


"""simple docstring"""



import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


__snake_case   			=    logging.get_logger(__name__)


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

__snake_case   			=    {
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

__snake_case   			=    {
    """allenai/led-base-16384""": 16384,
}







class _lowerCAmelCase				(      __UpperCamelCase	):
   __UpperCAmelCase :       Dict							  =  VOCAB_FILES_NAMES
   __UpperCAmelCase :       Union[str, Any]							  =  PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase :       Any							  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase :       List[Any]							  =  LEDTokenizer
   __UpperCAmelCase :       Optional[Any]							  =  ['''input_ids''', '''attention_mask''']







   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__						,	)	->		List[Any]:

       '''simple docstring'''




       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__						,	)

       snake_case    :      Union[str, Any]      					=    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
       if pre_tok_state.get("add_prefix_space"						,	UpperCamelCase__						) != add_prefix_space:
           snake_case    :      int      					=    getattr(UpperCamelCase__						,	pre_tok_state.pop("type"						)						)
           snake_case    :      Optional[int]      					=    add_prefix_space
           snake_case    :      List[str]      					=    pre_tok_class(**UpperCamelCase__						)

       snake_case    :      Optional[Any]      					=    add_prefix_space

       # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
       snake_case    :      str      					=    'post_processor'
       snake_case    :      Tuple      					=    getattr(self.backend_tokenizer						,	UpperCamelCase__						,	UpperCamelCase__						)
       if tokenizer_component_instance:
           snake_case    :      Any      					=    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:
               snake_case    :      int      					=    tuple(state["sep"]						)
           if "cls" in state:
               snake_case    :      Optional[int]      					=    tuple(state["cls"]						)

           snake_case    :      Optional[int]      					=    False

           if state.get("add_prefix_space"						,	UpperCamelCase__						) != add_prefix_space:
               snake_case    :      Dict      					=    add_prefix_space
               snake_case    :      Optional[Any]      					=    True

           if state.get("trim_offsets"						,	UpperCamelCase__						) != trim_offsets:
               snake_case    :      Any      					=    trim_offsets
               snake_case    :      Tuple      					=    True

           if changes_to_apply:
               snake_case    :      Tuple      					=    getattr(UpperCamelCase__						,	state.pop("type"						)						)
               snake_case    :      Any      					=    component_class(**UpperCamelCase__						)
               setattr(self.backend_tokenizer						,	UpperCamelCase__						,	UpperCamelCase__						)







   @property
   # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
   def  lowerCamelCase		(	self						)	->		str:

       '''simple docstring'''




       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  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		str:

       '''simple docstring'''




       snake_case    :      Any      					=    AddedToken(UpperCamelCase__						,	lstrip=UpperCamelCase__						,	rstrip=UpperCamelCase__						) if isinstance(UpperCamelCase__						,	UpperCamelCase__						) else value
       snake_case    :      Union[str, Any]      					=    value







   def  lowerCamelCase		(	self						,	*UpperCamelCase__						,	**UpperCamelCase__						)	->		BatchEncoding:

       '''simple docstring'''




       snake_case    :      Union[str, Any]      					=    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  lowerCamelCase		(	self						,	*UpperCamelCase__						,	**UpperCamelCase__						)	->		BatchEncoding:

       '''simple docstring'''




       snake_case    :      Optional[Any]      					=    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  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__ = None						)	->		Tuple[str]:

       '''simple docstring'''




       snake_case    :      Union[str, Any]      					=    self._tokenizer.model.save(UpperCamelCase__						,	name=UpperCamelCase__						)
       return tuple(UpperCamelCase__						)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__=None						)	->		str:

       '''simple docstring'''




       snake_case    :      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  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__ = None						)	->		List[int]:

       '''simple docstring'''




       snake_case    :      Dict      					=    [self.sep_token_id]
       snake_case    :      List[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]







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__ = None						,	UpperCamelCase__ = PaddingStrategy.DO_NOT_PAD						,	UpperCamelCase__ = None						,	UpperCamelCase__ = None						,	)	->		dict:

       '''simple docstring'''




       snake_case    :      List[str]      					=    super()._pad(
           encoded_inputs=UpperCamelCase__						,	max_length=UpperCamelCase__						,	padding_strategy=UpperCamelCase__						,	pad_to_multiple_of=UpperCamelCase__						,	return_attention_mask=UpperCamelCase__						,	)

       # Load from model defaults
       if return_attention_mask is None:
           snake_case    :      Tuple      					=    'attention_mask' in self.model_input_names

       if return_attention_mask and "global_attention_mask" in encoded_inputs:
           snake_case    :      List[Any]      					=    encoded_inputs[self.model_input_names[0]]
           # `global_attention_mask` need to have the same length as other (sequential) inputs.
           snake_case    :      Dict      					=    len(encoded_inputs["global_attention_mask"]						) != len(UpperCamelCase__						)

           if needs_to_be_padded:
               snake_case    :      Any      					=    len(UpperCamelCase__						) - len(encoded_inputs["global_attention_mask"]						)

               if self.padding_side == "right":
                   # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                   snake_case    :      List[str]      					=    (
                       encoded_inputs['global_attention_mask'] + [-1] * difference
                   )
               elif self.padding_side == "left":
                   snake_case    :      Dict      					=    [-1] * difference + encoded_inputs[
                       'global_attention_mask'
                   ]
               else:
                   raise ValueError("Invalid padding strategy:" + str(self.padding_side						)						)

       return encoded_inputs












import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel

class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		"""M-CLIP"""
 def __init__(      self  :      Optional[Any]			,     UpperCAmelCase  :      Union[str, Any]=1024			,     UpperCAmelCase  :      Tuple=768			,     **UpperCAmelCase  :      Optional[int]	)			->	Dict:
     lowerCamelCase__    :      Optional[int]					 =		transformerDimSize
     lowerCamelCase__    :      Optional[Any]					 =		imageDimSize
     super().__init__(**UpperCAmelCase	)


class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		MCLIPConfig
 def __init__(      self  :      List[Any]			,     UpperCAmelCase  :      Dict			,     *UpperCAmelCase  :      Any			,     **UpperCAmelCase  :      Dict	)			->	Dict:
     super().__init__(UpperCAmelCase			,     *UpperCAmelCase			,     **UpperCAmelCase	)
     lowerCamelCase__    :      Tuple					 =		XLMRobertaModel(UpperCAmelCase	)
     lowerCamelCase__    :      Union[str, Any]					 =		torch.nn.Linear(
         in_features=config.transformerDimensions			,     out_features=config.numDims	)







 def      A_	(      self  :      Optional[int]			,     UpperCAmelCase  :      Optional[Any]			,     UpperCAmelCase  :      Dict	)			->	Tuple:
     lowerCamelCase__    :      Any					 =		self.transformer(input_ids=UpperCAmelCase			,     attention_mask=UpperCAmelCase	)[0]
     lowerCamelCase__    :      int					 =		(embs * attention_mask.unsqueeze(2	)).sum(dim=1	) / attention_mask.sum(dim=1	)[:, None]
     return self.LinearTransformation(UpperCAmelCase	), embs





