
"""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
_A				=					'\\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
_A				=					'\\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
_A				=					''

# TODO: Add the licence for the dataset here if you can find it
_A				=					''

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






class 			_lowercase  (							datasets.GeneratorBasedBuilder						):
	lowercase_								=      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_								=      [
	    datasets.BuilderConfig(name='first_domain'							,			version=__UpperCAmelCase							,			description='This part of my dataset covers a first domain'						),
	    datasets.BuilderConfig(name='second_domain'							,			version=__UpperCAmelCase							,			description='This part of my dataset covers a second domain'						),
	]

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




	def      _UpperCamelCase       (					self   )						->				Union[str, 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
					lowerCamelCase	:     int            =       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	:     Optional[Any]            =       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=UpperCAmelCase_						,					homepage=_HOMEPAGE						,					license=_LICENSE						,					citation=_CITATION						,					)




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_   )						->				Tuple:
			# 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
			lowerCamelCase	:     Optional[Any]            =       _URLS[self.config.name]
			lowerCamelCase	:     List[Any]            =       dl_manager.download_and_extract(UpperCAmelCase_   )
			return [
			    datasets.SplitGenerator(
			        name=datasets.Split.TRAIN						,					gen_kwargs={
			            'filepath': os.path.join(UpperCAmelCase_						,					'train.jsonl'   ),
			            'split': 'train',
			        }						,					),
			    datasets.SplitGenerator(
			        name=datasets.Split.VALIDATION						,					gen_kwargs={
			            'filepath': os.path.join(UpperCAmelCase_						,					'dev.jsonl'   ),
			            'split': 'dev',
			        }						,					),
			    datasets.SplitGenerator(
			        name=datasets.Split.TEST						,					gen_kwargs={
			            'filepath': os.path.join(UpperCAmelCase_						,					'test.jsonl'   ),
			            'split': 'test'
			        }						,					),
			]




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_   )						->				int:
			# 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(UpperCAmelCase_						,					encoding='utf-8'   ) as f:
					for key, row in enumerate(UpperCAmelCase_   ):
							lowerCamelCase	:     int            =       json.loads(UpperCAmelCase_   )
							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"""

import sys

from transformers import AutoTokenizer


_A				=					sys.argv[1]
_A				=					sys.argv[2]
_A				=					int(sys.argv[3])

_A				=					0

_A				=					AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, 'rt') as f_p:
					for line in f_p:
										_A				=					line.rstrip()

										if not line:
															print(line)
															_A				=					0
															continue

										_A				=					line.split()[0]

										_A				=					len(tokenizer.tokenize(token))

										# Token contains strange control characters like \x96 or \x95
										# Just filter out the complete line
										if current_subwords_len == 0:
															continue

										if (subword_len_counter + current_subwords_len) > max_len:
															print('')
															print(line)
															_A				=					current_subwords_len
															continue

										subword_len_counter += current_subwords_len

										print(line)






