

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	:     Any		       = logging.get_logger(__name__)

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

_UpperCAmelCase	:     int		       = {
    """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	:     str		       = {
    """facebook/blenderbot_small-90M""": 512,
}







class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				Tuple     =    VOCAB_FILES_NAMES
				__SCREAMING_SNAKE_CASE      :				List[str]     =    PRETRAINED_VOCAB_FILES_MAP
				__SCREAMING_SNAKE_CASE      :				Dict     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__SCREAMING_SNAKE_CASE      :				str     =    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   ,     ):
											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   ,     )
											snake_case_           =					add_prefix_space




				def 					a							(      self   ,     snake_case   ,     snake_case=None ):
											snake_case_           =					[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   ,     snake_case   ,     snake_case = None ):
											snake_case_           =					[self.sep_token_id]
											snake_case_           =					[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 collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCAmelCase	:     int		       = logging.get_logger(__name__)

_UpperCAmelCase	:     Any		       = {
    """facebook/levit-128S""": """https://huggingface.co/facebook/levit-128S/resolve/main/config.json""",
    # See all LeViT models at https://huggingface.co/models?filter=levit
}







class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				int     =    '''levit'''




				def __init__(      self   ,     snake_case=224   ,     snake_case=3   ,     snake_case=3   ,     snake_case=2   ,     snake_case=1   ,     snake_case=16   ,     snake_case=[128, 256, 384]   ,     snake_case=[4, 8, 12]   ,     snake_case=[4, 4, 4]   ,     snake_case=[16, 16, 16]   ,     snake_case=0   ,     snake_case=[2, 2, 2]   ,     snake_case=[2, 2, 2]   ,     snake_case=0.02   ,     **snake_case   ,     ):
											super().__init__(**snake_case )
											snake_case_           =					image_size
											snake_case_           =					num_channels
											snake_case_           =					kernel_size
											snake_case_           =					stride
											snake_case_           =					padding
											snake_case_           =					hidden_sizes
											snake_case_           =					num_attention_heads
											snake_case_           =					depths
											snake_case_           =					key_dim
											snake_case_           =					drop_path_rate
											snake_case_           =					patch_size
											snake_case_           =					attention_ratio
											snake_case_           =					mlp_ratio
											snake_case_           =					initializer_range
											snake_case_           =					[
											    ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
											    ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
											]







class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				Dict     =    version.parse('''1.11'''    )




				@property
				def 					a							(      self ):
											return OrderedDict(
											    [
											        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
											    ] )




				@property
				def 					a							(      self ):
											return 1e-4




