

import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


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


@dataclass
class 			_SCREAMING_SNAKE_CASE      :
						_UpperCamelCase:nn.Module
						_UpperCamelCase:List[nn.Module]	=		field(default_factory=lowerCAmelCase__)
						_UpperCamelCase:list	=		field(default_factory=lowerCAmelCase__)







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							)->							Tuple:
													lowerCamelCase_								=len(list(m.modules()							)							) == 1 or isinstance(_SCREAMING_SNAKE_CASE     ,						nn.Convad							) or isinstance(_SCREAMING_SNAKE_CASE     ,						nn.BatchNormad							)
													if has_not_submodules:
																				if not isinstance(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							):
																											self.traced.append(_SCREAMING_SNAKE_CASE							)







						def __call__(			self     ,						_SCREAMING_SNAKE_CASE							)->							Union[str, Any]:
													for m in self.module.modules():
																				self.handles.append(m.register_forward_hook(self._forward_hook							)							)
													self.module(_SCREAMING_SNAKE_CASE							)
													[x.remove() for x in self.handles]
													return self







						@property
						def        _snake_case				(			self							)->							Tuple:
													# check the len of the state_dict keys to see if we have learnable params
													return list(filter(lambda _SCREAMING_SNAKE_CASE							: len(list(x.state_dict().keys()							)							) > 0     ,						self.traced							)							)


@dataclass
class 			_SCREAMING_SNAKE_CASE      :
						_UpperCamelCase:nn.Module
						_UpperCamelCase:nn.Module
						_UpperCamelCase:int	=		0
						_UpperCamelCase:List	=		field(default_factory=lowerCAmelCase__)
						_UpperCamelCase:List	=		field(default_factory=lowerCAmelCase__)







						def __call__(			self     ,						_SCREAMING_SNAKE_CASE							)->							Union[str, Any]:
													lowerCamelCase_								=Tracker(self.dest							)(_SCREAMING_SNAKE_CASE							).parametrized
													lowerCamelCase_								=Tracker(self.src							)(_SCREAMING_SNAKE_CASE							).parametrized

													lowerCamelCase_								=list(filter(lambda _SCREAMING_SNAKE_CASE							: type(_SCREAMING_SNAKE_CASE							) not in self.src_skip     ,						_SCREAMING_SNAKE_CASE							)							)
													lowerCamelCase_								=list(filter(lambda _SCREAMING_SNAKE_CASE							: type(_SCREAMING_SNAKE_CASE							) not in self.dest_skip     ,						_SCREAMING_SNAKE_CASE							)							)

													if len(_SCREAMING_SNAKE_CASE							) != len(_SCREAMING_SNAKE_CASE							):
																				raise Exception(
																				    f'Numbers of operations are different. Source module has {len(_SCREAMING_SNAKE_CASE							)} operations while'
																				    f' destination module has {len(_SCREAMING_SNAKE_CASE							)}.'							)

													for dest_m, src_m in zip(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							):
																				dest_m.load_state_dict(src_m.state_dict()							)
																				if self.verbose == 1:
																											print(f'Transfered from={src_m} to={dest_m}'							)




def        __UpperCamelCase     (      _A					:							nn.Module      ,  _A					:							nn.Module		)				->nn.Module:



							"""simple docstring"""
							# nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
							lowerCamelCase_								=from_model.state_dict()
							lowerCamelCase_								=our_model.state_dict()
							lowerCamelCase_								=our_model.config
							lowerCamelCase_								=[]
							for stage_idx in range(len(config.hidden_sizes		)		):
														for block_id in range(config.depths[stage_idx]		):
																					lowerCamelCase_								=f'block{stage_idx + 1}.{block_id}.layer_scale_1'
																					lowerCamelCase_								=f'van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'

																					all_keys.append((from_key, to_key)		)
																					lowerCamelCase_								=f'block{stage_idx + 1}.{block_id}.layer_scale_2'
																					lowerCamelCase_								=f'van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight'

																					all_keys.append((from_key, to_key)		)

							for from_key, to_key in all_keys:
														lowerCamelCase_								=from_state_dict.pop(_A		)

							our_model.load_state_dict(_A		)
							return our_model




def        __UpperCamelCase     (      _A					:							str      ,  _A					:							VanConfig      ,  _A					:							str      ,  _A					:							nn.Module      ,  _A					:							Path      ,  _A					:							bool = True      ,  )				->Any:



							"""simple docstring"""
							print(f'Downloading weights for {name}...'		)
							lowerCamelCase_								=cached_download(_A		)
							print(f'Converting {name}...'		)
							lowerCamelCase_								=torch.load(_A		)["""state_dict"""]
							from_model.load_state_dict(_A		)
							from_model.eval()
							with torch.no_grad():
														lowerCamelCase_								=VanForImageClassification(_A		).eval()
														lowerCamelCase_								=ModuleTransfer(src=_A      ,  dest=_A		)
														lowerCamelCase_								=torch.randn((1, 3, 224, 224)		)
														module_transfer(_A		)
														lowerCamelCase_								=copy_parameters(_A      ,  _A		)

							if not torch.allclose(from_model(_A		)      ,  our_model(_A		).logits		):
														raise ValueError("""The model logits don't match the original one."""		)

							lowerCamelCase_								=name
							print(_A		)

							if push_to_hub:
														our_model.push_to_hub(
														    repo_path_or_name=save_directory / checkpoint_name      ,  commit_message="""Add model"""      ,  use_temp_dir=_A      ,  )

