


'''simple docstring'''

import copy
import tempfile
import unittest

from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError

from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test





class      snake_case    (    unittest.TestCase			):





  """simple docstring"""




  @parameterized.expand([(None,), ("foo.json",)]     )
  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  GenerationConfig(
       do_sample=UpperCamelCase			,  temperature=0.7			,  length_penalty=1.0			,  bad_words_ids=[[1, 2, 3], [4, 5]]			,  )
   with tempfile.TemporaryDirectory() as tmp_dir:
    config.save_pretrained(UpperCamelCase			,  config_name=UpperCamelCase     )
    lowerCamelCase_      =  GenerationConfig.from_pretrained(UpperCamelCase			,  config_name=UpperCamelCase     )

   # Checks parameters that were specified
   self.assertEqual(loaded_config.do_sample			,  UpperCamelCase     )
   self.assertEqual(loaded_config.temperature			,  0.7     )
   self.assertEqual(loaded_config.length_penalty			,  1.0     )
   self.assertEqual(loaded_config.bad_words_ids			,  [[1, 2, 3], [4, 5]]     )

   # Checks parameters that were not specified (defaults)
   self.assertEqual(loaded_config.top_k			,  50     )
   self.assertEqual(loaded_config.max_length			,  20     )
   self.assertEqual(loaded_config.max_time			,  UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  AutoConfig.from_pretrained("gpt2"     )
   lowerCamelCase_      =  GenerationConfig.from_model_config(UpperCamelCase     )
   lowerCamelCase_      =  GenerationConfig()

   # The generation config has loaded a few non-default parameters from the model config
   self.assertNotEqual(UpperCamelCase			,  UpperCamelCase     )

   # One of those parameters is eos_token_id -- check if it matches
   self.assertNotEqual(generation_config_from_model.eos_token_id			,  default_generation_config.eos_token_id     )
   self.assertEqual(generation_config_from_model.eos_token_id			,  model_config.eos_token_id     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  GenerationConfig()
   lowerCamelCase_      =  {
       "max_new_tokens": 1024,
       "foo": "bar",
   }
   lowerCamelCase_      =  copy.deepcopy(UpperCamelCase     )
   lowerCamelCase_      =  generation_config.update(**UpperCamelCase     )

   # update_kwargs was not modified (no side effects)
   self.assertEqual(UpperCamelCase			,  UpperCamelCase     )

   # update_kwargs was used to update the config on valid attributes
   self.assertEqual(generation_config.max_new_tokens			,  1024     )

   # `.update()` returns a dictionary of unused kwargs
   self.assertEqual(UpperCamelCase			,  {"foo": "bar"}     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  GenerationConfig()
   lowerCamelCase_      =  "bar"

   with tempfile.TemporaryDirectory("test-generation-config"     ) as tmp_dir:
    generation_config.save_pretrained(UpperCamelCase     )

    lowerCamelCase_      =  GenerationConfig.from_pretrained(UpperCamelCase     )
   # update_kwargs was used to update the config on valid attributes
   self.assertEqual(new_config.foo			,  "bar"     )

   lowerCamelCase_      =  GenerationConfig.from_model_config(UpperCamelCase     )
   assert not hasattr(UpperCamelCase			,  "foo"     )  # no new kwargs should be initialized if from config







  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  GenerationConfig()
   self.assertEqual(default_config.temperature			,  1.0     )
   self.assertEqual(default_config.do_sample			,  UpperCamelCase     )
   self.assertEqual(default_config.num_beams			,  1     )

   lowerCamelCase_      =  GenerationConfig(
       do_sample=UpperCamelCase			,  temperature=0.7			,  length_penalty=1.0			,  bad_words_ids=[[1, 2, 3], [4, 5]]			,  )
   self.assertEqual(config.temperature			,  0.7     )
   self.assertEqual(config.do_sample			,  UpperCamelCase     )
   self.assertEqual(config.num_beams			,  1     )

   with tempfile.TemporaryDirectory() as tmp_dir:
    config.save_pretrained(UpperCamelCase     )
    lowerCamelCase_      =  GenerationConfig.from_pretrained(UpperCamelCase			,  temperature=1.0     )

   self.assertEqual(loaded_config.temperature			,  1.0     )
   self.assertEqual(loaded_config.do_sample			,  UpperCamelCase     )
   self.assertEqual(loaded_config.num_beams			,  1     )  # default value





@is_staging_test
class      snake_case    (    unittest.TestCase			):





  """simple docstring"""




  @classmethod
  def   snake_case		(    cls     ):







   """simple docstring"""
   lowerCamelCase_      =  TOKEN
   HfFolder.save_token(UpperCamelCase     )






  @classmethod
  def   snake_case		(    cls     ):







