
import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE    :      int          =			logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE    :      Dict          =			'https://openaipublic.azureedge.net/jukebox/models/'
_SCREAMING_SNAKE_CASE    :      Dict          =			{
    'jukebox-1b-lyrics': [
        '5b/vqvae.pth.tar',
        '5b/prior_level_0.pth.tar',
        '5b/prior_level_1.pth.tar',
        '1b_lyrics/prior_level_2.pth.tar',
    ],
    'jukebox-5b-lyrics': [
        '5b/vqvae.pth.tar',
        '5b/prior_level_0.pth.tar',
        '5b/prior_level_1.pth.tar',
        '5b_lyrics/prior_level_2.pth.tar',
    ],
}

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Dict       ) ->       Optional[Any]:

				"""simple docstring"""







				if key.endswith('''.model.1.bias'''       ) and len(key.split('''.'''       )       ) > 10:
								A__  :	Union[str, Any]         =							key.replace('''.model.1.bias'''							,						'''.conv1d_1.bias'''       )
				elif key.endswith('''.model.1.weight'''       ) and len(key.split('''.'''       )       ) > 10:
								A__  :	Optional[int]         =							key.replace('''.model.1.weight'''							,						'''.conv1d_1.weight'''       )
				elif key.endswith('''.model.3.bias'''       ) and len(key.split('''.'''       )       ) > 10:
								A__  :	Optional[Any]         =							key.replace('''.model.3.bias'''							,						'''.conv1d_2.bias'''       )
				elif key.endswith('''.model.3.weight'''       ) and len(key.split('''.'''       )       ) > 10:
								A__  :	Dict         =							key.replace('''.model.3.weight'''							,						'''.conv1d_2.weight'''       )

				if "conditioner_blocks.0." in key:
								A__  :	Optional[int]         =							key.replace('''conditioner_blocks.0'''							,						'''conditioner_blocks'''       )

				if "prime_prior" in key:
								A__  :	int         =							key.replace('''prime_prior'''							,						'''encoder'''       )

				if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
								A__  :	Optional[Any]         =							key.replace('''.emb.'''							,						'''.'''       )

				if key.endswith('''k'''       ):  # replace vqvae.X.k with vqvae.X.codebook
								return key.replace('''.k'''							,						'''.codebook'''       )
				if "y_emb." in key:
								return key.replace('''y_emb.'''							,						'''metadata_embedding.'''       )

				if "x_emb.emb." in key:
								A__  :	int         =							key.replace('''0.x_emb.emb'''							,						'''embed_tokens'''       )

				if "prime_state_ln" in key:
								return key.replace('''prime_state_ln'''							,						'''encoder.final_layer_norm'''       )
				if ".ln" in key:
								return key.replace('''.ln'''							,						'''.layer_norm'''       )
				if "_ln" in key:
								return key.replace('''_ln'''							,						'''_layer_norm'''       )

				if "prime_state_proj" in key:
								return key.replace('''prime_state_proj'''							,						'''encoder.proj_in'''       )
				if "prime_x_out" in key:
								return key.replace('''prime_x_out'''							,						'''encoder.lm_head'''       )
				if "prior.x_out" in key:
								return key.replace('''x_out'''							,						'''fc_proj_out'''       )
				if "x_emb" in key:
								return key.replace('''x_emb'''							,						'''embed_tokens'''       )

				return key

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Optional[int]							,						__UpperCamelCase :						Tuple							,						__UpperCamelCase :						int							,						__UpperCamelCase :						Union[str, Any]       ) ->       List[Any]:

				"""simple docstring"""







				A__  :	str         =							{}
				import re

				A__  :	Any         =							re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''       )
				A__  :	Union[str, Any]         =							re.compile(
				    R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''       )
				A__  :	Dict         =							re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''       )

				A__  :	Union[str, Any]         =							re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''       )
				A__  :	List[Any]         =							re.compile(
				    R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''       )
				A__  :	Any         =							re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''       )

