


from dataclasses import dataclass
from typing import Dict, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin



@dataclass
class        a					(			_UpperCAmelCase   ):
     UpperCAmelCase__ :  torch.FloatTensor



class        a					(			_UpperCAmelCase     ,_UpperCAmelCase   ):





     @register_to_config
     def __init__(    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				int = 32    ,	SCREAMING_SNAKE_CASE_					:				int = 64    ,	SCREAMING_SNAKE_CASE_					:				int = 20    ,	SCREAMING_SNAKE_CASE_					:				int = 768    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=77    ,	SCREAMING_SNAKE_CASE_					:				str=4    ,	SCREAMING_SNAKE_CASE_					:				float = 0.0    ,	SCREAMING_SNAKE_CASE_					:				str = "silu"    ,	SCREAMING_SNAKE_CASE_					:				Optional[str] = None    ,	SCREAMING_SNAKE_CASE_					:				Optional[str] = None    ,	SCREAMING_SNAKE_CASE_					:				Optional[str] = "linear"    ,	SCREAMING_SNAKE_CASE_					:				Optional[str] = "prd"    ,	SCREAMING_SNAKE_CASE_					:				Optional[int] = None    ,	SCREAMING_SNAKE_CASE_					:				Optional[int] = None    ,	SCREAMING_SNAKE_CASE_					:				Optional[int] = None    ,	):
        super().__init__()
        __lowerCamelCase:				Dict     =	num_attention_heads
        __lowerCamelCase:				Optional[int]     =	attention_head_dim
        __lowerCamelCase:				List[Any]     =	num_attention_heads * attention_head_dim
        __lowerCamelCase:				Any     =	additional_embeddings

        __lowerCamelCase:				Any     =	time_embed_dim or inner_dim
        __lowerCamelCase:				int     =	embedding_proj_dim or embedding_dim
        __lowerCamelCase:				Union[str, Any]     =	clip_embed_dim or embedding_dim

        __lowerCamelCase:				Optional[Any]     =	Timesteps(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	0							)
        __lowerCamelCase:				Optional[Any]     =	TimestepEmbedding(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	out_dim=SCREAMING_SNAKE_CASE_    ,	act_fn=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[Any]     =	nn.Linear(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)

        if embedding_proj_norm_type is None:
           __lowerCamelCase:				Optional[Any]     =	None
        elif embedding_proj_norm_type == "layer":
           __lowerCamelCase:				List[str]     =	nn.LayerNorm(SCREAMING_SNAKE_CASE_							)
        else:
           raise ValueError(F'unsupported embedding_proj_norm_type: {embedding_proj_norm_type}'							)

        __lowerCamelCase:				Union[str, Any]     =	nn.Linear(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)

        if encoder_hid_proj_type is None:
           __lowerCamelCase:				Union[str, Any]     =	None
        elif encoder_hid_proj_type == "linear":
           __lowerCamelCase:				List[str]     =	nn.Linear(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
        else:
           raise ValueError(F'unsupported encoder_hid_proj_type: {encoder_hid_proj_type}'							)

        __lowerCamelCase:				List[str]     =	nn.Parameter(torch.zeros(1    ,	num_embeddings + additional_embeddings    ,	SCREAMING_SNAKE_CASE_							)							)

        if added_emb_type == "prd":
           __lowerCamelCase:				Optional[int]     =	nn.Parameter(torch.zeros(1    ,	1    ,	SCREAMING_SNAKE_CASE_							)							)
        elif added_emb_type is None:
           __lowerCamelCase:				Tuple     =	None
        else:
           raise ValueError(
               F'`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.'							)

        __lowerCamelCase:				Dict     =	nn.ModuleList(
            [
                BasicTransformerBlock(
                    SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	dropout=SCREAMING_SNAKE_CASE_    ,	activation_fn="""gelu"""    ,	attention_bias=SCREAMING_SNAKE_CASE_    ,	)
                for d in range(SCREAMING_SNAKE_CASE_							)
            ]							)

        if norm_in_type == "layer":
           __lowerCamelCase:				Any     =	nn.LayerNorm(SCREAMING_SNAKE_CASE_							)
        elif norm_in_type is None:
           __lowerCamelCase:				Any     =	None
        else:
           raise ValueError(F'Unsupported norm_in_type: {norm_in_type}.'							)

