import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


__lowerCamelCase					:	int       =  {
    '''gwf-440k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/gwf-440k.ckpt''',
        '''sample_rate''': 4_80_00,
        '''sample_size''': 6_55_36,
    },
    '''jmann-small-190k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt''',
        '''sample_rate''': 4_80_00,
        '''sample_size''': 6_55_36,
    },
    '''jmann-large-580k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt''',
        '''sample_rate''': 4_80_00,
        '''sample_size''': 13_10_72,
    },
    '''maestro-uncond-150k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt''',
        '''sample_rate''': 1_60_00,
        '''sample_size''': 6_55_36,
    },
    '''unlocked-uncond-250k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt''',
        '''sample_rate''': 1_60_00,
        '''sample_size''': 6_55_36,
    },
    '''honk-140k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/honk-140k.ckpt''',
        '''sample_rate''': 1_60_00,
        '''sample_size''': 6_55_36,
    },
}




def       _snake_case	(   lowerCAmelCase							:   Tuple   , lowerCAmelCase							:   str	):
		"""simple docstring"""







		return torch.atana(lowerCAmelCase   , lowerCAmelCase	) / math.pi * 2




def       _snake_case	(   lowerCAmelCase							:   Tuple	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    torch.sin(t * math.pi / 2	) ** 2
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    (1 - sigma**2) ** 0.5
		return alpha_sigma_to_t(lowerCAmelCase   , lowerCAmelCase	)






class 		a__						(					A__					):
					pass







class 		a__						(					nn.Module					):

					def __init__(    self      :       Any,_A      :       List[Any]    ):







							"""simple docstring"""






							super().__init__()

							SCREAMING_SNAKE_CASE_  :		int				      =    DiffusionAttnUnetaD(_A,n_attn_layers=4    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    deepcopy(self.diffusion    )
							SCREAMING_SNAKE_CASE_  :		int				      =    torch.quasirandom.SobolEngine(1,scramble=_A    )







def       _snake_case	(   lowerCAmelCase							:   Union[str, Any]	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		int				      =    MODELS_MAP[model_name]["url"]
		os.system(f'wget {url} ./'	)

		return f'./{model_name}.ckpt'


__lowerCamelCase					:	List[Any]       =  {
    '''1''': '''resnets.0''',
    '''2''': '''attentions.0''',
    '''3''': '''resnets.1''',
    '''4''': '''attentions.1''',
    '''5''': '''resnets.2''',
    '''6''': '''attentions.2''',
}
__lowerCamelCase					:	int       =  {
    '''8''': '''resnets.0''',
    '''9''': '''attentions.0''',
    '''10''': '''resnets.1''',
    '''11''': '''attentions.1''',
    '''12''': '''resnets.2''',
    '''13''': '''attentions.2''',
}
__lowerCamelCase					:	List[Any]       =  {
    '''1''': '''resnets.0''',
    '''2''': '''attentions.0''',
    '''3''': '''resnets.1''',
    '''4''': '''attentions.1''',
    '''5''': '''resnets.2''',
    '''6''': '''attentions.2''',
    '''8''': '''resnets.3''',
    '''9''': '''attentions.3''',
    '''10''': '''resnets.4''',
    '''11''': '''attentions.4''',
    '''12''': '''resnets.5''',
    '''13''': '''attentions.5''',
}
__lowerCamelCase					:	Optional[int]       =  {
    '''0''': '''resnets.0''',
    '''1''': '''resnets.1''',
    '''2''': '''resnets.2''',
    '''4''': '''resnets.0''',
    '''5''': '''resnets.1''',
    '''6''': '''resnets.2''',
}

__lowerCamelCase					:	Tuple       =  {
    '''skip''': '''conv_skip''',
    '''main.0''': '''conv_1''',
    '''main.1''': '''group_norm_1''',
    '''main.3''': '''conv_2''',
    '''main.4''': '''group_norm_2''',
}

__lowerCamelCase					:	Any       =  {
    '''norm''': '''group_norm''',
    '''qkv_proj''': ['''query''', '''key''', '''value'''],
    '''out_proj''': ['''proj_attn'''],
}




def       _snake_case	(   lowerCAmelCase							:   Optional[Any]	):
		"""simple docstring"""







		if name.startswith("skip"	):
				return name.replace("skip"   , RES_CONV_MAP["skip"]	)

