





from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset



class  __lowerCAmelCase	(      _a					):






			def       lowerCamelCase  (self   )					->       List[Any]:


							'''simple docstring'''



							return [
							    {"col_1": 3, "col_2": "a"},
							    {"col_1": 2, "col_2": "b"},
							    {"col_1": 1, "col_2": "c"},
							    {"col_1": 0, "col_2": "d"},
							]






			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							snake_case_						:   Any           =  {'''col_1''': [3, 2, 1, 0], '''col_2''': ['''a''', '''b''', '''c''', '''d''']}
							return Dataset.from_dict(__magic_name__   )






			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   List[str]           =  self._create_example_records()
							snake_case_						:   List[Any]           =  Dataset.from_list(__magic_name__   )
							self.assertListEqual(dset.column_names ,							['''col_1''', '''col_2''']   )
							for i, r in enumerate(__magic_name__   ):
											self.assertDictEqual(__magic_name__ ,							example_records[i]   )






			def       lowerCamelCase  (self   )					->       Tuple:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  self._create_example_records()
							snake_case_						:   List[str]           =  Dataset.from_list(__magic_name__   )
							snake_case_						:   Tuple           =  Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]}   )
							self.assertEqual(dset.info ,							dset_from_dict.info   )






			def       lowerCamelCase  (self   )					->       Any:  # checks what happens with missing columns


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  [{'''col_1''': 1}, {'''col_2''': '''x'''}]
							snake_case_						:   List[Any]           =  Dataset.from_list(__magic_name__   )
							self.assertDictEqual(dset[0] ,							{'''col_1''': 1}   )
							self.assertDictEqual(dset[1] ,							{'''col_1''': None}   )  # NB: first record is used for columns






			def       lowerCamelCase  (self   )					->       Any:  # checks if the type can be inferred from the second record


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  [{'''col_1''': []}, {'''col_1''': [1, 2]}]
							snake_case_						:   Union[str, Any]           =  Dataset.from_list(__magic_name__   )
							self.assertEqual(dset.info.features['''col_1'''] ,							Sequence(Value('''int64'''   )   )   )






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


							'''simple docstring'''



							snake_case_						:   Tuple           =  Dataset.from_list([]   )
							self.assertEqual(len(__magic_name__   ) ,							0   )
							self.assertListEqual(dset.column_names ,							[]   )







from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


lowerCAmelCase_    =    logging.get_logger(__name__)

lowerCAmelCase_    =    {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

lowerCAmelCase_    =    {
    '''vocab_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
    },
    '''tokenizer_config_file''': {
        '''facebook/blenderbot_small-90M''': (
            '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
        )
    },
}

lowerCAmelCase_    =    {
    '''facebook/blenderbot_small-90M''': 5_1_2,
}



class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					int	      =				VOCAB_FILES_NAMES
			lowerCamelCase_					:					List[Any]	      =				PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase_					:					int	      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase_					:					Optional[Any]	      =				BlenderbotSmallTokenizer






			def __init__(self ,							__magic_name__=None ,							__magic_name__=None ,							__magic_name__="<|endoftext|>" ,							__magic_name__="<|endoftext|>" ,							__magic_name__="<|endoftext|>" ,							__magic_name__=False ,							__magic_name__=True ,							**__magic_name__ ,							)					->       Union[str, Any]:


							'''simple docstring'''



							super().__init__(
							    ByteLevelBPETokenizer(
							        vocab=__magic_name__ ,							merges=__magic_name__ ,							add_prefix_space=__magic_name__ ,							trim_offsets=__magic_name__ ,							) ,							bos_token=__magic_name__ ,							eos_token=__magic_name__ ,							unk_token=__magic_name__ ,							**__magic_name__ ,							)
							snake_case_						:   Optional[int]           =  add_prefix_space






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__=None   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
											return output

							return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ = None   )					->       List[int]:


							'''simple docstring'''



							snake_case_						:   List[str]           =  [self.sep_token_id]
							snake_case_						:   Optional[Any]           =  [self.cls_token_id]

							if token_ids_a is None:
											return len(cls + token_ids_a + sep   ) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]
