'''simple docstring'''







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


UpperCamelCase_      	=	{
    '''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    lowerCamelCase       (			UpperCAmelCase__				:  str				,						UpperCAmelCase__				:  Tuple		)			->	Union[str, Any]:


							'''simple docstring'''






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


def    lowerCamelCase       (			UpperCAmelCase__				:  Union[str, Any]		)			->	str:


							'''simple docstring'''






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




class _SCREAMING_SNAKE_CASE(							__a					):
						pass




class _SCREAMING_SNAKE_CASE(							nn.Module					):




						def __init__(    self :       Dict							,							UpperCamelCase_ :       Tuple      )     ->				List[Any]:
													super().__init__()

													SCREAMING_SNAKE_CASE__     :Tuple						    = DiffusionAttnUnetaD(lowercase_							,							n_attn_layers=4      )
													SCREAMING_SNAKE_CASE__     :Dict						    = deepcopy(self.diffusion      )
													SCREAMING_SNAKE_CASE__     :Any						    = torch.quasirandom.SobolEngine(1							,							scramble=lowercase_      )


def    lowerCamelCase       (			UpperCAmelCase__				:  Tuple		)			->	Tuple:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :str						    = MODELS_MAP[model_name]['url']
							os.system(F'''wget {url} ./'''		)

							return F'''./{model_name}.ckpt'''


UpperCamelCase_      	=	{
    '''1''': '''resnets.0''',
    '''2''': '''attentions.0''',
    '''3''': '''resnets.1''',
    '''4''': '''attentions.1''',
    '''5''': '''resnets.2''',
    '''6''': '''attentions.2''',
}
UpperCamelCase_      	=	{
    '''8''': '''resnets.0''',
    '''9''': '''attentions.0''',
    '''10''': '''resnets.1''',
    '''11''': '''attentions.1''',
    '''12''': '''resnets.2''',
    '''13''': '''attentions.2''',
}
UpperCamelCase_      	=	{
    '''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''',
}
UpperCamelCase_      	=	{
    '''0''': '''resnets.0''',
    '''1''': '''resnets.1''',
    '''2''': '''resnets.2''',
    '''4''': '''resnets.0''',
    '''5''': '''resnets.1''',
    '''6''': '''resnets.2''',
}

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

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


def    lowerCamelCase       (			UpperCAmelCase__				:  List[str]		)			->	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    lowerCamelCase       (			UpperCAmelCase__				:  Any		)			->	str:


							'''simple docstring'''






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


def    lowerCamelCase       (			UpperCAmelCase__				:  List[str]				,						UpperCAmelCase__				:  str=1_3		)			->	List[str]:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :int						    = input_string

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

							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = 0
							if string.startswith('net.3.'		):
														depth += 1
														SCREAMING_SNAKE_CASE__     :List[str]						    = string[6:]
							elif string.startswith('net.'		):
														SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = string[4:]

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

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

							# mid block
							if string[:2].isdigit():
														SCREAMING_SNAKE_CASE__     :str						    = string[:2]
														SCREAMING_SNAKE_CASE__     :List[str]						    = string[2:]
							else:
														SCREAMING_SNAKE_CASE__     :str						    = string[0]
														SCREAMING_SNAKE_CASE__     :Optional[Any]						    = string[1:]

							if depth == max_depth:
														SCREAMING_SNAKE_CASE__     :List[Any]						    = MID_NUM_TO_LAYER[layer_num]
														SCREAMING_SNAKE_CASE__     :Tuple						    = 'mid_block'
							elif depth > 0 and int(SCREAMING_SNAKE_CASE_		) < 7:
														SCREAMING_SNAKE_CASE__     :List[Any]						    = DOWN_NUM_TO_LAYER[layer_num]
														SCREAMING_SNAKE_CASE__     :Tuple						    = F'''down_blocks.{depth}'''
							elif depth > 0 and int(SCREAMING_SNAKE_CASE_		) > 7:
														SCREAMING_SNAKE_CASE__     :List[str]						    = UP_NUM_TO_LAYER[layer_num]
														SCREAMING_SNAKE_CASE__     :Dict						    = F'''up_blocks.{max_depth - depth - 1}'''
							elif depth == 0:
														SCREAMING_SNAKE_CASE__     :int						    = DEPTH_0_TO_LAYER[layer_num]
														SCREAMING_SNAKE_CASE__     :Any						    = F'''up_blocks.{max_depth - 1}''' if int(SCREAMING_SNAKE_CASE_		) > 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__     :Dict						    = string_left[1:]

