


import argparse
import gc
import json
import os
import shutil
import warnings

import torch

from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer


try:
				from transformers import LlamaTokenizerFast
except ImportError as e:
				warnings.warn(e)
				warnings.warn(
				    'The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion'
				)
				lowerCAmelCase__			:      Dict								=None
lowerCAmelCase__			:      Union[str, Any]								={
    '7B': 1_10_08,
    '13B': 1_38_24,
    '30B': 1_79_20,
    '65B': 2_20_16,
    '70B': 2_86_72,
}
lowerCAmelCase__			:      List[str]								={
    '7B': 1,
    '7Bf': 1,
    '13B': 2,
    '13Bf': 2,
    '30B': 4,
    '65B': 8,
    '70B': 8,
    '70Bf': 8,
}






def 						a__			(				A__,      A__=1,      A__=2_5_6				):
			return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3				)				) + multiple_of - 1) // multiple_of)






def 						a__			(				A__				):
			with open(A__,      'r'				) as f:
						return json.load(A__				)






def 						a__			(				A__,      A__				):
			with open(A__,      'w'				) as f:
						json.dump(A__,      A__				)






def 						a__			(				A__,      A__,      A__,      A__=True				):
			os.makedirs(A__,      exist_ok=A__				)
			SCREAMING_SNAKE_CASE_		:     Dict					=					os.path.join(A__,      'tmp'				)
			os.makedirs(A__,      exist_ok=A__				)

			SCREAMING_SNAKE_CASE_		:     Any					=					read_json(os.path.join(A__,      'params.json'				)				)
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					NUM_SHARDS[model_size]
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					params['n_layers']
			SCREAMING_SNAKE_CASE_		:     Any					=					params['n_heads']
			SCREAMING_SNAKE_CASE_		:     Any					=					n_heads // num_shards
			SCREAMING_SNAKE_CASE_		:     int					=					params['dim']
			SCREAMING_SNAKE_CASE_		:     str					=					dim // n_heads
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					1_00_00.0
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					1.0 / (base ** (torch.arange(0,      A__,      2				).float() / dims_per_head))

			if "n_kv_heads" in params:
						SCREAMING_SNAKE_CASE_		:     List[str]					=					params['n_kv_heads']  # for GQA / MQA
						SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					n_heads_per_shard // num_key_value_heads
						SCREAMING_SNAKE_CASE_		:     Tuple					=					dim // num_key_value_heads
			else:  # compatibility with other checkpoints
						SCREAMING_SNAKE_CASE_		:     Dict					=					n_heads
						SCREAMING_SNAKE_CASE_		:     Tuple					=					n_heads_per_shard
						SCREAMING_SNAKE_CASE_		:     int					=					dim

			# permute for sliced rotary
			def permute(A__,      A__=n_heads,      A__=dim,      A__=dim				):
						return w.view(A__,      dima // n_heads // 2,      2,      A__				).transpose(1,      2				).reshape(A__,      A__				)

			print(F'''Fetching all parameters from the checkpoint at {input_base_path}.'''				)
			# Load weights
			if model_size == "7B":
						# Not sharded
						# (The sharded implementation would also work, but this is simpler.)
						SCREAMING_SNAKE_CASE_		:     List[str]					=					torch.load(os.path.join(A__,      'consolidated.00.pth'				),      map_location='cpu'				)
			else:
						# Sharded
						SCREAMING_SNAKE_CASE_		:     str					=					[
						    torch.load(os.path.join(A__,      F'''consolidated.{i:02d}.pth'''				),      map_location='cpu'				)
						    for i in range(A__				)
						]
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					0
			SCREAMING_SNAKE_CASE_		:     str					=					{'weight_map': {}}
			for layer_i in range(A__				):
						SCREAMING_SNAKE_CASE_		:     Any					=					F'''pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin'''
						if model_size == "7B":
									# Unsharded
									SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					{
									    F'''model.layers.{layer_i}.self_attn.q_proj.weight''': permute(
									        loaded[F'''layers.{layer_i}.attention.wq.weight''']				),
									    F'''model.layers.{layer_i}.self_attn.k_proj.weight''': permute(
									        loaded[F'''layers.{layer_i}.attention.wk.weight''']				),
									    F'''model.layers.{layer_i}.self_attn.v_proj.weight''': loaded[F'''layers.{layer_i}.attention.wv.weight'''],
									    F'''model.layers.{layer_i}.self_attn.o_proj.weight''': loaded[F'''layers.{layer_i}.attention.wo.weight'''],
									    F'''model.layers.{layer_i}.mlp.gate_proj.weight''': loaded[F'''layers.{layer_i}.feed_forward.w1.weight'''],
									    F'''model.layers.{layer_i}.mlp.down_proj.weight''': loaded[F'''layers.{layer_i}.feed_forward.w2.weight'''],
									    F'''model.layers.{layer_i}.mlp.up_proj.weight''': loaded[F'''layers.{layer_i}.feed_forward.w3.weight'''],
									    F'''model.layers.{layer_i}.input_layernorm.weight''': loaded[F'''layers.{layer_i}.attention_norm.weight'''],
									    F'''model.layers.{layer_i}.post_attention_layernorm.weight''': loaded[F'''layers.{layer_i}.ffn_norm.weight'''],
									}
						else:
									# Sharded
									# Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
									# the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
									# redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.

