


"""simple docstring"""
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


__SCREAMING_SNAKE_CASE							:    Any		=    logging.get_logger(__name__)  # pylint: disable=invalid-name

__SCREAMING_SNAKE_CASE							:    str		=    '\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "A red cartoon frog, 4k"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to("cuda")\n\n        >>> init_image = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/frog.png"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save("red_frog.png")\n        ```\n'







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE=8				)    ->				List[str]:
							snake_case_           =		height // scale_factor**2
							if height % scale_factor**2 != 0:
														new_height += 1
							snake_case_           =		width // scale_factor**2
							if width % scale_factor**2 != 0:
														new_width += 1
							return new_height * scale_factor, new_width * scale_factor







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE=512				, _SCREAMING_SNAKE_CASE=512				)    ->				List[Any]:
							snake_case_           =		pil_image.resize((w, h)				, resample=Image.BICUBIC				, reducing_gap=1				)
							snake_case_           =		np.array(pil_image.convert("""RGB"""				)				)
							snake_case_           =		arr.astype(np.floataa				) / 127.5 - 1
							snake_case_           =		np.transpose(_SCREAMING_SNAKE_CASE				, [2, 0, 1]				)
							snake_case_           =		torch.from_numpy(_SCREAMING_SNAKE_CASE				).unsqueeze(0				)
							return image





class      __A     (snake_case__):





							'''simple docstring'''

							def __init__(				self							:      Tuple		,       UpperCAmelCase_							:      UNetaDConditionModel		,       UpperCAmelCase_							:      DDPMScheduler		,       UpperCAmelCase_							:      VQModel		,       )					->Tuple:



														"""simple docstring"""



														super().__init__()

														self.register_modules(
														    unet=UpperCAmelCase_		,       scheduler=UpperCAmelCase_		,       movq=UpperCAmelCase_		,       )
														snake_case_           =		2 ** (len(self.movq.config.block_out_channels       ) - 1)



							def 		lowerCAmelCase       (				self							:      int		,       UpperCAmelCase_							:      Optional[int]		,       UpperCAmelCase_							:      Tuple		,       UpperCAmelCase_							:      Dict       )					->Optional[int]:



														"""simple docstring"""



														snake_case_           =		min(int(num_inference_steps * strength       )		,       UpperCAmelCase_       )

														snake_case_           =		max(num_inference_steps - init_timestep		,       0       )
														snake_case_           =		self.scheduler.timesteps[t_start:]

														return timesteps, num_inference_steps - t_start



							def 		lowerCAmelCase       (				self							:      Optional[int]		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      Union[str, Any]		,       UpperCAmelCase_							:      Optional[Any]		,       UpperCAmelCase_							:      Optional[Any]		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      Any=None       )					->Optional[Any]:



														"""simple docstring"""



														if not isinstance(UpperCAmelCase_		,       (torch.Tensor, PIL.Image.Image, list)       ):
																					raise ValueError(
																					    F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(UpperCAmelCase_       )}"""       )

														snake_case_           =		image.to(device=UpperCAmelCase_		,       dtype=UpperCAmelCase_       )

														snake_case_           =		batch_size * num_images_per_prompt

														if image.shape[1] == 4:
																					snake_case_           =		image

														else:
																					if isinstance(UpperCAmelCase_		,       UpperCAmelCase_       ) and len(UpperCAmelCase_       ) != batch_size:
																												raise ValueError(
																												    F"""You have passed a list of generators of length {len(UpperCAmelCase_       )}, but requested an effective batch"""
																												    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""       )

																					elif isinstance(UpperCAmelCase_		,       UpperCAmelCase_       ):
																												snake_case_           =		[
																												    self.movq.encode(image[i : i + 1]       ).latent_dist.sample(generator[i]       ) for i in range(UpperCAmelCase_       )
																												]
																												snake_case_           =		torch.cat(UpperCAmelCase_		,       dim=0       )
																					else:
																												snake_case_           =		self.movq.encode(UpperCAmelCase_       ).latent_dist.sample(UpperCAmelCase_       )

																					snake_case_           =		self.movq.config.scaling_factor * init_latents

														snake_case_           =		torch.cat([init_latents]		,       dim=0       )

														snake_case_           =		init_latents.shape
														snake_case_           =		randn_tensor(UpperCAmelCase_		,       generator=UpperCAmelCase_		,       device=UpperCAmelCase_		,       dtype=UpperCAmelCase_       )

