



'''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__       :	List[Any]    = logging.get_logger(__name__)


UpperCamelCase__       :	Optional[Any]    = '''https://openaipublic.azureedge.net/jukebox/models/'''
UpperCamelCase__       :	int    = {
    '''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_   (	_lowerCamelCase:   Optional[Any]					):
		if key.endswith(""".model.1.bias"""					) and len(key.split("""."""					)					) > 10:
				__SCREAMING_SNAKE_CASE       :		Any           =		key.replace(""".model.1.bias"""       ,      """.conv1d_1.bias"""					)
		elif key.endswith(""".model.1.weight"""					) and len(key.split("""."""					)					) > 10:
				__SCREAMING_SNAKE_CASE       :		Tuple           =		key.replace(""".model.1.weight"""       ,      """.conv1d_1.weight"""					)
		elif key.endswith(""".model.3.bias"""					) and len(key.split("""."""					)					) > 10:
				__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		key.replace(""".model.3.bias"""       ,      """.conv1d_2.bias"""					)
		elif key.endswith(""".model.3.weight"""					) and len(key.split("""."""					)					) > 10:
				__SCREAMING_SNAKE_CASE       :		List[Any]           =		key.replace(""".model.3.weight"""       ,      """.conv1d_2.weight"""					)

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

		if "prime_prior" in key:
				__SCREAMING_SNAKE_CASE       :		Dict           =		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       :		List[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 				lowerCAmelCase_   (	_lowerCamelCase:   List[str]       ,      _lowerCamelCase:   str       ,      _lowerCamelCase:   str       ,      _lowerCamelCase:   Optional[Any]					):
		__SCREAMING_SNAKE_CASE       :		List[str]           =		{}
		import re

		__SCREAMING_SNAKE_CASE       :		Tuple           =		re.compile(r"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""					)
		__SCREAMING_SNAKE_CASE       :		int           =		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       :		str           =		re.compile(r"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""					)
		__SCREAMING_SNAKE_CASE       :		Tuple           =		re.compile(
		    r"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""					)
		__SCREAMING_SNAKE_CASE       :		Dict           =		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       :		Tuple           =		re.compile(
		    r"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""					)
		__SCREAMING_SNAKE_CASE       :		int           =		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       :		List[str]           =		regex_match.groups()
						__SCREAMING_SNAKE_CASE       :		Optional[int]           =		int(groups[2]					) * 2 + int(groups[3]					)
						__SCREAMING_SNAKE_CASE       :		Optional[int]           =		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       :		Any           =		re_encoder_block_resnet.match(_lowerCamelCase					)
						__SCREAMING_SNAKE_CASE       :		List[str]           =		regex_match.groups()
						__SCREAMING_SNAKE_CASE       :		int           =		int(groups[2]					) * 2 + int(groups[3]					)
						__SCREAMING_SNAKE_CASE       :		str           =		{"""1""": 1, """3""": 2}[groups[-2]]
						__SCREAMING_SNAKE_CASE       :		Dict           =		F"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."
						__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
						__SCREAMING_SNAKE_CASE       :		Tuple           =		prefix + resnet_block
						__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		re_encoder_block_resnet.sub(_lowerCamelCase       ,      _lowerCamelCase					)

				elif re_encoder_block_proj_out.fullmatch(_lowerCamelCase					):
						__SCREAMING_SNAKE_CASE       :		Any           =		re_encoder_block_proj_out.match(_lowerCamelCase					)
						__SCREAMING_SNAKE_CASE       :		Any           =		regex_match.groups()
						__SCREAMING_SNAKE_CASE       :		int           =		F"encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"
						__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		re_encoder_block_proj_out.sub(_lowerCamelCase       ,      _lowerCamelCase					)

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

				elif re_decoder_block_resnet.fullmatch(_lowerCamelCase					):
						__SCREAMING_SNAKE_CASE       :		List[Any]           =		re_decoder_block_resnet.match(_lowerCamelCase					)
						__SCREAMING_SNAKE_CASE       :		str           =		regex_match.groups()
						__SCREAMING_SNAKE_CASE       :		Any           =		int(groups[2]					) * 2 + int(groups[3]					) - 2
						__SCREAMING_SNAKE_CASE       :		Any           =		{"""1""": 1, """3""": 2}[groups[-2]]
						__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		F"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."
						__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
						__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		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       :		List[str]           =		re_decoder_block_proj_in.match(_lowerCamelCase					)
						__SCREAMING_SNAKE_CASE       :		List[str]           =		regex_match.groups()
						__SCREAMING_SNAKE_CASE       :		Any           =		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       :		List[Any]           =		re_prior_cond_conv_out.match(_lowerCamelCase					)
						__SCREAMING_SNAKE_CASE       :		List[str]           =		regex_match.groups()
						__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		int(groups[1]					) * 2 + int(groups[2]					) - 2
						__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		F"conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"
						__SCREAMING_SNAKE_CASE       :		str           =		re_prior_cond_conv_out.sub(_lowerCamelCase       ,      _lowerCamelCase					)

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

				elif re_prior_cond_proj_in.fullmatch(_lowerCamelCase					):
						__SCREAMING_SNAKE_CASE       :		Dict           =		re_prior_cond_proj_in.match(_lowerCamelCase					)
						__SCREAMING_SNAKE_CASE       :		Any           =		regex_match.groups()
						__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		F"conditioner_blocks.upsampler.proj_in.{groups[-1]}"
						__SCREAMING_SNAKE_CASE       :		str           =		re_prior_cond_proj_in.sub(_lowerCamelCase       ,      _lowerCamelCase					)

