

"""simple docstring"""




import argparse
import json
import os

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_  :      Tuple							=      logging.get_logger(__name__)



def 						UpperCAmelCase__ (					A__ )   ->   List[Any]:




   """simple docstring"""
   lowerCamelCase__						       =      ConvNextVaConfig()

   if "atto" in checkpoint_url:
      lowerCamelCase__						       =      [2, 2, 6, 2]
      lowerCamelCase__						       =      [40, 80, 160, 320]
   if "femto" in checkpoint_url:
      lowerCamelCase__						       =      [2, 2, 6, 2]
      lowerCamelCase__						       =      [48, 96, 192, 384]
   if "pico" in checkpoint_url:
      lowerCamelCase__						       =      [2, 2, 6, 2]
      lowerCamelCase__						       =      [64, 128, 256, 512]
   if "nano" in checkpoint_url:
      lowerCamelCase__						       =      [2, 2, 8, 2]
      lowerCamelCase__						       =      [80, 160, 320, 640]
   if "tiny" in checkpoint_url:
      lowerCamelCase__						       =      [3, 3, 9, 3]
      lowerCamelCase__						       =      [96, 192, 384, 768]
   if "base" in checkpoint_url:
      lowerCamelCase__						       =      [3, 3, 27, 3]
      lowerCamelCase__						       =      [128, 256, 512, 1024]
   if "large" in checkpoint_url:
      lowerCamelCase__						       =      [3, 3, 27, 3]
      lowerCamelCase__						       =      [192, 384, 768, 1536]
   if "huge" in checkpoint_url:
      lowerCamelCase__						       =      [3, 3, 27, 3]
      lowerCamelCase__						       =      [352, 704, 1408, 2816]

   lowerCamelCase__						       =      1000
   lowerCamelCase__						       =      "imagenet-1k-id2label.json"
   lowerCamelCase__						       =      (1, 1000)

   lowerCamelCase__						       =      "huggingface/label-files"
   lowerCamelCase__						       =      num_labels
   lowerCamelCase__						       =      json.load(open(hf_hub_download(A__						,     A__						,     repo_type="dataset" )						,     "r" ) )
   lowerCamelCase__						       =      {int(A__ ): v for k, v in idalabel.items()}

   lowerCamelCase__						       =      idalabel
   lowerCamelCase__						       =      {v: k for k, v in idalabel.items()}
   lowerCamelCase__						       =      hidden_sizes
   lowerCamelCase__						       =      depths

   return config, expected_shape



def 						UpperCAmelCase__ (					A__ )   ->   int:




   """simple docstring"""
   if "downsample_layers.0.0" in name:
      lowerCamelCase__						       =      name.replace("downsample_layers.0.0"						,     "embeddings.patch_embeddings" )
   if "downsample_layers.0.1" in name:
      lowerCamelCase__						       =      name.replace("downsample_layers.0.1"						,     "embeddings.norm" )  # we rename to layernorm later on
   if "downsample_layers.1.0" in name:
      lowerCamelCase__						       =      name.replace("downsample_layers.1.0"						,     "stages.1.downsampling_layer.0" )
   if "downsample_layers.1.1" in name:
      lowerCamelCase__						       =      name.replace("downsample_layers.1.1"						,     "stages.1.downsampling_layer.1" )
   if "downsample_layers.2.0" in name:
      lowerCamelCase__						       =      name.replace("downsample_layers.2.0"						,     "stages.2.downsampling_layer.0" )
   if "downsample_layers.2.1" in name:
      lowerCamelCase__						       =      name.replace("downsample_layers.2.1"						,     "stages.2.downsampling_layer.1" )
   if "downsample_layers.3.0" in name:
      lowerCamelCase__						       =      name.replace("downsample_layers.3.0"						,     "stages.3.downsampling_layer.0" )
   if "downsample_layers.3.1" in name:
      lowerCamelCase__						       =      name.replace("downsample_layers.3.1"						,     "stages.3.downsampling_layer.1" )
   if "stages" in name and "downsampling_layer" not in name:
      # stages.0.0. for instance should be renamed to stages.0.layers.0.
      lowerCamelCase__						       =      name[: len("stages.0" )] + ".layers" + name[len("stages.0" ) :]
   if "gamma" in name:
      lowerCamelCase__						       =      name.replace("gamma"						,     "weight" )
   if "beta" in name:
      lowerCamelCase__						       =      name.replace("beta"						,     "bias" )
   if "stages" in name:
      lowerCamelCase__						       =      name.replace("stages"						,     "encoder.stages" )
   if "norm" in name:
      lowerCamelCase__						       =      name.replace("norm"						,     "layernorm" )
   if "head" in name:
      lowerCamelCase__						       =      name.replace("head"						,     "classifier" )

