






"""simple docstring"""




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


_snake_case          =   logging.get_logger(__name__)

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

_snake_case          =   {
    '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'
        )
    },
}

_snake_case          =   {
    'facebook/blenderbot_small-90M': 512,
}






class        UpperCamelCase			(	snake_case_      ):
 UpperCamelCase					:       List[Any]			=					VOCAB_FILES_NAMES
 UpperCamelCase					:       int			=					PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase					:       Any			=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCamelCase					:       List[Any]			=					BlenderbotSmallTokenizer


 def __init__(     self		:						int							,						UpperCAmelCase__		:						Any=None							,						UpperCAmelCase__		:						Dict=None							,						UpperCAmelCase__		:						Any="<|endoftext|>"							,						UpperCAmelCase__		:						List[str]="<|endoftext|>"							,						UpperCAmelCase__		:						Any="<|endoftext|>"							,						UpperCAmelCase__		:						Dict=False							,						UpperCAmelCase__		:						List[Any]=True							,						**UpperCAmelCase__		:						int							,						)					->       str:
   super().__init__(
       ByteLevelBPETokenizer(
           vocab=UpperCAmelCase__							,						merges=UpperCAmelCase__							,						add_prefix_space=UpperCAmelCase__							,						trim_offsets=UpperCAmelCase__							,						)							,						bos_token=UpperCAmelCase__							,						eos_token=UpperCAmelCase__							,						unk_token=UpperCAmelCase__							,						**UpperCAmelCase__							,						)
   _a  :       Dict             =	add_prefix_space


 def _lowercase	(     self		:						List[Any]							,						UpperCAmelCase__		:						List[str]							,						UpperCAmelCase__		:						List[str]=None							)					->       int:
   _a  :       Optional[Any]             =	[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 _lowercase	(     self		:						Dict							,						UpperCAmelCase__		:						List[int]							,						UpperCAmelCase__		:						Optional[List[int]] = None							)					->       List[int]:
   _a  :       Tuple             =	[self.sep_token_id]
   _a  :       Union[str, 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]









"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_snake_case          =   {
    'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOnnxConfig'],
    'tokenization_perceiver': ['PerceiverTokenizer'],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _snake_case          =   ['PerceiverFeatureExtractor']
 _snake_case          =   ['PerceiverImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _snake_case          =   [
     'PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'PerceiverForImageClassificationConvProcessing',
     'PerceiverForImageClassificationFourier',
     'PerceiverForImageClassificationLearned',
     'PerceiverForMaskedLM',
     'PerceiverForMultimodalAutoencoding',
     'PerceiverForOpticalFlow',
     'PerceiverForSequenceClassification',
     'PerceiverLayer',
     'PerceiverModel',
     'PerceiverPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
 from .tokenization_perceiver import PerceiverTokenizer

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_perceiver import PerceiverFeatureExtractor
  from .image_processing_perceiver import PerceiverImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_perceiver import (
      PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
      PerceiverForImageClassificationConvProcessing,
      PerceiverForImageClassificationFourier,
      PerceiverForImageClassificationLearned,
      PerceiverForMaskedLM,
      PerceiverForMultimodalAutoencoding,
      PerceiverForOpticalFlow,
      PerceiverForSequenceClassification,
      PerceiverLayer,
      PerceiverModel,
      PerceiverPreTrainedModel,
  )

else:
 import sys

 _snake_case          =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

