




"""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


lowercase__     						=						logging.get_logger(__name__)

lowercase__     						=						{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

lowercase__     						=						{
    """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"""
        )
    },
}

lowercase__     						=						{
    """facebook/blenderbot_small-90M""": 512,
}






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			lowerCamelCase__      =			VOCAB_FILES_NAMES
			lowerCamelCase__      =			PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__      =			BlenderbotSmallTokenizer
			def __init__(  self  ,      lowercase=None  ,      lowercase=None  ,      lowercase="<|endoftext|>"  ,      lowercase="<|endoftext|>"  ,      lowercase="<|endoftext|>"  ,      lowercase=False  ,      lowercase=True  ,      **lowercase  ,      ):
							super().__init__(
							    ByteLevelBPETokenizer(
							        vocab=lowercase  ,      merges=lowercase  ,      add_prefix_space=lowercase  ,      trim_offsets=lowercase  ,      )  ,      bos_token=lowercase  ,      eos_token=lowercase  ,      unk_token=lowercase  ,      **lowercase  ,      )
							_lowerCamelCase     :			Any    =    add_prefix_space
			def 					A_    (  self  ,      lowercase  ,      lowercase=None  ):
							_lowerCamelCase     :			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 					A_    (  self  ,      lowercase  ,      lowercase = None  ):
							_lowerCamelCase     :			List[str]    =    [self.sep_token_id]
							_lowerCamelCase     :			Optional[int]    =    [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]

from typing import TYPE_CHECKING

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


__lowerCAmelCase			:     str           =   {
    'configuration_funnel': ['FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FunnelConfig'],
    'convert_funnel_original_tf_checkpoint_to_pytorch': [],
    'tokenization_funnel': ['FunnelTokenizer'],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase			:     Any           =   ['FunnelTokenizerFast']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase			:     Optional[int]           =   [
        'FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
        'FunnelBaseModel',
        'FunnelForMaskedLM',
        'FunnelForMultipleChoice',
        'FunnelForPreTraining',
        'FunnelForQuestionAnswering',
        'FunnelForSequenceClassification',
        'FunnelForTokenClassification',
        'FunnelModel',
        'FunnelPreTrainedModel',
        'load_tf_weights_in_funnel',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase			:     Tuple           =   [
        'TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFFunnelBaseModel',
        'TFFunnelForMaskedLM',
        'TFFunnelForMultipleChoice',
        'TFFunnelForPreTraining',
        'TFFunnelForQuestionAnswering',
        'TFFunnelForSequenceClassification',
        'TFFunnelForTokenClassification',
        'TFFunnelModel',
        'TFFunnelPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
    from .tokenization_funnel import FunnelTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_funnel_fast import FunnelTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_funnel import (
            FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
            FunnelBaseModel,
            FunnelForMaskedLM,
            FunnelForMultipleChoice,
            FunnelForPreTraining,
            FunnelForQuestionAnswering,
            FunnelForSequenceClassification,
            FunnelForTokenClassification,
            FunnelModel,
            FunnelPreTrainedModel,
            load_tf_weights_in_funnel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_funnel import (
            TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFFunnelBaseModel,
            TFFunnelForMaskedLM,
            TFFunnelForMultipleChoice,
            TFFunnelForPreTraining,
            TFFunnelForQuestionAnswering,
            TFFunnelForSequenceClassification,
            TFFunnelForTokenClassification,
            TFFunnelModel,
            TFFunnelPreTrainedModel,
        )

else:
    import sys

    __lowerCAmelCase			:     List[Any]           =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