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

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


def    lowerCamelCase       (			UpperCAmelCase__				:  Optional[int]		)			->	List[Any]:


							'''simple docstring'''






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

														SCREAMING_SNAKE_CASE__     :Optional[int]						    = rename(SCREAMING_SNAKE_CASE_		)

														# check if we need to transform from Conv => Linear for attention
														if isinstance(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_		):
																					SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = transform_conv_attns(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_		)
														else:
																					SCREAMING_SNAKE_CASE__     :int						    = v

							return new_state_dict


def    lowerCamelCase       (			UpperCAmelCase__				:  List[Any]				,						UpperCAmelCase__				:  List[Any]				,						UpperCAmelCase__				:  str		)			->	List[Any]:


							'''simple docstring'''






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


def    lowerCamelCase       (			UpperCAmelCase__				:  Optional[Any]		)			->	str:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :List[str]						    = torch.device('cuda' if torch.cuda.is_available() else 'cpu'		)

							SCREAMING_SNAKE_CASE__     :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__     :int						    = download(SCREAMING_SNAKE_CASE_		)

							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = MODELS_MAP[model_name]['sample_rate']
							SCREAMING_SNAKE_CASE__     :str						    = MODELS_MAP[model_name]['sample_size']

							SCREAMING_SNAKE_CASE__     :str						    = Object()
							SCREAMING_SNAKE_CASE__     :Tuple						    = sample_size
							SCREAMING_SNAKE_CASE__     :Dict						    = sample_rate
							SCREAMING_SNAKE_CASE__     :Dict						    = 0

							SCREAMING_SNAKE_CASE__     :Tuple						    = UNetaDModel(sample_size=SCREAMING_SNAKE_CASE_				,						sample_rate=SCREAMING_SNAKE_CASE_		)
							SCREAMING_SNAKE_CASE__     :str						    = diffusers_model.state_dict()

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

							SCREAMING_SNAKE_CASE__     :List[Any]						    = set(renamed_state_dict.keys()		) - set(diffusers_state_dict.keys()		)
							SCREAMING_SNAKE_CASE__     :Any						    = set(diffusers_state_dict.keys()		) - set(renamed_state_dict.keys()		)

							assert len(SCREAMING_SNAKE_CASE_		) == 0, F'''Problem with {renamed_minus_diffusers}'''
							assert all(k.endswith('kernel'		) for k in list(SCREAMING_SNAKE_CASE_		)		), 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__     :Union[str, Any]						    = value.squeeze()

														SCREAMING_SNAKE_CASE__     :str						    = value

							diffusers_model.load_state_dict(SCREAMING_SNAKE_CASE_		)

							SCREAMING_SNAKE_CASE__     :int						    = 1_0_0
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = 3_3

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

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

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

							SCREAMING_SNAKE_CASE__     :Dict						    = DanceDiffusionPipeline(unet=SCREAMING_SNAKE_CASE_				,						scheduler=SCREAMING_SNAKE_CASE_		)

							SCREAMING_SNAKE_CASE__     :List[str]						    = torch.manual_seed(3_3		)
							SCREAMING_SNAKE_CASE__     :int						    = pipe(num_inference_steps=SCREAMING_SNAKE_CASE_				,						generator=SCREAMING_SNAKE_CASE_		).audios

							SCREAMING_SNAKE_CASE__     :Dict						    = sampling.iplms_sample(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						{}		)
							SCREAMING_SNAKE_CASE__     :Tuple						    = generated.clamp(-1				,						1		)

							SCREAMING_SNAKE_CASE__     :Optional[int]						    = (generated - audio).abs().sum()
							SCREAMING_SNAKE_CASE__     :Tuple						    = (generated - audio).abs().max()