		# name has to be of format main.{digit}
		if not name.startswith("main."	):
				raise ValueError(f'ResConvBlock error with {name}'	)

		return name.replace(name[:6]   , RES_CONV_MAP[name[:6]]	)




def       _snake_case	(   lowerCAmelCase							:   Any	):
		"""simple docstring"""







		for key, value in ATTN_MAP.items():
				if name.startswith(lowerCAmelCase	) and not isinstance(lowerCAmelCase   , lowerCAmelCase	):
						return name.replace(lowerCAmelCase   , lowerCAmelCase	)
				elif name.startswith(lowerCAmelCase	):
						return [name.replace(lowerCAmelCase   , lowerCAmelCase	) for v in value]
		raise ValueError(f'Attn error with {name}'	)




def       _snake_case	(   lowerCAmelCase							:   Tuple   , lowerCAmelCase							:   str=1_3	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		str				      =    input_string

		if string.split("."	)[0] == "timestep_embed":
				return string.replace("timestep_embed"   , "time_proj"	)

		SCREAMING_SNAKE_CASE_  :		Tuple				      =    0
		if string.startswith("net.3."	):
				depth += 1
				SCREAMING_SNAKE_CASE_  :		Dict				      =    string[6:]
		elif string.startswith("net."	):
				SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    string[4:]

		while string.startswith("main.7."	):
				depth += 1
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    string[7:]

		if string.startswith("main."	):
				SCREAMING_SNAKE_CASE_  :		Tuple				      =    string[5:]

		# mid block
		if string[:2].isdigit():
				SCREAMING_SNAKE_CASE_  :		Any				      =    string[:2]
				SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    string[2:]
		else:
				SCREAMING_SNAKE_CASE_  :		int				      =    string[0]
				SCREAMING_SNAKE_CASE_  :		Dict				      =    string[1:]

		if depth == max_depth:
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    MID_NUM_TO_LAYER[layer_num]
				SCREAMING_SNAKE_CASE_  :		Tuple				      =    "mid_block"
		elif depth > 0 and int(lowerCAmelCase	) < 7:
				SCREAMING_SNAKE_CASE_  :		Dict				      =    DOWN_NUM_TO_LAYER[layer_num]
				SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    f'down_blocks.{depth}'
		elif depth > 0 and int(lowerCAmelCase	) > 7:
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    UP_NUM_TO_LAYER[layer_num]
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    f'up_blocks.{max_depth - depth - 1}'
		elif depth == 0:
				SCREAMING_SNAKE_CASE_  :		Tuple				      =    DEPTH_0_TO_LAYER[layer_num]
				SCREAMING_SNAKE_CASE_  :		int				      =    f'up_blocks.{max_depth - 1}' if int(lowerCAmelCase	) > 3 else "down_blocks.0"

		if not string_left.startswith("."	):
				raise ValueError(f'Naming error with {input_string} and string_left: {string_left}.'	)

		SCREAMING_SNAKE_CASE_  :		List[str]				      =    string_left[1:]

		if "resnets" in new_layer:
				SCREAMING_SNAKE_CASE_  :		str				      =    convert_resconv_naming(lowerCAmelCase	)
		elif "attentions" in new_layer:
				SCREAMING_SNAKE_CASE_  :		str				      =    convert_attn_naming(lowerCAmelCase	)
				SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    new_string_left

		if not isinstance(lowerCAmelCase   , lowerCAmelCase	):
				SCREAMING_SNAKE_CASE_  :		int				      =    prefix + "." + new_layer + "." + string_left
		else:
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    [prefix + "." + new_layer + "." + s for s in string_left]
		return new_string




def       _snake_case	(   lowerCAmelCase							:   Tuple	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		int				      =    {}
		for k, v in state_dict.items():
				if k.endswith("kernel"	):
						# up- and downsample layers, don't have trainable weights
						continue

				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    rename(lowerCAmelCase	)

				# check if we need to transform from Conv => Linear for attention
				if isinstance(lowerCAmelCase   , lowerCAmelCase	):
						SCREAMING_SNAKE_CASE_  :		List[str]				      =    transform_conv_attns(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase	)
				else:
						SCREAMING_SNAKE_CASE_  :		Dict				      =    v

		return new_state_dict




def       _snake_case	(   lowerCAmelCase							:   Tuple   , lowerCAmelCase							:   Optional[Any]   , lowerCAmelCase							:   Optional[int]	):
		"""simple docstring"""







		if len(lowerCAmelCase	) == 1:
				if len(v.shape	) == 3:
						# weight
						SCREAMING_SNAKE_CASE_  :		str				      =    v[:, :, 0]
				else:
						# bias
						SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    v
		else:
				# qkv matrices
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    v.shape[0]
				SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    trippled_shape // 3
				for i in range(3	):
						if len(v.shape	) == 3:
								SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    v[i * single_shape : (i + 1) * single_shape, :, 0]
						else:
								SCREAMING_SNAKE_CASE_  :		int				      =    v[i * single_shape : (i + 1) * single_shape]
		return new_state_dict




def       _snake_case	(   lowerCAmelCase							:   List[Any]	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Any				      =    torch.device("cuda" if torch.cuda.is_available() else "cpu"	)