				A__  :	Tuple         =							re.compile(R'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'''       )
				A__  :	List[str]         =							re.compile(
				    R'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''       )
				A__  :	str         =							re.compile(R'''conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)'''       )

				for original_key, value in state_dict.items():
								# rename vqvae.encoder keys
								if re_encoder_block_conv_in.fullmatch(__UpperCamelCase       ):
												A__  :	Optional[Any]         =							re_encoder_block_conv_in.match(__UpperCamelCase       )
												A__  :	Optional[int]         =							regex_match.groups()
												A__  :	Optional[int]         =							int(groups[2]       ) * 2 + int(groups[3]       )
												A__  :	Dict         =							F"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"
												A__  :	Dict         =							re_encoder_block_conv_in.sub(__UpperCamelCase							,						__UpperCamelCase       )

								elif re_encoder_block_resnet.fullmatch(__UpperCamelCase       ):
												A__  :	str         =							re_encoder_block_resnet.match(__UpperCamelCase       )
												A__  :	Tuple         =							regex_match.groups()
												A__  :	List[str]         =							int(groups[2]       ) * 2 + int(groups[3]       )
												A__  :	Optional[Any]         =							{'1': 1, '3': 2}[groups[-2]]
												A__  :	str         =							F"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."
												A__  :	List[Any]         =							F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
												A__  :	Tuple         =							prefix + resnet_block
												A__  :	Optional[Any]         =							re_encoder_block_resnet.sub(__UpperCamelCase							,						__UpperCamelCase       )

								elif re_encoder_block_proj_out.fullmatch(__UpperCamelCase       ):
												A__  :	Tuple         =							re_encoder_block_proj_out.match(__UpperCamelCase       )
												A__  :	Optional[Any]         =							regex_match.groups()
												A__  :	Optional[int]         =							F"encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"
												A__  :	Union[str, Any]         =							re_encoder_block_proj_out.sub(__UpperCamelCase							,						__UpperCamelCase       )

								# rename vqvae.decoder keys
								elif re_decoder_block_conv_out.fullmatch(__UpperCamelCase       ):
												A__  :	Dict         =							re_decoder_block_conv_out.match(__UpperCamelCase       )
												A__  :	Union[str, Any]         =							regex_match.groups()
												A__  :	Tuple         =							int(groups[2]       ) * 2 + int(groups[3]       ) - 2
												A__  :	List[str]         =							F"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"
												A__  :	str         =							re_decoder_block_conv_out.sub(__UpperCamelCase							,						__UpperCamelCase       )

								elif re_decoder_block_resnet.fullmatch(__UpperCamelCase       ):
												A__  :	Any         =							re_decoder_block_resnet.match(__UpperCamelCase       )
												A__  :	int         =							regex_match.groups()
												A__  :	Dict         =							int(groups[2]       ) * 2 + int(groups[3]       ) - 2
												A__  :	int         =							{'1': 1, '3': 2}[groups[-2]]
												A__  :	Optional[int]         =							F"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."
												A__  :	Dict         =							F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
												A__  :	Union[str, Any]         =							prefix + resnet_block
												A__  :	Any         =							re_decoder_block_resnet.sub(__UpperCamelCase							,						__UpperCamelCase       )

								elif re_decoder_block_proj_in.fullmatch(__UpperCamelCase       ):
												A__  :	int         =							re_decoder_block_proj_in.match(__UpperCamelCase       )
												A__  :	Optional[Any]         =							regex_match.groups()
												A__  :	Optional[int]         =							F"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
												A__  :	Optional[int]         =							re_decoder_block_proj_in.sub(__UpperCamelCase							,						__UpperCamelCase       )