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

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

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

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


if __name__ == "__main__":
		UpperCamelCase_      	=	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.''')
		UpperCamelCase_      	=	parser.parse_args()

		main(args)







'''simple docstring'''


import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

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


if is_torch_available():
       import torch


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


__snake_case						  =		logging.get_logger(__name__)
__snake_case						  =		{"""vocab_file""": """spiece.model"""}

__snake_case						  =		{
    """vocab_file""": {
        """AI-Sweden/gpt-sw3-126m""": """https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-350m""": """https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-1.6b""": """https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-6.7b""": """https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-20b""": """https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model""",
    }
}

__snake_case						  =		{
    """AI-Sweden/gpt-sw3-126m""": 2048,
    """AI-Sweden/gpt-sw3-350m""": 2048,
    """AI-Sweden/gpt-sw3-1.6b""": 2048,
    """AI-Sweden/gpt-sw3-6.7b""": 2048,
    """AI-Sweden/gpt-sw3-20b""": 2048,
}





class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						VOCAB_FILES_NAMES
  A_    							=						PRETRAINED_VOCAB_FILES_MAP
  A_    							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  A_    							=						['''input_ids''', '''attention_mask''']




  def __init__(		self      :       str   ,				lowercase_      :       List[str]   ,				lowercase_      :       List[str]=False   ,				lowercase_      :       List[str]=False   ,				lowercase_      :       Optional[int]=False   ,				lowercase_      :       Dict=None   ,				lowercase_      :       Union[str, Any]=None   ,				lowercase_      :       List[Any]=None   ,				lowercase_      :       Union[str, Any]=None   ,				lowercase_      :       Optional[Dict[str, Any]] = None   ,				**lowercase_      :       Tuple   ,				):







    '''simple docstring'''


    lowercase_     =       {} if sp_model_kwargs is None else sp_model_kwargs

    lowercase_     =       kwargs.get("""name_or_path"""      )
    if name_or_path is None:
      logger.warning(
          """name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"""
          """ you are testing the model, this can safely be ignored"""      )
      lowercase_     =       """None"""

    # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
    lowercase_     =       """<|endoftext|>""" if eos_token is None else eos_token
    lowercase_     =       """<unk>""" if unk_token is None else unk_token
    if "gpt-sw3-7b" in name_or_path:
      lowercase_     =       unk_token if pad_token is None else pad_token
      lowercase_     =       eos_token if bos_token is None else bos_token
    else:
      lowercase_     =       """<pad>""" if pad_token is None else pad_token
      lowercase_     =       """<s>""" if bos_token is None else bos_token

    super().__init__(
        do_lower_case=lowercase_   ,				remove_space=lowercase_   ,				keep_accents=lowercase_   ,				bos_token=lowercase_   ,				eos_token=lowercase_   ,				unk_token=lowercase_   ,				pad_token=lowercase_   ,				sp_model_kwargs=self.sp_model_kwargs   ,				**lowercase_   ,				)

    lowercase_     =       do_lower_case
    lowercase_     =       remove_space
    lowercase_     =       keep_accents
    lowercase_     =       vocab_file

    lowercase_     =       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
    self.sp_model.Load(lowercase_      )

    # Used for whitespace normalization in input texts
    # fmt : off
    lowercase_     =       {""" """, """ """, """ """, """ """, """ """, """　""", """ """, """ """, """ """, """ """, """￼""", """"""}
    # fmt : on

    # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
    lowercase_     =       re.compile(
        F"""[{"".join(map(lowercase_   ,				list(range(0   ,				9      )      ) + list(range(11   ,				32      )      ) + list(range(127   ,				160      )      ) + [160, 173, 8_203]      )      )}]"""      )




  def __getstate__(		self      :       Tuple      ):







    '''simple docstring'''


    lowercase_     =       self.__dict__.copy()
    lowercase_     =       None
    return state




  def __setstate__(		self      :       Tuple   ,				lowercase_      :       Union[str, Any]      ):







    '''simple docstring'''


    lowercase_     =       d

    # for backward compatibility
    if not hasattr(self   ,				"""sp_model_kwargs"""      ):
      lowercase_     =       {}

    lowercase_     =       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
    self.sp_model.Load(self.vocab_file      )




