





'''simple docstring'''

import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import UperNetForSemanticSegmentation
			from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor

class       UpperCAmelCase :




		def __init__(self      :	List[Any]							,			A__      :	int							,			A__      :	int=1_3							,			A__      :	str=3_2							,			A__      :	List[Any]=3							,			A__      :	Union[str, Any]=4							,			A__      :	Tuple=[1_0, 2_0, 3_0, 4_0]							,			A__      :	Optional[int]=[2, 2, 3, 2]							,			A__      :	Optional[Any]=True							,			A__      :	Any=True							,			A__      :	Dict=3_7							,			A__      :	Optional[Any]="gelu"							,			A__      :	str=1_0							,			A__      :	List[Any]=0.0_2							,			A__      :	Optional[int]=["stage2", "stage3", "stage4"]							,			A__      :	Optional[int]=3							,			A__      :	str=None							,			)					-> Optional[int]:
						lowercase         = parent
						lowercase         = batch_size
						lowercase         = image_size
						lowercase         = num_channels
						lowercase         = num_stages
						lowercase         = hidden_sizes
						lowercase         = depths
						lowercase         = is_training
						lowercase         = use_labels
						lowercase         = intermediate_size
						lowercase         = hidden_act
						lowercase         = type_sequence_label_size
						lowercase         = initializer_range
						lowercase         = out_features
						lowercase         = num_labels
						lowercase         = scope
						lowercase         = num_stages




		def 				UpperCAmelCase__   (self      :	int				)					-> Dict:
						lowercase         = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

						lowercase         = None
						if self.use_labels:
										lowercase         = ids_tensor([self.batch_size]							,			self.type_sequence_label_size				)

						lowercase         = self.get_config()

						return config, pixel_values, labels




		def 				UpperCAmelCase__   (self      :	List[str]				)					-> List[str]:
						return ConvNextConfig(
						    num_channels=self.num_channels							,			num_stages=self.num_stages							,			hidden_sizes=self.hidden_sizes							,			depths=self.depths							,			is_training=self.is_training							,			intermediate_size=self.intermediate_size							,			hidden_act=self.hidden_act							,			out_features=self.out_features							,			)




		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> List[Any]:
						return UperNetConfig(
						    backbone_config=self.get_backbone_config()							,			hidden_size=5_1_2							,			pool_scales=[1, 2, 3, 6]							,			use_auxiliary_head=__UpperCamelCase							,			auxiliary_loss_weight=0.4							,			auxiliary_in_channels=4_0							,			auxiliary_channels=2_5_6							,			auxiliary_num_convs=1							,			auxiliary_concat_input=__UpperCamelCase							,			loss_ignore_index=2_5_5							,			num_labels=self.num_labels							,			)




		def 				UpperCAmelCase__   (self      :	Any							,			A__      :	Dict							,			A__      :	Union[str, Any]							,			A__      :	List[Any]				)					-> List[str]:
						lowercase         = UperNetForSemanticSegmentation(config=__UpperCamelCase				)
						model.to(__UpperCamelCase				)
						model.eval()
						lowercase         = model(__UpperCamelCase				)
						self.parent.assertEqual(
						    result.logits.shape							,			(self.batch_size, self.num_labels, self.image_size, self.image_size)				)





		def 				UpperCAmelCase__   (self      :	Tuple				)					-> Optional[Any]:
						lowercase         = self.prepare_config_and_inputs()
						(
						    (
						    lowercase
						)   ,							(
						    lowercase
						)   ,							(
						    lowercase
						)   ,							
						)         = config_and_inputs
						lowercase         = {"pixel_values": pixel_values}
						return config, inputs_dict




@require_torch
class       UpperCAmelCase (						_UpperCAmelCase			,       _UpperCAmelCase			,       unittest.TestCase  ):
		UpperCAmelCase					:	int 			=		(UperNetForSemanticSegmentation,) if is_torch_available() else ()
		UpperCAmelCase					:	List[Any] 			=		{'image-segmentation': UperNetForSemanticSegmentation} if is_torch_available() else {}
		UpperCAmelCase					:	Optional[int] 			=		False
		UpperCAmelCase					:	Dict 			=		False
		UpperCAmelCase					:	List[Any] 			=		False
		UpperCAmelCase					:	Optional[Any] 			=		False
		UpperCAmelCase					:	List[Any] 			=		False
		UpperCAmelCase					:	Optional[Any] 			=		False




