
'''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()
__lowercase	=    logging.get_logger(__name__)







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ):
		lowerCAmelCase_   :       Tuple    							=ConvNextVaConfig()

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

		lowerCAmelCase_   :       List[Any]    							=1000
		lowerCAmelCase_   :       Optional[int]    							='''imagenet-1k-id2label.json'''
		lowerCAmelCase_   :       int    							=(1, 1000)

		lowerCAmelCase_   :       str    							='''huggingface/label-files'''
		lowerCAmelCase_   :       Dict    							=num_labels
		lowerCAmelCase_   :       Dict    							=json.load(open(hf_hub_download(A__  ,					A__  ,					repo_type='''dataset'''  )  ,					'''r'''  )  )
		lowerCAmelCase_   :       Optional[int]    							={int(A__  ): v for k, v in idalabel.items()}

		lowerCAmelCase_   :       str    							=idalabel
		lowerCAmelCase_   :       Any    							={v: k for k, v in idalabel.items()}
		lowerCAmelCase_   :       Optional[int]    							=hidden_sizes
		lowerCAmelCase_   :       int    							=depths

		return config, expected_shape







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ):
		if "downsample_layers.0.0" in name:
				lowerCAmelCase_   :       Dict    							=name.replace('''downsample_layers.0.0'''  ,					'''embeddings.patch_embeddings'''  )
		if "downsample_layers.0.1" in name:
				lowerCAmelCase_   :       int    							=name.replace('''downsample_layers.0.1'''  ,					'''embeddings.norm'''  )  # we rename to layernorm later on
		if "downsample_layers.1.0" in name:
				lowerCAmelCase_   :       Any    							=name.replace('''downsample_layers.1.0'''  ,					'''stages.1.downsampling_layer.0'''  )
		if "downsample_layers.1.1" in name:
				lowerCAmelCase_   :       List[Any]    							=name.replace('''downsample_layers.1.1'''  ,					'''stages.1.downsampling_layer.1'''  )
		if "downsample_layers.2.0" in name:
				lowerCAmelCase_   :       Optional[Any]    							=name.replace('''downsample_layers.2.0'''  ,					'''stages.2.downsampling_layer.0'''  )
		if "downsample_layers.2.1" in name:
				lowerCAmelCase_   :       List[Any]    							=name.replace('''downsample_layers.2.1'''  ,					'''stages.2.downsampling_layer.1'''  )
		if "downsample_layers.3.0" in name:
				lowerCAmelCase_   :       List[str]    							=name.replace('''downsample_layers.3.0'''  ,					'''stages.3.downsampling_layer.0'''  )
		if "downsample_layers.3.1" in name:
				lowerCAmelCase_   :       Union[str, Any]    							=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_   :       Optional[Any]    							=name[: len('''stages.0'''  )] + '''.layers''' + name[len('''stages.0'''  ) :]
		if "gamma" in name:
				lowerCAmelCase_   :       List[Any]    							=name.replace('''gamma'''  ,					'''weight'''  )
		if "beta" in name:
				lowerCAmelCase_   :       List[str]    							=name.replace('''beta'''  ,					'''bias'''  )
		if "stages" in name:
				lowerCAmelCase_   :       Dict    							=name.replace('''stages'''  ,					'''encoder.stages'''  )
		if "norm" in name:
				lowerCAmelCase_   :       List[Any]    							=name.replace('''norm'''  ,					'''layernorm'''  )
		if "head" in name:
				lowerCAmelCase_   :       Union[str, Any]    							=name.replace('''head'''  ,					'''classifier'''  )

		return name







def 		SCREAMING_SNAKE_CASE__					(							):
		lowerCAmelCase_   :       int    							='''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowerCAmelCase_   :       Dict    							=Image.open(requests.get(A__  ,					stream=A__  ).raw  )
		return im







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ):
		if "224" in checkpoint_url:
				lowerCAmelCase_   :       Any    							=224
				lowerCAmelCase_   :       List[str]    							=224 / 256
		elif "384" in checkpoint_url:
				lowerCAmelCase_   :       Optional[int]    							=384
				lowerCAmelCase_   :       List[Any]    							=None
		else:
				lowerCAmelCase_   :       Optional[Any]    							=512
				lowerCAmelCase_   :       Optional[Any]    							=None

		return ConvNextImageProcessor(
		    size=A__  ,					crop_pct=A__  ,					image_mean=[0.4_85, 0.4_56, 0.4_06]  ,					image_std=[0.2_29, 0.2_24, 0.2_25]  ,					resample=PILImageResampling.BICUBIC  ,					)







