





'''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()
__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 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		if key.endswith(""".model.1.bias"""	) and len(key.split("""."""	)	) > 10:
				SCREAMING_SNAKE_CASE :				int		=			key.replace(""".model.1.bias"""			,			""".conv1d_1.bias"""	)
		elif key.endswith(""".model.1.weight"""	) and len(key.split("""."""	)	) > 10:
				SCREAMING_SNAKE_CASE :				int		=			key.replace(""".model.1.weight"""			,			""".conv1d_1.weight"""	)
		elif key.endswith(""".model.3.bias"""	) and len(key.split("""."""	)	) > 10:
				SCREAMING_SNAKE_CASE :				str		=			key.replace(""".model.3.bias"""			,			""".conv1d_2.bias"""	)
		elif key.endswith(""".model.3.weight"""	) and len(key.split("""."""	)	) > 10:
				SCREAMING_SNAKE_CASE :				Optional[int]		=			key.replace(""".model.3.weight"""			,			""".conv1d_2.weight"""	)

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

		if "prime_prior" in key:
				SCREAMING_SNAKE_CASE :				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 :				int		=			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 :				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 		__A		(  lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				List[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 :				Tuple		=			re.compile(
		    R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""	)
		SCREAMING_SNAKE_CASE :				Dict		=			re.compile(R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""	)

		SCREAMING_SNAKE_CASE :				List[str]		=			re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""	)
		SCREAMING_SNAKE_CASE :				Any		=			re.compile(
		    R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""	)
		SCREAMING_SNAKE_CASE :				int		=			re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""	)

		SCREAMING_SNAKE_CASE :				Any		=			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 :				List[str]		=			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 :				Optional[Any]		=			re_encoder_block_conv_in.match(lowerCamelCase_	)
						SCREAMING_SNAKE_CASE :				Tuple		=			regex_match.groups()
						SCREAMING_SNAKE_CASE :				List[Any]		=			int(groups[2]	) * 2 + int(groups[3]	)
						SCREAMING_SNAKE_CASE :				List[str]		=			f'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'''
						SCREAMING_SNAKE_CASE :				int		=			re_encoder_block_conv_in.sub(lowerCamelCase_			,			lowerCamelCase_	)

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

				elif re_encoder_block_proj_out.fullmatch(lowerCamelCase_	):
						SCREAMING_SNAKE_CASE :				Dict		=			re_encoder_block_proj_out.match(lowerCamelCase_	)
						SCREAMING_SNAKE_CASE :				Optional[Any]		=			regex_match.groups()
						SCREAMING_SNAKE_CASE :				List[Any]		=			f'''encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'''
						SCREAMING_SNAKE_CASE :				Union[str, Any]		=			re_encoder_block_proj_out.sub(lowerCamelCase_			,			lowerCamelCase_	)

				# rename vqvae.decoder keys
				elif re_decoder_block_conv_out.fullmatch(lowerCamelCase_	):
						SCREAMING_SNAKE_CASE :				Union[str, Any]		=			re_decoder_block_conv_out.match(lowerCamelCase_	)
						SCREAMING_SNAKE_CASE :				Optional[int]		=			regex_match.groups()
						SCREAMING_SNAKE_CASE :				Tuple		=			int(groups[2]	) * 2 + int(groups[3]	) - 2
						SCREAMING_SNAKE_CASE :				List[Any]		=			f'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'''
						SCREAMING_SNAKE_CASE :				Tuple		=			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 :				int		=			int(groups[2]	) * 2 + int(groups[3]	) - 2
						SCREAMING_SNAKE_CASE :				str		=			{"""1""": 1, """3""": 2}[groups[-2]]
						SCREAMING_SNAKE_CASE :				str		=			f'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'''
						SCREAMING_SNAKE_CASE :				List[Any]		=			f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
						SCREAMING_SNAKE_CASE :				Tuple		=			prefix + resnet_block
						SCREAMING_SNAKE_CASE :				List[str]		=			re_decoder_block_resnet.sub(lowerCamelCase_			,			lowerCamelCase_	)

				elif re_decoder_block_proj_in.fullmatch(lowerCamelCase_	):
						SCREAMING_SNAKE_CASE :				Optional[int]		=			re_decoder_block_proj_in.match(lowerCamelCase_	)
						SCREAMING_SNAKE_CASE :				Optional[Any]		=			regex_match.groups()
						SCREAMING_SNAKE_CASE :				Tuple		=			f'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
						SCREAMING_SNAKE_CASE :				int		=			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 :				int		=			re_prior_cond_conv_out.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 :				List[str]		=			f'''conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'''
						SCREAMING_SNAKE_CASE :				Optional[Any]		=			re_prior_cond_conv_out.sub(lowerCamelCase_			,			lowerCamelCase_	)

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

				elif re_prior_cond_proj_in.fullmatch(lowerCamelCase_	):
						SCREAMING_SNAKE_CASE :				Optional[int]		=			re_prior_cond_proj_in.match(lowerCamelCase_	)
						SCREAMING_SNAKE_CASE :				List[str]		=			regex_match.groups()
						SCREAMING_SNAKE_CASE :				Dict		=			f'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
						SCREAMING_SNAKE_CASE :				Tuple		=			re_prior_cond_proj_in.sub(lowerCamelCase_			,			lowerCamelCase_	)

