'''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()
SCREAMING_SNAKE_CASE_	   =			logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_	   =			'https://openaipublic.azureedge.net/jukebox/models/'
SCREAMING_SNAKE_CASE_	   =			{
    '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__					(					_lowercase    :				Optional[int]     )							->	int:
  if key.endswith(""".model.1.bias"""     ) and len(key.split("""."""     )     ) > 1_0:
    __UpperCAmelCase:					str		= key.replace(""".model.1.bias"""   ,    """.conv1d_1.bias"""     )
  elif key.endswith(""".model.1.weight"""     ) and len(key.split("""."""     )     ) > 1_0:
    __UpperCAmelCase:					Dict		= key.replace(""".model.1.weight"""   ,    """.conv1d_1.weight"""     )
  elif key.endswith(""".model.3.bias"""     ) and len(key.split("""."""     )     ) > 1_0:
    __UpperCAmelCase:					Optional[Any]		= key.replace(""".model.3.bias"""   ,    """.conv1d_2.bias"""     )
  elif key.endswith(""".model.3.weight"""     ) and len(key.split("""."""     )     ) > 1_0:
    __UpperCAmelCase:					str		= key.replace(""".model.3.weight"""   ,    """.conv1d_2.weight"""     )

  if "conditioner_blocks.0." in key:
    __UpperCAmelCase:					Optional[Any]		= key.replace("""conditioner_blocks.0"""   ,    """conditioner_blocks"""     )

  if "prime_prior" in key:
    __UpperCAmelCase:					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:
    __UpperCAmelCase:					Dict		= 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:					Tuple		= 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__					(					_lowercase    :				List[str]   ,    _lowercase    :				Dict   ,    _lowercase    :				int   ,    _lowercase    :				Dict     )							->	Optional[int]:
  __UpperCAmelCase:					Union[str, Any]		= {}
  import re

  __UpperCAmelCase:					Dict		= re.compile(r"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""     )
  __UpperCAmelCase:					Any		= re.compile(
      r"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""     )
  __UpperCAmelCase:					List[str]		= re.compile(r"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""     )

  __UpperCAmelCase:					Optional[int]		= re.compile(r"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""     )
  __UpperCAmelCase:					Tuple		= re.compile(
      r"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""     )
  __UpperCAmelCase:					Optional[int]		= re.compile(r"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""     )

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

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

    elif re_encoder_block_proj_out.fullmatch(_lowercase     ):
      __UpperCAmelCase:					str		= re_encoder_block_proj_out.match(_lowercase     )
      __UpperCAmelCase:					int		= regex_match.groups()
      __UpperCAmelCase:					Dict		= F'''encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'''
      __UpperCAmelCase:					Any		= re_encoder_block_proj_out.sub(_lowercase   ,    _lowercase     )

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

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

    elif re_decoder_block_proj_in.fullmatch(_lowercase     ):
      __UpperCAmelCase:					Tuple		= re_decoder_block_proj_in.match(_lowercase     )
      __UpperCAmelCase:					Optional[int]		= regex_match.groups()
      __UpperCAmelCase:					Any		= F'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
      __UpperCAmelCase:					Tuple		= re_decoder_block_proj_in.sub(_lowercase   ,    _lowercase     )

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

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

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

    # keep original key
    else:
      __UpperCAmelCase:					Any		= original_key

    __UpperCAmelCase:					Dict		= replace_key(_lowercase     )

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

    __UpperCAmelCase:					Optional[Any]		= original_key
    __UpperCAmelCase:					Any		= value

  return new_dict






@torch.no_grad()
def  UpperCamelCase__					(					_lowercase    :				Tuple=None   ,    _lowercase    :				Optional[int]=None     )							->	List[str]:
  for file in MODEL_MAPPING[model_name]:
    if not os.path.isfile(F'''{pytorch_dump_folder_path}/{file.split('/'     )[-1]}'''     ):
      __UpperCAmelCase:					List[Any]		= requests.get(F'''{PREFIX}{file}'''   ,    allow_redirects=_lowercase     )
      os.makedirs(F'''{pytorch_dump_folder_path}/'''   ,    exist_ok=_lowercase     )
      open(F'''{pytorch_dump_folder_path}/{file.split('/'     )[-1]}'''   ,    """wb"""     ).write(r.content     )

