

"""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()
A_							:			Dict											= logging.get_logger(__name__)


A_							:			int											= "https://openaipublic.azureedge.net/jukebox/models/"
A_							:			Union[str, 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 	A       (     snake_case__    ):




			'''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 	A       (     snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    ):




			'''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(__lowercase    ):
									SCREAMING_SNAKE_CASE__								=      re_encoder_block_conv_in.match(__lowercase    )
									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(__lowercase		,				__lowercase    )

						elif re_encoder_block_resnet.fullmatch(__lowercase    ):
									SCREAMING_SNAKE_CASE__								=      re_encoder_block_resnet.match(__lowercase    )
									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(__lowercase		,				__lowercase    )

						elif re_encoder_block_proj_out.fullmatch(__lowercase    ):
									SCREAMING_SNAKE_CASE__								=      re_encoder_block_proj_out.match(__lowercase    )
									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(__lowercase		,				__lowercase    )

						# rename vqvae.decoder keys
						elif re_decoder_block_conv_out.fullmatch(__lowercase    ):
									SCREAMING_SNAKE_CASE__								=      re_decoder_block_conv_out.match(__lowercase    )
									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(__lowercase		,				__lowercase    )

						elif re_decoder_block_resnet.fullmatch(__lowercase    ):
									SCREAMING_SNAKE_CASE__								=      re_decoder_block_resnet.match(__lowercase    )
									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(__lowercase		,				__lowercase    )

						elif re_decoder_block_proj_in.fullmatch(__lowercase    ):
									SCREAMING_SNAKE_CASE__								=      re_decoder_block_proj_in.match(__lowercase    )
									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(__lowercase		,				__lowercase    )

						# rename prior cond.model to upsampler.upsample_block and resnet
						elif re_prior_cond_conv_out.fullmatch(__lowercase    ):
									SCREAMING_SNAKE_CASE__								=      re_prior_cond_conv_out.match(__lowercase    )
									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(__lowercase		,				__lowercase    )

						elif re_prior_cond_resnet.fullmatch(__lowercase    ):
									SCREAMING_SNAKE_CASE__								=      re_prior_cond_resnet.match(__lowercase    )
									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(__lowercase		,				__lowercase    )

						elif re_prior_cond_proj_in.fullmatch(__lowercase    ):
									SCREAMING_SNAKE_CASE__								=      re_prior_cond_proj_in.match(__lowercase    )
									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(__lowercase		,				__lowercase    )

						# keep original key
						else:
									SCREAMING_SNAKE_CASE__								=      original_key

						SCREAMING_SNAKE_CASE__								=      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:
									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 	A       (     snake_case__=None		,				snake_case__=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__								=      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    )

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

			SCREAMING_SNAKE_CASE__								=      JukeboxConfig.from_pretrained(__lowercase    )
			SCREAMING_SNAKE_CASE__								=      JukeboxModel(__lowercase    )

			SCREAMING_SNAKE_CASE__								=      []
			SCREAMING_SNAKE_CASE__								=      {}
			for i, dict_name in enumerate(__lowercase    ):
						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(__lowercase		,				model.state_dict()		,				__lowercase		,				__lowercase    )
						weight_dict.append(__lowercase    )

			SCREAMING_SNAKE_CASE__								=      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__":
						A_							:			List[str]											= 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.",
						)
						A_							:			List[str]											= parser.parse_args()
						convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)







"""simple docstring"""




import pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline

@pytest.mark.integration
def 	A       (     ):




			'''simple docstring'''


			with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT    ):
						with pytest.raises(snake_case__    ):
									requests.request("""GET"""		,				"""https://huggingface.co"""    )
						with pytest.raises(requests.exceptions.ConnectTimeout    ):
									requests.request("""GET"""		,				"""https://huggingface.co"""		,				timeout=1.0    )

@pytest.mark.integration
def 	A       (     ):




			'''simple docstring'''


			with offline(OfflineSimulationMode.CONNECTION_FAILS    ):
						with pytest.raises(requests.exceptions.ConnectionError    ):
									requests.request("""GET"""		,				"""https://huggingface.co"""    )




def 	A       (     ):




			'''simple docstring'''


			with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1    ):
						with pytest.raises(snake_case__    ):
									http_head("""https://huggingface.co"""    )