   return name



def 						UpperCAmelCase__ (					)   ->   Any:




   """simple docstring"""
   lowerCamelCase__						       =      "http://images.cocodataset.org/val2017/000000039769.jpg"
   lowerCamelCase__						       =      Image.open(requests.get(A__						,     stream=A__ ).raw )
   return im



def 						UpperCAmelCase__ (					A__ )   ->   Dict:




   """simple docstring"""
   if "224" in checkpoint_url:
      lowerCamelCase__						       =      224
      lowerCamelCase__						       =      224 / 256
   elif "384" in checkpoint_url:
      lowerCamelCase__						       =      384
      lowerCamelCase__						       =      None
   else:
      lowerCamelCase__						       =      512
      lowerCamelCase__						       =      None

   return ConvNextImageProcessor(
       size=A__						,     crop_pct=A__						,     image_mean=[0.485, 0.456, 0.406]						,     image_std=[0.229, 0.224, 0.225]						,     resample=PILImageResampling.BICUBIC						,     )



@torch.no_grad()
def 						UpperCAmelCase__ (					A__						,     A__						,     A__						,     A__ )   ->   Optional[int]:




   """simple docstring"""
   print("Downloading original model from checkpoint..." )
   # define ConvNeXTV2 configuration based on URL
   lowerCamelCase__       ,							lowerCamelCase__						       =      get_convnextva_config(A__ )
   # load original state_dict from URL
   lowerCamelCase__						       =      torch.hub.load_state_dict_from_url(A__ )["model"]

   print("Converting model parameters..." )
   # rename keys
   for key in state_dict.copy().keys():
      lowerCamelCase__						       =      state_dict.pop(A__ )
      lowerCamelCase__						       =      val
   # add prefix to all keys expect classifier head
   for key in state_dict.copy().keys():
      lowerCamelCase__						       =      state_dict.pop(A__ )
      if not key.startswith("classifier" ):
         lowerCamelCase__						       =      "convnextv2." + key
      lowerCamelCase__						       =      val

   # load HuggingFace model
   lowerCamelCase__						       =      ConvNextVaForImageClassification(A__ )
   model.load_state_dict(A__ )
   model.eval()

   # Check outputs on an image, prepared by ConvNextImageProcessor
   lowerCamelCase__						       =      convert_preprocessor(A__ )
   lowerCamelCase__						       =      preprocessor(images=prepare_img()						,     return_tensors="pt" )
   lowerCamelCase__						       =      model(**A__ ).logits

