCamelCase     :    Optional[int]       =			version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
      from nltk import word_tokenize


UpperCamelCase     :    List[Any]       =			"\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n"

UpperCamelCase     :    Optional[int]       =			"\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"

UpperCamelCase     :    Tuple       =			"\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    \'meteor\': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric(\'meteor\')\n    >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n    >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results[\"meteor\"], 4))\n    0.6944\n"

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





     def 						UpperCAmelCase (     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/nltk/nltk/blob/develop/nltk/translate/meteor_score.py']     ,					reference_urls=[
              'https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score',
              'https://en.wikipedia.org/wiki/METEOR',
          ]     ,					)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




      import nltk

      nltk.download('wordnet'				)
      if NLTK_VERSION >= version.Version('3.6.5'				):
       nltk.download('punkt'				)
      if NLTK_VERSION >= version.Version('3.6.6'				):
       nltk.download('omw-1.4'				)







     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase=0.9     ,					__UpperCAmelCase=3     ,					__UpperCAmelCase=0.5				):

      '''simple docstring'''




      if NLTK_VERSION >= version.Version('3.6.5'				):
       __UpperCamelCase      =	[
           meteor_score.single_meteor_score(
               word_tokenize(__UpperCAmelCase				)     ,					word_tokenize(__UpperCAmelCase				)     ,					alpha=__UpperCAmelCase     ,					beta=__UpperCAmelCase     ,					gamma=__UpperCAmelCase				)
           for ref, pred in zip(__UpperCAmelCase     ,					__UpperCAmelCase				)
       ]
      else:
       __UpperCamelCase      =	[
           meteor_score.single_meteor_score(__UpperCAmelCase     ,					__UpperCAmelCase     ,					alpha=__UpperCAmelCase     ,					beta=__UpperCAmelCase     ,					gamma=__UpperCAmelCase				)
           for ref, pred in zip(__UpperCAmelCase     ,					__UpperCAmelCase				)
       ]

      return {"meteor": np.mean(__UpperCAmelCase				)}





"""simple docstring"""

import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

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 tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


UpperCamelCase     :    List[Any]       =			logging.get_logger(__name__)

UpperCamelCase     :    List[str]       =			{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

UpperCamelCase     :    List[Any]       =			{
    "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",
    },
    "tokenizer_file": {
        "Salesforce/codegen-350M-mono": (
            "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json"
        ),
    },
}

UpperCamelCase     :    List[Any]       =			{
    "Salesforce/codegen-350M-mono": 2_0_4_8,
}

class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):
     lowercase						       =						VOCAB_FILES_NAMES
     lowercase						       =						PRETRAINED_VOCAB_FILES_MAP
     lowercase						       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase						       =						["input_ids", "attention_mask"]
     lowercase						       =						CodeGenTokenizer





     def __init__(     self     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase="<|endoftext|>"     ,					__UpperCAmelCase="<|endoftext|>"     ,					__UpperCAmelCase="<|endoftext|>"     ,					__UpperCAmelCase=False     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




      super().__init__(
          __UpperCAmelCase     ,					__UpperCAmelCase     ,					tokenizer_file=__UpperCAmelCase     ,					unk_token=__UpperCAmelCase     ,					bos_token=__UpperCAmelCase     ,					eos_token=__UpperCAmelCase     ,					add_prefix_space=__UpperCAmelCase     ,					**__UpperCAmelCase     ,					)

      if kwargs.pop('add_bos_token'     ,					__UpperCAmelCase				):
       __UpperCamelCase      =	kwargs.pop('name_or_path'     ,					''				)
       raise ValueError(
           'Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token.'
           'Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n'
           F'`CodeGenTokenizer.from_pretrained(\'{model_id}\')`\nor\n'
           F'`AutoTokenizer.from_pretrained(\'{model_id}\', use_fast=False)`\n'
           'This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005.'
           ' so that the fast tokenizer works correctly.'				)

      __UpperCamelCase      =	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
      if pre_tok_state.get('add_prefix_space'     ,					__UpperCAmelCase				) != add_prefix_space:
       __UpperCamelCase      =	getattr(__UpperCAmelCase     ,					pre_tok_state.pop('type'				)				)
       __UpperCamelCase      =	add_prefix_space
       __UpperCamelCase      =	pre_tok_class(**__UpperCAmelCase				)

      __UpperCamelCase      =	add_prefix_space





     def 						UpperCAmelCase (     self     ,					*__UpperCAmelCase     ,					**__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	kwargs.get('is_split_into_words'     ,					__UpperCAmelCase				)
      assert self.add_prefix_space or not is_split_into_words, (
          F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
          "to use it with pretokenized inputs."
      )

      return super()._batch_encode_plus(*__UpperCAmelCase     ,					**__UpperCAmelCase				)





     def 						UpperCAmelCase (     self     ,					*__UpperCAmelCase     ,					**__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	kwargs.get('is_split_into_words'     ,					__UpperCAmelCase				)

      assert self.add_prefix_space or not is_split_into_words, (
          F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
          "to use it with pretokenized inputs."
      )

      return super()._encode_plus(*__UpperCAmelCase     ,					**__UpperCAmelCase				)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None				):

      '''simple docstring'''




      __UpperCamelCase      =	self._tokenizer.model.save(__UpperCAmelCase     ,					name=__UpperCAmelCase				)
      return tuple(__UpperCAmelCase				)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	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:
       __UpperCamelCase      =	self.truncate(__UpperCAmelCase     ,					__UpperCAmelCase				)

      return decoded_text







     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      def find_re(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):
       __UpperCamelCase      =	pattern.search(__UpperCAmelCase     ,					__UpperCAmelCase				)
       return m.start() if m else -1

      __UpperCamelCase      =	[re.compile(__UpperCAmelCase     ,					re.MULTILINE				) for pattern in truncate_before_pattern]

      __UpperCamelCase      =	list(re.finditer('^print'     ,					__UpperCAmelCase     ,					re.MULTILINE				)				)

      if len(__UpperCAmelCase				) > 1:
       __UpperCamelCase      =	completion[: prints[1].start()]

      __UpperCamelCase      =	list(re.finditer('^def'     ,					__UpperCAmelCase     ,					re.MULTILINE				)				)

      if len(__UpperCAmelCase				) > 1:
       __UpperCamelCase      =	completion[: defs[1].start()]

      __UpperCamelCase      =	0

      __UpperCamelCase      =	[
          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



