import argparse
import json

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

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation




def     UpperCamelCase						(	__lowercase						:			Any     ):
		'''simple docstring'''




		A_		:    List[str]         =				3_84
		if "tiny" in model_name:
				A_		:    int         =				[3, 3, 9, 3]
				A_		:    Dict         =				[96, 1_92, 3_84, 7_68]
		if "small" in model_name:
				A_		:    Any         =				[3, 3, 27, 3]
				A_		:    Union[str, Any]         =				[96, 1_92, 3_84, 7_68]
		if "base" in model_name:
				A_		:    str         =				[3, 3, 27, 3]
				A_		:    List[str]         =				[1_28, 2_56, 5_12, 10_24]
				A_		:    Optional[Any]         =				5_12
		if "large" in model_name:
				A_		:    str         =				[3, 3, 27, 3]
				A_		:    List[str]         =				[1_92, 3_84, 7_68, 15_36]
				A_		:    Union[str, Any]         =				7_68
		if "xlarge" in model_name:
				A_		:    Optional[Any]         =				[3, 3, 27, 3]
				A_		:    List[str]         =				[2_56, 5_12, 10_24, 20_48]
				A_		:    int         =				10_24

		# set label information
		A_		:    Tuple         =				1_50
		A_		:    Tuple         =				'huggingface/label-files'
		A_		:    Optional[int]         =				'ade20k-id2label.json'
		A_		:    Tuple         =				json.load(open(hf_hub_download(__lowercase						,__lowercase						,repo_type='dataset'     )						,'r'     )     )
		A_		:    List[Any]         =				{int(__lowercase     ): v for k, v in idalabel.items()}
		A_		:    List[Any]         =				{v: k for k, v in idalabel.items()}

		A_		:    Tuple         =				ConvNextConfig(
		    depths=__lowercase						,hidden_sizes=__lowercase						,out_features=['stage1', 'stage2', 'stage3', 'stage4']     )
		A_		:    Optional[int]         =				UperNetConfig(
		    backbone_config=__lowercase						,auxiliary_in_channels=__lowercase						,num_labels=__lowercase						,idalabel=__lowercase						,labelaid=__lowercase						,)

		return config




def     UpperCamelCase						(	__lowercase						:			List[str]     ):
		'''simple docstring'''




		A_		:    int         =				[]

		# fmt: off
		# stem
		rename_keys.append(('backbone.downsample_layers.0.0.weight', 'backbone.embeddings.patch_embeddings.weight')     )
		rename_keys.append(('backbone.downsample_layers.0.0.bias', 'backbone.embeddings.patch_embeddings.bias')     )
		rename_keys.append(('backbone.downsample_layers.0.1.weight', 'backbone.embeddings.layernorm.weight')     )
		rename_keys.append(('backbone.downsample_layers.0.1.bias', 'backbone.embeddings.layernorm.bias')     )
		# stages
		for i in range(len(config.backbone_config.depths     )     ):
				for j in range(config.backbone_config.depths[i]     ):
						rename_keys.append((f'''backbone.stages.{i}.{j}.gamma''', f'''backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter''')     )
						rename_keys.append((f'''backbone.stages.{i}.{j}.depthwise_conv.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.dwconv.weight''')     )
						rename_keys.append((f'''backbone.stages.{i}.{j}.depthwise_conv.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.dwconv.bias''')     )
						rename_keys.append((f'''backbone.stages.{i}.{j}.norm.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.layernorm.weight''')     )
						rename_keys.append((f'''backbone.stages.{i}.{j}.norm.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.layernorm.bias''')     )
						rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv1.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight''')     )
						rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv1.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias''')     )
						rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv2.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight''')     )
						rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv2.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias''')     )
				if i > 0:
						rename_keys.append((f'''backbone.downsample_layers.{i}.0.weight''', f'''backbone.encoder.stages.{i}.downsampling_layer.0.weight''')     )
						rename_keys.append((f'''backbone.downsample_layers.{i}.0.bias''', f'''backbone.encoder.stages.{i}.downsampling_layer.0.bias''')     )
						rename_keys.append((f'''backbone.downsample_layers.{i}.1.weight''', f'''backbone.encoder.stages.{i}.downsampling_layer.1.weight''')     )
						rename_keys.append((f'''backbone.downsample_layers.{i}.1.bias''', f'''backbone.encoder.stages.{i}.downsampling_layer.1.bias''')     )