				# keep original key
				else:
						SCREAMING_SNAKE_CASE :				int		=			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 :				List[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 :				int		=			original_key

				SCREAMING_SNAKE_CASE :				Dict		=			original_key
				SCREAMING_SNAKE_CASE :				str		=			value

		return new_dict



@torch.no_grad()
def 		__A		(  lowerCamelCase_=None			,			lowerCamelCase_=None	):


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

		SCREAMING_SNAKE_CASE :				Optional[int]		=			JukeboxConfig.from_pretrained(lowerCamelCase_	)
		SCREAMING_SNAKE_CASE :				Tuple		=			JukeboxModel(lowerCamelCase_	)

		SCREAMING_SNAKE_CASE :				Any		=			[]
		SCREAMING_SNAKE_CASE :				Union[str, Any]		=			{}
		for i, dict_name in enumerate(lowerCamelCase_	):
				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			torch.load(f'''{pytorch_dump_folder_path}/{dict_name.split("/"	)[-1]}'''	)["""model"""]

				SCREAMING_SNAKE_CASE :				Optional[int]		=			{}
				for k in old_dic.keys():
						if k.endswith(""".b"""	):
								SCREAMING_SNAKE_CASE :				Tuple		=			old_dic[k]
						elif k.endswith(""".w"""	):
								SCREAMING_SNAKE_CASE :				Optional[Any]		=			old_dic[k]
						elif "level_2" not in dict_name and "cond.model." in k:
								SCREAMING_SNAKE_CASE :				Optional[Any]		=			old_dic[k]
						else:
								SCREAMING_SNAKE_CASE :				Dict		=			old_dic[k]

				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			"""vqvae""" if i == 0 else f'''priors.{3 - i}'''
				SCREAMING_SNAKE_CASE :				Optional[Any]		=			fix_jukebox_keys(lowerCamelCase_			,			model.state_dict()			,			lowerCamelCase_			,			lowerCamelCase_	)
				weight_dict.append(lowerCamelCase_	)

		SCREAMING_SNAKE_CASE :				List[Any]		=			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            =       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)










'''simple docstring'''




from __future__ import annotations

from statistics import mean



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				List[Any]		=			[0] * no_of_processes
		SCREAMING_SNAKE_CASE :				int		=			[0] * no_of_processes

		# Initialize remaining_time to waiting_time.

		for i in range(lowerCamelCase_	):
				SCREAMING_SNAKE_CASE :				Optional[int]		=			burst_time[i]
		SCREAMING_SNAKE_CASE :				list[int]		=			[]

		SCREAMING_SNAKE_CASE :				List[str]		=			0
		SCREAMING_SNAKE_CASE :				List[Any]		=			0

		# When processes are not completed,
		# A process whose arrival time has passed \
		# and has remaining execution time is put into the ready_process.
		# The shortest process in the ready_process, target_process is executed.

		while completed != no_of_processes:
				SCREAMING_SNAKE_CASE :				Optional[int]		=			[]
				SCREAMING_SNAKE_CASE :				Any		=			-1

				for i in range(lowerCamelCase_	):
						if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
								ready_process.append(lowerCamelCase_	)

				if len(lowerCamelCase_	) > 0:
						SCREAMING_SNAKE_CASE :				Union[str, Any]		=			ready_process[0]
						for i in ready_process:
								if remaining_time[i] < remaining_time[target_process]:
										SCREAMING_SNAKE_CASE :				Any		=			i
						total_time += burst_time[target_process]
						completed += 1
						SCREAMING_SNAKE_CASE :				Any		=			0
						SCREAMING_SNAKE_CASE :				Optional[int]		=			(
						    total_time - arrival_time[target_process] - burst_time[target_process]
						)
				else:
						total_time += 1

		return waiting_time



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Any		=			[0] * no_of_processes
		for i in range(lowerCamelCase_	):
				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			burst_time[i] + waiting_time[i]
		return turn_around_time


if __name__ == "__main__":
							print("""[TEST CASE 01]""")

							__UpperCAmelCase            =       4
							__UpperCAmelCase            =       [2, 5, 3, 7]
							__UpperCAmelCase            =       [0, 0, 0, 0]
							__UpperCAmelCase            =       calculate_waitingtime(arrival_time, burst_time, no_of_processes)
							__UpperCAmelCase            =       calculate_turnaroundtime(
							    burst_time, no_of_processes, waiting_time
							)

							# Printing the Result
							print("""PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time""")
							for i, process_id in enumerate(list(range(1, 5))):
														print(
														    f'''{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t'''
														    f'''{waiting_time[i]}\t\t\t\t{turn_around_time[i]}'''
														)
							print(f'''\nAverage waiting time = {mean(waiting_time):.5f}''')
							print(f'''Average turnaround time = {mean(turn_around_time):.5f}''')



