






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


__snake_case      :      List[str]             =       {
    """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 		_UpperCamelCase				(      UpperCamelCase_    :   Dict      ,    UpperCamelCase_    :   Dict						)					->				str:

						"""simple docstring"""



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







def 		_UpperCamelCase				(      UpperCamelCase_    :   int						)					->				List[Any]:

						"""simple docstring"""



						lowerCAmelCase__        =  torch.sin(t * math.pi / 2						) ** 2
						lowerCAmelCase__        =  (1 - sigma**2) ** 0.5
						return alpha_sigma_to_t(UpperCamelCase_      ,    UpperCamelCase_						)


class       __SCREAMING_SNAKE_CASE   (				__lowercase):
						pass




class       __SCREAMING_SNAKE_CASE   (				nn.Module):






						def __init__(		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												super().__init__()

												lowerCAmelCase__        =  DiffusionAttnUnetaD(_UpperCamelCase       ,  n_attn_layers=4				)
												lowerCAmelCase__        =  deepcopy(self.diffusion				)
												lowerCAmelCase__        =  torch.quasirandom.SobolEngine(1       ,  scramble=_UpperCamelCase				)







def 		_UpperCamelCase				(      UpperCamelCase_    :   Union[str, Any]						)					->				Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__        =  MODELS_MAP[model_name]['url']
						os.system(F"wget {url} ./"						)

						return F"./{model_name}.ckpt"


__snake_case      :      Union[str, Any]             =       {
    """1""": """resnets.0""",
    """2""": """attentions.0""",
    """3""": """resnets.1""",
    """4""": """attentions.1""",
    """5""": """resnets.2""",
    """6""": """attentions.2""",
}
__snake_case      :      Tuple             =       {
    """8""": """resnets.0""",
    """9""": """attentions.0""",
    """10""": """resnets.1""",
    """11""": """attentions.1""",
    """12""": """resnets.2""",
    """13""": """attentions.2""",
}
__snake_case      :      int             =       {
    """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""",
}
__snake_case      :      Optional[Any]             =       {
    """0""": """resnets.0""",
    """1""": """resnets.1""",
    """2""": """resnets.2""",
    """4""": """resnets.0""",
    """5""": """resnets.1""",
    """6""": """resnets.2""",
}

__snake_case      :      int             =       {
    """skip""": """conv_skip""",
    """main.0""": """conv_1""",
    """main.1""": """group_norm_1""",
    """main.3""": """conv_2""",
    """main.4""": """group_norm_2""",
}

__snake_case      :      Optional[int]             =       {
    """norm""": """group_norm""",
    """qkv_proj""": ["""query""", """key""", """value"""],
    """out_proj""": ["""proj_attn"""],
}







def 		_UpperCamelCase				(      UpperCamelCase_    :   Union[str, Any]						)					->				Optional[int]:

						"""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 		_UpperCamelCase				(      UpperCamelCase_    :   Tuple						)					->				Optional[Any]:

						"""simple docstring"""



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







def 		_UpperCamelCase				(      UpperCamelCase_    :   Tuple      ,    UpperCamelCase_    :   List[Any]=13						)					->				Dict:

						"""simple docstring"""



						lowerCAmelCase__        =  input_string

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

						lowerCAmelCase__        =  0
						if string.startswith('net.3.'						):
												depth += 1
												lowerCAmelCase__        =  string[6:]
						elif string.startswith('net.'						):
												lowerCAmelCase__        =  string[4:]

						while string.startswith('main.7.'						):
												depth += 1
												lowerCAmelCase__        =  string[7:]

						if string.startswith('main.'						):
												lowerCAmelCase__        =  string[5:]

						# mid block
						if string[:2].isdigit():
												lowerCAmelCase__        =  string[:2]
												lowerCAmelCase__        =  string[2:]
						else:
												lowerCAmelCase__        =  string[0]
												lowerCAmelCase__        =  string[1:]

						if depth == max_depth:
												lowerCAmelCase__        =  MID_NUM_TO_LAYER[layer_num]
												lowerCAmelCase__        =  'mid_block'
						elif depth > 0 and int(UpperCamelCase_						) < 7:
												lowerCAmelCase__        =  DOWN_NUM_TO_LAYER[layer_num]
												lowerCAmelCase__        =  F"down_blocks.{depth}"
						elif depth > 0 and int(UpperCamelCase_						) > 7:
												lowerCAmelCase__        =  UP_NUM_TO_LAYER[layer_num]
												lowerCAmelCase__        =  F"up_blocks.{max_depth - depth - 1}"
						elif depth == 0:
												lowerCAmelCase__        =  DEPTH_0_TO_LAYER[layer_num]
												lowerCAmelCase__        =  F"up_blocks.{max_depth - 1}" if int(UpperCamelCase_						) > 3 else 'down_blocks.0'