				rename_keys.append((f'''backbone.norm{i}.weight''', f'''backbone.hidden_states_norms.stage{i+1}.weight''')     )
				rename_keys.append((f'''backbone.norm{i}.bias''', f'''backbone.hidden_states_norms.stage{i+1}.bias''')     )

		# decode head
		rename_keys.extend(
		    [
		        ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
		        ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
		        ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
		        ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
		    ]     )
		# fmt: on

		return rename_keys




def     UpperCamelCase						(	__lowercase						:			Union[str, Any]						,__lowercase						:			Optional[int]						,__lowercase						:			int     ):
		'''simple docstring'''




		A_		:    Any         =				dct.pop(__lowercase     )
		A_		:    Union[str, Any]         =				val




def     UpperCamelCase						(	__lowercase						:			Union[str, Any]						,__lowercase						:			Tuple						,__lowercase						:			int     ):
		'''simple docstring'''




		A_		:    Optional[Any]         =				{
		    'upernet-convnext-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth',
		    'upernet-convnext-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth',
		    'upernet-convnext-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth',
		    'upernet-convnext-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth',
		    'upernet-convnext-xlarge': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth',
		}
		A_		:    Tuple         =				model_name_to_url[model_name]
		A_		:    List[str]         =				torch.hub.load_state_dict_from_url(__lowercase						,map_location='cpu'     )['state_dict']

		A_		:    Tuple         =				get_upernet_config(__lowercase     )
		A_		:    Tuple         =				UperNetForSemanticSegmentation(__lowercase     )
		model.eval()

		# replace "bn" => "batch_norm"
		for key in state_dict.copy().keys():
				A_		:    Optional[Any]         =				state_dict.pop(__lowercase     )
				if "bn" in key:
						A_		:    List[str]         =				key.replace('bn'						,'batch_norm'     )
				A_		:    List[Any]         =				val

		# rename keys
		A_		:    Optional[int]         =				create_rename_keys(__lowercase     )
		for src, dest in rename_keys:
				rename_key(__lowercase						,__lowercase						,__lowercase     )

		model.load_state_dict(__lowercase     )

		# verify on image
		A_		:    List[Any]         =				'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
		A_		:    List[Any]         =				Image.open(requests.get(__lowercase						,stream=__lowercase     ).raw     ).convert('RGB'     )

		A_		:    Tuple         =				SegformerImageProcessor()
		A_		:    List[str]         =				processor(__lowercase						,return_tensors='pt'     ).pixel_values

		with torch.no_grad():
				A_		:    List[Any]         =				model(__lowercase     )

		if model_name == "upernet-convnext-tiny":
				A_		:    Tuple         =				torch.tensor(
				    [[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]]     )
		elif model_name == "upernet-convnext-small":
				A_		:    Optional[Any]         =				torch.tensor(
				    [[-8.82_36, -8.82_36, -8.67_71], [-8.82_36, -8.82_36, -8.67_71], [-8.76_38, -8.76_38, -8.62_40]]     )
		elif model_name == "upernet-convnext-base":
				A_		:    str         =				torch.tensor(
				    [[-8.85_58, -8.85_58, -8.69_05], [-8.85_58, -8.85_58, -8.69_05], [-8.76_69, -8.76_69, -8.60_21]]     )
		elif model_name == "upernet-convnext-large":
				A_		:    Optional[int]         =				torch.tensor(
				    [[-8.66_60, -8.66_60, -8.62_10], [-8.66_60, -8.66_60, -8.62_10], [-8.63_10, -8.63_10, -8.59_64]]     )
		elif model_name == "upernet-convnext-xlarge":
				A_		:    Optional[Any]         =				torch.tensor(
				    [[-8.49_80, -8.49_80, -8.39_77], [-8.49_80, -8.49_80, -8.39_77], [-8.43_79, -8.43_79, -8.34_12]]     )
		print('Logits:'						,outputs.logits[0, 0, :3, :3]     )
		assert torch.allclose(outputs.logits[0, 0, :3, :3]						,__lowercase						,atol=1e-4     )
		print('Looks ok!'     )