        __lowerCamelCase:				List[Any]     =	nn.LayerNorm(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Any     =	nn.Linear(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Optional[Any]     =	torch.full(
            [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]    ,	-1_0000.0							)
        causal_attention_mask.triu_(1							)
        __lowerCamelCase:				Any     =	causal_attention_mask[None, ...]
        self.register_buffer("""causal_attention_mask"""    ,	SCREAMING_SNAKE_CASE_    ,	persistent=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Any     =	nn.Parameter(torch.zeros(1    ,	SCREAMING_SNAKE_CASE_							)							)
        __lowerCamelCase:				int     =	nn.Parameter(torch.zeros(1    ,	SCREAMING_SNAKE_CASE_							)							)





     @property
     # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        __lowerCamelCase:				List[str]     =	{}

        def fn_recursive_add_processors(SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				torch.nn.Module    ,	SCREAMING_SNAKE_CASE_					:				Dict[str, AttentionProcessor]							):
           if hasattr(SCREAMING_SNAKE_CASE_    ,	"""set_processor"""							):
              __lowerCamelCase:				Union[str, Any]     =	module.processor

           for sub_name, child in module.named_children():
              fn_recursive_add_processors(F'{name}.{sub_name}'    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)

           return processors

        for name, module in self.named_children():
           fn_recursive_add_processors(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)

        return processors





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Union[AttentionProcessor, Dict[str, AttentionProcessor]]							):
        __lowerCamelCase:				Tuple     =	len(self.attn_processors.keys()							)

        if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) and len(SCREAMING_SNAKE_CASE_							) != count:
           raise ValueError(
               F'A dict of processors was passed, but the number of processors {len(SCREAMING_SNAKE_CASE_							)} does not match the'
               F' number of attention layers: {count}. Please make sure to pass {count} processor classes.'							)

        def fn_recursive_attn_processor(SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				torch.nn.Module    ,	SCREAMING_SNAKE_CASE_					:				str							):
           if hasattr(SCREAMING_SNAKE_CASE_    ,	"""set_processor"""							):
              if not isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							):
                 module.set_processor(SCREAMING_SNAKE_CASE_							)
              else:
                 module.set_processor(processor.pop(F'{name}.processor'							)							)

           for sub_name, child in module.named_children():
              fn_recursive_attn_processor(F'{name}.{sub_name}'    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)

        for name, module in self.named_children():
           fn_recursive_attn_processor(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        self.set_attn_processor(AttnProcessor()							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				Union[torch.Tensor, float, int]    ,	SCREAMING_SNAKE_CASE_					:				torch.FloatTensor    ,	SCREAMING_SNAKE_CASE_					:				Optional[torch.FloatTensor] = None    ,	SCREAMING_SNAKE_CASE_					:				Optional[torch.BoolTensor] = None    ,	SCREAMING_SNAKE_CASE_					:				bool = True    ,	):
        __lowerCamelCase:				Any     =	hidden_states.shape[0]

        __lowerCamelCase:				Any     =	timestep
        if not torch.is_tensor(SCREAMING_SNAKE_CASE_							):
           __lowerCamelCase:				int     =	torch.tensor([timesteps]    ,	dtype=torch.long    ,	device=hidden_states.device							)
        elif torch.is_tensor(SCREAMING_SNAKE_CASE_							) and len(timesteps.shape							) == 0:
           __lowerCamelCase:				Dict     =	timesteps[None].to(hidden_states.device							)

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        __lowerCamelCase:				Optional[int]     =	timesteps * torch.ones(SCREAMING_SNAKE_CASE_    ,	dtype=timesteps.dtype    ,	device=timesteps.device							)

        __lowerCamelCase:				Any     =	self.time_proj(SCREAMING_SNAKE_CASE_							)

        # timesteps does not contain any weights and will always return f32 tensors
        # but time_embedding might be fp16, so we need to cast here.
        __lowerCamelCase:				Any     =	timesteps_projected.to(dtype=self.dtype							)
        __lowerCamelCase:				List[Any]     =	self.time_embedding(SCREAMING_SNAKE_CASE_							)

        if self.embedding_proj_norm is not None:
           __lowerCamelCase:				Any     =	self.embedding_proj_norm(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				str     =	self.embedding_proj(SCREAMING_SNAKE_CASE_							)
        if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
           __lowerCamelCase:				List[Any]     =	self.encoder_hidden_states_proj(SCREAMING_SNAKE_CASE_							)
        elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
           raise ValueError("""`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set"""							)