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

						lowerCAmelCase__        =  string_left[1:]

						if "resnets" in new_layer:
												lowerCAmelCase__        =  convert_resconv_naming(UpperCamelCase_						)
						elif "attentions" in new_layer:
												lowerCAmelCase__        =  convert_attn_naming(UpperCamelCase_						)
												lowerCAmelCase__        =  new_string_left

						if not isinstance(UpperCamelCase_      ,    UpperCamelCase_						):
												lowerCAmelCase__        =  prefix + '.' + new_layer + '.' + string_left
						else:
												lowerCAmelCase__        =  [prefix + '.' + new_layer + '.' + s for s in string_left]
						return new_string







def 		_UpperCamelCase				(      UpperCamelCase_    :   Optional[int]						)					->				Tuple:

						"""simple docstring"""



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

												lowerCAmelCase__        =  rename(UpperCamelCase_						)

												# check if we need to transform from Conv => Linear for attention
												if isinstance(UpperCamelCase_      ,    UpperCamelCase_						):
																		lowerCAmelCase__        =  transform_conv_attns(UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_						)
												else:
																		lowerCAmelCase__        =  v

						return new_state_dict







def 		_UpperCamelCase				(      UpperCamelCase_    :   Dict      ,    UpperCamelCase_    :   List[Any]      ,    UpperCamelCase_    :   Optional[Any]						)					->				Optional[int]:

						"""simple docstring"""



						if len(UpperCamelCase_						) == 1:
												if len(v.shape						) == 3:
																		# weight
																		lowerCAmelCase__        =  v[:, :, 0]
												else:
																		# bias
																		lowerCAmelCase__        =  v
						else:
												# qkv matrices
												lowerCAmelCase__        =  v.shape[0]
												lowerCAmelCase__        =  trippled_shape // 3
												for i in range(3						):
																		if len(v.shape						) == 3:
																								lowerCAmelCase__        =  v[i * single_shape : (i + 1) * single_shape, :, 0]
																		else:
																								lowerCAmelCase__        =  v[i * single_shape : (i + 1) * single_shape]
						return new_state_dict







def 		_UpperCamelCase				(      UpperCamelCase_    :   List[Any]						)					->				Optional[int]:

						"""simple docstring"""



						lowerCAmelCase__        =  torch.device('cuda' if torch.cuda.is_available() else 'cpu'						)

						lowerCAmelCase__        =  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()}"
												lowerCAmelCase__        =  download(UpperCamelCase_						)

						lowerCAmelCase__        =  MODELS_MAP[model_name]['sample_rate']
						lowerCAmelCase__        =  MODELS_MAP[model_name]['sample_size']

						lowerCAmelCase__        =  Object()
						lowerCAmelCase__        =  sample_size
						lowerCAmelCase__        =  sample_rate
						lowerCAmelCase__        =  0

						lowerCAmelCase__        =  UNetaDModel(sample_size=UpperCamelCase_      ,    sample_rate=UpperCamelCase_						)
						lowerCAmelCase__        =  diffusers_model.state_dict()

						lowerCAmelCase__        =  DiffusionUncond(UpperCamelCase_						)
						orig_model.load_state_dict(torch.load(args.model_path      ,    map_location=UpperCamelCase_						)['state_dict']						)
						lowerCAmelCase__        =  orig_model.diffusion_ema.eval()
						lowerCAmelCase__        =  orig_model.state_dict()
						lowerCAmelCase__        =  rename_orig_weights(UpperCamelCase_						)

						lowerCAmelCase__        =  set(renamed_state_dict.keys()						) - set(diffusers_state_dict.keys()						)
						lowerCAmelCase__        =  set(diffusers_state_dict.keys()						) - set(renamed_state_dict.keys()						)

						assert len(UpperCamelCase_						) == 0, F"Problem with {renamed_minus_diffusers}"
						assert all(k.endswith('kernel'						) for k in list(UpperCamelCase_						)						), 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":
																		lowerCAmelCase__        =  value.squeeze()

												lowerCAmelCase__        =  value

						diffusers_model.load_state_dict(UpperCamelCase_						)

