



"""simple docstring"""
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  (			_lowerCAmelCase    ,						unittest.TestCase				):






					'''simple docstring'''





					_A		:			str						=     CTRLTokenizer
					_A		:			Union[str, Any]						=     False
					_A		:			Union[str, Any]						=     False






					def    lowerCAmelCase_				(       self:							Tuple						)   ->	List[Any]:
											super().setUp()

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

											snake_case_ :List[str]						=			os.path.join(self.tmpdirname						,     VOCAB_FILES_NAMES["""vocab_file"""]						)
											snake_case_ :Dict						=			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(snake_case						) + """\n"""						)
											with open(self.merges_file						,     """w"""						,     encoding="""utf-8"""						) as fp:
																	fp.write("""\n""".join(snake_case						)						)






					def    lowerCAmelCase_				(       self:							Dict						,     **snake_case:							Optional[int]						)   ->	str:
											kwargs.update(self.special_tokens_map						)
											return CTRLTokenizer.from_pretrained(self.tmpdirname						,     **snake_case						)






					def    lowerCAmelCase_				(       self:							Tuple						,     snake_case:							Tuple						)   ->	Optional[Any]:
											snake_case_ :Optional[int]						=			"""adapt react readapt apt"""
											snake_case_ :Tuple						=			"""adapt react readapt apt"""
											return input_text, output_text






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

											snake_case_ :Dict						=			tokens + [tokenizer.unk_token]

											snake_case_ :Optional[Any]						=			[0, 1, 2, 4, 5, 1, 0, 3, 6]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case						)						,     snake_case						)






from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


snake_case_ : Any    		=       logging.get_logger(__name__)


snake_case_ : Optional[int]    		=       TypeVar("DatasetType", Dataset, IterableDataset)


def    A						(__A   :    List[DatasetType]   ,				__A   :    Optional[List[float]] = None   ,				__A   :    Optional[int] = None   ,				__A   :    Optional[DatasetInfo] = None   ,				__A   :    Optional[NamedSplit] = None   ,				__A   :    Literal["first_exhausted", "all_exhausted"] = "first_exhausted"   ,				)   ->      DatasetType:







     """simple docstring"""


     from .arrow_dataset import Dataset
     from .iterable_dataset import IterableDataset

     if not datasets:
          raise ValueError('''Unable to interleave an empty list of datasets.'''       )
     for i, dataset in enumerate(__A       ):
          if not isinstance(__A   ,				(Dataset, IterableDataset)       ):
               if isinstance(__A   ,				(DatasetDict, IterableDatasetDict)       ):
                    if not dataset:
                         raise ValueError(
                             F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
                             '''is an empty dataset dictionary.'''       )
                    raise ValueError(
                        F"""Dataset at position {i} has at least one split: {list(__A       )}\n"""
                        F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__A       )       )}']"""       )
               raise ValueError(
                   F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__A       ).__name__}."""       )
          if i == 0:
               UpperCAmelCase_     ,       UpperCAmelCase_          =							(
                   (Dataset, IterableDataset) if isinstance(__A   ,				__A       ) else (IterableDataset, Dataset)
               )
          elif not isinstance(__A   ,				__A       ):
               raise ValueError(
                   F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""       )
     if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
          raise ValueError(F"""{stopping_strategy} is not supported. Please enter a valid stopping_strategy."""       )
     if dataset_type is Dataset:
          return _interleave_map_style_datasets(
              __A   ,				__A   ,				__A   ,				info=__A   ,				split=__A   ,				stopping_strategy=__A       )
     else:
          return _interleave_iterable_datasets(
              __A   ,				__A   ,				__A   ,				info=__A   ,				split=__A   ,				stopping_strategy=__A       )


def    A						(__A   :    List[DatasetType]   ,				__A   :    Optional[DatasetInfo] = None   ,				__A   :    Optional[NamedSplit] = None   ,				__A   :    int = 0   ,				)   ->      DatasetType:







     """simple docstring"""


     if not dsets:
          raise ValueError('''Unable to concatenate an empty list of datasets.'''       )
     for i, dataset in enumerate(__A       ):
          if not isinstance(__A   ,				(Dataset, IterableDataset)       ):
               if isinstance(__A   ,				(DatasetDict, IterableDatasetDict)       ):
                    if not dataset:
                         raise ValueError(
                             F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
                             '''is an empty dataset dictionary.'''       )
                    raise ValueError(
                        F"""Dataset at position {i} has at least one split: {list(__A       )}\n"""
                        F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__A       )       )}']"""       )
               raise ValueError(
                   F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__A       ).__name__}."""       )
          if i == 0:
               UpperCAmelCase_     ,       UpperCAmelCase_          =							(
                   (Dataset, IterableDataset) if isinstance(__A   ,				__A       ) else (IterableDataset, Dataset)
               )
          elif not isinstance(__A   ,				__A       ):
               raise ValueError(
                   F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""       )
     if dataset_type is Dataset:
          return _concatenate_map_style_datasets(__A   ,				info=__A   ,				split=__A   ,				axis=__A       )
     else:
          return _concatenate_iterable_datasets(__A   ,				info=__A   ,				split=__A   ,				axis=__A       )