									SCREAMING_SNAKE_CASE_		:     Dict					=					{
									    F'''model.layers.{layer_i}.input_layernorm.weight''': loaded[0][
									        F'''layers.{layer_i}.attention_norm.weight'''
									    ].clone(),
									    F'''model.layers.{layer_i}.post_attention_layernorm.weight''': loaded[0][
									        F'''layers.{layer_i}.ffn_norm.weight'''
									    ].clone(),
									}
									SCREAMING_SNAKE_CASE_		:     int					=					permute(
									    torch.cat(
									        [
									            loaded[i][F'''layers.{layer_i}.attention.wq.weight'''].view(A__,      A__,      A__				)
									            for i in range(A__				)
									        ],      dim=0,      ).reshape(A__,      A__				)				)
									SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					permute(
									    torch.cat(
									        [
									            loaded[i][F'''layers.{layer_i}.attention.wk.weight'''].view(
									                A__,      A__,      A__				)
									            for i in range(A__				)
									        ],      dim=0,      ).reshape(A__,      A__				),      A__,      A__,      A__,      )
									SCREAMING_SNAKE_CASE_		:     Tuple					=					torch.cat(
									    [
									        loaded[i][F'''layers.{layer_i}.attention.wv.weight'''].view(
									            A__,      A__,      A__				)
									        for i in range(A__				)
									    ],      dim=0,      ).reshape(A__,      A__				)

									SCREAMING_SNAKE_CASE_		:     Dict					=					torch.cat(
									    [loaded[i][F'''layers.{layer_i}.attention.wo.weight'''] for i in range(A__				)],      dim=1				)
									SCREAMING_SNAKE_CASE_		:     Tuple					=					torch.cat(
									    [loaded[i][F'''layers.{layer_i}.feed_forward.w1.weight'''] for i in range(A__				)],      dim=0				)
									SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					torch.cat(
									    [loaded[i][F'''layers.{layer_i}.feed_forward.w2.weight'''] for i in range(A__				)],      dim=1				)
									SCREAMING_SNAKE_CASE_		:     Any					=					torch.cat(
									    [loaded[i][F'''layers.{layer_i}.feed_forward.w3.weight'''] for i in range(A__				)],      dim=0				)

						SCREAMING_SNAKE_CASE_		:     str					=					inv_freq
						for k, v in state_dict.items():
									SCREAMING_SNAKE_CASE_		:     Any					=					filename
									param_count += v.numel()
						torch.save(A__,      os.path.join(A__,      A__				)				)

			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					F'''pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin'''
			if model_size == "7B":
						# Unsharded
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					{
						    'model.embed_tokens.weight': loaded['tok_embeddings.weight'],
						    'model.norm.weight': loaded['norm.weight'],
						    'lm_head.weight': loaded['output.weight'],
						}
			else:
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					{
						    'model.norm.weight': loaded[0]['norm.weight'],
						    'model.embed_tokens.weight': torch.cat(
						        [loaded[i]['tok_embeddings.weight'] for i in range(A__				)],      dim=1				),
						    'lm_head.weight': torch.cat([loaded[i]['output.weight'] for i in range(A__				)],      dim=0				),
						}

			for k, v in state_dict.items():
						SCREAMING_SNAKE_CASE_		:     Tuple					=					filename
						param_count += v.numel()
			torch.save(A__,      os.path.join(A__,      A__				)				)

			# Write configs
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					{'total_size': param_count * 2}
			write_json(A__,      os.path.join(A__,      'pytorch_model.bin.index.json'				)				)
			SCREAMING_SNAKE_CASE_		:     str					=					params['ffn_dim_multiplier'] if 'ffn_dim_multiplier' in params else 1
			SCREAMING_SNAKE_CASE_		:     Tuple					=					params['multiple_of'] if 'multiple_of' in params else 2_5_6
			SCREAMING_SNAKE_CASE_		:     List[str]					=					LlamaConfig(
			    hidden_size=A__,      intermediate_size=compute_intermediate_size(A__,      A__,      A__				),      num_attention_heads=params['n_heads'],      num_hidden_layers=params['n_layers'],      rms_norm_eps=params['norm_eps'],      num_key_value_heads=A__,      )
			config.save_pretrained(A__				)

