






"""simple docstring"""


import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
   from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

   from .test_feature_extraction_speechta import floats_list


_UpperCamelCase   :						Tuple     						= get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")






@require_torch
class __a   (   unittest.TestCase				):



  def 						A_				(self  :		Optional[int]   )		->							Optional[Any]:
   __A	:       str       =	tempfile.mkdtemp()

   __A	:       Optional[int]       =	SpeechTaTokenizer(lowercase_   )
   tokenizer.save_pretrained(self.tmpdirname   )

   __A	:       Union[str, Any]       =	{
       'feature_size': 1,
       'padding_value': 0.0,
       'sampling_rate': 16_000,
       'do_normalize': False,
       'num_mel_bins': 80,
       'hop_length': 16,
       'win_length': 64,
       'win_function': 'hann_window',
       'fmin': 80,
       'fmax': 7_600,
       'mel_floor': 1E-10,
       'reduction_factor': 2,
       'return_attention_mask': True,
   }

   __A	:       List[str]       =	os.path.join(self.tmpdirname				,			lowercase_   )
   with open(self.feature_extraction_file				,			'w'				,			encoding='utf-8'   ) as fp:
    fp.write(json.dumps(lowercase_   ) + '\n'   )



  def 						A_				(self  :		int				,			**lowercase_  :		str   )		->							Union[str, Any]:
   return SpeechTaTokenizer.from_pretrained(self.tmpdirname				,			**lowercase_   )



  def 						A_				(self  :		int				,			**lowercase_  :		str   )		->							Any:
   return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname				,			**lowercase_   )



  def 						A_				(self  :		Optional[Any]   )		->							List[str]:
   shutil.rmtree(self.tmpdirname   )



  def 						A_				(self  :		Tuple   )		->							Any:
   __A	:       int       =	self.get_tokenizer()
   __A	:       Optional[int]       =	self.get_feature_extractor()

   __A	:       Union[str, Any]       =	SpeechTaProcessor(tokenizer=lowercase_				,			feature_extractor=lowercase_   )

   processor.save_pretrained(self.tmpdirname   )
   __A	:       int       =	SpeechTaProcessor.from_pretrained(self.tmpdirname   )

   self.assertEqual(processor.tokenizer.get_vocab()				,			tokenizer.get_vocab()   )
   self.assertIsInstance(processor.tokenizer				,			lowercase_   )

   self.assertEqual(processor.feature_extractor.to_json_string()				,			feature_extractor.to_json_string()   )
   self.assertIsInstance(processor.feature_extractor				,			lowercase_   )



  def 						A_				(self  :		Union[str, Any]   )		->							Dict:
   __A	:       int       =	SpeechTaProcessor(tokenizer=self.get_tokenizer()				,			feature_extractor=self.get_feature_extractor()   )
   processor.save_pretrained(self.tmpdirname   )

   __A	:       Any       =	self.get_tokenizer(bos_token='(BOS)'				,			eos_token='(EOS)'   )
   __A	:       Tuple       =	self.get_feature_extractor(do_normalize=lowercase_				,			padding_value=1.0   )

   __A	:       List[Any]       =	SpeechTaProcessor.from_pretrained(
       self.tmpdirname				,			bos_token='(BOS)'				,			eos_token='(EOS)'				,			do_normalize=lowercase_				,			padding_value=1.0   )

   self.assertEqual(processor.tokenizer.get_vocab()				,			tokenizer_add_kwargs.get_vocab()   )
   self.assertIsInstance(processor.tokenizer				,			lowercase_   )

   self.assertEqual(processor.feature_extractor.to_json_string()				,			feature_extractor_add_kwargs.to_json_string()   )
   self.assertIsInstance(processor.feature_extractor				,			lowercase_   )



  def 						A_				(self  :		List[str]   )		->							str:
   __A	:       Optional[int]       =	self.get_feature_extractor()
   __A	:       Dict       =	self.get_tokenizer()

