
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()
lowerCamelCase__	=							logging.get_logger(__name__)


lowerCamelCase__	=							"https://openaipublic.azureedge.net/jukebox/models/"
lowerCamelCase__	=							{
    "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 							__A(lowerCAmelCase )		->				int:

		"""simple docstring"""






		if key.endswith(""".model.1.bias""" ) and len(key.split(""".""" ) ) > 1_0:
				_UpperCamelCase						    =	key.replace(""".model.1.bias"""						,  """.conv1d_1.bias""" )
		elif key.endswith(""".model.1.weight""" ) and len(key.split(""".""" ) ) > 1_0:
				_UpperCamelCase						    =	key.replace(""".model.1.weight"""						,  """.conv1d_1.weight""" )
		elif key.endswith(""".model.3.bias""" ) and len(key.split(""".""" ) ) > 1_0:
				_UpperCamelCase						    =	key.replace(""".model.3.bias"""						,  """.conv1d_2.bias""" )
		elif key.endswith(""".model.3.weight""" ) and len(key.split(""".""" ) ) > 1_0:
				_UpperCamelCase						    =	key.replace(""".model.3.weight"""						,  """.conv1d_2.weight""" )

		if "conditioner_blocks.0." in key:
				_UpperCamelCase						    =	key.replace("""conditioner_blocks.0"""						,  """conditioner_blocks""" )

		if "prime_prior" in key:
				_UpperCamelCase						    =	key.replace("""prime_prior"""						,  """encoder""" )

		if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
				_UpperCamelCase						    =	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:
				_UpperCamelCase						    =	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 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		_UpperCamelCase						    =	{}
		import re

		_UpperCamelCase						    =	re.compile(R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)""" )
		_UpperCamelCase						    =	re.compile(
		    R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)""" )
		_UpperCamelCase						    =	re.compile(R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)""" )

		_UpperCamelCase						    =	re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)""" )
		_UpperCamelCase						    =	re.compile(
		    R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)""" )
		_UpperCamelCase						    =	re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)""" )

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

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

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

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

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

				elif re_decoder_block_proj_in.fullmatch(lowerCAmelCase ):
						_UpperCamelCase						    =	re_decoder_block_proj_in.match(lowerCAmelCase )
						_UpperCamelCase						    =	regex_match.groups()
						_UpperCamelCase						    =	F'decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'
						_UpperCamelCase						    =	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 ):
						_UpperCamelCase						    =	re_prior_cond_conv_out.match(lowerCAmelCase )
						_UpperCamelCase						    =	regex_match.groups()
						_UpperCamelCase						    =	int(groups[1] ) * 2 + int(groups[2] ) - 2
						_UpperCamelCase						    =	F'conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'
						_UpperCamelCase						    =	re_prior_cond_conv_out.sub(lowerCAmelCase						,  lowerCAmelCase )

				elif re_prior_cond_resnet.fullmatch(lowerCAmelCase ):
						_UpperCamelCase						    =	re_prior_cond_resnet.match(lowerCAmelCase )
						_UpperCamelCase						    =	regex_match.groups()
						_UpperCamelCase						    =	int(groups[1] ) * 2 + int(groups[2] ) - 2
						_UpperCamelCase						    =	{"""1""": 1, """3""": 2}[groups[-2]]
						_UpperCamelCase						    =	F'conditioner_blocks.upsampler.upsample_block.{block_index}.'
						_UpperCamelCase						    =	F'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
						_UpperCamelCase						    =	prefix + resnet_block
						_UpperCamelCase						    =	re_prior_cond_resnet.sub(lowerCAmelCase						,  lowerCAmelCase )

				elif re_prior_cond_proj_in.fullmatch(lowerCAmelCase ):
						_UpperCamelCase						    =	re_prior_cond_proj_in.match(lowerCAmelCase )
						_UpperCamelCase						    =	regex_match.groups()
						_UpperCamelCase						    =	F'conditioner_blocks.upsampler.proj_in.{groups[-1]}'
						_UpperCamelCase						    =	re_prior_cond_proj_in.sub(lowerCAmelCase						,  lowerCAmelCase )

				# keep original key
				else:
						_UpperCamelCase						    =	original_key

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

				_UpperCamelCase						    =	original_key
				_UpperCamelCase						    =	value

		return new_dict


@torch.no_grad()
def 							__A(lowerCAmelCase=None						,  lowerCAmelCase=None )		->				int:

		"""simple docstring"""






		for file in MODEL_MAPPING[model_name]:
				if not os.path.isfile(F'{pytorch_dump_folder_path}/{file.split("/" )[-1]}' ):
						_UpperCamelCase						    =	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 )

		_UpperCamelCase						    =	MODEL_MAPPING[model_name.split("""/""" )[-1]]

		_UpperCamelCase						    =	JukeboxConfig.from_pretrained(lowerCAmelCase )
		_UpperCamelCase						    =	JukeboxModel(lowerCAmelCase )

		_UpperCamelCase						    =	[]
		_UpperCamelCase						    =	{}
		for i, dict_name in enumerate(lowerCAmelCase ):
				_UpperCamelCase						    =	torch.load(F'{pytorch_dump_folder_path}/{dict_name.split("/" )[-1]}' )["""model"""]

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

				_UpperCamelCase						    =	"""vqvae""" if i == 0 else F'priors.{3 - i}'
				_UpperCamelCase						    =	fix_jukebox_keys(lowerCAmelCase						,  model.state_dict()						,  lowerCAmelCase						,  lowerCAmelCase )
				weight_dict.append(lowerCAmelCase )

		_UpperCamelCase						    =	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__":
		lowerCamelCase__	=							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.",
		)
		lowerCamelCase__	=							parser.parse_args()
		convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCamelCase__	=							{"configuration_yolos": ["YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP", "YolosConfig", "YolosOnnxConfig"]}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase__	=							["YolosFeatureExtractor"]
		lowerCamelCase__	=							["YolosImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase__	=							[
		    "YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "YolosForObjectDetection",
		    "YolosModel",
		    "YolosPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_yolos import YolosFeatureExtractor
				from .image_processing_yolos import YolosImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_yolos import (
				    YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    YolosForObjectDetection,
				    YolosModel,
				    YolosPreTrainedModel,
				)


else:
		import sys

		lowerCamelCase__	=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