			# Make space so we can load the model properly now.
			del state_dict
			del loaded
			gc.collect()

			print('Loading the checkpoint in a Llama model.'				)
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					LlamaForCausalLM.from_pretrained(A__,      torch_dtype=torch.floataa,      low_cpu_mem_usage=A__				)
			# Avoid saving this as part of the config.
			del model.config._name_or_path

			print('Saving in the Transformers format.'				)
			model.save_pretrained(A__,      safe_serialization=A__				)
			shutil.rmtree(A__				)






def 						a__			(				A__,      A__				):
			# Initialize the tokenizer based on the `spm` model
			SCREAMING_SNAKE_CASE_		:     List[str]					=					LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
			print(F'''Saving a {tokenizer_class.__name__} to {tokenizer_path}.'''				)
			SCREAMING_SNAKE_CASE_		:     List[str]					=					tokenizer_class(A__				)
			tokenizer.save_pretrained(A__				)






def 						a__			(				):
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					argparse.ArgumentParser()
			parser.add_argument(
			    '--input_dir',      help='Location of LLaMA weights, which contains tokenizer.model and model folders',      )
			parser.add_argument(
			    '--model_size',      choices=['7B', '7Bf', '13B', '13Bf', '30B', '65B', '70B', '70Bf', 'tokenizer_only'],      )
			parser.add_argument(
			    '--output_dir',      help='Location to write HF model and tokenizer',      )
			parser.add_argument('--safe_serialization',      type=A__,      help='Whether or not to save using `safetensors`.'				)
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					parser.parse_args()
			if args.model_size != "tokenizer_only":
						write_model(
						    model_path=args.output_dir,      input_base_path=os.path.join(args.input_dir,      args.model_size				),      model_size=args.model_size,      safe_serialization=args.safe_serialization,      )
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					os.path.join(args.input_dir,      'tokenizer.model'				)
			write_tokenizer(args.output_dir,      A__				)


if __name__ == "__main__":
				main()











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()
UpperCamelCase__ :   Tuple						=							logging.get_logger(__name__)


UpperCamelCase__ :   Optional[int]						=							'''https://openaipublic.azureedge.net/jukebox/models/'''
UpperCamelCase__ :   Optional[Any]						=							{
    '''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        __UpperCAmelCase						(							lowerCamelCase_ :     List[str]  )       ->  int:







			"""simple docstring"""





			if key.endswith('.model.1.bias'  ) and len(key.split('.'  )  ) > 10:
						SCREAMING_SNAKE_CASE_   : List[str]					  =      key.replace('.model.1.bias'  ,							'.conv1d_1.bias'  )
			elif key.endswith('.model.1.weight'  ) and len(key.split('.'  )  ) > 10:
						SCREAMING_SNAKE_CASE_   : Any					  =      key.replace('.model.1.weight'  ,							'.conv1d_1.weight'  )
			elif key.endswith('.model.3.bias'  ) and len(key.split('.'  )  ) > 10:
						SCREAMING_SNAKE_CASE_   : Tuple					  =      key.replace('.model.3.bias'  ,							'.conv1d_2.bias'  )
			elif key.endswith('.model.3.weight'  ) and len(key.split('.'  )  ) > 10:
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      key.replace('.model.3.weight'  ,							'.conv1d_2.weight'  )

			if "conditioner_blocks.0." in key:
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      key.replace('conditioner_blocks.0'  ,							'conditioner_blocks'  )

			if "prime_prior" in key:
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      key.replace('prime_prior'  ,							'encoder'  )

			if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
						SCREAMING_SNAKE_CASE_   : Union[str, 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:
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      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        __UpperCAmelCase						(							lowerCamelCase_ :     Dict  ,							lowerCamelCase_ :     List[Any]  ,							lowerCamelCase_ :     str  ,							lowerCamelCase_ :     List[Any]  )       ->  List[Any]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Any					  =      {}
			import re