   # note: the logits below were obtained without center cropping
   if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
      lowerCamelCase__						       =      torch.tensor([-0.3930, 0.1747, -0.5246, 0.4177, 0.4295] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
      lowerCamelCase__						       =      torch.tensor([-0.1727, -0.5341, -0.7818, -0.4745, -0.6566] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
      lowerCamelCase__						       =      torch.tensor([-0.0333, 0.1563, -0.9137, 0.1054, 0.0381] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
      lowerCamelCase__						       =      torch.tensor([-0.1744, -0.1555, -0.0713, 0.0950, -0.1431] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
      lowerCamelCase__						       =      torch.tensor([0.9996, 0.1966, -0.4386, -0.3472, 0.6661] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
      lowerCamelCase__						       =      torch.tensor([-0.2553, -0.6708, -0.1359, 0.2518, -0.2488] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
      lowerCamelCase__						       =      torch.tensor([-0.0673, -0.5627, -0.3753, -0.2722, 0.0178] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
      lowerCamelCase__						       =      torch.tensor([-0.6377, -0.7458, -0.2150, 0.1184, -0.0597] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
      lowerCamelCase__						       =      torch.tensor([1.0799, 0.2322, -0.8860, 1.0219, 0.6231] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
      lowerCamelCase__						       =      torch.tensor([0.3766, 0.4917, -1.1426, 0.9942, 0.6024] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
      lowerCamelCase__						       =      torch.tensor([0.4220, -0.6919, -0.4317, -0.2881, -0.6609] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
      lowerCamelCase__						       =      torch.tensor([0.1082, -0.8286, -0.5095, 0.4681, -0.8085] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
      lowerCamelCase__						       =      torch.tensor([-0.2419, -0.6221, 0.2176, -0.0980, -0.7527] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
      lowerCamelCase__						       =      torch.tensor([0.0391, -0.4371, 0.3786, 0.1251, -0.2784] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
      lowerCamelCase__						       =      torch.tensor([-0.0504, 0.5636, -0.1729, -0.6507, -0.3949] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
      lowerCamelCase__						       =      torch.tensor([0.3560, 0.9486, 0.3149, -0.2667, -0.5138] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
      lowerCamelCase__						       =      torch.tensor([-0.2469, -0.4550, -0.5853, -0.0810, 0.0309] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
      lowerCamelCase__						       =      torch.tensor([-0.3090, 0.0802, -0.0682, -0.1979, -0.2826] )
   else:
      raise ValueError(f'Unknown URL: {checkpoint_url}' )

   assert torch.allclose(logits[0, :5]						,     A__						,     atol=1E-3 )
   assert logits.shape == expected_shape
   print("Model outputs match the original results!" )

   if save_model:
      print("Saving model to local..." )
      # Create folder to save model
      if not os.path.isdir(A__ ):
         os.mkdir(A__ )

      model.save_pretrained(A__ )
      preprocessor.save_pretrained(A__ )

   lowerCamelCase__						       =      "convnextv2"
   if "atto" in checkpoint_url:
      model_name += "-atto"
   if "femto" in checkpoint_url:
      model_name += "-femto"
   if "pico" in checkpoint_url:
      model_name += "-pico"
   if "nano" in checkpoint_url:
      model_name += "-nano"
   elif "tiny" in checkpoint_url:
      model_name += "-tiny"
   elif "base" in checkpoint_url:
      model_name += "-base"
   elif "large" in checkpoint_url:
      model_name += "-large"
   elif "huge" in checkpoint_url:
      model_name += "-huge"
   if "22k" in checkpoint_url and "1k" not in checkpoint_url:
      model_name += "-22k"
   elif "22k" in checkpoint_url and "1k" in checkpoint_url:
      model_name += "-22k-1k"
   elif "1k" in checkpoint_url:
      model_name += "-1k"
   if "224" in checkpoint_url:
      model_name += "-224"
   elif "384" in checkpoint_url:
      model_name += "-384"
   elif "512" in checkpoint_url:
      model_name += "-512"

   if push_to_hub:
      print(f'Pushing {model_name} to the hub...' )
      model.push_to_hub(A__ )
      preprocessor.push_to_hub(A__ )


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_  :      Union[str, Any]							=      argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--checkpoint_url''',
         default='''https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt''',
         type=str,
         help='''URL of the original ConvNeXTV2 checkpoint you\'d like to convert.''',
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''',
         default='''model''',
         type=str,
         help='''Path to the output PyTorch model directory.''',
     )
     parser.add_argument('''--save_model''', action='''store_true''', help='''Save model to local''')
     parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image preprocessor to the hub''')

     SCREAMING_SNAKE_CASE_  :      List[Any]							=      parser.parse_args()
     convert_convnextva_checkpoint(
         args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
     )