		SCREAMING_SNAKE_CASE_  :		List[str]				      =    args.model_path.split("/"	)[-1].split("."	)[0]
		if not os.path.isfile(args.model_path	):
				assert (
				    model_name == args.model_path
				), f'Make sure to provide one of the official model names {MODELS_MAP.keys()}'
				SCREAMING_SNAKE_CASE_  :		str				      =    download(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		Dict				      =    MODELS_MAP[model_name]["sample_rate"]
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    MODELS_MAP[model_name]["sample_size"]

		SCREAMING_SNAKE_CASE_  :		str				      =    Object()
		SCREAMING_SNAKE_CASE_  :		Any				      =    sample_size
		SCREAMING_SNAKE_CASE_  :		Dict				      =    sample_rate
		SCREAMING_SNAKE_CASE_  :		Any				      =    0

		SCREAMING_SNAKE_CASE_  :		Any				      =    UNetaDModel(sample_size=lowerCAmelCase   , sample_rate=lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    diffusers_model.state_dict()

		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    DiffusionUncond(lowerCAmelCase	)
		orig_model.load_state_dict(torch.load(args.model_path   , map_location=lowerCAmelCase	)["state_dict"]	)
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    orig_model.diffusion_ema.eval()
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    orig_model.state_dict()
		SCREAMING_SNAKE_CASE_  :		int				      =    rename_orig_weights(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		List[str]				      =    set(renamed_state_dict.keys()	) - set(diffusers_state_dict.keys()	)
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    set(diffusers_state_dict.keys()	) - set(renamed_state_dict.keys()	)

		assert len(lowerCAmelCase	) == 0, f'Problem with {renamed_minus_diffusers}'
		assert all(k.endswith("kernel"	) for k in list(lowerCAmelCase	)	), f'Problem with {diffusers_minus_renamed}'

		for key, value in renamed_state_dict.items():
				assert (
				    diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
				), f'Shape for {key} doesn\'t match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}'
				if key == "time_proj.weight":
						SCREAMING_SNAKE_CASE_  :		int				      =    value.squeeze()

				SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    value

		diffusers_model.load_state_dict(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		int				      =    1_0_0
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    3_3

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    IPNDMScheduler(num_train_timesteps=lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    torch.manual_seed(lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		Dict				      =    torch.randn([1, 2, config.sample_size]   , generator=lowerCAmelCase	).to(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    torch.linspace(1   , 0   , steps + 1   , device=lowerCAmelCase	)[:-1]
		SCREAMING_SNAKE_CASE_  :		Dict				      =    get_crash_schedule(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		int				      =    DanceDiffusionPipeline(unet=lowerCAmelCase   , scheduler=lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		Tuple				      =    torch.manual_seed(3_3	)
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    pipe(num_inference_steps=lowerCAmelCase   , generator=lowerCAmelCase	).audios

		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    sampling.iplms_sample(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase   , {}	)
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    generated.clamp(-1   , 1	)

		SCREAMING_SNAKE_CASE_  :		str				      =    (generated - audio).abs().sum()
		SCREAMING_SNAKE_CASE_  :		Dict				      =    (generated - audio).abs().max()

		if args.save:
				pipe.save_pretrained(args.checkpoint_path	)

		print("Diff sum"   , lowerCAmelCase	)
		print("Diff max"   , lowerCAmelCase	)

		assert diff_max < 1E-3, f'Diff max: {diff_max} is too much :-/'

		print(f'Conversion for {model_name} successful!'	)


if __name__ == "__main__":
		__lowerCamelCase					:	Optional[Any]       =  argparse.ArgumentParser()

		parser.add_argument('''--model_path''', default=None, type=str, required=True, help='''Path to the model to convert.''')
		parser.add_argument(
		    '''--save''', default=True, type=bool, required=False, help='''Whether to save the converted model or not.'''
		)
		parser.add_argument('''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the output model.''')
		__lowerCamelCase					:	Union[str, Any]       =  parser.parse_args()

		main(args)

