"""simple docstring"""




import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


__lowerCamelCase											=  logging.get_logger(__name__)


__lowerCamelCase											=  {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

# See all BART models at https://huggingface.co/models?filter=bart
__lowerCamelCase											=  {
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json",
    },
}

__lowerCamelCase											=  {
    "facebook/bart-base": 10_24,
    "facebook/bart-large": 10_24,
    "facebook/bart-large-mnli": 10_24,
    "facebook/bart-large-cnn": 10_24,
    "facebook/bart-large-xsum": 10_24,
    "yjernite/bart_eli5": 10_24,
}
class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						Dict													=		VOCAB_FILES_NAMES
						lowerCAmelCase__  :						List[str]													=		PRETRAINED_VOCAB_FILES_MAP
						lowerCAmelCase__  :						Any													=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowerCAmelCase__  :						Optional[Any]													=		['input_ids', 'attention_mask']
						lowerCAmelCase__  :						Any													=		BartTokenizer





						def __init__(       self		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase="replace"		,__UpperCAmelCase="<s>"		,__UpperCAmelCase="</s>"		,__UpperCAmelCase="</s>"		,__UpperCAmelCase="<s>"		,__UpperCAmelCase="<unk>"		,__UpperCAmelCase="<pad>"		,__UpperCAmelCase="<mask>"		,__UpperCAmelCase=False		,__UpperCAmelCase=True		,**__UpperCAmelCase		,)					->				str:
													super().__init__(
													    __UpperCAmelCase		,__UpperCAmelCase		,tokenizer_file=__UpperCAmelCase		,errors=__UpperCAmelCase		,bos_token=__UpperCAmelCase		,eos_token=__UpperCAmelCase		,sep_token=__UpperCAmelCase		,cls_token=__UpperCAmelCase		,unk_token=__UpperCAmelCase		,pad_token=__UpperCAmelCase		,mask_token=__UpperCAmelCase		,add_prefix_space=__UpperCAmelCase		,trim_offsets=__UpperCAmelCase		,**__UpperCAmelCase		,)

													A__	     =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
													if pre_tok_state.get('add_prefix_space'		,__UpperCAmelCase     ) != add_prefix_space:
																				A__	     =							getattr(__UpperCAmelCase		,pre_tok_state.pop('type'     )     )
																				A__	     =							add_prefix_space
																				A__	     =							pre_tok_class(**__UpperCAmelCase     )

													A__	     =							add_prefix_space

													# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
													A__	     =							'post_processor'
													A__	     =							getattr(self.backend_tokenizer		,__UpperCAmelCase		,__UpperCAmelCase     )
													if tokenizer_component_instance:
																				A__	     =							json.loads(tokenizer_component_instance.__getstate__()     )

																				# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
																				if "sep" in state:
																											A__	     =							tuple(state['sep']     )
																				if "cls" in state:
																											A__	     =							tuple(state['cls']     )

																				A__	     =							False

																				if state.get('add_prefix_space'		,__UpperCAmelCase     ) != add_prefix_space:
																											A__	     =							add_prefix_space
																											A__	     =							True

																				if state.get('trim_offsets'		,__UpperCAmelCase     ) != trim_offsets:
																											A__	     =							trim_offsets
																											A__	     =							True

																				if changes_to_apply:
																											A__	     =							getattr(__UpperCAmelCase		,state.pop('type'     )     )
																											A__	     =							component_class(**__UpperCAmelCase     )
																											setattr(self.backend_tokenizer		,__UpperCAmelCase		,__UpperCAmelCase     )





						@property
						def 				snake_case__	(       self     )					->				str:
													if self._mask_token is None:
																				if self.verbose:
																											logger.error('Using mask_token, but it is not set yet.'     )
																				return None
													return str(self._mask_token     )





						@mask_token.setter
						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				Optional[int]:
													A__	     =							AddedToken(__UpperCAmelCase		,lstrip=__UpperCAmelCase		,rstrip=__UpperCAmelCase     ) if isinstance(__UpperCAmelCase		,__UpperCAmelCase     ) else value
													A__	     =							value





						def 				snake_case__	(       self		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				BatchEncoding:
													A__	     =							kwargs.get('is_split_into_words'		,__UpperCAmelCase     )

													if is_split_into_words and not self.add_prefix_space:
																				raise ValueError(
																				    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
																				    'to use it with pretokenized inputs.'     )

													return super()._batch_encode_plus(*__UpperCAmelCase		,**__UpperCAmelCase     )





						def 				snake_case__	(       self		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				BatchEncoding:
													A__	     =							kwargs.get('is_split_into_words'		,__UpperCAmelCase     )

													if is_split_into_words and not self.add_prefix_space:
																				raise ValueError(
																				    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
																				    'to use it with pretokenized inputs.'     )

													return super()._encode_plus(*__UpperCAmelCase		,**__UpperCAmelCase     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase = None     )					->				Tuple[str]:
													A__	     =							self._tokenizer.model.save(__UpperCAmelCase		,name=__UpperCAmelCase     )
													return tuple(__UpperCAmelCase     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase=None     )					->				str:
													A__	     =							[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		,__UpperCAmelCase		,__UpperCAmelCase = None     )					->				List[int]:
													A__	     =							[self.sep_token_id]
													A__	     =							[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"""




import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						Union[str, Any]													=		'M-CLIP'





						def __init__(       self		,__UpperCAmelCase=10_24		,__UpperCAmelCase=7_68		,**__UpperCAmelCase     )					->				List[str]:
													A__	     =							transformerDimSize
													A__	     =							imageDimSize
													super().__init__(**__UpperCAmelCase     )






class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						Union[str, Any]													=		MCLIPConfig





						def __init__(       self		,__UpperCAmelCase		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				List[Any]:
													super().__init__(__UpperCAmelCase		,*__UpperCAmelCase		,**__UpperCAmelCase     )
													A__	     =							XLMRobertaModel(__UpperCAmelCase     )
													A__	     =							torch.nn.Linear(
													    in_features=config.transformerDimensions		,out_features=config.numDims     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Tuple:
													A__	     =							self.transformer(input_ids=__UpperCAmelCase		,attention_mask=__UpperCAmelCase     )[0]
													A__	     =							(embs * attention_mask.unsqueeze(2     )).sum(dim=1     ) / attention_mask.sum(dim=1     )[:, None]
													return self.LinearTransformation(__UpperCAmelCase     ), embs





