





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_					:   int 							=						logging.get_logger(__name__)

UpperCAmelCase_					:   Union[str, Any] 							=						{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

UpperCAmelCase_					:   Dict 							=						{
    """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_					:   Optional[Any] 							=						{
    """facebook/blenderbot_small-90M""": 5_12,
}





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  VOCAB_FILES_NAMES
  __UpperCamelCase	     =  PRETRAINED_VOCAB_FILES_MAP
  __UpperCamelCase	     =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __UpperCamelCase	     =  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_					:       Dict    =		add_prefix_space







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase=None						):
       lowerCAmelCase_					:       List[str]    =		[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 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = None						):
       lowerCAmelCase_					:       Any    =		[self.sep_token_id]
       lowerCAmelCase_					:       str    =		[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]







import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  """M-CLIP"""







  def __init__(	self    ,						_lowercase=1_024    ,						_lowercase=768    ,						**_lowercase						):
       lowerCAmelCase_					:       Tuple    =		transformerDimSize
       lowerCAmelCase_					:       Optional[Any]    =		imageDimSize
       super().__init__(**_lowercase						)





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  MCLIPConfig







  def __init__(	self    ,						_lowercase    ,						*_lowercase    ,						**_lowercase						):
       super().__init__(_lowercase    ,						*_lowercase    ,						**_lowercase						)
       lowerCAmelCase_					:       str    =		XLMRobertaModel(_lowercase						)
       lowerCAmelCase_					:       str    =		torch.nn.Linear(
           in_features=config.transformerDimensions    ,						out_features=config.numDims						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase						):
       lowerCAmelCase_					:       Optional[int]    =		self.transformer(input_ids=_lowercase    ,						attention_mask=_lowercase						)[0]
       lowerCAmelCase_					:       Any    =		(embs * attention_mask.unsqueeze(2						)).sum(dim=1						) / attention_mask.sum(dim=1						)[:, None]
       return self.LinearTransformation(_lowercase						), embs