   __A	:       List[Any]       =	SpeechTaProcessor(tokenizer=lowercase_				,			feature_extractor=lowercase_   )

   __A	:       Optional[Any]       =	floats_list((3, 1_000)   )

   __A	:       List[Any]       =	feature_extractor(audio=lowercase_				,			return_tensors='np'   )
   __A	:       Optional[Any]       =	processor(audio=lowercase_				,			return_tensors='np'   )

   for key in input_feat_extract.keys():
    self.assertAlmostEqual(input_feat_extract[key].sum()				,			input_processor[key].sum()				,			delta=1E-2   )



  def 						A_				(self  :		str   )		->							Optional[int]:
   __A	:       Dict       =	self.get_feature_extractor()
   __A	:       Optional[Any]       =	self.get_tokenizer()

   __A	:       str       =	SpeechTaProcessor(tokenizer=lowercase_				,			feature_extractor=lowercase_   )

   __A	:       Tuple       =	floats_list((3, 1_000)   )

   __A	:       str       =	feature_extractor(audio_target=lowercase_				,			return_tensors='np'   )
   __A	:       str       =	processor(audio_target=lowercase_				,			return_tensors='np'   )

   for key in input_feat_extract.keys():
    self.assertAlmostEqual(input_feat_extract[key].sum()				,			input_processor[key].sum()				,			delta=1E-2   )



  def 						A_				(self  :		Union[str, Any]   )		->							Any:
   __A	:       Optional[int]       =	self.get_feature_extractor()
   __A	:       Any       =	self.get_tokenizer()

   __A	:       Optional[Any]       =	SpeechTaProcessor(tokenizer=lowercase_				,			feature_extractor=lowercase_   )

   __A	:       Any       =	'This is a test string'

   __A	:       Any       =	processor(text=lowercase_   )
   __A	:       Union[str, Any]       =	tokenizer(lowercase_   )

   for key in encoded_tok.keys():
    self.assertListEqual(encoded_tok[key]				,			encoded_processor[key]   )



  def 						A_				(self  :		List[Any]   )		->							List[Any]:
   __A	:       Dict       =	self.get_feature_extractor()
   __A	:       List[str]       =	self.get_tokenizer()

   __A	:       Dict       =	SpeechTaProcessor(tokenizer=lowercase_				,			feature_extractor=lowercase_   )

   __A	:       List[str]       =	'This is a test string'

   __A	:       List[Any]       =	processor(text_target=lowercase_   )
   __A	:       str       =	tokenizer(lowercase_   )

   for key in encoded_tok.keys():
    self.assertListEqual(encoded_tok[key]				,			encoded_processor[key]   )



  def 						A_				(self  :		Optional[Any]   )		->							int:
   __A	:       str       =	self.get_feature_extractor()
   __A	:       Tuple       =	self.get_tokenizer()

   __A	:       List[str]       =	SpeechTaProcessor(tokenizer=lowercase_				,			feature_extractor=lowercase_   )

   __A	:       Optional[Any]       =	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

   __A	:       Union[str, Any]       =	processor.batch_decode(lowercase_   )
   __A	:       Optional[Any]       =	tokenizer.batch_decode(lowercase_   )

   self.assertListEqual(lowercase_				,			lowercase_   )



  def 						A_				(self  :		Optional[Any]   )		->							Optional[Any]:
   __A	:       Union[str, Any]       =	self.get_feature_extractor()
   __A	:       Optional[int]       =	self.get_tokenizer()

   __A	:       str       =	SpeechTaProcessor(tokenizer=lowercase_				,			feature_extractor=lowercase_   )

   self.assertListEqual(
       processor.model_input_names				,			feature_extractor.model_input_names				,			msg='`processor` and `feature_extractor` model input names do not match'				,			)













"""simple docstring"""


import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_UpperCamelCase   :						List[Any]     						= """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_UpperCamelCase   :						Any     						= """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
_UpperCamelCase   :						Dict     						= """"""