		if pytorch_dump_folder_path is not None:
				print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''     )
				model.save_pretrained(__lowercase     )
				print(f'''Saving processor to {pytorch_dump_folder_path}'''     )
				processor.save_pretrained(__lowercase     )

		if push_to_hub:
				print(f'''Pushing model and processor for {model_name} to hub'''     )
				model.push_to_hub(f'''openmmlab/{model_name}'''     )
				processor.push_to_hub(f'''openmmlab/{model_name}'''     )


if __name__ == "__main__":
	_UpperCAmelCase            =		argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--model_name""",
	    default="""upernet-convnext-tiny""",
	    type=str,
	    choices=[F"""upernet-convnext-{size}""" for size in ["""tiny""", """small""", """base""", """large""", """xlarge"""]],
	    help="""Name of the ConvNext UperNet model you'd like to convert.""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
	)
	parser.add_argument(
	    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
	)

	_UpperCAmelCase            =		parser.parse_args()
	convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







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

import regex as re

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


_UpperCAmelCase            =		logging.get_logger(__name__)


_UpperCAmelCase            =		{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

_UpperCAmelCase            =		{
    """vocab_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
    },
}

_UpperCAmelCase            =		{
    """allenai/longformer-base-4096""": 4096,
    """allenai/longformer-large-4096""": 4096,
    """allenai/longformer-large-4096-finetuned-triviaqa""": 4096,
    """allenai/longformer-base-4096-extra.pos.embd.only""": 4096,
    """allenai/longformer-large-4096-extra.pos.embd.only""": 4096,
}




@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def     UpperCamelCase						(	):
		'''simple docstring'''




		A_		:    Optional[int]         =				(
		    list(range(ord('!'     )						,ord('~'     ) + 1     )     ) + list(range(ord('¡'     )						,ord('¬'     ) + 1     )     ) + list(range(ord('®'     )						,ord('ÿ'     ) + 1     )     )
		)
		A_		:    Any         =				bs[:]
		A_		:    Optional[Any]         =				0
		for b in range(2**8     ):
				if b not in bs:
						bs.append(__lowercase     )
						cs.append(2**8 + n     )
						n += 1
		A_		:    str         =				[chr(__lowercase     ) for n in cs]
		return dict(zip(__lowercase						,__lowercase     )     )




def     UpperCamelCase						(	__lowercase						:			Dict     ):
		'''simple docstring'''




		A_		:    List[str]         =				set()
		A_		:    List[Any]         =				word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)     )
				A_		:    Union[str, Any]         =				char
		return pairs





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				lowerCamelCase_    = VOCAB_FILES_NAMES
				lowerCamelCase_    = PRETRAINED_VOCAB_FILES_MAP
				lowerCamelCase_    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCamelCase_    = ['''input_ids''', '''attention_mask''']
				def __init__(						self		,     lowercase		,     lowercase		,     lowercase="replace"		,     lowercase="<s>"		,     lowercase="</s>"		,     lowercase="</s>"		,     lowercase="<s>"		,     lowercase="<unk>"		,     lowercase="<pad>"		,     lowercase="<mask>"		,     lowercase=False		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    Union[str, Any]         =				AddedToken(lowercase		,     lstrip=lowercase		,     rstrip=lowercase ) if isinstance(lowercase		,     lowercase ) else bos_token
						A_		:    str         =				AddedToken(lowercase		,     lstrip=lowercase		,     rstrip=lowercase ) if isinstance(lowercase		,     lowercase ) else eos_token
						A_		:    str         =				AddedToken(lowercase		,     lstrip=lowercase		,     rstrip=lowercase ) if isinstance(lowercase		,     lowercase ) else sep_token
						A_		:    Tuple         =				AddedToken(lowercase		,     lstrip=lowercase		,     rstrip=lowercase ) if isinstance(lowercase		,     lowercase ) else cls_token
						A_		:    List[str]         =				AddedToken(lowercase		,     lstrip=lowercase		,     rstrip=lowercase ) if isinstance(lowercase		,     lowercase ) else unk_token
						A_		:    Optional[int]         =				AddedToken(lowercase		,     lstrip=lowercase		,     rstrip=lowercase ) if isinstance(lowercase		,     lowercase ) else pad_token