  __UpperCAmelCase:					Optional[Any]		= MODEL_MAPPING[model_name.split("""/"""     )[-1]]

  __UpperCAmelCase:					int		= JukeboxConfig.from_pretrained(_lowercase     )
  __UpperCAmelCase:					List[Any]		= JukeboxModel(_lowercase     )

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

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

    __UpperCAmelCase:					int		= """vqvae""" if i == 0 else F'''priors.{3 - i}'''
    __UpperCAmelCase:					List[str]		= fix_jukebox_keys(_lowercase   ,    model.state_dict()   ,    _lowercase   ,    _lowercase     )
    weight_dict.append(_lowercase     )

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

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

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

  return weight_dict


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_	   =			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.',
    )
    SCREAMING_SNAKE_CASE_	   =			parser.parse_args()
    convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)
'''simple docstring'''






import numpy

# List of input, output pairs
SCREAMING_SNAKE_CASE_	   =			(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
SCREAMING_SNAKE_CASE_	   =			(((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
SCREAMING_SNAKE_CASE_	   =			[2, 4, 1, 5]
SCREAMING_SNAKE_CASE_	   =			len(train_data)
SCREAMING_SNAKE_CASE_	   =			0.009






def  UpperCamelCase__					(					_lowercase    :				Union[str, Any]   ,    _lowercase    :				List[Any]="train"     )							->	int:
  return calculate_hypothesis_value(_lowercase   ,    _lowercase     ) - output(
      _lowercase   ,    _lowercase     )






def  UpperCamelCase__					(					_lowercase    :				Dict     )							->	Optional[Any]:
  __UpperCAmelCase:					List[Any]		= 0
  for i in range(len(_lowercase     ) - 1     ):
    hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
  hyp_val += parameter_vector[0]
  return hyp_val






def  UpperCamelCase__					(					_lowercase    :				List[str]   ,    _lowercase    :				List[str]     )							->	Optional[Any]:
  if data_set == "train":
    return train_data[example_no][1]
  elif data_set == "test":
    return test_data[example_no][1]
  return None






def  UpperCamelCase__					(					_lowercase    :				Dict   ,    _lowercase    :				str     )							->	Dict:
  if data_set == "train":
    return _hypothesis_value(train_data[example_no][0]     )
  elif data_set == "test":
    return _hypothesis_value(test_data[example_no][0]     )
  return None






def  UpperCamelCase__					(					_lowercase    :				Optional[int]   ,    _lowercase    :				Union[str, Any]=m     )							->	Optional[Any]:
  __UpperCAmelCase:					int		= 0
  for i in range(_lowercase     ):
    if index == -1:
      summation_value += _error(_lowercase     )
    else:
      summation_value += _error(_lowercase     ) * train_data[i][0][index]
  return summation_value






def  UpperCamelCase__					(					_lowercase    :				Tuple     )							->	Optional[int]:
  __UpperCAmelCase:					Any		= summation_of_cost_derivative(_lowercase   ,    _lowercase     ) / m
  return cost_derivative_value






def  UpperCamelCase__					(					)							->	Tuple:
  global parameter_vector
  # Tune these values to set a tolerance value for predicted output
  __UpperCAmelCase:					str		= 0.00_00_02
  __UpperCAmelCase:					List[Any]		= 0
  __UpperCAmelCase:					Optional[Any]		= 0
  while True:
    j += 1
    __UpperCAmelCase:					Any		= [0, 0, 0, 0]
    for i in range(0   ,    len(_lowercase     )     ):
      __UpperCAmelCase:					str		= get_cost_derivative(i - 1     )
      __UpperCAmelCase:					List[str]		= (
          parameter_vector[i] - LEARNING_RATE * cost_derivative
      )
    if numpy.allclose(
        _lowercase   ,    _lowercase   ,    atol=_lowercase   ,    rtol=_lowercase   ,    ):
      break
    __UpperCAmelCase:					List[Any]		= temp_parameter_vector
  print(("""Number of iterations:""", j)     )






def  UpperCamelCase__					(					)							->	Dict:
  for i in range(len(_lowercase     )     ):
    print(("""Actual output value:""", output(_lowercase   ,    """test"""     ))     )
    print(("""Hypothesis output:""", calculate_hypothesis_value(_lowercase   ,    """test"""     ))     )


if __name__ == "__main__":
    run_gradient_descent()
    print('\nTesting gradient descent for a linear hypothesis function.\n')
    test_gradient_descent()