        __lowerCamelCase:				List[Any]     =	self.proj_in(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				str     =	self.positional_embedding.to(hidden_states.dtype							)

        __lowerCamelCase:				List[str]     =	[]
        __lowerCamelCase:				Dict     =	0

        if encoder_hidden_states is not None:
           additional_embeds.append(SCREAMING_SNAKE_CASE_							)
           additional_embeddings_len += encoder_hidden_states.shape[1]

        if len(proj_embeddings.shape							) == 2:
           __lowerCamelCase:				Dict     =	proj_embeddings[:, None, :]

        if len(hidden_states.shape							) == 2:
           __lowerCamelCase:				int     =	hidden_states[:, None, :]

        __lowerCamelCase:				List[Any]     =	additional_embeds + [
            proj_embeddings,
            time_embeddings[:, None, :],
            hidden_states,
        ]

        if self.prd_embedding is not None:
           __lowerCamelCase:				Dict     =	self.prd_embedding.to(hidden_states.dtype							).expand(SCREAMING_SNAKE_CASE_    ,	-1    ,	-1							)
           additional_embeds.append(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Union[str, Any]     =	torch.cat(
            SCREAMING_SNAKE_CASE_    ,	dim=1    ,	)

        # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
        __lowerCamelCase:				Optional[Any]     =	additional_embeddings_len + proj_embeddings.shape[1] + 1
        if positional_embeddings.shape[1] < hidden_states.shape[1]:
           __lowerCamelCase:				Any     =	F.pad(
               SCREAMING_SNAKE_CASE_    ,	(
                   0,
                   0,
                   additional_embeddings_len,
                   self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
               )    ,	value=0.0    ,	)

        __lowerCamelCase:				List[str]     =	hidden_states + positional_embeddings

        if attention_mask is not None:
           __lowerCamelCase:				Tuple     =	(1 - attention_mask.to(hidden_states.dtype							)) * -1_0000.0
           __lowerCamelCase:				Dict     =	F.pad(SCREAMING_SNAKE_CASE_    ,	(0, self.additional_embeddings)    ,	value=0.0							)
           __lowerCamelCase:				Optional[int]     =	(attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype							)
           __lowerCamelCase:				Dict     =	attention_mask.repeat_interleave(self.config.num_attention_heads    ,	dim=0							)

        if self.norm_in is not None:
           __lowerCamelCase:				List[Any]     =	self.norm_in(SCREAMING_SNAKE_CASE_							)

        for block in self.transformer_blocks:
           __lowerCamelCase:				List[str]     =	block(SCREAMING_SNAKE_CASE_    ,	attention_mask=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Dict     =	self.norm_out(SCREAMING_SNAKE_CASE_							)

        if self.prd_embedding is not None:
           __lowerCamelCase:				List[Any]     =	hidden_states[:, -1]
        else:
           __lowerCamelCase:				List[str]     =	hidden_states[:, additional_embeddings_len:]

        __lowerCamelCase:				Any     =	self.proj_to_clip_embeddings(SCREAMING_SNAKE_CASE_							)

        if not return_dict:
           return (predicted_image_embedding,)

        return PriorTransformerOutput(predicted_image_embedding=SCREAMING_SNAKE_CASE_							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Any							):
        __lowerCamelCase:				List[str]     =	(prior_latents * self.clip_std) + self.clip_mean
        return prior_latents






import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


_A		:				List[str]									=       logging.get_logger(__name__)


_A		:				Any									=       {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all LED models at https://huggingface.co/models?filter=LED
_A		:				int									=       {
    '''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		:				Dict									=       {
    '''allenai/led-base-16384''': 16_384,
}