  @property
  # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
  def 		lowerCamelCase__  (		self      :       int      ):







    '''simple docstring'''


    return len(self.sp_model      )




  def 		lowerCamelCase__  (		self      :       List[Any]   ,				lowercase_      :       str      ):







    '''simple docstring'''


    lowercase_     =       self.non_printing_characters_re.sub(""""""   ,				lowercase_      )

    # Normalize whitespaces
    lowercase_     =       """""".join([char if char not in self.whitespaces else """ """ for char in text]      )

    # NFC Unicode normalization
    lowercase_     =       unicodedata.normalize("""NFC"""   ,				lowercase_      )
    return text




  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       str   ,				**lowercase_      :       Any      ):







    '''simple docstring'''


    lowercase_     =       self.preprocess_text(lowercase_      )
    return self.sp_model.encode(lowercase_   ,				out_type=lowercase_      )




  def 		lowerCamelCase__  (		self      :       Dict   ,				lowercase_      :       str      ):







    '''simple docstring'''


    return self.sp_model.PieceToId(lowercase_      )




  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       int      ):







    '''simple docstring'''


    return self.sp_model.IdToPiece(lowercase_      )




  @staticmethod
  def 		lowerCamelCase__  (		lowercase_      :       str      ):







    '''simple docstring'''


    return out_string




  def 		lowerCamelCase__  (		self      :       Dict   ,				lowercase_      :       List[str]      ):







    '''simple docstring'''


    lowercase_     =       []
    lowercase_     =       """"""
    lowercase_     =       False
    for token in tokens:
      # make sure that special tokens are not decoded using sentencepiece model
      if token in self.all_special_tokens:
        # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
        if not prev_is_special:
          out_string += " "

        out_string += self.sp_model.decode(lowercase_      ) + token
        lowercase_     =       True
        lowercase_     =       []
      else:
        current_sub_tokens.append(lowercase_      )
        lowercase_     =       False
    out_string += self.sp_model.decode(lowercase_      )

    return out_string




  def 		lowerCamelCase__  (		self      :       Tuple      ):







    '''simple docstring'''


    lowercase_     =       {self.convert_ids_to_tokens(lowercase_      ): i for i in range(self.vocab_size      )}
    vocab.update(self.added_tokens_encoder      )
    return vocab




  def 		lowerCamelCase__  (		self      :       Tuple   ,				lowercase_      :       str   ,				lowercase_      :       Optional[str] = None      ):







    '''simple docstring'''


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

    if os.path.abspath(self.vocab_file      ) != os.path.abspath(lowercase_      ) and os.path.isfile(self.vocab_file      ):
      copyfile(self.vocab_file   ,				lowercase_      )
    elif not os.path.isfile(self.vocab_file      ):
      with open(lowercase_   ,				"""wb"""      ) as fi:
        lowercase_     =       self.sp_model.serialized_model_proto()
        fi.write(lowercase_      )

    return (out_vocab_file,)




  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       Union[str, List[str]]   ,				lowercase_      :       Union[str, bool] = False      ):







    '''simple docstring'''


    if isinstance(lowercase_   ,				lowercase_      ):
      lowercase_     =       self.preprocess_text(lowercase_      )
      lowercase_     =       self.sp_model.encode(lowercase_      )
    else:
      lowercase_     =       [self.preprocess_text(lowercase_      ) for t in text]
      lowercase_     =       self.sp_model.encode(lowercase_      )

    if return_tensors is True or return_tensors == "pt":
      lowercase_     =       torch.tensor(lowercase_      )

    return token_ids




  def 		lowerCamelCase__  (		self      :       Union[str, Any]   ,				lowercase_      :       Union[int, List[int]]      ):







    '''simple docstring'''


    return self.sp_model.decode(lowercase_      )





  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       "Conversation"      ):







    '''simple docstring'''


    lowercase_     =       [F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
    lowercase_     =       (
        F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(lowercase_      ) + F"""{self.bos_token}Bot:"""
    )
    return self.encode(text=lowercase_      )

