






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 				=	logging.get_logger(__name__)


UpperCamelCase 				=	"https://openaipublic.azureedge.net/jukebox/models/"
UpperCamelCase 				=	{
    "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__		)							->  str:






						"""simple docstring"""





						if key.endswith(""".model.1.bias"""		) and len(key.split("""."""		)		) > 10:
												_SCREAMING_SNAKE_CASE	      =  key.replace(""".model.1.bias"""       ,""".conv1d_1.bias"""		)
						elif key.endswith(""".model.1.weight"""		) and len(key.split("""."""		)		) > 10:
												_SCREAMING_SNAKE_CASE	      =  key.replace(""".model.1.weight"""       ,""".conv1d_1.weight"""		)
						elif key.endswith(""".model.3.bias"""		) and len(key.split("""."""		)		) > 10:
												_SCREAMING_SNAKE_CASE	      =  key.replace(""".model.3.bias"""       ,""".conv1d_2.bias"""		)
						elif key.endswith(""".model.3.weight"""		) and len(key.split("""."""		)		) > 10:
												_SCREAMING_SNAKE_CASE	      =  key.replace(""".model.3.weight"""       ,""".conv1d_2.weight"""		)

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

						if "prime_prior" in key:
												_SCREAMING_SNAKE_CASE	      =  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	      =  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	      =  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"""





						_SCREAMING_SNAKE_CASE	      =  {}
						import re

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

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

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

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

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

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

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

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

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

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

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

												# keep original key
												else:
																		_SCREAMING_SNAKE_CASE	      =  original_key

												_SCREAMING_SNAKE_CASE	      =  replace_key(_A		)

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

												_SCREAMING_SNAKE_CASE	      =  original_key
												_SCREAMING_SNAKE_CASE	      =  value

						return new_dict

@torch.no_grad()
def        __lowerCamelCase			(		snake_case__=None       ,snake_case__=None		)							->  List[Any]:






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

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

						_SCREAMING_SNAKE_CASE	      =  JukeboxConfig.from_pretrained(_A		)
						_SCREAMING_SNAKE_CASE	      =  JukeboxModel(_A		)

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

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

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

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

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

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

						return weight_dict


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





import math
import random
from typing import Any

from .hill_climbing import SearchProblem

def       lowercase_		(		_A			:    Optional[int]    ,					_A			:    bool = True    ,					_A			:    float = math.inf    ,					_A			:    float = -math.inf    ,					_A			:    float = math.inf    ,					_A			:    float = -math.inf    ,					_A			:    bool = False    ,					_A			:    float = 100    ,					_A			:    float = 0.01    ,					_A			:    float = 1    ,					):

      """simple docstring"""




      lowerCamelCase__		:				List[Any]    =  False
      lowerCamelCase__		:				Optional[Any]    =  search_prob
      lowerCamelCase__		:				List[str]    =  start_temperate
      lowerCamelCase__		:				Optional[int]    =  []
      lowerCamelCase__		:				Any    =  0
      lowerCamelCase__		:				Optional[Any]    =  None

      while not search_end:
            lowerCamelCase__		:				Optional[Any]    =  current_state.score()
            if best_state is None or current_score > best_state.score():
                  lowerCamelCase__		:				List[Any]    =  current_state
            scores.append(_A						)
            iterations += 1
            lowerCamelCase__		:				List[Any]    =  None
            lowerCamelCase__		:				Optional[Any]    =  current_state.get_neighbors()
            while (
                next_state is None and neighbors
            ):  # till we do not find a neighbor that we can move to
                  lowerCamelCase__		:				Any    =  random.randint(0    ,					len(_A						) - 1						)  # picking a random neighbor
                  lowerCamelCase__		:				List[Any]    =  neighbors.pop(_A						)
                  lowerCamelCase__		:				Dict    =  picked_neighbor.score() - current_score

                  if (
                      picked_neighbor.x > max_x
                      or picked_neighbor.x < min_x
                      or picked_neighbor.y > max_y
                      or picked_neighbor.y < min_y
                  ):
                        continue  # neighbor outside our bounds

                  if not find_max:
                        lowerCamelCase__		:				Dict    =  change * -1  # in case we are finding minimum
                  if change > 0:  # improves the solution
                        lowerCamelCase__		:				Optional[Any]    =  picked_neighbor
                  else:
                        lowerCamelCase__		:				str    =  (math.e) ** (
                            change / current_temp
                        )  # probability generation function
                        if random.random() < probability:  # random number within probability
                              lowerCamelCase__		:				Optional[int]    =  picked_neighbor
            lowerCamelCase__		:				Any    =  current_temp - (current_temp * rate_of_decrease)

            if current_temp < threshold_temp or next_state is None:
                  # temperature below threshold, or could not find a suitable neighbor
                  lowerCamelCase__		:				int    =  True
            else:
                  lowerCamelCase__		:				Optional[int]    =  next_state

      if visualization:
            from matplotlib import pyplot as plt

            plt.plot(range(_A						)    ,					_A						)
            plt.xlabel("Iterations"						)
            plt.ylabel("Function values"						)
            plt.show()
      return best_state


if __name__ == "__main__":

    def       lowercase_		(		_A			:    List[str]    ,					_A			:    int						):

          """simple docstring"""




          return (x**2) + (y**2)

    # starting the problem with initial coordinates (12, 47)
    A      :    Any		=					SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
    A      :    Dict		=					simulated_annealing(
        prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
    )
    print(
        "The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
        f'and 50 > y > - 5 found via hill climbing: {local_min.score()}'
    )

    # starting the problem with initial coordinates (12, 47)
    A      :    str		=					SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
    A      :    int		=					simulated_annealing(
        prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
    )
    print(
        "The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
        f'and 50 > y > - 5 found via hill climbing: {local_min.score()}'
    )

    def       lowercase_		(		_A			:    int    ,					_A			:    Any						):

          """simple docstring"""




          return (3 * x**2) - (6 * y)

    A      :    Optional[int]		=					SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
    A      :    Union[str, Any]		=					simulated_annealing(prob, find_max=False, visualization=True)
    print(
        "The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
        f'{local_min.score()}'
    )

    A      :    Any		=					SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
    A      :    Optional[int]		=					simulated_annealing(prob, find_max=True, visualization=True)


    print(
        "The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
        f'{local_min.score()}'
    )