								# rename prior cond.model to upsampler.upsample_block and resnet
								elif re_prior_cond_conv_out.fullmatch(__UpperCamelCase       ):
												A__  :	str         =							re_prior_cond_conv_out.match(__UpperCamelCase       )
												A__  :	Optional[int]         =							regex_match.groups()
												A__  :	List[Any]         =							int(groups[1]       ) * 2 + int(groups[2]       ) - 2
												A__  :	Tuple         =							F"conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"
												A__  :	Tuple         =							re_prior_cond_conv_out.sub(__UpperCamelCase							,						__UpperCamelCase       )

								elif re_prior_cond_resnet.fullmatch(__UpperCamelCase       ):
												A__  :	Dict         =							re_prior_cond_resnet.match(__UpperCamelCase       )
												A__  :	Optional[int]         =							regex_match.groups()
												A__  :	Union[str, Any]         =							int(groups[1]       ) * 2 + int(groups[2]       ) - 2
												A__  :	Any         =							{'1': 1, '3': 2}[groups[-2]]
												A__  :	Any         =							F"conditioner_blocks.upsampler.upsample_block.{block_index}."
												A__  :	List[str]         =							F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
												A__  :	int         =							prefix + resnet_block
												A__  :	List[str]         =							re_prior_cond_resnet.sub(__UpperCamelCase							,						__UpperCamelCase       )

								elif re_prior_cond_proj_in.fullmatch(__UpperCamelCase       ):
												A__  :	Optional[Any]         =							re_prior_cond_proj_in.match(__UpperCamelCase       )
												A__  :	str         =							regex_match.groups()
												A__  :	Optional[Any]         =							F"conditioner_blocks.upsampler.proj_in.{groups[-1]}"
												A__  :	Tuple         =							re_prior_cond_proj_in.sub(__UpperCamelCase							,						__UpperCamelCase       )

								# keep original key
								else:
												A__  :	int         =							original_key

								A__  :	Optional[int]         =							replace_key(__UpperCamelCase       )

								if F"{key_prefix}.{key}" not in model_state_dict or key is None:
												print(F"failed converting {original_key} to {key}, does not match"       )

								# handle missmatched shape
								elif value.shape != model_state_dict[F"{key_prefix}.{key}"].shape:
												A__  :	str         =							model_state_dict[F"{key_prefix}.{key}"]
												print(F"{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"       )
												A__  :	List[str]         =							original_key

								A__  :	List[Any]         =							original_key
								A__  :	Union[str, Any]         =							value

				return new_dict

@torch.no_grad()
def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						str=None							,						__UpperCamelCase :						int=None       ) ->       str:

				"""simple docstring"""







				for file in MODEL_MAPPING[model_name]:
								if not os.path.isfile(F"{pytorch_dump_folder_path}/{file.split('/'       )[-1]}"       ):
												A__  :	Union[str, Any]         =							requests.get(F"{PREFIX}{file}"							,						allow_redirects=__UpperCamelCase       )
												os.makedirs(F"{pytorch_dump_folder_path}/"							,						exist_ok=__UpperCamelCase       )
												open(F"{pytorch_dump_folder_path}/{file.split('/'       )[-1]}"							,						'''wb'''       ).write(r.content       )

				A__  :	Union[str, Any]         =							MODEL_MAPPING[model_name.split('''/'''       )[-1]]

				A__  :	Any         =							JukeboxConfig.from_pretrained(__UpperCamelCase       )
				A__  :	Optional[Any]         =							JukeboxModel(__UpperCamelCase       )

				A__  :	Optional[int]         =							[]
				A__  :	Union[str, Any]         =							{}
				for i, dict_name in enumerate(__UpperCamelCase       ):
								A__  :	List[Any]         =							torch.load(F"{pytorch_dump_folder_path}/{dict_name.split('/'       )[-1]}"       )['model']

								A__  :	List[Any]         =							{}
								for k in old_dic.keys():
												if k.endswith('''.b'''       ):
																A__  :	Any         =							old_dic[k]
												elif k.endswith('''.w'''       ):
																A__  :	List[Any]         =							old_dic[k]
												elif "level_2" not in dict_name and "cond.model." in k:
																A__  :	str         =							old_dic[k]
												else:
																A__  :	List[Any]         =							old_dic[k]