@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def     __lowerCAmelCase						(							)       ->							List[Any]:
   __lowerCamelCase:				Optional[Any]     =	(
       list(range(ord("""!""" )						,      ord("""~""" ) + 1 ) ) + list(range(ord("""¡""" )						,      ord("""¬""" ) + 1 ) ) + list(range(ord("""®""" )						,      ord("""ÿ""" ) + 1 ) )
   )
   __lowerCamelCase:				List[str]     =	bs[:]
   __lowerCamelCase:				Tuple     =	0
   for b in range(2**8 ):
      if b not in bs:
         bs.append(snake_case )
         cs.append(2**8 + n )
         n += 1
   __lowerCamelCase:				List[Any]     =	[chr(snake_case ) for n in cs]
   return dict(zip(snake_case						,      snake_case ) )



def     __lowerCAmelCase						(							snake_case	:    List[str] )       ->							str:
   __lowerCamelCase:				int     =	set()
   __lowerCamelCase:				str     =	word[0]
   for char in word[1:]:
      pairs.add((prev_char, char) )
      __lowerCamelCase:				Any     =	char
   return pairs







class        a					(			_UpperCAmelCase   ):
     UpperCAmelCase__ :  Optional[Any]						      =	VOCAB_FILES_NAMES
     UpperCAmelCase__ :  Optional[Any]						      =	PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__ :  List[str]						      =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__ :  List[str]						      =	["input_ids", "attention_mask"]





     def __init__(    self					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Dict="replace"    ,	SCREAMING_SNAKE_CASE_					:				Dict="<s>"    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]="</s>"    ,	SCREAMING_SNAKE_CASE_					:				int="</s>"    ,	SCREAMING_SNAKE_CASE_					:				Any="<s>"    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]="<unk>"    ,	SCREAMING_SNAKE_CASE_					:				Tuple="<pad>"    ,	SCREAMING_SNAKE_CASE_					:				Dict="<mask>"    ,	SCREAMING_SNAKE_CASE_					:				int=False    ,	**SCREAMING_SNAKE_CASE_					:				List[str]    ,	):
        __lowerCamelCase:				Tuple     =	AddedToken(SCREAMING_SNAKE_CASE_    ,	lstrip=SCREAMING_SNAKE_CASE_    ,	rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) else bos_token
        __lowerCamelCase:				Union[str, Any]     =	AddedToken(SCREAMING_SNAKE_CASE_    ,	lstrip=SCREAMING_SNAKE_CASE_    ,	rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) else eos_token
        __lowerCamelCase:				List[Any]     =	AddedToken(SCREAMING_SNAKE_CASE_    ,	lstrip=SCREAMING_SNAKE_CASE_    ,	rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) else sep_token
        __lowerCamelCase:				Dict     =	AddedToken(SCREAMING_SNAKE_CASE_    ,	lstrip=SCREAMING_SNAKE_CASE_    ,	rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) else cls_token
        __lowerCamelCase:				Any     =	AddedToken(SCREAMING_SNAKE_CASE_    ,	lstrip=SCREAMING_SNAKE_CASE_    ,	rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) else unk_token
        __lowerCamelCase:				Tuple     =	AddedToken(SCREAMING_SNAKE_CASE_    ,	lstrip=SCREAMING_SNAKE_CASE_    ,	rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) else pad_token

        # Mask token behave like a normal word, i.e. include the space before it
        __lowerCamelCase:				List[str]     =	AddedToken(SCREAMING_SNAKE_CASE_    ,	lstrip=SCREAMING_SNAKE_CASE_    ,	rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) else mask_token

        super().__init__(
            errors=SCREAMING_SNAKE_CASE_    ,	bos_token=SCREAMING_SNAKE_CASE_    ,	eos_token=SCREAMING_SNAKE_CASE_    ,	unk_token=SCREAMING_SNAKE_CASE_    ,	sep_token=SCREAMING_SNAKE_CASE_    ,	cls_token=SCREAMING_SNAKE_CASE_    ,	pad_token=SCREAMING_SNAKE_CASE_    ,	mask_token=SCREAMING_SNAKE_CASE_    ,	add_prefix_space=SCREAMING_SNAKE_CASE_    ,	**SCREAMING_SNAKE_CASE_    ,	)