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


__lowerCamelCase					:	List[str]       =  logging.get_logger(__name__)


__lowerCamelCase					:	Tuple       =  {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

__lowerCamelCase					:	List[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'''
        ),
    },
}

__lowerCamelCase					:	int       =  {
    '''allenai/longformer-base-4096''': 40_96,
    '''allenai/longformer-large-4096''': 40_96,
    '''allenai/longformer-large-4096-finetuned-triviaqa''': 40_96,
    '''allenai/longformer-base-4096-extra.pos.embd.only''': 40_96,
    '''allenai/longformer-large-4096-extra.pos.embd.only''': 40_96,
}




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







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




def       _snake_case	(   lowerCAmelCase							:   List[str]	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[str]				      =    set()
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)	)
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    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      :       Union[str, Any],_A      :       List[Any],_A      :       Tuple,_A      :       str="replace",_A      :       Optional[int]="<s>",_A      :       Dict="</s>",_A      :       Any="</s>",_A      :       Optional[Any]="<s>",_A      :       Union[str, Any]="<unk>",_A      :       int="<pad>",_A      :       Dict="<mask>",_A      :       int=False,**_A      :       Dict,):







							"""simple docstring"""






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

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

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

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

							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
							SCREAMING_SNAKE_CASE_  :		List[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 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							return len(self.encoder    )

					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






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

					def 				__UpperCamelCase   (    self      :       Any,_A      :       int    ):







							"""simple docstring"""






							if token in self.cache:
									return self.cache[token]
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    tuple(_A    )
							SCREAMING_SNAKE_CASE_  :		str				      =    get_pairs(_A    )

							if not pairs:
									return token

							while True:
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    min(_A,key=lambda _A    : self.bpe_ranks.get(_A,float("inf"    )    )    )
									if bigram not in self.bpe_ranks:
											break
									SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Tuple				      =    bigram
									SCREAMING_SNAKE_CASE_  :		int				      =    []
									SCREAMING_SNAKE_CASE_  :		Dict				      =    0
									while i < len(_A    ):
											try:
													SCREAMING_SNAKE_CASE_  :		Tuple				      =    word.index(_A,_A    )
											except ValueError:
													new_word.extend(word[i:]    )
													break
											else:
													new_word.extend(word[i:j]    )
													SCREAMING_SNAKE_CASE_  :		str				      =    j

											if word[i] == first and i < len(_A    ) - 1 and word[i + 1] == second:
													new_word.append(first + second    )
													i += 2
											else:
													new_word.append(word[i]    )
													i += 1
									SCREAMING_SNAKE_CASE_  :		Dict				      =    tuple(_A    )
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    new_word
									if len(_A    ) == 1:
											break
									else:
											SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    get_pairs(_A    )
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    " ".join(_A    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    word
							return word

					def 				__UpperCamelCase   (    self      :       Dict,_A      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    []
							for token in re.findall(self.pat,_A    ):
									SCREAMING_SNAKE_CASE_  :		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(_A    ).split(" "    )    )
							return bpe_tokens

					def 				__UpperCamelCase   (    self      :       Optional[int],_A      :       str    ):







							"""simple docstring"""






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

					def 				__UpperCamelCase   (    self      :       Tuple,_A      :       str    ):







							"""simple docstring"""






							return self.decoder.get(_A    )

					def 				__UpperCamelCase   (    self      :       List[str],_A      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    "".join(_A    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    bytearray([self.byte_decoder[c] for c in text]    ).decode("utf-8",errors=self.errors    )
							return text

					def 				__UpperCamelCase   (    self      :       List[Any],_A      :       str,_A      :       Optional[str] = None    ):







							"""simple docstring"""






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

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

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    0
							with open(_A,"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 _A    : 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!"    )
													SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    token_index
											writer.write(" ".join(_A    ) + "\n"    )
											index += 1

							return vocab_file, merge_file

					def 				__UpperCamelCase   (    self      :       Optional[Any],_A      :       List[int],_A      :       Optional[List[int]] = None    ):







							"""simple docstring"""






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

					def 				__UpperCamelCase   (    self      :       str,_A      :       List[int],_A      :       Optional[List[int]] = None,_A      :       bool = False    ):







							"""simple docstring"""






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

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

					def 				__UpperCamelCase   (    self      :       Any,_A      :       List[int],_A      :       Optional[List[int]] = None    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    [self.sep_token_id]
							SCREAMING_SNAKE_CASE_  :		Optional[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 				__UpperCamelCase   (    self      :       Any,_A      :       Union[str, Any],_A      :       Any=False,**_A      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    kwargs.pop("add_prefix_space",self.add_prefix_space    )
							if (is_split_into_words or add_prefix_space) and (len(_A    ) > 0 and not text[0].isspace()):
									SCREAMING_SNAKE_CASE_  :		str				      =    " " + text
							return (text, kwargs)