		def 				UpperCAmelCase__   (self      :	str				)					-> int:
						lowercase         = UperNetModelTester(self				)
						lowercase         = ConfigTester(self							,			config_class=__UpperCamelCase							,			has_text_modality=__UpperCamelCase							,			hidden_size=3_7				)




		def 				UpperCAmelCase__   (self      :	List[str]				)					-> int:
						self.create_and_test_config_common_properties()
						self.config_tester.create_and_test_config_to_json_string()
						self.config_tester.create_and_test_config_to_json_file()
						self.config_tester.create_and_test_config_from_and_save_pretrained()
						self.config_tester.create_and_test_config_with_num_labels()
						self.config_tester.check_config_can_be_init_without_params()
						self.config_tester.check_config_arguments_init()




		def 				UpperCAmelCase__   (self      :	Dict				)					-> Optional[Any]:
						return




		def 				UpperCAmelCase__   (self      :	Dict				)					-> Optional[Any]:
						lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										lowercase         = model_class(__UpperCamelCase				)
										lowercase         = inspect.signature(model.forward				)
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										lowercase         = [*signature.parameters.keys()]

										lowercase         = ["pixel_values"]
										self.assertListEqual(arg_names[:1]							,			__UpperCamelCase				)




		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Any:
						lowercase         = self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCamelCase				)




		@unittest.skip(reason="UperNet does not use inputs_embeds"				)
		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> Optional[int]:
						pass




		@unittest.skip(reason="UperNet does not support input and output embeddings"				)
		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> Union[str, Any]:
						pass




		@unittest.skip(reason="UperNet does not have a base model"				)
		def 				UpperCAmelCase__   (self      :	Dict				)					-> List[Any]:
						pass




		@unittest.skip(reason="UperNet does not have a base model"				)
		def 				UpperCAmelCase__   (self      :	int				)					-> List[Any]:
						pass




		@require_torch_multi_gpu
		@unittest.skip(reason="UperNet has some layers using `add_module` which doesn't work well with `nn.DataParallel`"				)
		def 				UpperCAmelCase__   (self      :	Dict				)					-> Any:
						pass




		@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."				)
		def 				UpperCAmelCase__   (self      :	Tuple				)					-> Optional[int]:
						pass




		def 				UpperCAmelCase__   (self      :	Dict				)					-> int:






						def check_hidden_states_output(A__      :	int							,			A__      :	Union[str, Any]							,			A__      :	Dict				):
										lowercase         = model_class(__UpperCamelCase				)
										model.to(__UpperCamelCase				)
										model.eval()

										with torch.no_grad():
														lowercase         = model(**self._prepare_for_class(__UpperCamelCase							,			__UpperCamelCase				)				)

										lowercase         = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

										lowercase         = self.model_tester.num_stages
										self.assertEqual(len(__UpperCamelCase				)							,			expected_num_stages + 1				)

										# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
										self.assertListEqual(
										    list(hidden_states[0].shape[-2:]				)							,			[self.model_tester.image_size // 4, self.model_tester.image_size // 4]							,			)

						lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										lowercase         = True
										check_hidden_states_output(__UpperCamelCase							,			__UpperCamelCase							,			__UpperCamelCase				)

										# check that output_hidden_states also work using config
										del inputs_dict["output_hidden_states"]
										lowercase         = True

										check_hidden_states_output(__UpperCamelCase							,			__UpperCamelCase							,			__UpperCamelCase				)




		def 				UpperCAmelCase__   (self      :	Any				)					-> Dict:
						lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs_for_common()

						lowercase         = _config_zero_init(__UpperCamelCase				)
						lowercase         = _config_zero_init(configs_no_init.backbone_config				)
						for model_class in self.all_model_classes:
										lowercase         = model_class(config=__UpperCamelCase				)
										for name, param in model.named_parameters():
														if param.requires_grad:
																		self.assertIn(
																		    ((param.data.mean() * 1e9).round() / 1e9).item()							,			[0.0, 1.0]							,			msg=f'Parameter {name} of model {model_class} seems not properly initialized'							,			)




		@unittest.skip(reason="UperNet does not have tied weights"				)
		def 				UpperCAmelCase__   (self      :	int				)					-> Dict:
						pass