														# we can use the convnext one
														lowerCamelCase_								=AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""		)
														image_processor.push_to_hub(
														    repo_path_or_name=save_directory / checkpoint_name      ,  commit_message="""Add image processor"""      ,  use_temp_dir=_A      ,  )

														print(f'Pushed {checkpoint_name}'		)




def        __UpperCamelCase     (      _A					:							Path      ,  _A					:							str = None      ,  _A					:							bool = True		)				->List[Any]:



							"""simple docstring"""
							lowerCamelCase_								="""imagenet-1k-id2label.json"""
							lowerCamelCase_								=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_								=partial(_A      ,  num_labels=_A      ,  idalabel=_A      ,  labelaid=_A		)

							lowerCamelCase_								={
							    """van-tiny""": ImageNetPreTrainedConfig(
							        hidden_sizes=[32, 64, 160, 256]      ,  depths=[3, 3, 5, 2]      ,  mlp_ratios=[8, 8, 4, 4]      ,  ),
							    """van-small""": ImageNetPreTrainedConfig(
							        hidden_sizes=[64, 128, 320, 512]      ,  depths=[2, 2, 4, 2]      ,  mlp_ratios=[8, 8, 4, 4]      ,  ),
							    """van-base""": ImageNetPreTrainedConfig(
							        hidden_sizes=[64, 128, 320, 512]      ,  depths=[3, 3, 12, 3]      ,  mlp_ratios=[8, 8, 4, 4]      ,  ),
							    """van-large""": ImageNetPreTrainedConfig(
							        hidden_sizes=[64, 128, 320, 512]      ,  depths=[3, 5, 27, 3]      ,  mlp_ratios=[8, 8, 4, 4]      ,  ),
							}

							lowerCamelCase_								={
							    """van-tiny""": van_tiny,
							    """van-small""": van_small,
							    """van-base""": van_base,
							    """van-large""": van_large,
							}

							lowerCamelCase_								={
							    """van-tiny""": (
							        """https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar"""
							    ),
							    """van-small""": (
							        """https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar"""
							    ),
							    """van-base""": (
							        """https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar"""
							    ),
							    """van-large""": (
							        """https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar"""
							    ),
							}

							if model_name:
														convert_weight_and_push(
														    _A      ,  names_to_config[model_name]      ,  checkpoint=names_to_original_checkpoints[model_name]      ,  from_model=names_to_original_models[model_name]()      ,  save_directory=_A      ,  push_to_hub=_A      ,  )
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(
																					    _A      ,  _A      ,  checkpoint=names_to_original_checkpoints[model_name]      ,  from_model=names_to_original_models[model_name]()      ,  save_directory=_A      ,  push_to_hub=_A      ,  )


if __name__ == "__main__":
	__A	:   Optional[int]	=					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--model-name',
	    default=None,
	    type=str,
	    help=(
	        'The name of the model you wish to convert, it must be one of the supported resnet* architecture,'
	        ' currently: van-tiny/small/base/large. If `None`, all of them will the converted.'
	    ),
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path',
	    default=None,
	    type=Path,
	    required=True,
	    help='Path to the output PyTorch model directory.',
	)
	parser.add_argument(
	    '--van_dir',
	    required=True,
	    type=Path,
	    help=(
	        'A path to VAN\'s original implementation directory. You can download from here:'
	        ' https://github.com/Visual-Attention-Network/VAN-Classification'
	    ),
	)
	parser.add_argument(
	    '--push_to_hub',
	    default=True,
	    type=bool,
	    required=False,
	    help='If True, push model and image processor to the hub.',
	)

	__A	:   List[str]	=					parser.parse_args()
	__A	:   Path	=					args.pytorch_dump_folder_path
	pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
	__A	:   int	=					args.van_dir
	# append the path to the parents to maskformer dir
	sys.path.append(str(van_dir.parent))
	from van.models.van import van_base, van_large, van_small, van_tiny

	convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)



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

__A	:   Any	=					logging.get_logger(__name__)

__A	:   Union[str, Any]	=					{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

__A	:   Optional[int]	=					{
    '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',
    },
}

__A	:   Optional[Any]	=					{
    'gpt2': 10_24,
    'gpt2-medium': 10_24,
    'gpt2-large': 10_24,
    'gpt2-xl': 10_24,
    'distilgpt2': 10_24,
}




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



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






class 			_SCREAMING_SNAKE_CASE      (  lowerCAmelCase__):
						_UpperCamelCase:Any	=		VOCAB_FILES_NAMES
						_UpperCamelCase:Any	=		PRETRAINED_VOCAB_FILES_MAP
						_UpperCamelCase:Optional[int]	=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_UpperCamelCase:List[str]	=		["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     ,						)->							Optional[Any]:
													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        _snake_case				(			self							)->							str:
													return len(self.encoder							)







						def        _snake_case				(			self							)->							Optional[Any]:
													return dict(self.encoder     ,						**self.added_tokens_encoder							)







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE							)->							int:
													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        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE=None							)->							Any:
													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        _snake_case				(			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        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE							)->							Any:
													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        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE							)->							List[str]:
													return self.encoder.get(_SCREAMING_SNAKE_CASE     ,						self.encoder.get(self.unk_token							)							)







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE							)->							int:
													return self.decoder.get(_SCREAMING_SNAKE_CASE							)







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE							)->							int:
													lowerCamelCase_								="""""".join(_SCREAMING_SNAKE_CASE							)
													lowerCamelCase_								=bytearray([self.byte_decoder[c] for c in text]							).decode("""utf-8"""     ,						errors=self.errors							)
													return text







						def        _snake_case				(			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        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE=False     ,						**_SCREAMING_SNAKE_CASE							)->							Optional[Any]:
													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        _snake_case				(			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
