





"""simple docstring"""






import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase						=			logging.get_logger(__name__)

set_seed(770)


__UpperCAmelCase						=			{
    '''c_attn''': '''att_proj''',
    '''c_proj''': '''out_proj''',
    '''c_fc''': '''in_proj''',
    '''transformer.''': '''''',
    '''h.''': '''layers.''',
    '''ln_1''': '''layernorm_1''',
    '''ln_2''': '''layernorm_2''',
    '''ln_f''': '''layernorm_final''',
    '''wpe''': '''position_embeds_layer''',
    '''wte''': '''input_embeds_layer''',
}


__UpperCAmelCase						=			{
    '''text_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''text.pt''',
    },
    '''coarse_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''coarse.pt''',
    },
    '''fine_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''fine.pt''',
    },
    '''text''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''text_2.pt''',
    },
    '''coarse''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''coarse_2.pt''',
    },
    '''fine''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''fine_2.pt''',
    },
}

__UpperCAmelCase						=			os.path.dirname(os.path.abspath(__file__))
__UpperCAmelCase						=			os.path.join(os.path.expanduser('''~'''), '''.cache''')
__UpperCAmelCase						=			os.path.join(os.getenv('''XDG_CACHE_HOME''', default_cache_dir), '''suno''', '''bark_v0''')






def      lowercase__ (					lowerCAmelCase__   :			Tuple							,			lowerCAmelCase__   :			List[Any]=False			)    ->			List[Any]:



			'''simple docstring'''
			a__  :	Optional[int]		 =			model_type
			if use_small:
						key += "_small"
			return os.path.join(lowerCAmelCase__							,			REMOTE_MODEL_PATHS[key]["file_name"]			)






def      lowercase__ (					lowerCAmelCase__   :			str							,			lowerCAmelCase__   :			Optional[int]			)    ->			List[Any]:



			'''simple docstring'''
			os.makedirs(lowerCAmelCase__							,			exist_ok=lowerCAmelCase__			)
			hf_hub_download(repo_id=lowerCAmelCase__							,			filename=lowerCAmelCase__							,			local_dir=lowerCAmelCase__			)






def      lowercase__ (					lowerCAmelCase__   :			str							,			lowerCAmelCase__   :			str							,			lowerCAmelCase__   :			List[Any]=False							,			lowerCAmelCase__   :			Tuple="text"			)    ->			Any:



			'''simple docstring'''
			if model_type == "text":
						a__  :	Dict		 =			BarkSemanticModel
						a__  :	Dict		 =			BarkSemanticConfig
						a__  :	int		 =			BarkSemanticGenerationConfig
			elif model_type == "coarse":
						a__  :	str		 =			BarkCoarseModel
						a__  :	Union[str, Any]		 =			BarkCoarseConfig
						a__  :	List[str]		 =			BarkCoarseGenerationConfig
			elif model_type == "fine":
						a__  :	Any		 =			BarkFineModel
						a__  :	Union[str, Any]		 =			BarkFineConfig
						a__  :	str		 =			BarkFineGenerationConfig
			else:
						raise NotImplementedError()
			a__  :	List[str]		 =			F"{model_type}_small" if use_small else model_type
			a__  :	Optional[Any]		 =			REMOTE_MODEL_PATHS[model_key]
			if not os.path.exists(lowerCAmelCase__			):
						logger.info(F"{model_type} model not found, downloading into `{CACHE_DIR}`."			)
						_download(model_info["repo_id"]							,			model_info["file_name"]			)
			a__  :	int		 =			torch.load(lowerCAmelCase__							,			map_location=lowerCAmelCase__			)
			# this is a hack
			a__  :	Any		 =			checkpoint["model_args"]
			if "input_vocab_size" not in model_args:
						a__  :	Union[str, Any]		 =			model_args["vocab_size"]
						a__  :	str		 =			model_args["vocab_size"]
						del model_args["vocab_size"]