				# keep original key
				else:
						__SCREAMING_SNAKE_CASE       :		Dict           =		original_key

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

				__SCREAMING_SNAKE_CASE       :		List[Any]           =		original_key
				__SCREAMING_SNAKE_CASE       :		str           =		value

		return new_dict


@torch.no_grad()
def 				lowerCAmelCase_   (	_lowerCamelCase:   Optional[Any]=None       ,      _lowerCamelCase:   Any=None					):
		for file in MODEL_MAPPING[model_name]:
				if not os.path.isfile(F"{pytorch_dump_folder_path}/{file.split('/'					)[-1]}"					):
						__SCREAMING_SNAKE_CASE       :		Any           =		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       :		Optional[Any]           =		MODEL_MAPPING[model_name.split("""/"""					)[-1]]

		__SCREAMING_SNAKE_CASE       :		List[Any]           =		JukeboxConfig.from_pretrained(_lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		Dict           =		JukeboxModel(_lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		[]
		__SCREAMING_SNAKE_CASE       :		List[str]           =		{}
		for i, dict_name in enumerate(_lowerCamelCase					):
				__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		torch.load(F"{pytorch_dump_folder_path}/{dict_name.split('/'					)[-1]}"					)["""model"""]

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

				__SCREAMING_SNAKE_CASE       :		Dict           =		"""vqvae""" if i == 0 else F"priors.{3 - i}"
				__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		fix_jukebox_keys(_lowerCamelCase       ,      model.state_dict()       ,      _lowerCamelCase       ,      _lowerCamelCase					)
				weight_dict.append(_lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		Tuple           =		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__       :	Union[str, 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.''',
						)
						UpperCamelCase__       :	Tuple    = parser.parse_args()
						convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)




'''simple docstring'''




# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


UpperCamelCase__       :	List[Any]    = [
    '''EAGER''',
    '''AOT_EAGER''',
    '''INDUCTOR''',
    '''NVFUSER''',
    '''AOT_NVFUSER''',
    '''AOT_CUDAGRAPHS''',
    '''OFI''',
    '''FX2TRT''',
    '''ONNXRT''',
    '''IPEX''',
]


def 				lowerCAmelCase_   (	_lowerCamelCase:   str       ,      _lowerCamelCase:   Union[str, Any]=None       ,      _lowerCamelCase:   Optional[int]=None       ,      _lowerCamelCase:   str=None					):
		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		True
		while ask_again:
				__SCREAMING_SNAKE_CASE       :		Tuple           =		input(_lowerCamelCase					)
				try:
						if default is not None and len(_lowerCamelCase					) == 0:
								return default
						return convert_value(_lowerCamelCase					) if convert_value is not None else result
				except Exception:
						if error_message is not None:
								print(_lowerCamelCase					)


def 				lowerCAmelCase_   (	_lowerCamelCase:   Tuple       ,      _lowerCamelCase:   Union[str, Any]=[]       ,      _lowerCamelCase:   List[Any]=None       ,      _lowerCamelCase:   Optional[Any]=0					):
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		BulletMenu(_lowerCamelCase       ,      _lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		Dict           =		menu.run(default_choice=_lowerCamelCase					)
		return convert_value(_lowerCamelCase					) if convert_value is not None else result


def 				lowerCAmelCase_   (	_lowerCamelCase:   Optional[Any]					):
		__SCREAMING_SNAKE_CASE       :		List[str]           =		int(_lowerCamelCase					)
		return ComputeEnvironment(["""LOCAL_MACHINE""", """AMAZON_SAGEMAKER"""][value]					)


def 				lowerCAmelCase_   (	_lowerCamelCase:   Any					):
		__SCREAMING_SNAKE_CASE       :		str           =		int(_lowerCamelCase					)
		return DistributedType(["""NO""", """MULTI_CPU""", """MULTI_XPU""", """MULTI_GPU""", """MULTI_NPU""", """TPU"""][value]					)


def 				lowerCAmelCase_   (	_lowerCamelCase:   Tuple					):
		__SCREAMING_SNAKE_CASE       :		Tuple           =		int(_lowerCamelCase					)
		return DynamoBackend(DYNAMO_BACKENDS[value]					).value


def 				lowerCAmelCase_   (	_lowerCamelCase:   Union[str, Any]					):
		__SCREAMING_SNAKE_CASE       :		List[str]           =		int(_lowerCamelCase					)
		return PrecisionType(["""no""", """fp16""", """bf16""", """fp8"""][value]					)


def 				lowerCAmelCase_   (	_lowerCamelCase:   Tuple					):
		__SCREAMING_SNAKE_CASE       :		int           =		int(_lowerCamelCase					)
		return SageMakerDistributedType(["""NO""", """DATA_PARALLEL""", """MODEL_PARALLEL"""][value]					)


def 				lowerCAmelCase_   (	_lowerCamelCase:   List[Any]					):
		return {"yes": True, "no": False}[value.lower()]






class 						_UpperCamelCase  (							argparse.RawDescriptionHelpFormatter    ):


			'''simple docstring'''

			def UpperCamelCase__					(							self     : Tuple							,     lowerCAmelCase__     : Optional[Any]							,     lowerCAmelCase__     : Dict							,     lowerCAmelCase__     : List[Any]							,     lowerCAmelCase__     : List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		super()._format_usage(lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Any           =		usage.replace("""<command> [<args>] """							,     """"""						)
					return usage