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

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

			SCREAMING_SNAKE_CASE_   : Dict					  =      re.compile(R'conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'  )
			SCREAMING_SNAKE_CASE_   : Dict					  =      re.compile(
			    R'conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'  )
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      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(lowerCamelCase_  ):
									SCREAMING_SNAKE_CASE_   : List[str]					  =      re_encoder_block_conv_in.match(lowerCamelCase_  )
									SCREAMING_SNAKE_CASE_   : int					  =      regex_match.groups()
									SCREAMING_SNAKE_CASE_   : List[str]					  =      int(groups[2]  ) * 2 + int(groups[3]  )
									SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      F'encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'
									SCREAMING_SNAKE_CASE_   : Optional[int]					  =      re_encoder_block_conv_in.sub(lowerCamelCase_  ,							lowerCamelCase_  )

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

						elif re_encoder_block_proj_out.fullmatch(lowerCamelCase_  ):
									SCREAMING_SNAKE_CASE_   : Tuple					  =      re_encoder_block_proj_out.match(lowerCamelCase_  )
									SCREAMING_SNAKE_CASE_   : int					  =      regex_match.groups()
									SCREAMING_SNAKE_CASE_   : int					  =      F'encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'
									SCREAMING_SNAKE_CASE_   : Tuple					  =      re_encoder_block_proj_out.sub(lowerCamelCase_  ,							lowerCamelCase_  )

						# rename vqvae.decoder keys
						elif re_decoder_block_conv_out.fullmatch(lowerCamelCase_  ):
									SCREAMING_SNAKE_CASE_   : Any					  =      re_decoder_block_conv_out.match(lowerCamelCase_  )
									SCREAMING_SNAKE_CASE_   : Any					  =      regex_match.groups()
									SCREAMING_SNAKE_CASE_   : List[Any]					  =      int(groups[2]  ) * 2 + int(groups[3]  ) - 2
									SCREAMING_SNAKE_CASE_   : Optional[int]					  =      F'decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'
									SCREAMING_SNAKE_CASE_   : Optional[int]					  =      re_decoder_block_conv_out.sub(lowerCamelCase_  ,							lowerCamelCase_  )

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

						elif re_decoder_block_proj_in.fullmatch(lowerCamelCase_  ):
									SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      re_decoder_block_proj_in.match(lowerCamelCase_  )
									SCREAMING_SNAKE_CASE_   : List[Any]					  =      regex_match.groups()
									SCREAMING_SNAKE_CASE_   : int					  =      F'decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'
									SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      re_decoder_block_proj_in.sub(lowerCamelCase_  ,							lowerCamelCase_  )

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

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

						elif re_prior_cond_proj_in.fullmatch(lowerCamelCase_  ):
									SCREAMING_SNAKE_CASE_   : Any					  =      re_prior_cond_proj_in.match(lowerCamelCase_  )
									SCREAMING_SNAKE_CASE_   : List[str]					  =      regex_match.groups()
									SCREAMING_SNAKE_CASE_   : List[Any]					  =      F'conditioner_blocks.upsampler.proj_in.{groups[-1]}'
									SCREAMING_SNAKE_CASE_   : List[Any]					  =      re_prior_cond_proj_in.sub(lowerCamelCase_  ,							lowerCamelCase_  )

						# keep original key
						else:
									SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      original_key

						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      replace_key(lowerCamelCase_  )

						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:
									SCREAMING_SNAKE_CASE_   : str					  =      model_state_dict[F'{key_prefix}.{key}']
									print(F'{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match'  )
									SCREAMING_SNAKE_CASE_   : Dict					  =      original_key

						SCREAMING_SNAKE_CASE_   : int					  =      original_key
						SCREAMING_SNAKE_CASE_   : int					  =      value

			return new_dict





@torch.no_grad()
def        __UpperCAmelCase						(							lowerCamelCase_ :     int=None  ,							lowerCamelCase_ :     int=None  )       ->  Dict:







			"""simple docstring"""





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

			SCREAMING_SNAKE_CASE_   : List[str]					  =      MODEL_MAPPING[model_name.split('/'  )[-1]]

			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      JukeboxConfig.from_pretrained(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : str					  =      JukeboxModel(lowerCamelCase_  )

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

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

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

			SCREAMING_SNAKE_CASE_   : Tuple					  =      weight_dict.pop(0  )
			model.vqvae.load_state_dict(lowerCamelCase_  )
			for i in range(len(lowerCamelCase_  )  ):
						model.priors[i].load_state_dict(weight_dict[2 - i]  )

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

			print(F'Saving model {model_name} to {pytorch_dump_folder_path}'  )
			model.save_pretrained(lowerCamelCase_  )

			return weight_dict


if __name__ == "__main__":
						UpperCamelCase__ :   Optional[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.''',
						)
						UpperCamelCase__ :   str						=							parser.parse_args()
						convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)