			# convert Bark model arguments to HF Bark model arguments
			a__  :	Dict		 =			model_args.pop("n_head"			)
			a__  :	Optional[int]		 =			model_args.pop("n_embd"			)
			a__  :	Optional[Any]		 =			model_args.pop("n_layer"			)

			a__  :	Dict		 =			ConfigClass(**checkpoint["model_args"]			)
			a__  :	Dict		 =			ModelClass(config=lowerCAmelCase__			)
			a__  :	str		 =			GenerationConfigClass()

			a__  :	Optional[int]		 =			model_generation_config
			a__  :	Optional[int]		 =			checkpoint["model"]
			# fixup checkpoint
			a__  :	Any		 =			"_orig_mod."
			for k, v in list(state_dict.items()			):
						if k.startswith(lowerCAmelCase__			):
									# replace part of the key with corresponding layer name in HF implementation
									a__  :	str		 =			k[len(lowerCAmelCase__			) :]
									for old_layer_name in new_layer_name_dict:
												a__  :	Tuple		 =			new_k.replace(lowerCAmelCase__							,			new_layer_name_dict[old_layer_name]			)

									a__  :	Dict		 =			state_dict.pop(lowerCAmelCase__			)

			a__  :	Optional[int]		 =			set(state_dict.keys()			) - set(model.state_dict().keys()			)
			a__  :	Optional[int]		 =			{k for k in extra_keys if not k.endswith(".attn.bias"			)}
			a__  :	Union[str, Any]		 =			set(model.state_dict().keys()			) - set(state_dict.keys()			)
			a__  :	List[str]		 =			{k for k in missing_keys if not k.endswith(".attn.bias"			)}
			if len(lowerCAmelCase__			) != 0:
						raise ValueError(F"extra keys found: {extra_keys}"			)
			if len(lowerCAmelCase__			) != 0:
						raise ValueError(F"missing keys: {missing_keys}"			)
			model.load_state_dict(lowerCAmelCase__							,			strict=lowerCAmelCase__			)
			a__  :	Optional[int]		 =			model.num_parameters(exclude_embeddings=lowerCAmelCase__			)
			a__  :	int		 =			checkpoint["best_val_loss"].item()
			logger.info(F"model loaded: {round(n_params/1E6							,			1			)}M params, {round(lowerCAmelCase__							,			3			)} loss"			)
			model.eval()
			model.to(lowerCAmelCase__			)
			del checkpoint, state_dict

			return model






def      lowercase__ (					lowerCAmelCase__   :			Tuple							,			lowerCAmelCase__   :			str=False							,			lowerCAmelCase__   :			List[str]="text"			)    ->			List[Any]:



			'''simple docstring'''
			if model_type not in ("text", "coarse", "fine"):
						raise NotImplementedError()

			a__  :	str		 =			"cpu"  # do conversion on cpu

			a__  :	str		 =			_get_ckpt_path(lowerCAmelCase__							,			use_small=lowerCAmelCase__			)
			a__  :	int		 =			_load_model(lowerCAmelCase__							,			lowerCAmelCase__							,			model_type=lowerCAmelCase__							,			use_small=lowerCAmelCase__			)

			# load bark initial model
			a__  :	Union[str, Any]		 =			_bark_load_model(lowerCAmelCase__							,			"cpu"							,			model_type=lowerCAmelCase__							,			use_small=lowerCAmelCase__			)

			if model_type == "text":
						a__  :	List[str]		 =			bark_model["model"]

			if model.num_parameters(exclude_embeddings=lowerCAmelCase__			) != bark_model.get_num_params():
						raise ValueError("initial and new models don't have the same number of parameters"			)

			# check if same output as the bark model
			a__  :	Dict		 =			5
			a__  :	Optional[int]		 =			1_0

			if model_type in ["text", "coarse"]:
						a__  :	Any		 =			torch.randint(2_5_6							,			(batch_size, sequence_length)							,			dtype=torch.int			)
						a__  :	Dict		 =			bark_model(lowerCAmelCase__			)[0]

						a__  :	Union[str, Any]		 =			model(lowerCAmelCase__			)