						lowerCAmelCase__        =  100
						lowerCAmelCase__        =  33

						lowerCAmelCase__        =  IPNDMScheduler(num_train_timesteps=UpperCamelCase_						)

						lowerCAmelCase__        =  torch.manual_seed(UpperCamelCase_						)
						lowerCAmelCase__        =  torch.randn([1, 2, config.sample_size]      ,    generator=UpperCamelCase_						).to(UpperCamelCase_						)

						lowerCAmelCase__        =  torch.linspace(1      ,    0      ,    steps + 1      ,    device=UpperCamelCase_						)[:-1]
						lowerCAmelCase__        =  get_crash_schedule(UpperCamelCase_						)

						lowerCAmelCase__        =  DanceDiffusionPipeline(unet=UpperCamelCase_      ,    scheduler=UpperCamelCase_						)

						lowerCAmelCase__        =  torch.manual_seed(33						)
						lowerCAmelCase__        =  pipe(num_inference_steps=UpperCamelCase_      ,    generator=UpperCamelCase_						).audios

						lowerCAmelCase__        =  sampling.iplms_sample(UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    {}						)
						lowerCAmelCase__        =  generated.clamp(-1      ,    1						)

						lowerCAmelCase__        =  (generated - audio).abs().sum()
						lowerCAmelCase__        =  (generated - audio).abs().max()

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

						print('Diff sum'      ,    UpperCamelCase_						)
						print('Diff max'      ,    UpperCamelCase_						)

						assert diff_max < 1e-3, F"Diff max: {diff_max} is too much :-/"

						print(F"Conversion for {model_name} successful!"						)


if __name__ == "__main__":
					__snake_case      :      Union[str, 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.""")
					__snake_case      :      Dict             =       parser.parse_args()

					main(args)













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

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


__snake_case      :      Any             =       logging.get_logger(__name__)


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

# See all LED models at https://huggingface.co/models?filter=LED
__snake_case      :      Optional[Any]             =       {
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

__snake_case      :      List[str]             =       {
    """allenai/led-base-16384""": 1_63_84,
}







@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
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(UpperCamelCase_						)
																		cs.append(2**8 + n						)
																		n += 1
						lowerCAmelCase__        =  [chr(UpperCamelCase_						) for n in cs]
						return dict(zip(UpperCamelCase_      ,    UpperCamelCase_						)						)







def 		_UpperCamelCase				(      UpperCamelCase_    :   Optional[Any]						)					->				Dict:

						"""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   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    Optional[int]						=   VOCAB_FILES_NAMES
						_SCREAMING_SNAKE_CASE		:    Optional[int]						=   PRETRAINED_VOCAB_FILES_MAP
						_SCREAMING_SNAKE_CASE		:    Optional[Any]						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_SCREAMING_SNAKE_CASE		:    Any						=   ['''input_ids''', '''attention_mask''']






						def __init__(		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase="replace"       ,  _UpperCamelCase="<s>"       ,  _UpperCamelCase="</s>"       ,  _UpperCamelCase="</s>"       ,  _UpperCamelCase="<s>"       ,  _UpperCamelCase="<unk>"       ,  _UpperCamelCase="<pad>"       ,  _UpperCamelCase="<mask>"       ,  _UpperCamelCase=False       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  AddedToken(_UpperCamelCase       ,  lstrip=_UpperCamelCase       ,  rstrip=_UpperCamelCase				) if isinstance(_UpperCamelCase       ,  _UpperCamelCase				) else bos_token
												lowerCAmelCase__        =  AddedToken(_UpperCamelCase       ,  lstrip=_UpperCamelCase       ,  rstrip=_UpperCamelCase				) if isinstance(_UpperCamelCase       ,  _UpperCamelCase				) else eos_token
												lowerCAmelCase__        =  AddedToken(_UpperCamelCase       ,  lstrip=_UpperCamelCase       ,  rstrip=_UpperCamelCase				) if isinstance(_UpperCamelCase       ,  _UpperCamelCase				) else sep_token
												lowerCAmelCase__        =  AddedToken(_UpperCamelCase       ,  lstrip=_UpperCamelCase       ,  rstrip=_UpperCamelCase				) if isinstance(_UpperCamelCase       ,  _UpperCamelCase				) else cls_token
												lowerCAmelCase__        =  AddedToken(_UpperCamelCase       ,  lstrip=_UpperCamelCase       ,  rstrip=_UpperCamelCase				) if isinstance(_UpperCamelCase       ,  _UpperCamelCase				) else unk_token
												lowerCAmelCase__        =  AddedToken(_UpperCamelCase       ,  lstrip=_UpperCamelCase       ,  rstrip=_UpperCamelCase				) if isinstance(_UpperCamelCase       ,  _UpperCamelCase				) else pad_token

