



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


__A     =    logging.get_logger(__name__)


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

__A     =    {
    "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",
    },
}

__A     =    {
    "allenai/led-base-16384": 1_6384,
}



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      VOCAB_FILES_NAMES
 lowercase_		=      PRETRAINED_VOCAB_FILES_MAP
 lowercase_		=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_		=      LEDTokenizer
 lowercase_		=      ["input_ids", "attention_mask"]

 def __init__(self    :       str	,			UpperCAmelCase_    :       Tuple=None	,			UpperCAmelCase_    :       int=None	,			UpperCAmelCase_    :       Dict=None	,			UpperCAmelCase_    :       List[Any]="replace"	,			UpperCAmelCase_    :       str="<s>"	,			UpperCAmelCase_    :       Dict="</s>"	,			UpperCAmelCase_    :       str="</s>"	,			UpperCAmelCase_    :       str="<s>"	,			UpperCAmelCase_    :       List[str]="<unk>"	,			UpperCAmelCase_    :       int="<pad>"	,			UpperCAmelCase_    :       Dict="<mask>"	,			UpperCAmelCase_    :       Tuple=False	,			UpperCAmelCase_    :       Any=True	,			**UpperCAmelCase_    :       List[str]	,			)	->Tuple:



   '''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_	,			)

   lowerCamelCase__:				Any        =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
   if pre_tok_state.get("add_prefix_space"	,			UpperCAmelCase_) != add_prefix_space:
     lowerCamelCase__:				int        =getattr(UpperCAmelCase_	,			pre_tok_state.pop("type"))
     lowerCamelCase__:				Tuple        =add_prefix_space
     lowerCamelCase__:				List[Any]        =pre_tok_class(**UpperCAmelCase_)

   lowerCamelCase__:				Optional[Any]        =add_prefix_space

   # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
   lowerCamelCase__:				List[Any]        ="post_processor"
   lowerCamelCase__:				Dict        =getattr(self.backend_tokenizer	,			UpperCAmelCase_	,			UpperCAmelCase_)
   if tokenizer_component_instance:
     lowerCamelCase__:				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:
       lowerCamelCase__:				Dict        =tuple(state["sep"])
     if "cls" in state:
       lowerCamelCase__:				List[Any]        =tuple(state["cls"])

     lowerCamelCase__:				Union[str, Any]        =False

     if state.get("add_prefix_space"	,			UpperCAmelCase_) != add_prefix_space:
       lowerCamelCase__:				int        =add_prefix_space
       lowerCamelCase__:				Optional[int]        =True

     if state.get("trim_offsets"	,			UpperCAmelCase_) != trim_offsets:
       lowerCamelCase__:				Any        =trim_offsets
       lowerCamelCase__:				Any        =True

     if changes_to_apply:
       lowerCamelCase__:				Any        =getattr(UpperCAmelCase_	,			state.pop("type"))
       lowerCamelCase__:				Union[str, 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 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->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 							SCREAMING_SNAKE_CASE_ (self    :       List[str]	,			UpperCAmelCase_    :       Any)	->int:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =AddedToken(UpperCAmelCase_	,			lstrip=UpperCAmelCase_	,			rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_	,			UpperCAmelCase_) else value
   lowerCamelCase__:				Dict        =value

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str]	,			*UpperCAmelCase_    :       str	,			**UpperCAmelCase_    :       Union[str, Any])	->BatchEncoding:



   '''simple docstring'''



   lowerCamelCase__:				List[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 							SCREAMING_SNAKE_CASE_ (self    :       Any	,			*UpperCAmelCase_    :       Any	,			**UpperCAmelCase_    :       Union[str, Any])	->BatchEncoding:



   '''simple docstring'''



   lowerCamelCase__:				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 							SCREAMING_SNAKE_CASE_ (self    :       List[str]	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       Optional[str] = None)	->Tuple[str]:



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        =self._tokenizer.model.save(UpperCAmelCase_	,			name=UpperCAmelCase_)
   return tuple(UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       int	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       Any=None)	->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:				Union[str, Any]        =[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 							SCREAMING_SNAKE_CASE_ (self    :       Any	,			UpperCAmelCase_    :       List[int]	,			UpperCAmelCase_    :       Optional[List[int]] = None)	->List[int]:



