




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	:       Dict     =	logging.get_logger(__name__)


__lowerCAmelCase	:       Union[str, Any]     =	'https://openaipublic.azureedge.net/jukebox/models/'
__lowerCAmelCase	:       Optional[Any]     =	{
    '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       (				A	:      Dict     ):






			'''simple docstring'''







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

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

			if "prime_prior" in key:
						a					=    key.replace("prime_prior",						"encoder"     )

			if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
						a					=    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:
						a					=    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       (				A	:      List[str],						A	:      Any,						A	:      List[str],						A	:      List[Any]     ):






			'''simple docstring'''







			a					=    {}
			import re

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

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

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

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

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

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

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

						elif re_decoder_block_proj_in.fullmatch(A     ):
									a					=    re_decoder_block_proj_in.match(A     )
									a					=    regex_match.groups()
									a					=    F"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
									a					=    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     ):
									a					=    re_prior_cond_conv_out.match(A     )
									a					=    regex_match.groups()
									a					=    int(groups[1]     ) * 2 + int(groups[2]     ) - 2
									a					=    F"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
									a					=    re_prior_cond_conv_out.sub(A,						A     )

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

						elif re_prior_cond_proj_in.fullmatch(A     ):
									a					=    re_prior_cond_proj_in.match(A     )
									a					=    regex_match.groups()
									a					=    F"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
									a					=    re_prior_cond_proj_in.sub(A,						A     )

						# keep original key
						else:
									a					=    original_key

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

						a					=    original_key
						a					=    value

			return new_dict




@torch.no_grad()
def     _UpperCamelCase       (				A	:      Optional[Any]=None,						A	:      Tuple=None     ):






			'''simple docstring'''







			for file in MODEL_MAPPING[model_name]:
						if not os.path.isfile(F"""{pytorch_dump_folder_path}/{file.split("/"     )[-1]}"""     ):
									a					=    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     )

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

			a					=    JukeboxConfig.from_pretrained(A     )
			a					=    JukeboxModel(A     )

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

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

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

			a					=    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__":
		__lowerCAmelCase	:       Optional[Any]     =	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	:       List[Any]     =	parser.parse_args()
		convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)












import json
import multiprocessing
import os
import re
from collections import defaultdict

import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList


__lowerCAmelCase	:       Union[str, Any]     =	['\nclass', '\ndef', '\n#', '\n@', '\nprint', '\nif']




class 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				def __init__(   self       :  Any      ,   __lowerCamelCase       :  List[str]      ,   __lowerCamelCase       :  Union[str, Any]      ,   __lowerCamelCase       :  Union[str, Any]=None      ,   __lowerCamelCase       :  Any=1							)      ->  Union[str, Any]:
							a					=    tokenizer
							a					=    dataset
							a					=    len(__lowerCamelCase							) if n_tasks is None else n_tasks
							a					=    n_copies





				def __iter__(   self       :  Tuple							)      ->  str:
							a					=    []
							for task in range(self.n_tasks							):
										# without strip, the model generate commented codes ...
										prompts.append(self.tokenizer.eos_token + self.dataset[task]["prompt"].strip()							)
							a					=    self.tokenizer(__lowerCamelCase      ,   padding=__lowerCamelCase      ,   return_tensors="pt"							)
							for task in range(self.n_tasks							):
										for _ in range(self.n_copies							):
													yield {
													    "ids": outputs.input_ids[task],
													    "task_id": task,
													    "input_len": outputs.attention_mask[task].sum(),
													}




class 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				def __init__(   self       :  int      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  Tuple							)      ->  Optional[Any]:
							a					=    start_length
							a					=    eof_strings
							a					=    tokenizer





				def __call__(   self       :  int      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  List[str]      ,   **__lowerCamelCase       :  Optional[int]							)      ->  Optional[Any]:
							a					=    self.tokenizer.batch_decode(input_ids[:, self.start_length :]							)
							a					=    []
							for decoded_generation in decoded_generations:
										done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings							)							)
							return all(__lowerCamelCase							)




def     __magic_name__       (				A	:      List[Any]     ):