"""simple docstring"""




import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation

SCREAMING_SNAKE_CASE_  :      Optional[int]							=      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_  :      Optional[int]							=      {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

SCREAMING_SNAKE_CASE_  :      Dict							=      {
    '''vocab_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/vocab.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/vocab.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/vocab.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/vocab.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/merges.txt''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/merges.txt''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/merges.txt''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/merges.txt''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/merges.txt''',
    },
}

SCREAMING_SNAKE_CASE_  :      Optional[Any]							=      {
    '''gpt2''': 1024,
    '''gpt2-medium''': 1024,
    '''gpt2-large''': 1024,
    '''gpt2-xl''': 1024,
    '''distilgpt2''': 1024,
}



@lru_cache()
def 						UpperCAmelCase__ (					)   ->   Optional[int]:




   """simple docstring"""
   lowerCamelCase__						       =      (
       list(range(ord("!" )						,     ord("~" ) + 1 ) ) + list(range(ord("¡" )						,     ord("¬" ) + 1 ) ) + list(range(ord("®" )						,     ord("ÿ" ) + 1 ) )
   )
   lowerCamelCase__						       =      bs[:]
   lowerCamelCase__						       =      0
   for b in range(2**8 ):
      if b not in bs:
         bs.append(A__ )
         cs.append(2**8 + n )
         n += 1
   lowerCamelCase__						       =      [chr(A__ ) for n in cs]
   return dict(zip(A__						,     A__ ) )



def 						UpperCAmelCase__ (					A__ )   ->   int:




   """simple docstring"""
   lowerCamelCase__						       =      set()
   lowerCamelCase__						       =      word[0]
   for char in word[1:]:
      pairs.add((prev_char, char) )
      lowerCamelCase__						       =      char
   return pairs


class 		_A							(      __a       ):
      __a             =						VOCAB_FILES_NAMES
      __a             =						PRETRAINED_VOCAB_FILES_MAP
      __a             =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __a             =						['input_ids', 'attention_mask']





      def __init__(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__="replace"     ,    SCREAMING_SNAKE_CASE__="<|endoftext|>"     ,    SCREAMING_SNAKE_CASE__="<|endoftext|>"     ,    SCREAMING_SNAKE_CASE__="<|endoftext|>"     ,    SCREAMING_SNAKE_CASE__=None     ,    SCREAMING_SNAKE_CASE__=False     ,    SCREAMING_SNAKE_CASE__=False     ,    **SCREAMING_SNAKE_CASE__     ,    ) ->       List[str]:
         lowerCamelCase__						       =      AddedToken(SCREAMING_SNAKE_CASE__     ,    lstrip=SCREAMING_SNAKE_CASE__     ,    rstrip=SCREAMING_SNAKE_CASE__		) if isinstance(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		) else bos_token
         lowerCamelCase__						       =      AddedToken(SCREAMING_SNAKE_CASE__     ,    lstrip=SCREAMING_SNAKE_CASE__     ,    rstrip=SCREAMING_SNAKE_CASE__		) if isinstance(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		) else eos_token
         lowerCamelCase__						       =      AddedToken(SCREAMING_SNAKE_CASE__     ,    lstrip=SCREAMING_SNAKE_CASE__     ,    rstrip=SCREAMING_SNAKE_CASE__		) if isinstance(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		) else unk_token
         lowerCamelCase__						       =      AddedToken(SCREAMING_SNAKE_CASE__     ,    lstrip=SCREAMING_SNAKE_CASE__     ,    rstrip=SCREAMING_SNAKE_CASE__		) if isinstance(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		) else pad_token
         super().__init__(
             errors=SCREAMING_SNAKE_CASE__     ,    unk_token=SCREAMING_SNAKE_CASE__     ,    bos_token=SCREAMING_SNAKE_CASE__     ,    eos_token=SCREAMING_SNAKE_CASE__     ,    pad_token=SCREAMING_SNAKE_CASE__     ,    add_prefix_space=SCREAMING_SNAKE_CASE__     ,    add_bos_token=SCREAMING_SNAKE_CASE__     ,    **SCREAMING_SNAKE_CASE__     ,    )
         lowerCamelCase__						       =      add_bos_token