						# take last logits
						a__  :	str		 =			output_new_model_total.logits[:, [-1], :]

			else:
						a__  :	List[Any]		 =			3
						a__  :	Dict		 =			8
						a__  :	Optional[Any]		 =			torch.randint(2_5_6							,			(batch_size, sequence_length, n_codes_total)							,			dtype=torch.int			)

						a__  :	int		 =			model(lowerCAmelCase__							,			lowerCAmelCase__			)
						a__  :	str		 =			bark_model(lowerCAmelCase__							,			lowerCAmelCase__			)

						a__  :	Dict		 =			output_new_model_total.logits

			# output difference should come from the difference of self-attention implementation design
			if output_new_model.shape != output_old_model.shape:
						raise ValueError("initial and new outputs don't have the same shape"			)
			if (output_new_model - output_old_model).abs().max().item() > 1E-3:
						raise ValueError("initial and new outputs are not equal"			)

			Path(lowerCAmelCase__			).mkdir(exist_ok=lowerCAmelCase__			)
			model.save_pretrained(lowerCAmelCase__			)






def      lowercase__ (					lowerCAmelCase__   :			Optional[int]							,			lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			Any							,			lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			Optional[Any]							,			lowerCAmelCase__   :			List[str]							,			)    ->			Any:



			'''simple docstring'''
			a__  :	Any		 =			os.path.join(lowerCAmelCase__							,			lowerCAmelCase__			)

			a__  :	Optional[int]		 =			BarkSemanticConfig.from_pretrained(os.path.join(lowerCAmelCase__							,			"config.json"			)			)
			a__  :	Optional[Any]		 =			BarkCoarseConfig.from_pretrained(os.path.join(lowerCAmelCase__							,			"config.json"			)			)
			a__  :	Union[str, Any]		 =			BarkFineConfig.from_pretrained(os.path.join(lowerCAmelCase__							,			"config.json"			)			)
			a__  :	Union[str, Any]		 =			EncodecConfig.from_pretrained("facebook/encodec_24khz"			)

			a__  :	Dict		 =			BarkSemanticModel.from_pretrained(lowerCAmelCase__			)
			a__  :	List[Any]		 =			BarkCoarseModel.from_pretrained(lowerCAmelCase__			)
			a__  :	Dict		 =			BarkFineModel.from_pretrained(lowerCAmelCase__			)
			a__  :	int		 =			EncodecModel.from_pretrained("facebook/encodec_24khz"			)

			a__  :	int		 =			BarkConfig.from_sub_model_configs(
			    lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__			)

			a__  :	Any		 =			BarkGenerationConfig.from_sub_model_configs(
			    semantic.generation_config							,			coarseAcoustic.generation_config							,			fineAcoustic.generation_config			)

			a__  :	Union[str, Any]		 =			BarkModel(lowerCAmelCase__			)

			a__  :	List[str]		 =			semantic
			a__  :	Dict		 =			coarseAcoustic
			a__  :	List[str]		 =			fineAcoustic
			a__  :	Dict		 =			codec

			a__  :	List[str]		 =			bark_generation_config

			Path(lowerCAmelCase__			).mkdir(exist_ok=lowerCAmelCase__			)
			bark.save_pretrained(lowerCAmelCase__							,			repo_id=lowerCAmelCase__							,			push_to_hub=lowerCAmelCase__			)


if __name__ == "__main__":
		__UpperCAmelCase						=			argparse.ArgumentParser()
		# Required parameters

		parser.add_argument('''model_type''', type=str, help='''text, coarse or fine.''')
		parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
		parser.add_argument('''--is_small''', action='''store_true''', help='''convert the small version instead of the large.''')

		__UpperCAmelCase						=			parser.parse_args()

		load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)






"""simple docstring"""






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

import regex as re

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


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation

__UpperCAmelCase						=			logging.get_logger(__name__)


__UpperCAmelCase						=			{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

__UpperCAmelCase						=			{
    '''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
    '''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
    '''tokenizer_config_file''': {
        '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
    },
}

__UpperCAmelCase						=			{'''facebook/blenderbot-3B''': 128}






@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def      lowercase__ (					)    ->			Any:



