

import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402


a_    :						Optional[Any]    		=      get_tests_dir("fixtures/dummy-config.json")




class      UpperCamelCase (     unittest.TestCase   ):

    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			0

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     self.assertIsNotNone(transformers.models.auto.__spec__    )
     self.assertIsNotNone(importlib.util.find_spec('transformers.models.auto'    )    )

    def UpperCamelCase			(			self :      List[str]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			AutoConfig.from_pretrained('bert-base-uncased'    )
     self.assertIsInstance(snake_case__							,    snake_case__    )

    def UpperCamelCase			(			self :      List[str]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			AutoConfig.from_pretrained(snake_case__    )
     self.assertIsInstance(snake_case__							,    snake_case__    )

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			AutoConfig.from_pretrained(snake_case__    )
     self.assertIsInstance(snake_case__							,    snake_case__    )

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			AutoConfig.for_model('roberta'    )
     self.assertIsInstance(snake_case__							,    snake_case__    )

    def UpperCamelCase			(			self :      Dict    ):

     """simple docstring"""






     with tempfile.TemporaryDirectory() as tmp_dir:
      # This model name contains bert and roberta, but roberta ends up being picked.
      SCREAMING_SNAKE_CASE								=			os.path.join(snake_case__							,    'fake-roberta'    )
      os.makedirs(snake_case__							,    exist_ok=snake_case__    )
      with open(os.path.join(snake_case__							,    'config.json'    )							,    'w'    ) as f:
       f.write(json.dumps({}    )    )
      SCREAMING_SNAKE_CASE								=			AutoConfig.from_pretrained(snake_case__    )
      self.assertEqual(type(snake_case__    )							,    snake_case__    )

    def UpperCamelCase			(			self :      Tuple    ):

     """simple docstring"""






     try:
      AutoConfig.register('custom'							,    snake_case__    )
      # Wrong model type will raise an error
      with self.assertRaises(snake_case__    ):
       AutoConfig.register('model'							,    snake_case__    )
      # Trying to register something existing in the Transformers library will raise an error
      with self.assertRaises(snake_case__    ):
       AutoConfig.register('bert'							,    snake_case__    )

      # Now that the config is registered, it can be used as any other config with the auto-API
      SCREAMING_SNAKE_CASE								=			CustomConfig()
      with tempfile.TemporaryDirectory() as tmp_dir:
       config.save_pretrained(snake_case__    )
       SCREAMING_SNAKE_CASE								=			AutoConfig.from_pretrained(snake_case__    )
       self.assertIsInstance(snake_case__							,    snake_case__    )

     finally:
      if "custom" in CONFIG_MAPPING._extra_content:
       del CONFIG_MAPPING._extra_content["custom"]

    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






     with self.assertRaisesRegex(
         snake_case__							,    'bert-base is not a local folder and is not a valid model identifier'    ):
      SCREAMING_SNAKE_CASE								=			AutoConfig.from_pretrained('bert-base'    )

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

     """simple docstring"""






     with self.assertRaisesRegex(
         snake_case__							,    r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'    ):
      SCREAMING_SNAKE_CASE								=			AutoConfig.from_pretrained(snake_case__							,    revision='aaaaaa'    )

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     with self.assertRaisesRegex(
         snake_case__							,    'hf-internal-testing/no-config-test-repo does not appear to have a file named config.json.'							,    ):
      SCREAMING_SNAKE_CASE								=			AutoConfig.from_pretrained('hf-internal-testing/no-config-test-repo'    )

    def UpperCamelCase			(			self :      List[str]    ):

     """simple docstring"""






     with self.assertRaises(snake_case__    ):
      SCREAMING_SNAKE_CASE								=			AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model'    )
     # If remote code is disabled, we can't load this config.
     with self.assertRaises(snake_case__    ):
      SCREAMING_SNAKE_CASE								=			AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model'							,    trust_remote_code=snake_case__    )

     SCREAMING_SNAKE_CASE								=			AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model'							,    trust_remote_code=snake_case__    )
     self.assertEqual(config.__class__.__name__							,    'NewModelConfig'    )

     # Test config can be reloaded.
     with tempfile.TemporaryDirectory() as tmp_dir:
      config.save_pretrained(snake_case__    )
      SCREAMING_SNAKE_CASE								=			AutoConfig.from_pretrained(snake_case__							,    trust_remote_code=snake_case__    )
     self.assertEqual(reloaded_config.__class__.__name__							,    'NewModelConfig'    )




    def UpperCamelCase			(			self :      Dict    ):

     """simple docstring"""






     class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
         __UpperCamelCase         ="new-model"

     try:
      AutoConfig.register('new-model'							,    snake_case__    )
      # If remote code is not set, the default is to use local
      SCREAMING_SNAKE_CASE								=			AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model'    )
      self.assertEqual(config.__class__.__name__							,    'NewModelConfigLocal'    )

      # If remote code is disabled, we load the local one.
      SCREAMING_SNAKE_CASE								=			AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model'							,    trust_remote_code=snake_case__    )
      self.assertEqual(config.__class__.__name__							,    'NewModelConfigLocal'    )

      # If remote is enabled, we load from the Hub
      SCREAMING_SNAKE_CASE								=			AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model'							,    trust_remote_code=snake_case__    )
      self.assertEqual(config.__class__.__name__							,    'NewModelConfig'    )

     finally:
      if "new-model" in CONFIG_MAPPING._extra_content:
       del CONFIG_MAPPING._extra_content["new-model"]




import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


a_    :						List[str]    		=      version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
      from nltk import word_tokenize


a_    :						Dict    		=      "\\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"

a_    :						str    		=      "\\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"

a_    :						int    		=      "\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      UpperCamelCase (     datasets.Metric   ):

    def UpperCamelCase			(			self :      str    ):

     """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 :      Dict							,    snake_case__ :      int    ):

     """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 :      Union[str, Any]							,    snake_case__ :      str							,    snake_case__ :      List[Any]							,    snake_case__ :      List[Any]=0.9							,    snake_case__ :      Optional[Any]=3							,    snake_case__ :      Any=0.5    ):

     """simple docstring"""






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

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

