

'''simple docstring'''
from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403


def 			_lowerCAmelCase   (		_UpperCamelCase		:	str				,					_UpperCamelCase		:	complex				,					_UpperCamelCase		:	str = "x"				,					_UpperCamelCase		:	float = 10**-10				,					_UpperCamelCase		:	int = 1				,					) ->     complex:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE				       =symbols(lowerCamelCase_							)
     _SCREAMING_SNAKE_CASE				       =lambdify(lowerCamelCase_				,					lowerCamelCase_							)
     _SCREAMING_SNAKE_CASE				       =lambdify(lowerCamelCase_				,					diff(lowerCamelCase_				,					lowerCamelCase_							)							)

     _SCREAMING_SNAKE_CASE				       =starting_point

     while True:
          if diff_function(lowerCamelCase_							) != 0:
               _SCREAMING_SNAKE_CASE				       =prev_guess - multiplicity * func(lowerCamelCase_							) / diff_function(
                   lowerCamelCase_							)
          else:
               raise ZeroDivisionError('Could not find root'							) from None

          # Precision is checked by comparing the difference of consecutive guesses
          if abs(next_guess - prev_guess							) < precision:
               return next_guess

          _SCREAMING_SNAKE_CASE				       =next_guess


# Let's Execute
if __name__ == "__main__":
     # Find root of trigonometric function
     # Find value of pi
     print(f'''The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}''')

     # Find root of polynomial
     # Find fourth Root of 5
     print(f'''The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5j)}''')

     # Find value of e
     print(
         "The root of log(y) - 1 = 0 is ",
         f'''{newton_raphson("log(y) - 1", 2, variable="y")}''',
     )

     # Exponential Roots
     print(
         "The root of exp(x) - 1 = 0 is",
         f'''{newton_raphson("exp(x) - 1", 1_0, precision=0.0_0_5)}''',
     )

     # Find root of cos(x)
     print(f'''The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}''')






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)