			'''simple docstring'''
			a__  :	Any		 =			(
			    list(range(ord("!"			)							,			ord("~"			) + 1			)			) + list(range(ord("¡"			)							,			ord("¬"			) + 1			)			) + list(range(ord("®"			)							,			ord("ÿ"			) + 1			)			)
			)
			a__  :	Any		 =			bs[:]
			a__  :	int		 =			0
			for b in range(2**8			):
						if b not in bs:
									bs.append(lowerCAmelCase__			)
									cs.append(2**8 + n			)
									n += 1
			a__  :	Tuple		 =			[chr(lowerCAmelCase__			) for n in cs]
			return dict(zip(lowerCAmelCase__							,			lowerCAmelCase__			)			)






def      lowercase__ (					lowerCAmelCase__   :			List[str]			)    ->			Any:



			'''simple docstring'''
			a__  :	Tuple		 =			set()
			a__  :	Union[str, Any]		 =			word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)			)
						a__  :	Tuple		 =			char
			return pairs





class 					__UpperCAmelCase							(						_UpperCamelCase  ):
				__lowerCamelCase				:	int				     =				VOCAB_FILES_NAMES
				__lowerCamelCase				:	List[Any]				     =				PRETRAINED_VOCAB_FILES_MAP
				__lowerCamelCase				:	Optional[int]				     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__lowerCamelCase				:	List[str]				     =				["input_ids", "attention_mask"]
				def __init__(		self    :			str	, a_    :			Optional[Any]	, a_    :			Union[str, Any]	, a_    :			int="replace"	, a_    :			Optional[Any]="<s>"	, a_    :			Optional[Any]="</s>"	, a_    :			Any="</s>"	, a_    :			Dict="<s>"	, a_    :			Optional[int]="<unk>"	, a_    :			Optional[int]="<pad>"	, a_    :			Any="<mask>"	, a_    :			Optional[Any]=False	, **a_    :			str	, )	->	Tuple:



							'''simple docstring'''



							a__  :	Optional[int]		 =			AddedToken(a_	, lstrip=a_	, rstrip=a_    ) if isinstance(a_	, a_    ) else bos_token
							a__  :	Any		 =			AddedToken(a_	, lstrip=a_	, rstrip=a_    ) if isinstance(a_	, a_    ) else eos_token
							a__  :	List[str]		 =			AddedToken(a_	, lstrip=a_	, rstrip=a_    ) if isinstance(a_	, a_    ) else sep_token
							a__  :	str		 =			AddedToken(a_	, lstrip=a_	, rstrip=a_    ) if isinstance(a_	, a_    ) else cls_token
							a__  :	Optional[Any]		 =			AddedToken(a_	, lstrip=a_	, rstrip=a_    ) if isinstance(a_	, a_    ) else unk_token
							a__  :	Tuple		 =			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
							a__  :	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:
										a__  :	List[Any]		 =			json.load(a_    )
							a__  :	str		 =			{v: k for k, v in self.encoder.items()}
							a__  :	int		 =			errors  # how to handle errors in decoding
							a__  :	int		 =			bytes_to_unicode()
							a__  :	List[Any]		 =			{v: k for k, v in self.byte_encoder.items()}
							with open(a_	, encoding="utf-8"    ) as merges_handle:
										a__  :	Dict		 =			merges_handle.read().split("\n"    )[1:-1]
							a__  :	Dict		 =			[tuple(merge.split()    ) for merge in bpe_merges]
							a__  :	str		 =			dict(zip(a_	, range(len(a_    )    )    )    )
							a__  :	Tuple		 =			{}
							a__  :	Optional[int]		 =			add_prefix_space

							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
							a__  :	List[str]		 =			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.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
				def       UpperCAmelCase      (		self    :			Any    )	->	Optional[int]:



							'''simple docstring'''



							return len(self.encoder    )
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	List[str]:



							'''simple docstring'''



							return dict(self.encoder	, **self.added_tokens_encoder    )
				def       UpperCAmelCase      (		self    :			Dict	, a_    :			str    )	->	int:



