




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


_lowercase  =			"""https://openaipublic.azureedge.net/jukebox/models/"""
_lowercase  =			{
    """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 						lowerCamelCase__   (				a							):
						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 						lowerCamelCase__   (				a    ,	a    ,	a    ,	a							):
						__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(a							):
																		__snake_case						 =   re_encoder_block_conv_in.match(a							)
																		__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(a    ,	a							)

												elif re_encoder_block_resnet.fullmatch(a							):
																		__snake_case						 =   re_encoder_block_resnet.match(a							)
																		__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(a    ,	a							)

												elif re_encoder_block_proj_out.fullmatch(a							):
																		__snake_case						 =   re_encoder_block_proj_out.match(a							)
																		__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(a    ,	a							)

												# rename vqvae.decoder keys
												elif re_decoder_block_conv_out.fullmatch(a							):
																		__snake_case						 =   re_decoder_block_conv_out.match(a							)
																		__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(a    ,	a							)

												elif re_decoder_block_resnet.fullmatch(a							):
																		__snake_case						 =   re_decoder_block_resnet.match(a							)
																		__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(a    ,	a							)

												elif re_decoder_block_proj_in.fullmatch(a							):
																		__snake_case						 =   re_decoder_block_proj_in.match(a							)
																		__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(a    ,	a							)

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

												elif re_prior_cond_resnet.fullmatch(a							):
																		__snake_case						 =   re_prior_cond_resnet.match(a							)
																		__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(a    ,	a							)

												elif re_prior_cond_proj_in.fullmatch(a							):
																		__snake_case						 =   re_prior_cond_proj_in.match(a							)
																		__snake_case						 =   regex_match.groups()
																		__snake_case						 =   f'conditioner_blocks.upsampler.proj_in.{groups[-1]}'
																		__snake_case						 =   re_prior_cond_proj_in.sub(a    ,	a							)

												# keep original key
												else:
																		__snake_case						 =   original_key

												__snake_case						 =   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:
																		__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 						lowerCamelCase__   (				a=None    ,	a=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=a							)
																		os.makedirs(f'{pytorch_dump_folder_path}/'    ,	exist_ok=a							)
																		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(a							)
						__snake_case						 =   JukeboxModel(a							)

						__snake_case						 =   []
						__snake_case						 =   {}
						for i, dict_name in enumerate(a							):
												__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(a    ,	model.state_dict()    ,	a    ,	a							)
												weight_dict.append(a							)

						__snake_case						 =   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__":
	_lowercase  =			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.""",
	)
	_lowercase  =			parser.parse_args()
	convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_lowercase  =			{
    """configuration_groupvit""": [
        """GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """GroupViTConfig""",
        """GroupViTOnnxConfig""",
        """GroupViTTextConfig""",
        """GroupViTVisionConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase  =			[
	    """GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GroupViTModel""",
	    """GroupViTPreTrainedModel""",
	    """GroupViTTextModel""",
	    """GroupViTVisionModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase  =			[
	    """TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFGroupViTModel""",
	    """TFGroupViTPreTrainedModel""",
	    """TFGroupViTTextModel""",
	    """TFGroupViTVisionModel""",
	]

if TYPE_CHECKING:
	from .configuration_groupvit import (
	    GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    GroupViTConfig,
	    GroupViTOnnxConfig,
	    GroupViTTextConfig,
	    GroupViTVisionConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_groupvit import (
		    GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GroupViTModel,
		    GroupViTPreTrainedModel,
		    GroupViTTextModel,
		    GroupViTVisionModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_groupvit import (
		    TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFGroupViTModel,
		    TFGroupViTPreTrainedModel,
		    TFGroupViTTextModel,
		    TFGroupViTVisionModel,
		)

else:
	import sys

	_lowercase  =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

