
"""simple docstring"""





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()
_a														=		logging.get_logger(__name__)


_a														=		"""https://openaipublic.azureedge.net/jukebox/models/"""
_a														=		{
    """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 				lowerCamelCase__		(      __snake_case     )							->		Optional[Any]:


					"""simple docstring"""





					if key.endswith('''.model.1.bias'''     ) and len(key.split('''.'''     )     ) > 10:
										_UpperCamelCase										=  key.replace('''.model.1.bias''',					'''.conv1d_1.bias'''     )
					elif key.endswith('''.model.1.weight'''     ) and len(key.split('''.'''     )     ) > 10:
										_UpperCamelCase										=  key.replace('''.model.1.weight''',					'''.conv1d_1.weight'''     )
					elif key.endswith('''.model.3.bias'''     ) and len(key.split('''.'''     )     ) > 10:
										_UpperCamelCase										=  key.replace('''.model.3.bias''',					'''.conv1d_2.bias'''     )
					elif key.endswith('''.model.3.weight'''     ) and len(key.split('''.'''     )     ) > 10:
										_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 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case,					__snake_case     )							->		Tuple:


					"""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(__snake_case     ):
															_UpperCamelCase										=  re_encoder_block_conv_in.match(__snake_case     )
															_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(__snake_case,					__snake_case     )

										elif re_encoder_block_resnet.fullmatch(__snake_case     ):
															_UpperCamelCase										=  re_encoder_block_resnet.match(__snake_case     )
															_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(__snake_case,					__snake_case     )

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

										# rename vqvae.decoder keys
										elif re_decoder_block_conv_out.fullmatch(__snake_case     ):
															_UpperCamelCase										=  re_decoder_block_conv_out.match(__snake_case     )
															_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(__snake_case,					__snake_case     )

										elif re_decoder_block_resnet.fullmatch(__snake_case     ):
															_UpperCamelCase										=  re_decoder_block_resnet.match(__snake_case     )
															_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(__snake_case,					__snake_case     )

										elif re_decoder_block_proj_in.fullmatch(__snake_case     ):
															_UpperCamelCase										=  re_decoder_block_proj_in.match(__snake_case     )
															_UpperCamelCase										=  regex_match.groups()
															_UpperCamelCase										=  F'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
															_UpperCamelCase										=  re_decoder_block_proj_in.sub(__snake_case,					__snake_case     )

										# rename prior cond.model to upsampler.upsample_block and resnet
										elif re_prior_cond_conv_out.fullmatch(__snake_case     ):
															_UpperCamelCase										=  re_prior_cond_conv_out.match(__snake_case     )
															_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(__snake_case,					__snake_case     )

										elif re_prior_cond_resnet.fullmatch(__snake_case     ):
															_UpperCamelCase										=  re_prior_cond_resnet.match(__snake_case     )
															_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(__snake_case,					__snake_case     )

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

										# keep original key
										else:
															_UpperCamelCase										=  original_key

										_UpperCamelCase										=  replace_key(__snake_case     )

										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 				lowerCamelCase__		(      __snake_case=None,					__snake_case=None     )							->		Optional[Any]:


					"""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=__snake_case     )
															os.makedirs(F'''{pytorch_dump_folder_path}/''',					exist_ok=__snake_case     )
															open(F'''{pytorch_dump_folder_path}/{file.split("/"     )[-1]}''',					'''wb'''     ).write(r.content     )

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

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

					_UpperCamelCase										=  []
					_UpperCamelCase										=  {}
					for i, dict_name in enumerate(__snake_case     ):
										_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(__snake_case,					model.state_dict(),					__snake_case,					__snake_case     )
										weight_dict.append(__snake_case     )

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

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

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

					return weight_dict


if __name__ == "__main__":
	_a														=		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.""",
	)
	_a														=		parser.parse_args()
	convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)








"""simple docstring"""





import numpy






class    _UpperCAmelCase:



		def __init__( self    ,		__a    ,		__a)	->	None:






							'''simple docstring'''
							_UpperCamelCase										=  input_array

							# Random initial weights are assigned where first argument is the
							# number of nodes in previous layer and second argument is the
							# number of nodes in the next layer.

							# Random initial weights are assigned.
							# self.input_array.shape[1] is used to represent number of nodes in input layer.
							# First hidden layer consists of 4 nodes.
							_UpperCamelCase										=  numpy.random.rand(
							    self.input_array.shape[1]    ,		4)

							# Random initial values for the first hidden layer.
							# First hidden layer has 4 nodes.
							# Second hidden layer has 3 nodes.
							_UpperCamelCase										=  numpy.random.rand(
							    4    ,		3)

							# Random initial values for the second hidden layer.
							# Second hidden layer has 3 nodes.
							# Output layer has 1 node.
							_UpperCamelCase										=  numpy.random.rand(3    ,		1)

							# Real output values provided.
							_UpperCamelCase										=  output_array

							# Predicted output values by the neural network.
							# Predicted_output array initially consists of zeroes.
							_UpperCamelCase										=  numpy.zeros(output_array.shape)



		def 						UpperCAmelCase							( self)	->	numpy.ndarray:






							'''simple docstring'''
							_UpperCamelCase										=  sigmoid(
							    numpy.dot(self.input_array    ,		self.input_layer_and_first_hidden_layer_weights))

							# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
							# connecting the first hidden set of nodes with the second hidden set of nodes.
							_UpperCamelCase										=  sigmoid(
							    numpy.dot(
							        self.layer_between_input_and_first_hidden_layer    ,		self.first_hidden_layer_and_second_hidden_layer_weights    ,		))

							# layer_between_second_hidden_layer_and_output is the layer connecting
							# second hidden layer with the output node.
							_UpperCamelCase										=  sigmoid(
							    numpy.dot(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer    ,		self.second_hidden_layer_and_output_layer_weights    ,		))

							return self.layer_between_second_hidden_layer_and_output



		def 						UpperCAmelCase							( self)	->	None:






							'''simple docstring'''
							_UpperCamelCase										=  numpy.dot(
							    self.layer_between_first_hidden_layer_and_second_hidden_layer.T    ,		2
							    * (self.output_array - self.predicted_output)
							    * sigmoid_derivative(self.predicted_output)    ,		)
							_UpperCamelCase										=  numpy.dot(
							    self.layer_between_input_and_first_hidden_layer.T    ,		numpy.dot(
							        2
							        * (self.output_array - self.predicted_output)
							        * sigmoid_derivative(self.predicted_output)    ,		self.second_hidden_layer_and_output_layer_weights.T    ,		)
							    * sigmoid_derivative(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer)    ,		)
							_UpperCamelCase										=  numpy.dot(
							    self.input_array.T    ,		numpy.dot(
							        numpy.dot(
							            2
							            * (self.output_array - self.predicted_output)
							            * sigmoid_derivative(self.predicted_output)    ,		self.second_hidden_layer_and_output_layer_weights.T    ,		)
							        * sigmoid_derivative(
							            self.layer_between_first_hidden_layer_and_second_hidden_layer)    ,		self.first_hidden_layer_and_second_hidden_layer_weights.T    ,		)
							    * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer)    ,		)

							self.input_layer_and_first_hidden_layer_weights += (
							    updated_input_layer_and_first_hidden_layer_weights
							)
							self.first_hidden_layer_and_second_hidden_layer_weights += (
							    updated_first_hidden_layer_and_second_hidden_layer_weights
							)
							self.second_hidden_layer_and_output_layer_weights += (
							    updated_second_hidden_layer_and_output_layer_weights
							)



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a)	->	None:






							'''simple docstring'''
							for iteration in range(1    ,		iterations + 1):
												_UpperCamelCase										=  self.feedforward()
												self.back_propagation()
												if give_loss:
																	_UpperCamelCase										=  numpy.mean(numpy.square(output - self.feedforward()))
																	print(F'''Iteration {iteration} Loss: {loss}''')




		def 						UpperCAmelCase							( self    ,		__a)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  input_arr

							_UpperCamelCase										=  sigmoid(
							    numpy.dot(self.array    ,		self.input_layer_and_first_hidden_layer_weights))

							_UpperCamelCase										=  sigmoid(
							    numpy.dot(
							        self.layer_between_input_and_first_hidden_layer    ,		self.first_hidden_layer_and_second_hidden_layer_weights    ,		))

							_UpperCamelCase										=  sigmoid(
							    numpy.dot(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer    ,		self.second_hidden_layer_and_output_layer_weights    ,		))

							return int(self.layer_between_second_hidden_layer_and_output > 0.6)





def 				lowerCamelCase__		(      __snake_case     )							->		numpy.ndarray:


					"""simple docstring"""





					return 1 / (1 + numpy.exp(-value     ))



def 				lowerCamelCase__		(      __snake_case     )							->		numpy.ndarray:


					"""simple docstring"""





					return (value) * (1 - (value))



def 				lowerCamelCase__		(      )							->		int:


					"""simple docstring"""





					_UpperCamelCase										=  numpy.array(
					    (
					        [0, 0, 0],
					        [0, 0, 1],
					        [0, 1, 0],
					        [0, 1, 1],
					        [1, 0, 0],
					        [1, 0, 1],
					        [1, 1, 0],
					        [1, 1, 1],
					    ),					dtype=numpy.floataa,					)

					# True output values for the given input values.
					_UpperCamelCase										=  numpy.array(([0], [1], [1], [0], [1], [0], [0], [1]),					dtype=numpy.floataa     )

					# Calling neural network class.
					_UpperCamelCase										=  TwoHiddenLayerNeuralNetwork(
					    input_array=__snake_case,					output_array=__snake_case     )

					# Calling training function.
					# Set give_loss to True if you want to see loss in every iteration.
					neural_network.train(output=__snake_case,					iterations=10,					give_loss=__snake_case     )

					return neural_network.predict(numpy.array(([1, 1, 1]),					dtype=numpy.floataa     )     )


if __name__ == "__main__":
				example()