        with open(SCREAMING_SNAKE_CASE_    ,	encoding="""utf-8"""							) as vocab_handle:
           __lowerCamelCase:				str     =	json.load(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[Any]     =	{v: k for k, v in self.encoder.items()}
        __lowerCamelCase:				Any     =	errors  # how to handle errors in decoding
        __lowerCamelCase:				List[Any]     =	bytes_to_unicode()
        __lowerCamelCase:				Optional[int]     =	{v: k for k, v in self.byte_encoder.items()}
        with open(SCREAMING_SNAKE_CASE_    ,	encoding="""utf-8"""							) as merges_handle:
           __lowerCamelCase:				List[Any]     =	merges_handle.read().split("""\n"""							)[1:-1]
        __lowerCamelCase:				Tuple     =	[tuple(merge.split()							) for merge in bpe_merges]
        __lowerCamelCase:				str     =	dict(zip(SCREAMING_SNAKE_CASE_    ,	range(len(SCREAMING_SNAKE_CASE_							)							)							)							)
        __lowerCamelCase:				List[str]     =	{}
        __lowerCamelCase:				Optional[int]     =	add_prefix_space

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        __lowerCamelCase:				str     =	re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""							)





     @property
     # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        return len(self.encoder							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        return dict(self.encoder    ,	**self.added_tokens_encoder							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				int							):
        if token in self.cache:
           return self.cache[token]
        __lowerCamelCase:				List[str]     =	tuple(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[Any]     =	get_pairs(SCREAMING_SNAKE_CASE_							)

        if not pairs:
           return token

        while True:
           __lowerCamelCase:				List[str]     =	min(SCREAMING_SNAKE_CASE_    ,	key=lambda SCREAMING_SNAKE_CASE_							: self.bpe_ranks.get(SCREAMING_SNAKE_CASE_    ,	float("""inf"""							)							)							)
           if bigram not in self.bpe_ranks:
              break
           __lowerCamelCase   ,     __lowerCamelCase:				List[str]     =	bigram
           __lowerCamelCase:				Optional[int]     =	[]
           __lowerCamelCase:				Optional[Any]     =	0
           while i < len(SCREAMING_SNAKE_CASE_							):
              try:
                 __lowerCamelCase:				int     =	word.index(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
              except ValueError:
                 new_word.extend(word[i:]							)
                 break
              else:
                 new_word.extend(word[i:j]							)
                 __lowerCamelCase:				Tuple     =	j

              if word[i] == first and i < len(SCREAMING_SNAKE_CASE_							) - 1 and word[i + 1] == second:
                 new_word.append(first + second							)
                 i += 2
              else:
                 new_word.append(word[i]							)
                 i += 1
           __lowerCamelCase:				Optional[int]     =	tuple(SCREAMING_SNAKE_CASE_							)
           __lowerCamelCase:				Any     =	new_word
           if len(SCREAMING_SNAKE_CASE_							) == 1:
              break
           else:
              __lowerCamelCase:				Tuple     =	get_pairs(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	""" """.join(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Any     =	word
        return word





     def  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				Any							):
        __lowerCamelCase:				Any     =	[]
        for token in re.findall(self.pat    ,	SCREAMING_SNAKE_CASE_							):
           __lowerCamelCase:				int     =	"""""".join(
               self.byte_encoder[b] for b in token.encode("""utf-8"""							)							)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
           bpe_tokens.extend(bpe_token for bpe_token in self.bpe(SCREAMING_SNAKE_CASE_							).split(""" """							)							)
        return bpe_tokens





     def  SCREAMING_SNAKE_CASE__       (    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				Tuple							):
        return self.encoder.get(SCREAMING_SNAKE_CASE_    ,	self.encoder.get(self.unk_token							)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				str							):
        return self.decoder.get(SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]							):
        __lowerCamelCase:				Any     =	"""""".join(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				str     =	bytearray([self.byte_decoder[c] for c in text]							).decode("""utf-8"""    ,	errors=self.errors							)
        return text





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				Optional[str] = None							):
        if not os.path.isdir(SCREAMING_SNAKE_CASE_							):
           logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
           return
        __lowerCamelCase:				Dict     =	os.path.join(
            SCREAMING_SNAKE_CASE_    ,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)
        __lowerCamelCase:				str     =	os.path.join(
            SCREAMING_SNAKE_CASE_    ,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]							)

        with open(SCREAMING_SNAKE_CASE_    ,	"""w"""    ,	encoding="""utf-8"""							) as f:
           f.write(json.dumps(self.encoder    ,	indent=2    ,	sort_keys=SCREAMING_SNAKE_CASE_    ,	ensure_ascii=SCREAMING_SNAKE_CASE_							) + """\n"""							)

