


import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipaConfig,
    BlipaForConditionalGeneration,
    BlipaProcessor,
    BlipaVisionConfig,
    BlipImageProcessor,
    OPTConfig,
    TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD





def 		lowercase_  (     ):


			'''simple docstring'''







			__lowercase 					=   '''https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'''
			__lowercase 					=   Image.open(requests.get(_UpperCamelCase       ,			stream=_UpperCamelCase  ).raw  ).convert('''RGB'''  )

			return image





def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   []
			# fmt: off

			# vision encoder
			rename_keys.append(('''visual_encoder.cls_token''', '''vision_model.embeddings.class_embedding''')  )
			rename_keys.append(('''visual_encoder.pos_embed''', '''vision_model.embeddings.position_embedding''')  )
			rename_keys.append(('''visual_encoder.patch_embed.proj.weight''', '''vision_model.embeddings.patch_embedding.weight''')  )
			rename_keys.append(('''visual_encoder.patch_embed.proj.bias''', '''vision_model.embeddings.patch_embedding.bias''')  )
			rename_keys.append(('''ln_vision.weight''', '''vision_model.post_layernorm.weight''')  )
			rename_keys.append(('''ln_vision.bias''', '''vision_model.post_layernorm.bias''')  )

			for i in range(config.vision_config.num_hidden_layers  ):
						rename_keys.append((F'visual_encoder.blocks.{i}.norm1.weight', F'vision_model.encoder.layers.{i}.layer_norm1.weight')  )
						rename_keys.append((F'visual_encoder.blocks.{i}.norm1.bias', F'vision_model.encoder.layers.{i}.layer_norm1.bias')  )
						rename_keys.append((F'visual_encoder.blocks.{i}.norm2.weight', F'vision_model.encoder.layers.{i}.layer_norm2.weight')  )
						rename_keys.append((F'visual_encoder.blocks.{i}.norm2.bias', F'vision_model.encoder.layers.{i}.layer_norm2.bias')  )
						rename_keys.append((F'visual_encoder.blocks.{i}.attn.qkv.weight', F'vision_model.encoder.layers.{i}.self_attn.qkv.weight')  )
						rename_keys.append((F'visual_encoder.blocks.{i}.attn.proj.weight', F'vision_model.encoder.layers.{i}.self_attn.projection.weight',)  )
						rename_keys.append((F'visual_encoder.blocks.{i}.attn.proj.bias', F'vision_model.encoder.layers.{i}.self_attn.projection.bias')  )
						rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc1.weight', F'vision_model.encoder.layers.{i}.mlp.fc1.weight')  )
						rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc1.bias', F'vision_model.encoder.layers.{i}.mlp.fc1.bias')  )
						rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc2.weight', F'vision_model.encoder.layers.{i}.mlp.fc2.weight')  )
						rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc2.bias', F'vision_model.encoder.layers.{i}.mlp.fc2.bias')  )

			# QFormer
			rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.weight''', '''qformer.layernorm.weight''')  )
			rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.bias''', '''qformer.layernorm.bias''')  )

			# fmt: on
			return rename_keys





def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   dct.pop(_UpperCamelCase  )
			__lowercase 					=   val





def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			for i in range(config.vision_config.num_hidden_layers  ):
						# read in original q and v biases
						__lowercase 					=   state_dict.pop(F'visual_encoder.blocks.{i}.attn.q_bias'  )
						__lowercase 					=   state_dict.pop(F'visual_encoder.blocks.{i}.attn.v_bias'  )

						# next, set bias in the state dict
						__lowercase 					=   torch.cat((q_bias, torch.zeros_like(_UpperCamelCase       ,			requires_grad=_UpperCamelCase  ), v_bias)  )
						__lowercase 					=   qkv_bias





def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   3_64 if '''coco''' in model_name else 2_24
			__lowercase 					=   BlipaVisionConfig(image_size=_UpperCamelCase  ).to_dict()

			# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
			# seems like flan-T5 models don't have bos_token_id properly set?
			if "opt-2.7b" in model_name:
						__lowercase 					=   OPTConfig.from_pretrained('''facebook/opt-2.7b'''       ,			eos_token_id=_UpperCamelCase  ).to_dict()
			elif "opt-6.7b" in model_name:
						__lowercase 					=   OPTConfig.from_pretrained('''facebook/opt-6.7b'''       ,			eos_token_id=_UpperCamelCase  ).to_dict()
			elif "t5-xl" in model_name:
						__lowercase 					=   TaConfig.from_pretrained('''google/flan-t5-xl'''       ,			dense_act_fn='''gelu'''       ,			bos_token_id=1  ).to_dict()
			elif "t5-xxl" in model_name:
						__lowercase 					=   TaConfig.from_pretrained('''google/flan-t5-xxl'''       ,			dense_act_fn='''gelu'''       ,			bos_token_id=1  ).to_dict()

			__lowercase 					=   BlipaConfig(vision_config=_UpperCamelCase       ,			text_config=_UpperCamelCase  )

			return config, image_size





@torch.no_grad()
def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase=None       ,			_UpperCamelCase=False  ):


			'''simple docstring'''







			__lowercase 					=   (
			    AutoTokenizer.from_pretrained('''facebook/opt-2.7b'''  )
			    if '''opt''' in model_name
			    else AutoTokenizer.from_pretrained('''google/flan-t5-xl'''  )
			)
			__lowercase 					=   tokenizer('''\n'''       ,			add_special_tokens=_UpperCamelCase  ).input_ids[0]
			__lowercase					, __lowercase 					=   get_blipa_config(_UpperCamelCase       ,			eos_token_id=_UpperCamelCase  )

			__lowercase 					=   BlipaForConditionalGeneration(_UpperCamelCase  ).eval()

			__lowercase 					=   {
			    '''blip2-opt-2.7b''': ('''blip2_opt''', '''pretrain_opt2.7b'''),
			    '''blip2-opt-6.7b''': ('''blip2_opt''', '''pretrain_opt6.7b'''),
			    '''blip2-opt-2.7b-coco''': ('''blip2_opt''', '''caption_coco_opt2.7b'''),
			    '''blip2-opt-6.7b-coco''': ('''blip2_opt''', '''caption_coco_opt6.7b'''),
			    '''blip2-flan-t5-xl''': ('''blip2_t5''', '''pretrain_flant5xl'''),
			    '''blip2-flan-t5-xl-coco''': ('''blip2_t5''', '''caption_coco_flant5xl'''),
			    '''blip2-flan-t5-xxl''': ('''blip2_t5''', '''pretrain_flant5xxl'''),
			}

			__lowercase					, __lowercase 					=   model_name_to_original[model_name]

			# load original model
			print('''Loading original model...'''  )
			__lowercase 					=   '''cuda''' if torch.cuda.is_available() else '''cpu'''
			__lowercase					, __lowercase					, __lowercase 					=   load_model_and_preprocess(
			    name=_UpperCamelCase       ,			model_type=_UpperCamelCase       ,			is_eval=_UpperCamelCase       ,			device=_UpperCamelCase  )
			original_model.eval()
			print('''Done!'''  )

			# update state dict keys
			__lowercase 					=   original_model.state_dict()
			__lowercase 					=   create_rename_keys(_UpperCamelCase  )
			for src, dest in rename_keys:
						rename_key(_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  )

			# some keys can be renamed efficiently
			for key, val in state_dict.copy().items():
						__lowercase 					=   state_dict.pop(_UpperCamelCase  )
						if key.startswith('''Qformer.bert'''  ):
									__lowercase 					=   key.replace('''Qformer.bert'''       ,			'''qformer'''  )
						if "attention.self" in key:
									__lowercase 					=   key.replace('''self'''       ,			'''attention'''  )
						if "opt_proj" in key:
									__lowercase 					=   key.replace('''opt_proj'''       ,			'''language_projection'''  )
						if "t5_proj" in key:
									__lowercase 					=   key.replace('''t5_proj'''       ,			'''language_projection'''  )
						if key.startswith('''opt'''  ):
									__lowercase 					=   key.replace('''opt'''       ,			'''language'''  )
						if key.startswith('''t5'''  ):
									__lowercase 					=   key.replace('''t5'''       ,			'''language'''  )
						__lowercase 					=   val