   """simple docstring"""
   try:
    delete_repo(token=cls._token			,  repo_id="test-generation-config"     )
   except HTTPError:
    pass

   try:
    delete_repo(token=cls._token			,  repo_id="valid_org/test-generation-config-org"     )
   except HTTPError:
    pass






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  GenerationConfig(
       do_sample=UpperCamelCase			,  temperature=0.7			,  length_penalty=1.0			,  )
   config.push_to_hub("test-generation-config"			,  use_auth_token=self._token     )

   lowerCamelCase_      =  GenerationConfig.from_pretrained(f'''{USER}/test-generation-config'''     )
   for k, v in config.to_dict().items():
    if k != "transformers_version":
     self.assertEqual(UpperCamelCase			,  getattr(UpperCamelCase			,  UpperCamelCase     )     )

        # Reset repo
   delete_repo(token=self._token			,  repo_id="test-generation-config"     )

   # Push to hub via save_pretrained
   with tempfile.TemporaryDirectory() as tmp_dir:
    config.save_pretrained(
        UpperCamelCase			,  repo_id="test-generation-config"			,  push_to_hub=UpperCamelCase			,  use_auth_token=self._token     )

   lowerCamelCase_      =  GenerationConfig.from_pretrained(f'''{USER}/test-generation-config'''     )
   for k, v in config.to_dict().items():
    if k != "transformers_version":
     self.assertEqual(UpperCamelCase			,  getattr(UpperCamelCase			,  UpperCamelCase     )     )







  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  GenerationConfig(
       do_sample=UpperCamelCase			,  temperature=0.7			,  length_penalty=1.0			,  )
   config.push_to_hub("valid_org/test-generation-config-org"			,  use_auth_token=self._token     )

   lowerCamelCase_      =  GenerationConfig.from_pretrained("valid_org/test-generation-config-org"     )
   for k, v in config.to_dict().items():
    if k != "transformers_version":
     self.assertEqual(UpperCamelCase			,  getattr(UpperCamelCase			,  UpperCamelCase     )     )

        # Reset repo
   delete_repo(token=self._token			,  repo_id="valid_org/test-generation-config-org"     )

   # Push to hub via save_pretrained
   with tempfile.TemporaryDirectory() as tmp_dir:
    config.save_pretrained(
        UpperCamelCase			,  repo_id="valid_org/test-generation-config-org"			,  push_to_hub=UpperCamelCase			,  use_auth_token=self._token     )

   lowerCamelCase_      =  GenerationConfig.from_pretrained("valid_org/test-generation-config-org"     )
   for k, v in config.to_dict().items():
    if k != "transformers_version":
     self.assertEqual(UpperCamelCase			,  getattr(UpperCamelCase			,  UpperCamelCase     )     )







'''simple docstring'''

import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


a_  :				str						= """\
@inproceedings{lin-2004-rouge,
    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",
    author = \"Lin, Chin-Yew\",
    booktitle = \"Text Summarization Branches Out\",
    month = jul,
    year = \"2004\",
    address = \"Barcelona, Spain\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W04-1013\",
    pages = \"74--81\",
}
"""

a_  :				int						= """\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
"""

a_  :				Tuple						= """
Calculates average rouge scores for a list of hypotheses and references
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,
        `\"rougeL\"`: Longest common subsequence based scoring.
        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric('rouge')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']
    >>> print(results[\"rouge1\"])
    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
    >>> print(results[\"rouge1\"].mid.fmeasure)
    1.0
"""





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





  """simple docstring"""




  def   snake_case		(    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/google-research/google-research/tree/master/rouge"]			,  reference_urls=[
           "https://en.wikipedia.org/wiki/ROUGE_(metric)",
           "https://github.com/google-research/google-research/tree/master/rouge",
       ]			,  )







  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase=None			,  UpperCamelCase=True			,  UpperCamelCase=False     ):







   """simple docstring"""
   if rouge_types is None:
    lowerCamelCase_      =  ["rouge1", "rouge2", "rougeL", "rougeLsum"]

   lowerCamelCase_      =  rouge_scorer.RougeScorer(rouge_types=UpperCamelCase			,  use_stemmer=UpperCamelCase     )
   if use_aggregator:
    lowerCamelCase_      =  scoring.BootstrapAggregator()
   else:
    lowerCamelCase_      =  []

   for ref, pred in zip(UpperCamelCase			,  UpperCamelCase     ):
    lowerCamelCase_      =  scorer.score(UpperCamelCase			,  UpperCamelCase     )
    if use_aggregator:
     aggregator.add_scores(UpperCamelCase     )
    else:
     scores.append(UpperCamelCase     )

   if use_aggregator:
    lowerCamelCase_      =  aggregator.aggregate()
   else:
    lowerCamelCase_      =  {}
    for key in scores[0]:
     lowerCamelCase_      =  [score[key] for score in scores]

   return result