		@slow
		def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> Optional[int]:






						for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowercase         = UperNetForSemanticSegmentation.from_pretrained(__UpperCamelCase				)
										self.assertIsNotNone(__UpperCamelCase				)







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






				lowercase         = hf_hub_download(
				    repo_id="hf-internal-testing/fixtures_ade20k"		,							repo_type="dataset"		,							filename="ADE_val_00000001.jpg"							)
				lowercase         = Image.open(_A							).convert("RGB"							)
				return image


@require_torch
@require_vision
@slow
class       UpperCAmelCase (						unittest.TestCase  ):




		def 				UpperCAmelCase__   (self      :	Tuple				)					-> Dict:
						lowercase         = AutoImageProcessor.from_pretrained("openmmlab/upernet-swin-tiny"				)
						lowercase         = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-swin-tiny"				).to(__UpperCamelCase				)

						lowercase         = prepare_img()
						lowercase         = processor(images=__UpperCamelCase							,			return_tensors="pt"				).to(__UpperCamelCase				)

						with torch.no_grad():
										lowercase         = model(**__UpperCamelCase				)

						lowercase         = torch.Size((1, model.config.num_labels, 5_1_2, 5_1_2)				)
						self.assertEqual(outputs.logits.shape							,			__UpperCamelCase				)

						lowercase         = torch.tensor(
						    [[-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.4_7_9_7, -7.4_7_9_7, -7.3_0_6_8]]				).to(__UpperCamelCase				)
						self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]							,			__UpperCamelCase							,			atol=1e-4				)				)





		def 				UpperCAmelCase__   (self      :	str				)					-> Union[str, Any]:
						lowercase         = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-tiny"				)
						lowercase         = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-tiny"				).to(__UpperCamelCase				)

						lowercase         = prepare_img()
						lowercase         = processor(images=__UpperCamelCase							,			return_tensors="pt"				).to(__UpperCamelCase				)

						with torch.no_grad():
										lowercase         = model(**__UpperCamelCase				)

						lowercase         = torch.Size((1, model.config.num_labels, 5_1_2, 5_1_2)				)
						self.assertEqual(outputs.logits.shape							,			__UpperCamelCase				)

						lowercase         = torch.tensor(
						    [[-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.7_7_4_6, -8.7_7_4_6, -8.6_1_3_0]]				).to(__UpperCamelCase				)
						self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]							,			__UpperCamelCase							,			atol=1e-4				)				)











'''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()
__lowerCamelCase : List[str]											=			logging.get_logger(__name__)


__lowerCamelCase : List[Any]											=			"https://openaipublic.azureedge.net/jukebox/models/"
__lowerCamelCase : Tuple											=			{
    "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_    (	lowerCAmelCase_							):

				"""simple docstring"""






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

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

				if "prime_prior" in key:
								lowercase         = 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         = 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         = 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_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = {}
				import re

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

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

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

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

								elif re_encoder_block_proj_out.fullmatch(lowerCAmelCase_							):
												lowercase         = re_encoder_block_proj_out.match(lowerCAmelCase_							)
												lowercase         = regex_match.groups()
												lowercase         = f'encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'
												lowercase         = re_encoder_block_proj_out.sub(lowerCAmelCase_		,							lowerCAmelCase_							)

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

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

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

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

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

								# keep original key
								else:
												lowercase         = original_key

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

								lowercase         = original_key
								lowercase         = value

				return new_dict







@torch.no_grad()
def 							UpperCAmelCase_    (	lowerCAmelCase_=None		,							lowerCAmelCase_=None							):

				"""simple docstring"""






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

				lowercase         = JukeboxConfig.from_pretrained(lowerCAmelCase_							)
				lowercase         = JukeboxModel(lowerCAmelCase_							)

				lowercase         = []
				lowercase         = {}
				for i, dict_name in enumerate(lowerCAmelCase_							):
								lowercase         = torch.load(f'{pytorch_dump_folder_path}/{dict_name.split("/"							)[-1]}'							)["model"]

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

								lowercase         = "vqvae" if i == 0 else f'priors.{3 - i}'
								lowercase         = fix_jukebox_keys(lowerCAmelCase_		,							model.state_dict()		,							lowerCAmelCase_		,							lowerCAmelCase_							)
								weight_dict.append(lowerCAmelCase_							)

				lowercase         = 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__":
			__lowerCamelCase : int											=			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[str]											=			parser.parse_args()
			convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)