			# read in qv biases
			read_in_q_v_bias(_UpperCamelCase       ,			_UpperCamelCase  )

			__lowercase					, __lowercase 					=   hf_model.load_state_dict(_UpperCamelCase       ,			strict=_UpperCamelCase  )
			assert len(_UpperCamelCase  ) == 0
			assert unexpected_keys == ["qformer.embeddings.position_ids"]

			__lowercase 					=   load_demo_image()
			__lowercase 					=   vis_processors['''eval'''](_UpperCamelCase  ).unsqueeze(0  ).to(_UpperCamelCase  )
			__lowercase 					=   tokenizer(['''\n''']       ,			return_tensors='''pt'''  ).input_ids.to(_UpperCamelCase  )

			# create processor
			__lowercase 					=   BlipImageProcessor(
			    size={'''height''': image_size, '''width''': image_size}       ,			image_mean=_UpperCamelCase       ,			image_std=_UpperCamelCase  )
			__lowercase 					=   BlipaProcessor(image_processor=_UpperCamelCase       ,			tokenizer=_UpperCamelCase  )
			__lowercase 					=   processor(images=_UpperCamelCase       ,			return_tensors='''pt'''  ).pixel_values.to(_UpperCamelCase  )

			# make sure processor creates exact same pixel values
			assert torch.allclose(_UpperCamelCase       ,			_UpperCamelCase  )

			original_model.to(_UpperCamelCase  )
			hf_model.to(_UpperCamelCase  )
			with torch.no_grad():
						if "opt" in model_name:
									__lowercase 					=   original_model({'''image''': original_pixel_values, '''text_input''': ['''''']}  ).logits
									__lowercase 					=   hf_model(_UpperCamelCase       ,			_UpperCamelCase  ).logits
						else:
									__lowercase 					=   original_model(
									    {'''image''': original_pixel_values, '''text_input''': ['''\n'''], '''text_output''': ['''\n''']}  ).logits
									__lowercase 					=   input_ids.masked_fill(input_ids == tokenizer.pad_token_id       ,			-1_00  )
									__lowercase 					=   hf_model(_UpperCamelCase       ,			_UpperCamelCase       ,			labels=_UpperCamelCase  ).logits

			assert original_logits.shape == logits.shape
			print('''First values of original logits:'''       ,			original_logits[0, :3, :3]  )
			print('''First values of HF logits:'''       ,			logits[0, :3, :3]  )

			# assert values
			if model_name == "blip2-flan-t5-xl":
						__lowercase 					=   torch.tensor(
						    [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]]       ,			device=_UpperCamelCase  )
						assert torch.allclose(logits[0, :3, :3]       ,			_UpperCamelCase       ,			atol=1E-4  )
			elif model_name == "blip2-flan-t5-xl-coco":
						__lowercase 					=   torch.tensor(
						    [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]]       ,			device=_UpperCamelCase  )
			else:
						# cast to same type
						__lowercase 					=   logits.dtype
						assert torch.allclose(original_logits.to(_UpperCamelCase  )       ,			_UpperCamelCase       ,			atol=1E-2  )
			print('''Looks ok!'''  )

			print('''Generating a caption...'''  )
			__lowercase 					=   ''''''
			__lowercase 					=   tokenizer(_UpperCamelCase       ,			return_tensors='''pt'''  ).input_ids.to(_UpperCamelCase  )

