


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


__UpperCamelCase    		=       logging.get_logger(__name__)

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

__UpperCamelCase    		=       {
    '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'
        )
    },
}

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







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					str    =		VOCAB_FILES_NAMES
			_UpperCamelCase		:					Tuple    =		PRETRAINED_VOCAB_FILES_MAP
			_UpperCamelCase		:					int    =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCamelCase		:					List[Any]    =		BlenderbotSmallTokenizer




			def __init__(							self      ,						snake_case=None      ,						snake_case=None      ,						snake_case="<|endoftext|>"      ,						snake_case="<|endoftext|>"      ,						snake_case="<|endoftext|>"      ,						snake_case=False      ,						snake_case=True      ,						**snake_case      ,						):

						'''simple docstring'''


						super().__init__(
						    ByteLevelBPETokenizer(
						        vocab=snake_case      ,						merges=snake_case      ,						add_prefix_space=snake_case      ,						trim_offsets=snake_case      ,						)      ,						bos_token=snake_case      ,						eos_token=snake_case      ,						unk_token=snake_case      ,						**snake_case      ,						)
						UpperCamelCase__					=       add_prefix_space




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case=None     ):

						'''simple docstring'''


						UpperCamelCase__					=       [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 				snake_case__				(							self      ,						snake_case      ,						snake_case = None     ):

						'''simple docstring'''


						UpperCamelCase__					=       [self.sep_token_id]
						UpperCamelCase__					=       [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_torch_available, is_vision_available


__UpperCamelCase    		=       {
    'configuration_mobilenet_v2': [
        'MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileNetV2Config',
        'MobileNetV2OnnxConfig',
    ],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase    		=       ['MobileNetV2FeatureExtractor']
						__UpperCamelCase    		=       ['MobileNetV2ImageProcessor']


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase    		=       [
						    'MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'MobileNetV2ForImageClassification',
						    'MobileNetV2ForSemanticSegmentation',
						    'MobileNetV2Model',
						    'MobileNetV2PreTrainedModel',
						    'load_tf_weights_in_mobilenet_v2',
						]


if TYPE_CHECKING:
						from .configuration_mobilenet_va import (
						    MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    MobileNetVaConfig,
						    MobileNetVaOnnxConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
												from .image_processing_mobilenet_va import MobileNetVaImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mobilenet_va import (
												    MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MobileNetVaForImageClassification,
												    MobileNetVaForSemanticSegmentation,
												    MobileNetVaModel,
												    MobileNetVaPreTrainedModel,
												    load_tf_weights_in_mobilenet_va,
												)


else:
						import sys

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