# TODO: Add the licence for the dataset here if you can find it
_UpperCamelCase   :						Tuple     						= """"""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_UpperCamelCase   :						str     						= {
    """first_domain""": """https://huggingface.co/great-new-dataset-first_domain.zip""",
    """second_domain""": """https://huggingface.co/great-new-dataset-second_domain.zip""",
}






class __a   (   datasets.GeneratorBasedBuilder				):
  __lowercase    :    int						=					datasets.Version('1.1.0'				)

  # This is an example of a dataset with multiple configurations.
  # If you don't want/need to define several sub-sets in your dataset,
  # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

  # If you need to make complex sub-parts in the datasets with configurable options
  # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
  # BUILDER_CONFIG_CLASS = MyBuilderConfig

  # You will be able to load one or the other configurations in the following list with
  # data = datasets.load_dataset('my_dataset', 'first_domain')
  # data = datasets.load_dataset('my_dataset', 'second_domain')
  __lowercase    :    Optional[Any]						=					[
      datasets.BuilderConfig(name='first_domain' ,version=snake_case_ ,description='This part of my dataset covers a first domain'				),
      datasets.BuilderConfig(name='second_domain' ,version=snake_case_ ,description='This part of my dataset covers a second domain'				),
  ]

  __lowercase    :    Any						=					'first_domain'  # It's not mandatory to have a default configuration. Just use one if it make sense.



  def 						A_				(self  :		Optional[Any]   )		->							List[Any]:
   # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
   if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
    __A	:       Dict       =	datasets.Features(
        {
            'sentence': datasets.Value('string'   ),
            'option1': datasets.Value('string'   ),
            'answer': datasets.Value('string'   )
            # These are the features of your dataset like images, labels ...
        }   )
   else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
    __A	:       Optional[int]       =	datasets.Features(
        {
            'sentence': datasets.Value('string'   ),
            'option2': datasets.Value('string'   ),
            'second_domain_answer': datasets.Value('string'   )
            # These are the features of your dataset like images, labels ...
        }   )
   return datasets.DatasetInfo(
       # This is the description that will appear on the datasets page.
       description=_DESCRIPTION				,			features=lowercase_				,			homepage=_HOMEPAGE				,			license=_LICENSE				,			citation=_CITATION				,			)



  def 						A_				(self  :		Dict				,			lowercase_  :		Union[str, Any]   )		->							Union[str, Any]:
   # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
   # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

   # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
   # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
   # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
   __A	:       Optional[int]       =	_URLS[self.config.name]
   __A	:       Any       =	dl_manager.download_and_extract(lowercase_   )
   return [
       datasets.SplitGenerator(
           name=datasets.Split.TRAIN				,			gen_kwargs={
               'filepath': os.path.join(lowercase_				,			'train.jsonl'   ),
               'split': 'train',
           }				,			),
       datasets.SplitGenerator(
           name=datasets.Split.VALIDATION				,			gen_kwargs={
               'filepath': os.path.join(lowercase_				,			'dev.jsonl'   ),
               'split': 'dev',
           }				,			),
       datasets.SplitGenerator(
           name=datasets.Split.TEST				,			gen_kwargs={
               'filepath': os.path.join(lowercase_				,			'test.jsonl'   ),
               'split': 'test'
           }				,			),
   ]



  def 						A_				(self  :		Tuple				,			lowercase_  :		Dict				,			lowercase_  :		Optional[Any]   )		->							Tuple:
   # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
   # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
   with open(lowercase_				,			encoding='utf-8'   ) as f:
    for key, row in enumerate(lowercase_   ):
     __A	:       int       =	json.loads(lowercase_   )
     if self.config.name == "first_domain":
      # Yields examples as (key, example) tuples
      yield key, {
          "sentence": data["sentence"],
          "option1": data["option1"],
          "answer": "" if split == "test" else data["answer"],
      }
     else:
      yield key, {
          "sentence": data["sentence"],
          "option2": data["option2"],
          "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
      }