@torch.no_grad()
def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		print('''Downloading original model from checkpoint...'''  )
		# define ConvNeXTV2 configuration based on URL
		lowerCAmelCase_							,		lowerCAmelCase_   :       List[Any]    							=get_convnextva_config(A__  )
		# load original state_dict from URL
		lowerCAmelCase_   :       Tuple    							=torch.hub.load_state_dict_from_url(A__  )['''model''']

		print('''Converting model parameters...'''  )
		# rename keys
		for key in state_dict.copy().keys():
				lowerCAmelCase_   :       Optional[Any]    							=state_dict.pop(A__  )
				lowerCAmelCase_   :       Tuple    							=val
		# add prefix to all keys expect classifier head
		for key in state_dict.copy().keys():
				lowerCAmelCase_   :       List[Any]    							=state_dict.pop(A__  )
				if not key.startswith('''classifier'''  ):
						lowerCAmelCase_   :       Optional[int]    							='''convnextv2.''' + key
				lowerCAmelCase_   :       List[Any]    							=val

		# load HuggingFace model
		lowerCAmelCase_   :       List[str]    							=ConvNextVaForImageClassification(A__  )
		model.load_state_dict(A__  )
		model.eval()

		# Check outputs on an image, prepared by ConvNextImageProcessor
		lowerCAmelCase_   :       Optional[Any]    							=convert_preprocessor(A__  )
		lowerCAmelCase_   :       List[str]    							=preprocessor(images=prepare_img()  ,					return_tensors='''pt'''  )
		lowerCAmelCase_   :       Optional[int]    							=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_   :       Tuple    							=torch.tensor([-0.39_30, 0.17_47, -0.52_46, 0.41_77, 0.42_95]  )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
				lowerCAmelCase_   :       List[str]    							=torch.tensor([-0.17_27, -0.53_41, -0.78_18, -0.47_45, -0.65_66]  )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
				lowerCAmelCase_   :       Optional[int]    							=torch.tensor([-0.03_33, 0.15_63, -0.91_37, 0.10_54, 0.03_81]  )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
				lowerCAmelCase_   :       Any    							=torch.tensor([-0.17_44, -0.15_55, -0.07_13, 0.09_50, -0.14_31]  )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
				lowerCAmelCase_   :       Union[str, Any]    							=torch.tensor([0.99_96, 0.19_66, -0.43_86, -0.34_72, 0.66_61]  )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
				lowerCAmelCase_   :       str    							=torch.tensor([-0.25_53, -0.67_08, -0.13_59, 0.25_18, -0.24_88]  )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
				lowerCAmelCase_   :       Optional[Any]    							=torch.tensor([-0.06_73, -0.56_27, -0.37_53, -0.27_22, 0.01_78]  )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
				lowerCAmelCase_   :       List[str]    							=torch.tensor([-0.63_77, -0.74_58, -0.21_50, 0.11_84, -0.05_97]  )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
				lowerCAmelCase_   :       Union[str, Any]    							=torch.tensor([1.07_99, 0.23_22, -0.88_60, 1.02_19, 0.62_31]  )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
				lowerCAmelCase_   :       List[Any]    							=torch.tensor([0.37_66, 0.49_17, -1.14_26, 0.99_42, 0.60_24]  )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
				lowerCAmelCase_   :       Dict    							=torch.tensor([0.42_20, -0.69_19, -0.43_17, -0.28_81, -0.66_09]  )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
				lowerCAmelCase_   :       Union[str, Any]    							=torch.tensor([0.10_82, -0.82_86, -0.50_95, 0.46_81, -0.80_85]  )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
				lowerCAmelCase_   :       int    							=torch.tensor([-0.24_19, -0.62_21, 0.21_76, -0.09_80, -0.75_27]  )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
				lowerCAmelCase_   :       Tuple    							=torch.tensor([0.03_91, -0.43_71, 0.37_86, 0.12_51, -0.27_84]  )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
				lowerCAmelCase_   :       Optional[Any]    							=torch.tensor([-0.05_04, 0.56_36, -0.17_29, -0.65_07, -0.39_49]  )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
				lowerCAmelCase_   :       str    							=torch.tensor([0.35_60, 0.94_86, 0.31_49, -0.26_67, -0.51_38]  )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
				lowerCAmelCase_   :       Optional[int]    							=torch.tensor([-0.24_69, -0.45_50, -0.58_53, -0.08_10, 0.03_09]  )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
				lowerCAmelCase_   :       Optional[Any]    							=torch.tensor([-0.30_90, 0.08_02, -0.06_82, -0.19_79, -0.28_26]  )
		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_   :       Union[str, Any]    							='''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__":
							__lowercase	=    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''')

							__lowercase	=    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




