




"""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   :  Optional[int]					=      logging.get_logger(__name__)


__A   :  Any					=      'https://openaipublic.azureedge.net/jukebox/models/'
__A   :  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 						snake_case__       (					_lowerCamelCase				)			->Tuple:






		"""simple docstring"""


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

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

		if "prime_prior" in key:
				__lowercase				:		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:
				__lowercase				:		Optional[Any]												=				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:
				__lowercase				:		Optional[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 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)			->Tuple:






		"""simple docstring"""


		__lowercase				:		Tuple												=				{}
		import re

		__lowercase				:		List[str]												=				re.compile(R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"				)
		__lowercase				:		Any												=				re.compile(
		    R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"				)
		__lowercase				:		Dict												=				re.compile(R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"				)

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

		__lowercase				:		List[Any]												=				re.compile(R"conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)"				)
		__lowercase				:		str												=				re.compile(
		    R"conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"				)
		__lowercase				:		Any												=				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				):
						__lowercase				:		List[Any]												=				re_encoder_block_conv_in.match(_lowerCamelCase				)
						__lowercase				:		Union[str, Any]												=				regex_match.groups()
						__lowercase				:		int												=				int(groups[2]				) * 2 + int(groups[3]				)
						__lowercase				:		Tuple												=				F'encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'
						__lowercase				:		Dict												=				re_encoder_block_conv_in.sub(_lowerCamelCase,  _lowerCamelCase				)

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

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

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

				elif re_decoder_block_resnet.fullmatch(_lowerCamelCase				):
						__lowercase				:		str												=				re_decoder_block_resnet.match(_lowerCamelCase				)
						__lowercase				:		int												=				regex_match.groups()
						__lowercase				:		Any												=				int(groups[2]				) * 2 + int(groups[3]				) - 2
						__lowercase				:		Tuple												=				{"1": 1, "3": 2}[groups[-2]]
						__lowercase				:		int												=				F'decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'
						__lowercase				:		Any												=				F'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
						__lowercase				:		List[Any]												=				prefix + resnet_block
						__lowercase				:		Any												=				re_decoder_block_resnet.sub(_lowerCamelCase,  _lowerCamelCase				)

				elif re_decoder_block_proj_in.fullmatch(_lowerCamelCase				):
						__lowercase				:		int												=				re_decoder_block_proj_in.match(_lowerCamelCase				)
						__lowercase				:		Dict												=				regex_match.groups()
						__lowercase				:		Optional[int]												=				F'decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'
						__lowercase				:		Optional[Any]												=				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				):
						__lowercase				:		Optional[Any]												=				re_prior_cond_conv_out.match(_lowerCamelCase				)
						__lowercase				:		str												=				regex_match.groups()
						__lowercase				:		Optional[Any]												=				int(groups[1]				) * 2 + int(groups[2]				) - 2
						__lowercase				:		str												=				F'conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'
						__lowercase				:		Dict												=				re_prior_cond_conv_out.sub(_lowerCamelCase,  _lowerCamelCase				)

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

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

				# keep original key
				else:
						__lowercase				:		Tuple												=				original_key

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

				__lowercase				:		Union[str, Any]												=				original_key
				__lowercase				:		List[str]												=				value

		return new_dict

@torch.no_grad()
def 						snake_case__       (					_lowerCamelCase=None,  _lowerCamelCase=None				)			->Tuple:






		"""simple docstring"""


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

		__lowercase				:		Tuple												=				MODEL_MAPPING[model_name.split("/"				)[-1]]

		__lowercase				:		List[Any]												=				JukeboxConfig.from_pretrained(_lowerCamelCase				)
		__lowercase				:		str												=				JukeboxModel(_lowerCamelCase				)

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

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

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

		__lowercase				:		Optional[int]												=				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__":
						__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   :  Dict					=      parser.parse_args()
						convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)






"""simple docstring"""







import os
import sys
import unittest


__A   :  Optional[Any]					=      os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__A   :  Optional[Any]					=      os.path.join(git_repo_path, 'src', 'diffusers')




class       lowerCAmelCase__       (			unittest.TestCase    ):


				"""simple docstring"""





				def       snake_case    (     self					:	str						):
						__lowercase				:		int												=				find_backend("    if not is_torch_available():"						)
						self.assertEqual(lowercase__    ,   "torch"						)

						# backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
						# self.assertEqual(backend_with_underscore, "tensorflow_text")

						__lowercase				:		int												=				find_backend("    if not (is_torch_available() and is_transformers_available()):"						)
						self.assertEqual(lowercase__    ,   "torch_and_transformers"						)

						# double_backend_with_underscore = find_backend(
						#    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
						# )
						# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

						__lowercase				:		str												=				find_backend(
						    "    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):"						)
						self.assertEqual(lowercase__    ,   "torch_and_transformers_and_onnx"						)



				def       snake_case    (     self					:	Any						):
						__lowercase				:		List[Any]												=				read_init()
						# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
						self.assertIn("torch"    ,   lowercase__						)
						self.assertIn("torch_and_transformers"    ,   lowercase__						)
						self.assertIn("flax_and_transformers"    ,   lowercase__						)
						self.assertIn("torch_and_transformers_and_onnx"    ,   lowercase__						)

						# Likewise, we can't assert on the exact content of a key
						self.assertIn("UNet2DModel"    ,   objects["torch"]						)
						self.assertIn("FlaxUNet2DConditionModel"    ,   objects["flax"]						)
						self.assertIn("StableDiffusionPipeline"    ,   objects["torch_and_transformers"]						)
						self.assertIn("FlaxStableDiffusionPipeline"    ,   objects["flax_and_transformers"]						)
						self.assertIn("LMSDiscreteScheduler"    ,   objects["torch_and_scipy"]						)
						self.assertIn("OnnxStableDiffusionPipeline"    ,   objects["torch_and_transformers_and_onnx"]						)



				def       snake_case    (     self					:	Dict						):
						__lowercase				:		Tuple												=				create_dummy_object("CONSTANT"    ,   "'torch'"						)
						self.assertEqual(lowercase__    ,   "\nCONSTANT = None\n"						)

						__lowercase				:		Union[str, Any]												=				create_dummy_object("function"    ,   "'torch'"						)
						self.assertEqual(
						    lowercase__    ,   "\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"						)

						__lowercase				:		Tuple												=				"\nclass FakeClass(metaclass=DummyObject):\n    _backends = 'torch'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, 'torch')\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n"
						__lowercase				:		Dict												=				create_dummy_object("FakeClass"    ,   "'torch'"						)
						self.assertEqual(lowercase__    ,   lowercase__						)



				def       snake_case    (     self					:	List[Any]						):
						__lowercase				:		Optional[int]												=				"# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n"
						__lowercase				:		List[str]												=				create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]}						)
						self.assertEqual(dummy_files["torch"]    ,   lowercase__						)