						# Mask token behave like a normal word, i.e. include the space before it
						A_		:    int         =				AddedToken(lowercase		,     lstrip=lowercase		,     rstrip=lowercase ) if isinstance(lowercase		,     lowercase ) else mask_token

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

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

						# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
						A_		:    Optional[Any]         =				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 ):



						"""simple docstring"""





						return len(self.encoder )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						return dict(self.encoder		,     **self.added_tokens_encoder )
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						if token in self.cache:
								return self.cache[token]
						A_		:    List[Any]         =				tuple(lowercase )
						A_		:    List[Any]         =				get_pairs(lowercase )

						if not pairs:
								return token

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

										if word[i] == first and i < len(lowercase ) - 1 and word[i + 1] == second:
												new_word.append(first + second )
												i += 2
										else:
												new_word.append(word[i] )
												i += 1
								A_		:    Union[str, Any]         =				tuple(lowercase )
								A_		:    Tuple         =				new_word
								if len(lowercase ) == 1:
										break
								else:
										A_		:    Optional[int]         =				get_pairs(lowercase )
						A_		:    Optional[Any]         =				' '.join(lowercase )
						A_		:    str         =				word
						return word
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						A_		:    Optional[int]         =				[]
						for token in re.findall(self.pat		,     lowercase ):
								A_		:    Optional[Any]         =				''.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(lowercase ).split(' ' ) )
						return bpe_tokens
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						return self.encoder.get(lowercase		,     self.encoder.get(self.unk_token ) )
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						return self.decoder.get(lowercase )
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						A_		:    Union[str, Any]         =				''.join(lowercase )
						A_		:    List[Any]         =				bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8'		,     errors=self.errors )
						return text
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





						if not os.path.isdir(lowercase ):
								logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
								return
						A_		:    Optional[int]         =				os.path.join(
						    lowercase		,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
						A_		:    Dict         =				os.path.join(
						    lowercase		,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )

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

						A_		:    Dict         =				0
						with open(lowercase		,     '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 lowercase : 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!' )
												A_		:    int         =				token_index
										writer.write(' '.join(lowercase ) + '\n' )
										index += 1

						return vocab_file, merge_file
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





						if token_ids_a is None:
								return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						A_		:    Tuple         =				[self.cls_token_id]
						A_		:    Any         =				[self.sep_token_id]
						return cls + token_ids_a + sep + sep + token_ids_a + sep
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None		,     lowercase = False ):



						"""simple docstring"""





						if already_has_special_tokens:
								return super().get_special_tokens_mask(
								    token_ids_a=lowercase		,     token_ids_a=lowercase		,     already_has_special_tokens=lowercase )

						if token_ids_a is None:
								return [1] + ([0] * len(lowercase )) + [1]
						return [1] + ([0] * len(lowercase )) + [1, 1] + ([0] * len(lowercase )) + [1]
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





						A_		:    Optional[int]         =				[self.sep_token_id]
						A_		:    List[Any]         =				[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   lowerCAmelCase_       (						self		,     lowercase		,     lowercase=False		,     **lowercase ):



						"""simple docstring"""





						A_		:    Tuple         =				kwargs.pop('add_prefix_space'		,     self.add_prefix_space )
						if (is_split_into_words or add_prefix_space) and (len(lowercase ) > 0 and not text[0].isspace()):
								A_		:    int         =				' ' + text
						return (text, kwargs)