			__lowercase 					=   original_model.generate({'''image''': original_pixel_values}  )
			__lowercase 					=   hf_model.generate(
			    _UpperCamelCase       ,			_UpperCamelCase       ,			do_sample=_UpperCamelCase       ,			num_beams=5       ,			max_length=30       ,			min_length=1       ,			top_p=0.9       ,			repetition_penalty=1.0       ,			length_penalty=1.0       ,			temperature=1       ,			)
			print('''Original generation:'''       ,			_UpperCamelCase  )
			__lowercase 					=   input_ids.shape[1]
			__lowercase 					=   processor.batch_decode(outputs[:, prompt_length:]       ,			skip_special_tokens=_UpperCamelCase  )
			__lowercase 					=   [text.strip() for text in output_text]
			print('''HF generation:'''       ,			_UpperCamelCase  )

			if pytorch_dump_folder_path is not None:
						processor.save_pretrained(_UpperCamelCase  )
						hf_model.save_pretrained(_UpperCamelCase  )

			if push_to_hub:
						processor.push_to_hub(F'nielsr/{model_name}'  )
						hf_model.push_to_hub(F'nielsr/{model_name}'  )


if __name__ == "__main__":
	a : Dict		=				argparse.ArgumentParser()
	a : Dict		=				[
	    '''blip2-opt-2.7b''',
	    '''blip2-opt-6.7b''',
	    '''blip2-opt-2.7b-coco''',
	    '''blip2-opt-6.7b-coco''',
	    '''blip2-flan-t5-xl''',
	    '''blip2-flan-t5-xl-coco''',
	    '''blip2-flan-t5-xxl''',
	]
	parser.add_argument(
	    '''--model_name''',
	    default='''blip2-opt-2.7b''',
	    choices=choices,
	    type=str,
	    help='''Path to hf config.json of model to convert''',
	)
	parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
	parser.add_argument(
	    '''--push_to_hub''',
	    action='''store_true''',
	    help='''Whether to push the model and processor to the hub after converting''',
	)

	a : Tuple		=				parser.parse_args()

	convert_blipa_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


a : Union[str, Any]		=				logging.get_logger(__name__)


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

a : Union[str, Any]		=				{
    '''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'''
        ),
    },
}

a : Dict		=				{
    '''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 		lowercase_  (     ):


			'''simple docstring'''







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


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   set()
			__lowercase 					=   word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)  )
						__lowercase 					=   char
			return pairs





class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				VOCAB_FILES_NAMES
					__UpperCAmelCase            =				PRETRAINED_VOCAB_FILES_MAP
					__UpperCAmelCase            =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__UpperCAmelCase            =				["input_ids", "attention_mask"]






					def __init__(				self ,       snake_case_ ,       snake_case_ ,       snake_case_="replace" ,       snake_case_="<s>" ,       snake_case_="</s>" ,       snake_case_="</s>" ,       snake_case_="<s>" ,       snake_case_="<unk>" ,       snake_case_="<pad>" ,       snake_case_="<mask>" ,       snake_case_=False ,       **snake_case_ ,       )   ->			Optional[int]:







								'''simple docstring'''

								__lowercase 					=   AddedToken(snake_case_ ,       lstrip=snake_case_ ,       rstrip=snake_case_						) if isinstance(snake_case_ ,       snake_case_						) else bos_token
								__lowercase 					=   AddedToken(snake_case_ ,       lstrip=snake_case_ ,       rstrip=snake_case_						) if isinstance(snake_case_ ,       snake_case_						) else eos_token
								__lowercase 					=   AddedToken(snake_case_ ,       lstrip=snake_case_ ,       rstrip=snake_case_						) if isinstance(snake_case_ ,       snake_case_						) else sep_token
								__lowercase 					=   AddedToken(snake_case_ ,       lstrip=snake_case_ ,       rstrip=snake_case_						) if isinstance(snake_case_ ,       snake_case_						) else cls_token
								__lowercase 					=   AddedToken(snake_case_ ,       lstrip=snake_case_ ,       rstrip=snake_case_						) if isinstance(snake_case_ ,       snake_case_						) else unk_token
								__lowercase 					=   AddedToken(snake_case_ ,       lstrip=snake_case_ ,       rstrip=snake_case_						) if isinstance(snake_case_ ,       snake_case_						) else pad_token

