
import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class 			lowerCAmelCase_		(a__				,      unittest.TestCase						):


				"""simple docstring"""



				__UpperCamelCase   :		Optional[Any]       =     CTRLTokenizer
				__UpperCamelCase   :		str       =     False
				__UpperCamelCase   :		Optional[Any]       =     False



				def 			__magic_name__					(self					)      ->	Union[str, Any]:



											"""simple docstring"""




											super().setUp()

											# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
											SCREAMING_SNAKE_CASE__		: Any     =     ["""adapt""", """re@@""", """a@@""", """apt""", """c@@""", """t""", """<unk>"""]
											SCREAMING_SNAKE_CASE__		: List[str]     =     dict(zip(SCREAMING_SNAKE_CASE__     , range(len(SCREAMING_SNAKE_CASE__					)					)					)					)
											SCREAMING_SNAKE_CASE__		: str     =     ["""#version: 0.2""", """a p""", """ap t</w>""", """r e""", """a d""", """ad apt</w>""", """"""]
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     {"""unk_token""": """<unk>"""}

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     os.path.join(self.tmpdirname     , VOCAB_FILES_NAMES["""vocab_file"""]					)
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     os.path.join(self.tmpdirname     , VOCAB_FILES_NAMES["""merges_file"""]					)
											with open(self.vocab_file     , """w"""     , encoding="""utf-8"""					) as fp:
																		fp.write(json.dumps(SCREAMING_SNAKE_CASE__					) + """\n"""					)
											with open(self.merges_file     , """w"""     , encoding="""utf-8"""					) as fp:
																		fp.write("""\n""".join(SCREAMING_SNAKE_CASE__					)					)



				def 			__magic_name__					(self     , **SCREAMING_SNAKE_CASE__					)      ->	Any:



											"""simple docstring"""




											kwargs.update(self.special_tokens_map					)
											return CTRLTokenizer.from_pretrained(self.tmpdirname     , **SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     """adapt react readapt apt"""
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     """adapt react readapt apt"""
											return input_text, output_text



				def 			__magic_name__					(self					)      ->	List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[str]     =     CTRLTokenizer(self.vocab_file     , self.merges_file     , **self.special_tokens_map					)
											SCREAMING_SNAKE_CASE__		: str     =     """adapt react readapt apt"""
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     """adapt re@@ a@@ c@@ t re@@ adapt apt""".split()
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     tokenizer.tokenize(SCREAMING_SNAKE_CASE__					)
											self.assertListEqual(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: str     =     tokens + [tokenizer.unk_token]

											SCREAMING_SNAKE_CASE__		: int     =     [0, 1, 2, 4, 5, 1, 0, 3, 6]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__					)     , SCREAMING_SNAKE_CASE__					)






"""simple docstring"""




import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast



@dataclass
class 			lowerCAmelCase_		(datasets.BuilderConfig						):


				"""simple docstring"""



				__UpperCamelCase   :		Optional[datasets.Features]       =     None






class 			lowerCAmelCase_		(datasets.ArrowBasedBuilder						):


				"""simple docstring"""



				__UpperCamelCase   :		Union[str, Any]       =     PandasConfig



				def 			__magic_name__					(self					)      ->	List[str]:



											"""simple docstring"""




											return datasets.DatasetInfo(features=self.config.features					)



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	Optional[int]:



											"""simple docstring"""




											if not self.config.data_files:
																		raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}'''					)
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     dl_manager.download_and_extract(self.config.data_files					)
											if isinstance(SCREAMING_SNAKE_CASE__     , (str, list, tuple)					):
																		SCREAMING_SNAKE_CASE__		: Tuple     =     data_files
																		if isinstance(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					):
																									SCREAMING_SNAKE_CASE__		: Optional[Any]     =     [files]
																		# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																		SCREAMING_SNAKE_CASE__		: str     =     [dl_manager.iter_files(SCREAMING_SNAKE_CASE__					) for file in files]
																		return [datasets.SplitGenerator(name=datasets.Split.TRAIN     , gen_kwargs={"""files""": files}					)]
											SCREAMING_SNAKE_CASE__		: int     =     []
											for split_name, files in data_files.items():
																		if isinstance(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					):
																									SCREAMING_SNAKE_CASE__		: List[str]     =     [files]
																		# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																		SCREAMING_SNAKE_CASE__		: Dict     =     [dl_manager.iter_files(SCREAMING_SNAKE_CASE__					) for file in files]
																		splits.append(datasets.SplitGenerator(name=SCREAMING_SNAKE_CASE__     , gen_kwargs={"""files""": files}					)					)
											return splits



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	pa.Table:



											"""simple docstring"""




											if self.config.features is not None:
																		# more expensive cast to support nested features with keys in a different order
																		# allows str <-> int/float or str to Audio for example
																		SCREAMING_SNAKE_CASE__		: Tuple     =     table_cast(SCREAMING_SNAKE_CASE__     , self.config.features.arrow_schema					)
											return pa_table



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	Optional[Any]:



											"""simple docstring"""




											for i, file in enumerate(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE__					)					):
																		with open(SCREAMING_SNAKE_CASE__     , """rb"""					) as f:
																									SCREAMING_SNAKE_CASE__		: List[str]     =     pa.Table.from_pandas(pd.read_pickle(SCREAMING_SNAKE_CASE__					)					)
																									yield i, self._cast_table(SCREAMING_SNAKE_CASE__					)