							'''simple docstring'''



							if token in self.cache:
										return self.cache[token]
							a__  :	List[str]		 =			tuple(a_    )
							a__  :	Dict		 =			get_pairs(a_    )

							if not pairs:
										return token

							while True:
										a__  :	Dict		 =			min(a_	, key=lambda a_    : self.bpe_ranks.get(a_	, float("inf"    )    )    )
										if bigram not in self.bpe_ranks:
													break
										a__ ,	a__  :	List[Any]		 =			bigram
										a__  :	int		 =			[]
										a__  :	List[Any]		 =			0
										while i < len(a_    ):
													try:
																a__  :	List[str]		 =			word.index(a_	, a_    )
													except ValueError:
																new_word.extend(word[i:]    )
																break
													else:
																new_word.extend(word[i:j]    )
																a__  :	Dict		 =			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
										a__  :	List[Any]		 =			tuple(a_    )
										a__  :	str		 =			new_word
										if len(a_    ) == 1:
													break
										else:
													a__  :	Optional[int]		 =			get_pairs(a_    )
							a__  :	Union[str, Any]		 =			" ".join(a_    )
							a__  :	str		 =			word
							return word
				def       UpperCAmelCase      (		self    :			Optional[int]	, a_    :			Union[str, Any]    )	->	List[str]:



							'''simple docstring'''



							a__  :	List[Any]		 =			[]
							for token in re.findall(self.pat	, a_    ):
										a__  :	Dict		 =			"".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    :			Union[str, Any]	, a_    :			Optional[int]    )	->	Optional[int]:



							'''simple docstring'''



							return self.encoder.get(a_	, self.encoder.get(self.unk_token    )    )
				def       UpperCAmelCase      (		self    :			Optional[Any]	, a_    :			Optional[Any]    )	->	Optional[Any]:



							'''simple docstring'''



							return self.decoder.get(a_    )
				def       UpperCAmelCase      (		self    :			str	, a_    :			List[Any]    )	->	Optional[int]:



							'''simple docstring'''



							a__  :	str		 =			"".join(a_    )
							a__  :	List[str]		 =			bytearray([self.byte_decoder[c] for c in text]    ).decode("utf-8"	, errors=self.errors    )
							return text
				def       UpperCAmelCase      (		self    :			Any	, a_    :			str	, a_    :			Optional[str] = None    )	->	Tuple[str]:



							'''simple docstring'''



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

							a__  :	Dict		 =			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!"    )
																a__  :	Optional[Any]		 =			token_index
													writer.write(" ".join(a_    ) + "\n"    )
													index += 1

							return vocab_file, merge_file
				def       UpperCAmelCase      (		self    :			List[Any]	, a_    :			List[int]	, a_    :			Optional[List[int]] = None	, a_    :			bool = False    )	->	List[int]:



							'''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    :			Optional[Any]	, a_    :			List[int]	, a_    :			Optional[List[int]] = None    )	->	List[int]:



							'''simple docstring'''



							a__  :	Dict		 =			[self.sep_token_id]
							a__  :	Tuple		 =			[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_    :			Optional[Any]	, a_    :			Union[str, Any]=False	, **a_    :			Dict    )	->	Dict:



							'''simple docstring'''



							a__  :	int		 =			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()):
										a__  :	int		 =			" " + text
							return (text, kwargs)
				def       UpperCAmelCase      (		self    :			str	, a_    :			List[int]	, a_    :			Optional[List[int]] = None    )	->	Optional[int]:



							'''simple docstring'''



							return token_ids_a + [self.eos_token_id]






				def       UpperCAmelCase      (		self    :			str	, a_    :			"Conversation"    )	->	List[int]:



							'''simple docstring'''



							a__  :	Union[str, Any]		 =			[]
							for is_user, text in conversation.iter_texts():
										if is_user:
													# We need to space prefix as it's being done within blenderbot
													inputs.append(" " + text    )
										else:
													# Generated responses should contain them already.
													inputs.append(a_    )

							a__  :	Dict		 =			"  ".join(a_    )
							a__  :	List[str]		 =			self.encode(a_    )
							if len(a_    ) > self.model_max_length:
										a__  :	Union[str, Any]		 =			input_ids[-self.model_max_length :]
										logger.warning(F"Trimmed input from conversation as it was longer than {self.model_max_length} tokens."    )
							return input_ids