			'''simple docstring'''







			a					=    re.split("(%s)" % "|".join(A     ),						A     )
			# last string should be ""
			return "".join(string_list[:-2]     )




def     __magic_name__       (				A	:      Union[str, Any],						A	:      Optional[Any],						A	:      List[Any],						A	:      Optional[Any],						A	:      List[str],						A	:      List[Any]=20,						**A	:      Union[str, Any]     ):






			'''simple docstring'''







			a					=    defaultdict(A     )  # dict of list of generated tokens
			for step, batch in tqdm(enumerate(A     )     ):
						with torch.no_grad():
									a					=    batch["ids"].shape[-1]
									a					=    accelerator.unwrap_model(A     ).generate(
									    input_ids=batch["ids"][:, : batch["input_len"]],						num_return_sequences=A,						**A     )
									# each task is generated batch_size times
									a					=    batch["task_id"].repeat(A     )
									a					=    accelerator.pad_across_processes(
									    A,						dim=1,						pad_index=tokenizer.pad_token_id     )

									a					,	a					=    accelerator.gather((generated_tokens, generated_tasks)     )
									a					=    generated_tokens.cpu().numpy()
									a					=    generated_tasks.cpu().numpy()

									for task, generated_tokens in zip(A,						A     ):
												gen_token_dict[task].append(A     )

			a					=    [[] for _ in range(A     )]
			for task, generated_tokens in gen_token_dict.items():
						for s in generated_tokens:
									a					=    tokenizer.decode(A,						skip_special_tokens=A,						clean_up_tokenization_spaces=A     )
									code_gens[task].append(remove_last_block(A     )     )
			return code_gens




def     __magic_name__       (				):






			'''simple docstring'''







			a					=    HfArgumentParser(A     )
			a					=    parser.parse_args()

			transformers.logging.set_verbosity_error()
			# enables code execution in code_eval metric
			a					=    args.HF_ALLOW_CODE_EVAL
			# make sure tokenizer plays nice with multiprocessing
			a					=    "false"

			if args.num_workers is None:
						a					=    multiprocessing.cpu_count()

			# Use dataset load to feed to accelerate
			a					=    Accelerator()
			set_seed(args.seed,						device_specific=A     )

			# Load model and tokenizer
			a					=    AutoTokenizer.from_pretrained(args.model_ckpt     )
			a					=    tokenizer.eos_token
			a					=    AutoModelForCausalLM.from_pretrained(args.model_ckpt     )

			# Generation settings
			a					=    {
			    "do_sample": args.do_sample,
			    "temperature": args.temperature,
			    "max_new_tokens": args.max_new_tokens,
			    "top_p": args.top_p,
			    "top_k": args.top_k,
			    "stopping_criteria": StoppingCriteriaList([EndOfFunctionCriteria(0,						A,						A     )]     ),
			}

			# Load evaluation dataset and metric
			a					=    load_dataset("openai_humaneval"     )
			a					=    load_metric("code_eval"     )

			a					=    args.num_tasks if args.num_tasks is not None else len(human_eval["test"]     )
			a					=    args.n_samples // args.batch_size

			a					=    TokenizedDataset(A,						human_eval["test"],						n_copies=A,						n_tasks=A     )
			# do not confuse args.batch_size, which is actually the num_return_sequences
			a					=    DataLoader(A,						batch_size=1     )

			# Run a quick test to see if code evaluation is enabled
			try:
						a					=    code_eval_metric.compute(references=[""],						predictions=[[""]]     )
			except ValueError as exception:
						print(
						    "Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL=\"1\"`"
						    " flag to enable code evaluation."     )
						raise exception

			a					,	a					=    accelerator.prepare(A,						A     )

			a					=    complete_code(
			    A,						A,						A,						A,						n_tasks=A,						batch_size=args.batch_size,						**A,						)

			if accelerator.is_main_process:
						a					=    []

						for task in tqdm(range(A     )     ):
									a					=    human_eval["test"][task]["test"]
									a					=    F"""check({human_eval["test"][task]["entry_point"]})"""
									references.append("\n" + test_func + "\n" + entry_point     )

						# Evaluate completions with "code_eval" metric
						a					,	a					=    code_eval_metric.compute(
						    references=A,						predictions=A,						num_workers=args.num_workers     )
						print(F"""Results: {pass_at_k}"""     )

						# Save results to json file
						with open(args.output_file,						"w"     ) as fp:
									json.dump(A,						A     )


# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
		main()