        __lowerCamelCase:				List[str]     =	0
        with open(SCREAMING_SNAKE_CASE_    ,	"""w"""    ,	encoding="""utf-8"""							) as writer:
           writer.write("""#version: 0.2\n"""							)
           for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,	key=lambda SCREAMING_SNAKE_CASE_							: kv[1]							):
              if index != token_index:
                 logger.warning(
                     F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                     """ Please check that the tokenizer is not corrupted!"""							)
                 __lowerCamelCase:				Tuple     =	token_index
              writer.write(""" """.join(SCREAMING_SNAKE_CASE_							) + """\n"""							)
              index += 1

        return vocab_file, merge_file





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				List[int]    ,	SCREAMING_SNAKE_CASE_					:				Optional[List[int]] = None							):
        if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        __lowerCamelCase:				List[str]     =	[self.cls_token_id]
        __lowerCamelCase:				int     =	[self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep





     def  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				List[int]    ,	SCREAMING_SNAKE_CASE_					:				Optional[List[int]] = None    ,	SCREAMING_SNAKE_CASE_					:				bool = False							):
        if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=SCREAMING_SNAKE_CASE_    ,	token_ids_a=SCREAMING_SNAKE_CASE_    ,	already_has_special_tokens=SCREAMING_SNAKE_CASE_							)

        if token_ids_a is None:
           return [1] + ([0] * len(SCREAMING_SNAKE_CASE_							)) + [1]
        return [1] + ([0] * len(SCREAMING_SNAKE_CASE_							)) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE_							)) + [1]





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				List[int]    ,	SCREAMING_SNAKE_CASE_					:				Optional[List[int]] = None							):
        __lowerCamelCase:				int     =	[self.sep_token_id]
        __lowerCamelCase:				Tuple     =	[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					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=False    ,	**SCREAMING_SNAKE_CASE_					:				Union[str, Any]							):
        __lowerCamelCase:				Any     =	kwargs.pop("""add_prefix_space"""    ,	self.add_prefix_space							)
        if (is_split_into_words or add_prefix_space) and (len(SCREAMING_SNAKE_CASE_							) > 0 and not text[0].isspace()):
           __lowerCamelCase:				Dict     =	""" """ + text
        return (text, kwargs)







     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Union[Dict[str, EncodedInput], BatchEncoding]    ,	SCREAMING_SNAKE_CASE_					:				Optional[int] = None    ,	SCREAMING_SNAKE_CASE_					:				PaddingStrategy = PaddingStrategy.DO_NOT_PAD    ,	SCREAMING_SNAKE_CASE_					:				Optional[int] = None    ,	SCREAMING_SNAKE_CASE_					:				Optional[bool] = None    ,	):
        __lowerCamelCase:				Optional[Any]     =	super()._pad(
            encoded_inputs=SCREAMING_SNAKE_CASE_    ,	max_length=SCREAMING_SNAKE_CASE_    ,	padding_strategy=SCREAMING_SNAKE_CASE_    ,	pad_to_multiple_of=SCREAMING_SNAKE_CASE_    ,	return_attention_mask=SCREAMING_SNAKE_CASE_    ,	)

        # Load from model defaults
        if return_attention_mask is None:
           __lowerCamelCase:				Optional[Any]     =	"""attention_mask""" in self.model_input_names

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

           if needs_to_be_padded:
              __lowerCamelCase:				Union[str, Any]     =	len(SCREAMING_SNAKE_CASE_							) - 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:				str     =	(
                     encoded_inputs["""global_attention_mask"""] + [-1] * difference
                 )
              elif self.padding_side == "left":
                 __lowerCamelCase:				Optional[int]     =	[-1] * difference + encoded_inputs[
                     """global_attention_mask"""
                 ]
              else:
                 raise ValueError("""Invalid padding strategy:""" + str(self.padding_side							)							)

        return encoded_inputs