   '''simple docstring'''



   lowerCamelCase__:				Any        =[self.sep_token_id]
   lowerCamelCase__:				Dict        =[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 							SCREAMING_SNAKE_CASE_ (self    :       Dict	,			UpperCAmelCase_    :       Union[Dict[str, EncodedInput], BatchEncoding]	,			UpperCAmelCase_    :       Optional[int] = None	,			UpperCAmelCase_    :       PaddingStrategy = PaddingStrategy.DO_NOT_PAD	,			UpperCAmelCase_    :       Optional[int] = None	,			UpperCAmelCase_    :       Optional[bool] = None	,			)	->dict:



   '''simple docstring'''



   lowerCamelCase__:				int        =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:
     lowerCamelCase__:				Tuple        ="attention_mask" in self.model_input_names

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

     if needs_to_be_padded:
       lowerCamelCase__:				Dict        =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`
         lowerCamelCase__:				Any        =(
             encoded_inputs["global_attention_mask"] + [-1] * difference
         )
       elif self.padding_side == "left":
         lowerCamelCase__:				str        =[-1] * difference + encoded_inputs[
             "global_attention_mask"
         ]
       else:
         raise ValueError("Invalid padding strategy:" + str(self.padding_side))

   return encoded_inputs








from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''

 def __init__(self    :       int	,			UpperCAmelCase_    :       Distribution	,			UpperCAmelCase_    :       Dict=None	,			UpperCAmelCase_    :       Tuple=None	,			UpperCAmelCase_    :       Tuple=0)	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        =1.0 if scale is None else scale
   lowerCamelCase__:				List[Any]        =0.0 if loc is None else loc

   super().__init__(UpperCAmelCase_	,			[AffineTransform(loc=self.loc	,			scale=self.scale	,			event_dim=UpperCAmelCase_)])

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->int:



   '''simple docstring'''



   return self.base_dist.mean * self.scale + self.loc

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Dict:



   '''simple docstring'''



   return self.base_dist.variance * self.scale**2







 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Optional[Any]:



   '''simple docstring'''



   return self.variance.sqrt()



class        _SCREAMING_SNAKE_CASE						(     nn.Module   ):







 '''simple docstring'''

 def __init__(self    :       Tuple	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       Dict[str, int]	,			UpperCAmelCase_    :       Callable[..., Tuple[torch.Tensor]]	,			**UpperCAmelCase_    :       Dict)	->None:



   '''simple docstring'''



   super().__init__(**UpperCAmelCase_)
   lowerCamelCase__:				Tuple        =args_dim
   lowerCamelCase__:				Any        =nn.ModuleList([nn.Linear(UpperCAmelCase_	,			UpperCAmelCase_) for dim in args_dim.values()])
   lowerCamelCase__:				Any        =domain_map







 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any]	,			UpperCAmelCase_    :       torch.Tensor)	->Tuple[torch.Tensor]:



   '''simple docstring'''



   lowerCamelCase__:				Any        =[proj(UpperCAmelCase_) for proj in self.proj]

   return self.domain_map(*UpperCAmelCase_)



class        _SCREAMING_SNAKE_CASE						(     nn.Module   ):







 '''simple docstring'''

 def __init__(self    :       str	,			UpperCAmelCase_    :       Optional[Any])	->Optional[int]:



   '''simple docstring'''



   super().__init__()
   lowerCamelCase__:				Any        =function







 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict	,			UpperCAmelCase_    :       Optional[Any]	,			*UpperCAmelCase_    :       Union[str, Any])	->Union[str, Any]:



   '''simple docstring'''



   return self.function(UpperCAmelCase_	,			*UpperCAmelCase_)



class        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''
 lowercase_		=      42
 lowercase_		=      42
 lowercase_		=      42

 def __init__(self    :       str	,			UpperCAmelCase_    :       int = 1)	->None:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =dim
   lowerCamelCase__:				int        ={k: dim * self.args_dim[k] for k in self.args_dim}

 def 							SCREAMING_SNAKE_CASE_ (self    :       int	,			UpperCAmelCase_    :       str)	->Optional[Any]:



   '''simple docstring'''



   if self.dim == 1:
     return self.distribution_class(*UpperCAmelCase_)
   else:
     return Independent(self.distribution_class(*UpperCAmelCase_)	,			1)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Any	,			UpperCAmelCase_    :       List[Any]	,			UpperCAmelCase_    :       Optional[torch.Tensor] = None	,			UpperCAmelCase_    :       Optional[torch.Tensor] = None	,			)	->Distribution:



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        =self._base_distribution(UpperCAmelCase_)
   if loc is None and scale is None:
     return distr
   else:
     return AffineTransformed(UpperCAmelCase_	,			loc=UpperCAmelCase_	,			scale=UpperCAmelCase_	,			event_dim=self.event_dim)

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Tuple:



   '''simple docstring'''



   return () if self.dim == 1 else (self.dim,)

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any])	->int:



   '''simple docstring'''



   return len(self.event_shape)

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->float:



   '''simple docstring'''



   return 0.0

 def 							SCREAMING_SNAKE_CASE_ (self    :       str	,			UpperCAmelCase_    :       int)	->nn.Module:



   '''simple docstring'''



   return ParameterProjection(
       in_features=UpperCAmelCase_	,			args_dim=self.args_dim	,			domain_map=LambdaLayer(self.domain_map)	,			)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Any	,			*UpperCAmelCase_    :       torch.Tensor)	->Any:



   '''simple docstring'''



   raise NotImplementedError()







 @staticmethod
 def 							SCREAMING_SNAKE_CASE_ (UpperCAmelCase_    :       torch.Tensor)	->torch.Tensor:



   '''simple docstring'''



   return (x + torch.sqrt(torch.square(UpperCAmelCase_) + 4.0)) / 2.0



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      {"df": 1, "loc": 1, "scale": 1}
 lowercase_		=      StudentT

 @classmethod
 def 							SCREAMING_SNAKE_CASE_ (cls    :       Optional[int]	,			UpperCAmelCase_    :       torch.Tensor	,			UpperCAmelCase_    :       torch.Tensor	,			UpperCAmelCase_    :       torch.Tensor)	->Dict:



   '''simple docstring'''



   lowerCamelCase__:				str        =cls.squareplus(UpperCAmelCase_).clamp_min(torch.finfo(scale.dtype).eps)
   lowerCamelCase__:				Any        =2.0 + cls.squareplus(UpperCAmelCase_)
   return df.squeeze(-1), loc.squeeze(-1), scale.squeeze(-1)



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      {"loc": 1, "scale": 1}
 lowercase_		=      Normal

 @classmethod
 def 							SCREAMING_SNAKE_CASE_ (cls    :       Optional[Any]	,			UpperCAmelCase_    :       torch.Tensor	,			UpperCAmelCase_    :       torch.Tensor)	->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:				List[Any]        =cls.squareplus(UpperCAmelCase_).clamp_min(torch.finfo(scale.dtype).eps)
   return loc.squeeze(-1), scale.squeeze(-1)



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      {"total_count": 1, "logits": 1}
 lowercase_		=      NegativeBinomial

 @classmethod
 def 							SCREAMING_SNAKE_CASE_ (cls    :       Optional[int]	,			UpperCAmelCase_    :       torch.Tensor	,			UpperCAmelCase_    :       torch.Tensor)	->List[str]:



   '''simple docstring'''



   lowerCamelCase__:				Tuple        =cls.squareplus(UpperCAmelCase_)
   return total_count.squeeze(-1), logits.squeeze(-1)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple	,			UpperCAmelCase_    :       Tuple)	->Distribution:



   '''simple docstring'''



   lowerCamelCase__  , lowerCamelCase__:				Any        =distr_args
   if self.dim == 1:
     return self.distribution_class(total_count=UpperCAmelCase_	,			logits=UpperCAmelCase_)
   else:
     return Independent(self.distribution_class(total_count=UpperCAmelCase_	,			logits=UpperCAmelCase_)	,			1)







 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			UpperCAmelCase_    :       Optional[Any]	,			UpperCAmelCase_    :       Optional[torch.Tensor] = None	,			UpperCAmelCase_    :       Optional[torch.Tensor] = None)	->Distribution:



   '''simple docstring'''



   lowerCamelCase__  , lowerCamelCase__:				Union[str, Any]        =distr_args

   if scale is not None:
     # See scaling property of Gamma.
     logits += scale.log()

   return self._base_distribution((total_count, logits))