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

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

												with open(_UpperCamelCase       ,  encoding='utf-8'				) as vocab_handle:
																		lowerCAmelCase__        =  json.load(_UpperCamelCase				)
												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(_UpperCamelCase       ,  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(_UpperCamelCase       ,  range(len(_UpperCamelCase				)				)				)				)
												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
						# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return len(self.encoder				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return dict(self.encoder       ,  **self.added_tokens_encoder				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												if token in self.cache:
																		return self.cache[token]
												lowerCAmelCase__        =  tuple(_UpperCamelCase				)
												lowerCAmelCase__        =  get_pairs(_UpperCamelCase				)

												if not pairs:
																		return token

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

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






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  []
												for token in re.findall(self.pat       ,  _UpperCamelCase				):
																		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(_UpperCamelCase				).split(' '				)				)
												return bpe_tokens






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												return self.encoder.get(_UpperCamelCase       ,  self.encoder.get(self.unk_token				)				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												return self.decoder.get(_UpperCamelCase				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  ''.join(_UpperCamelCase				)
												lowerCAmelCase__        =  bytearray([self.byte_decoder[c] for c in text]				).decode('utf-8'       ,  errors=self.errors				)
												return text






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None				):






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

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

												lowerCAmelCase__        =  0
												with open(_UpperCamelCase       ,  '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 _UpperCamelCase				: 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(_UpperCamelCase				) + '\n'				)
																								index += 1

												return vocab_file, merge_file






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None				):






												"""simple docstring"""
												if token_ids_a is None:
																		return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
												lowerCAmelCase__        =  [self.cls_token_id]
												lowerCAmelCase__        =  [self.sep_token_id]
												return cls + token_ids_a + sep + sep + token_ids_a + sep






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  _UpperCamelCase = False				):






												"""simple docstring"""
												if already_has_special_tokens:
																		return super().get_special_tokens_mask(
																		    token_ids_a=_UpperCamelCase       ,  token_ids_a=_UpperCamelCase       ,  already_has_special_tokens=_UpperCamelCase				)

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






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None				):






												"""simple docstring"""
												lowerCAmelCase__        =  [self.sep_token_id]
												lowerCAmelCase__        =  [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       ,  _UpperCamelCase       ,  _UpperCamelCase=False       ,  **_UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  kwargs.pop('add_prefix_space'       ,  self.add_prefix_space				)
												if (is_split_into_words or add_prefix_space) and (len(_UpperCamelCase				) > 0 and not text[0].isspace()):
																		lowerCAmelCase__        =  ' ' + text
												return (text, kwargs)







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  _UpperCamelCase = PaddingStrategy.DO_NOT_PAD       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  super()._pad(
												    encoded_inputs=_UpperCamelCase       ,  max_length=_UpperCamelCase       ,  padding_strategy=_UpperCamelCase       ,  pad_to_multiple_of=_UpperCamelCase       ,  return_attention_mask=_UpperCamelCase       ,  )

												# Load from model defaults
												if return_attention_mask is None:
																		lowerCAmelCase__        =  'attention_mask' in self.model_input_names

												if return_attention_mask and "global_attention_mask" in encoded_inputs:
																		lowerCAmelCase__        =  encoded_inputs[self.model_input_names[0]]
																		# `global_attention_mask` need to have the same length as other (sequential) inputs.
																		lowerCAmelCase__        =  len(encoded_inputs['global_attention_mask']				) != len(_UpperCamelCase				)

																		if needs_to_be_padded:
																								lowerCAmelCase__        =  len(_UpperCamelCase				) - len(encoded_inputs['global_attention_mask']				)

																								if self.padding_side == "right":
																														# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																														lowerCAmelCase__        =  (
																														    encoded_inputs['global_attention_mask'] + [-1] * difference
																														)
																								elif self.padding_side == "left":
																														lowerCAmelCase__        =  [-1] * difference + encoded_inputs[
																														    'global_attention_mask'
																														]
																								else:
																														raise ValueError('Invalid padding strategy:' + str(self.padding_side				)				)

												return encoded_inputs





