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

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

							if "prime_prior" in key:
														_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:
														_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:
														_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  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  {}
							import re

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

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

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

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

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

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

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

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

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

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

														elif re_prior_cond_proj_in.fullmatch(_SCREAMING_SNAKE_CASE				):
																					_snake_case       =  re_prior_cond_proj_in.match(_SCREAMING_SNAKE_CASE				)
																					_snake_case       =  regex_match.groups()
																					_snake_case       =  f"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
																					_snake_case       =  re_prior_cond_proj_in.sub(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)

														# keep original key
														else:
																					_snake_case       =  original_key

														_snake_case       =  replace_key(_SCREAMING_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:
																					_snake_case       =  model_state_dict[f"""{key_prefix}.{key}"""]
																					print(f"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"""				)
																					_snake_case       =  original_key

														_snake_case       =  original_key
														_snake_case       =  value

							return new_dict






@torch.no_grad()
def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE=None							, _SCREAMING_SNAKE_CASE=None				):
							for file in MODEL_MAPPING[model_name]:
														if not os.path.isfile(f"""{pytorch_dump_folder_path}/{file.split("/"				)[-1]}"""				):
																					_snake_case       =  requests.get(f"""{PREFIX}{file}"""							, allow_redirects=_SCREAMING_SNAKE_CASE				)
																					os.makedirs(f"""{pytorch_dump_folder_path}/"""							, exist_ok=_SCREAMING_SNAKE_CASE				)
																					open(f"""{pytorch_dump_folder_path}/{file.split("/"				)[-1]}"""							, """wb"""				).write(r.content				)

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

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

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

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

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

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

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

							print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""				)
							model.save_pretrained(_SCREAMING_SNAKE_CASE				)

							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)

'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey


class       _lowerCAmelCase   :







							'''simple docstring'''

							def __init__(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       List[str]:
														if dst_width < 0 or dst_height < 0:
																					raise ValueError("""Destination width/height should be > 0"""  )

														_snake_case       =  img
														_snake_case       =  img.shape[1]
														_snake_case       =  img.shape[0]
														_snake_case       =  dst_width
														_snake_case       =  dst_height

														_snake_case       =  self.src_w / self.dst_w
														_snake_case       =  self.src_h / self.dst_h

														_snake_case       =  _snake_case       =  (
														    np.ones((self.dst_h, self.dst_w, 3)  ,  np.uinta  ) * 255
														)

							def 	lowercase							(self  )					->       List[Any]:
														for i in range(self.dst_h  ):
																					for j in range(self.dst_w  ):
																												_snake_case       =  self.img[self.get_y(UpperCAmelCase  )][self.get_x(UpperCAmelCase  )]

							def 	lowercase							(self  ,  UpperCAmelCase  )					->       int:
														return int(self.ratio_x * x  )





							def 	lowercase							(self  ,  UpperCAmelCase  )					->       int:
														return int(self.ratio_y * y  )


if __name__ == "__main__":
				__lowerCAmelCase							,    __lowerCAmelCase												=	800, 600
				__lowerCAmelCase												=	imread('image_data/lena.jpg', 1)
				__lowerCAmelCase												=	NearestNeighbour(im, dst_w, dst_h)
				n.process()

				imshow(
				    f'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
				)
				waitKey(0)
				destroyAllWindows()