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

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

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

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






					@property
					def A      (				self						)   ->			List[str]:







								'''simple docstring'''

								return len(self.encoder						)






					def A      (				self						)   ->			Union[str, Any]:







								'''simple docstring'''

								return dict(self.encoder ,       **self.added_tokens_encoder						)






					def A      (				self ,       snake_case_						)   ->			List[str]:







								'''simple docstring'''

								if token in self.cache:
											return self.cache[token]
								__lowercase 					=   tuple(snake_case_						)
								__lowercase 					=   get_pairs(snake_case_						)

								if not pairs:
											return token

								while True:
											__lowercase 					=   min(snake_case_ ,       key=lambda snake_case_						: self.bpe_ranks.get(snake_case_ ,       float('''inf'''						)						)						)
											if bigram not in self.bpe_ranks:
														break
											__lowercase					, __lowercase 					=   bigram
											__lowercase 					=   []
											__lowercase 					=   0
											while i < len(snake_case_						):
														try:
																	__lowercase 					=   word.index(snake_case_ ,       snake_case_						)
														except ValueError:
																	new_word.extend(word[i:]						)
																	break
														else:
																	new_word.extend(word[i:j]						)
																	__lowercase 					=   j

														if word[i] == first and i < len(snake_case_						) - 1 and word[i + 1] == second:
																	new_word.append(first + second						)
																	i += 2
														else:
																	new_word.append(word[i]						)
																	i += 1
											__lowercase 					=   tuple(snake_case_						)
											__lowercase 					=   new_word
											if len(snake_case_						) == 1:
														break
											else:
														__lowercase 					=   get_pairs(snake_case_						)
								__lowercase 					=   ''' '''.join(snake_case_						)
								__lowercase 					=   word
								return word






					def A      (				self ,       snake_case_						)   ->			Union[str, Any]:







								'''simple docstring'''

								__lowercase 					=   []
								for token in re.findall(self.pat ,       snake_case_						):
											__lowercase 					=   ''''''.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(snake_case_						).split(''' '''						)						)
								return bpe_tokens






					def A      (				self ,       snake_case_						)   ->			Any:







								'''simple docstring'''

								return self.encoder.get(snake_case_ ,       self.encoder.get(self.unk_token						)						)






					def A      (				self ,       snake_case_						)   ->			Dict:







								'''simple docstring'''

								return self.decoder.get(snake_case_						)






					def A      (				self ,       snake_case_						)   ->			List[str]:







								'''simple docstring'''

								__lowercase 					=   ''''''.join(snake_case_						)
								__lowercase 					=   bytearray([self.byte_decoder[c] for c in text]						).decode('''utf-8''' ,       errors=self.errors						)
								return text






					def A      (				self ,       snake_case_ ,       snake_case_ = None						)   ->			Tuple[str]:







								'''simple docstring'''

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

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

								__lowercase 					=   0
								with open(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 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!'''						)
																	__lowercase 					=   token_index
														writer.write(''' '''.join(snake_case_						) + '''\n'''						)
														index += 1

								return vocab_file, merge_file






					def A      (				self ,       snake_case_ ,       snake_case_ = None						)   ->			List[int]:







								'''simple docstring'''

								if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								__lowercase 					=   [self.cls_token_id]
								__lowercase 					=   [self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep






					def A      (				self ,       snake_case_ ,       snake_case_ = None ,       snake_case_ = False						)   ->			List[int]:







								'''simple docstring'''

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

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






					def A      (				self ,       snake_case_ ,       snake_case_ = None						)   ->			List[int]:







								'''simple docstring'''

								__lowercase 					=   [self.sep_token_id]
								__lowercase 					=   [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 A      (				self ,       snake_case_ ,       snake_case_=False ,       **snake_case_						)   ->			Union[str, Any]:







								'''simple docstring'''

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


