
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    			=					"\\n@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n"

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

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

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

# 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    			=					{
    "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
    "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
}

class 		__magic_name__				( datasets.GeneratorBasedBuilder					):


  """simple docstring"""





  _UpperCamelCase						 = 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')
  _UpperCamelCase						 = [
      datasets.BuilderConfig(name="first_domain"						,version=_A						,description="This part of my dataset covers a first domain"					),
      datasets.BuilderConfig(name="second_domain"						,version=_A						,description="This part of my dataset covers a second domain"					),
  ]

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


  def     _UpperCAmelCase  (					self		):





    if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
      _lowerCamelCase      =						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"
      _lowerCamelCase      =						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=__A	,		homepage=_HOMEPAGE	,		license=_LICENSE	,		citation=_CITATION	,		)


  def     _UpperCAmelCase  (					self	,		a__		):
    _lowerCamelCase      =						_URLS[self.config.name]
    _lowerCamelCase      =						dl_manager.download_and_extract(__A		)
    return [
        datasets.SplitGenerator(
            name=datasets.Split.TRAIN	,		gen_kwargs={
                '''filepath''': os.path.join(__A	,		'''train.jsonl'''		),
                '''split''': '''train''',
            }	,		),
        datasets.SplitGenerator(
            name=datasets.Split.VALIDATION	,		gen_kwargs={
                '''filepath''': os.path.join(__A	,		'''dev.jsonl'''		),
                '''split''': '''dev''',
            }	,		),
        datasets.SplitGenerator(
            name=datasets.Split.TEST	,		gen_kwargs={
                '''filepath''': os.path.join(__A	,		'''test.jsonl'''		),
                '''split''': '''test'''
            }	,		),
    ]


  def     _UpperCAmelCase  (					self	,		a__	,		a__		):





    with open(__A	,		encoding='''utf-8'''		) as f:
      for key, row in enumerate(__A		):
        _lowerCamelCase      =						json.loads(__A		)
        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"],
          }









'''simple docstring'''







def 			_lowerCAmelCase(      UpperCAmelCase_      :	str						) ->							str:
							lowerCAmelCase__  =   word.upper()
							lowerCAmelCase__  =   0
							lowerCAmelCase__  =   {}
							lowerCAmelCase__  =   []

							for letter in word:
														if letter not in letter_nums:
																					lowerCAmelCase__  =   str(UpperCAmelCase_						)
																					next_num += 1
														word_pattern.append(letter_nums[letter]						)
							return ".".join(UpperCAmelCase_						)


if __name__ == "__main__":
		import pprint
		import time

		_UpperCamelCase  						=			time.time()
		with open("""dictionary.txt""") as in_file:
				_UpperCamelCase  						=			in_file.read().splitlines()

		_UpperCamelCase  						=			{}
		for word in word_list:
				_UpperCamelCase  						=			get_word_pattern(word)
				if pattern in all_patterns:
						all_patterns[pattern].append(word)
				else:
						_UpperCamelCase  						=			[word]

		with open("""word_patterns.txt""", """w""") as out_file:
				out_file.write(pprint.pformat(all_patterns))

		_UpperCamelCase  						=			round(time.time() - start_time, 2)
		print(f'Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.')
		# Done!  9,581 word patterns found in 0.58 seconds.