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

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





      @property
      def        _lowerCamelCase	(	self		) ->       Optional[int]:
         return len(self.encoder		)





      def        _lowerCamelCase	(	self		) ->       str:
         return dict(self.encoder     ,    **self.added_tokens_encoder		)





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__		) ->       Any:
         if token in self.cache:
            return self.cache[token]
         lowerCamelCase__						       =      tuple(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      get_pairs(SCREAMING_SNAKE_CASE__		)

         if not pairs:
            return token

         while True:
            lowerCamelCase__						       =      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__						       =      bigram
            lowerCamelCase__						       =      []
            lowerCamelCase__						       =      0
            while i < len(SCREAMING_SNAKE_CASE__		):
               try:
                  lowerCamelCase__						       =      word.index(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)
               except ValueError:
                  new_word.extend(word[i:]		)
                  break
               else:
                  new_word.extend(word[i:j]		)
                  lowerCamelCase__						       =      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__						       =      tuple(SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						       =      new_word
            if len(SCREAMING_SNAKE_CASE__		) == 1:
               break
            else:
               lowerCamelCase__						       =      get_pairs(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      " ".join(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      word
         return word





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__=None		) ->       Optional[int]:
         if self.add_bos_token:
            lowerCamelCase__						       =      [self.bos_token_id]
         else:
            lowerCamelCase__						       =      []

         lowerCamelCase__						       =      bos_token_ids + token_ids_a

         if token_ids_a is None:
            return output

         return output + bos_token_ids + token_ids_a





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__ = None     ,    SCREAMING_SNAKE_CASE__ = False		) ->       List[int]:
         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 not self.add_bos_token:
            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__		))
         return [1] + ([0] * len(SCREAMING_SNAKE_CASE__		)) + [1] + ([0] * len(SCREAMING_SNAKE_CASE__		))





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__		) ->       Tuple:
         lowerCamelCase__						       =      []
         for token in re.findall(self.pat     ,    SCREAMING_SNAKE_CASE__		):
            lowerCamelCase__						       =      "".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        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__		) ->       Tuple:
         return self.encoder.get(SCREAMING_SNAKE_CASE__     ,    self.encoder.get(self.unk_token		)		)





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__		) ->       Any:
         return self.decoder.get(SCREAMING_SNAKE_CASE__		)





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__		) ->       str:
         lowerCamelCase__						       =      "".join(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      bytearray([self.byte_decoder[c] for c in text]		).decode("utf-8"     ,    errors=self.errors		)
         return text





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__ = None		) ->       Tuple[str]:
         if not os.path.isdir(SCREAMING_SNAKE_CASE__		):
            logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
            return
         lowerCamelCase__						       =      os.path.join(
             SCREAMING_SNAKE_CASE__     ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
         lowerCamelCase__						       =      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__						       =      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__						       =      token_index
               writer.write(" ".join(SCREAMING_SNAKE_CASE__		) + "\n"		)
               index += 1

         return vocab_file, merge_file





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__=False     ,    **SCREAMING_SNAKE_CASE__		) ->       int:
         lowerCamelCase__						       =      kwargs.pop("add_prefix_space"     ,    self.add_prefix_space		)
         if is_split_into_words or add_prefix_space:
            lowerCamelCase__						       =      " " + text
         return (text, kwargs)





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__		) ->       List[int]:
         lowerCamelCase__						       =      []
         for is_user, text in conversation.iter_texts():
            input_ids.extend(self.encode(SCREAMING_SNAKE_CASE__     ,    add_special_tokens=SCREAMING_SNAKE_CASE__		) + [self.eos_token_id]		)
         if len(SCREAMING_SNAKE_CASE__		) > self.model_max_length:
            lowerCamelCase__						       =      input_ids[-self.model_max_length :]
         return input_ids