								A__  :	Union[str, Any]         =							'vqvae' if i == 0 else F"priors.{3 - i}"
								A__  :	Any         =							fix_jukebox_keys(__UpperCamelCase							,						model.state_dict()							,						__UpperCamelCase							,						__UpperCamelCase       )
								weight_dict.append(__UpperCamelCase       )

				A__  :	Any         =							weight_dict.pop(0       )
				model.vqvae.load_state_dict(__UpperCamelCase       )
				for i in range(len(__UpperCamelCase       )       ):
								model.priors[i].load_state_dict(weight_dict[2 - i]       )

				Path(__UpperCamelCase       ).mkdir(exist_ok=__UpperCamelCase       )
				with open(F"{pytorch_dump_folder_path}/mapping.json"							,						'''w'''       ) as txtfile:
								json.dump(__UpperCamelCase							,						__UpperCamelCase       )

				print(F"Saving model {model_name} to {pytorch_dump_folder_path}"       )
				model.save_pretrained(__UpperCamelCase       )

				return weight_dict


if __name__ == "__main__":
			_SCREAMING_SNAKE_CASE    :      int          =			argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--model_name',
			    default='jukebox-5b-lyrics',
			    type=str,
			    help='Name of the model you\'d like to convert.',
			)
			parser.add_argument(
			    '--pytorch_dump_folder_path',
			    default='jukebox-5b-lyrics-converted',
			    type=str,
			    help='Path to the output PyTorch model directory.',
			)
			_SCREAMING_SNAKE_CASE    :      List[str]          =			parser.parse_args()
			convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


_SCREAMING_SNAKE_CASE    :      Optional[int]          =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

_SCREAMING_SNAKE_CASE    :      List[str]          =			{
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

_SCREAMING_SNAKE_CASE    :      Dict          =			{
    'gpt-neox-20b': 2_0_4_8,
}







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				VOCAB_FILES_NAMES
	_lowerCAmelCase							       =				PRETRAINED_VOCAB_FILES_MAP
	_lowerCAmelCase							       =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowerCAmelCase							       =				["input_ids", "attention_mask"]





	def __init__(			self				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__="<|endoftext|>"				,       UpperCamelCase__="<|endoftext|>"				,       UpperCamelCase__="<|endoftext|>"				,       UpperCamelCase__=False				,       **UpperCamelCase__				,       ):
					super().__init__(
					    UpperCamelCase__				,       UpperCamelCase__				,       tokenizer_file=UpperCamelCase__				,       unk_token=UpperCamelCase__				,       bos_token=UpperCamelCase__				,       eos_token=UpperCamelCase__				,       add_prefix_space=UpperCamelCase__				,       **UpperCamelCase__				,       )

					A__  :	Optional[Any]         =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
					if pre_tok_state.get('''add_prefix_space'''				,       UpperCamelCase__		) != add_prefix_space:
									A__  :	Union[str, Any]         =							getattr(UpperCamelCase__				,       pre_tok_state.pop('''type'''		)		)
									A__  :	List[Any]         =							add_prefix_space
									A__  :	Any         =							pre_tok_class(**UpperCamelCase__		)

					A__  :	List[Any]         =							add_prefix_space





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__ = None		):
					A__  :	Any         =							self._tokenizer.model.save(UpperCamelCase__				,       name=UpperCamelCase__		)
					return tuple(UpperCamelCase__		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__  :	List[str]         =							[]
					for is_user, text in conversation.iter_texts():
									input_ids.extend(self.encode(UpperCamelCase__				,       add_special_tokens=UpperCamelCase__		) + [self.eos_token_id]		)

					if len(UpperCamelCase__		) > self.model_max_length:
									A__  :	Tuple         =							input_ids[-self.model_max_length :]
					return input_ids