														# get latents
														snake_case_           =		self.scheduler.add_noise(UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_       )

														snake_case_           =		init_latents

														return latents



							def 		lowerCAmelCase       (				self							:      Dict		,       UpperCAmelCase_							:      List[Any]=0       )					->Any:



														"""simple docstring"""



														if is_accelerate_available():
																					from accelerate import cpu_offload
														else:
																					raise ImportError("""Please install accelerate via `pip install accelerate`"""       )

														snake_case_           =		torch.device(F"""cuda:{gpu_id}"""       )

														snake_case_           =		[
														    self.unet,
														    self.movq,
														]
														for cpu_offloaded_model in models:
																					if cpu_offloaded_model is not None:
																												cpu_offload(UpperCAmelCase_		,       UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      List[Any]		,       UpperCAmelCase_							:      Dict=0       )					->int:



														"""simple docstring"""



														if is_accelerate_available() and is_accelerate_version(""">="""		,       """0.17.0.dev0"""       ):
																					from accelerate import cpu_offload_with_hook
														else:
																					raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""       )

														snake_case_           =		torch.device(F"""cuda:{gpu_id}"""       )

														if self.device.type != "cpu":
																					self.to("""cpu"""		,       silence_dtype_warnings=UpperCAmelCase_       )
																					torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

														snake_case_           =		None
														for cpu_offloaded_model in [self.unet, self.movq]:
																					snake_case_       ,						snake_case_           =		cpu_offload_with_hook(UpperCAmelCase_		,       UpperCAmelCase_		,       prev_module_hook=UpperCAmelCase_       )

														# We'll offload the last model manually.
														snake_case_           =		hook



							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->str:



														"""simple docstring"""



														if not hasattr(self.unet		,       """_hf_hook"""       ):
																					return self.device
														for module in self.unet.modules():
																					if (
																					    hasattr(UpperCAmelCase_		,       """_hf_hook"""       )
																					    and hasattr(module._hf_hook		,       """execution_device"""       )
																					    and module._hf_hook.execution_device is not None
																					):
																												return torch.device(module._hf_hook.execution_device       )
														return self.device



							@torch.no_grad()
							@replace_example_docstring(UpperCAmelCase_       )
							def __call__(				self							:      str		,       UpperCAmelCase_							:      Union[torch.FloatTensor, List[torch.FloatTensor]]		,       UpperCAmelCase_							:      Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]		,       UpperCAmelCase_							:      Union[torch.FloatTensor, List[torch.FloatTensor]]		,       UpperCAmelCase_							:      int = 512		,       UpperCAmelCase_							:      int = 512		,       UpperCAmelCase_							:      int = 100		,       UpperCAmelCase_							:      float = 4.0		,       UpperCAmelCase_							:      float = 0.3		,       UpperCAmelCase_							:      int = 1		,       UpperCAmelCase_							:      Optional[Union[torch.Generator, List[torch.Generator]]] = None		,       UpperCAmelCase_							:      Optional[str] = "pil"		,       UpperCAmelCase_							:      bool = True		,       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		self._execution_device

														snake_case_           =		guidance_scale > 1.0

														if isinstance(UpperCAmelCase_		,       UpperCAmelCase_       ):
																					snake_case_           =		torch.cat(UpperCAmelCase_		,       dim=0       )
														snake_case_           =		image_embeds.shape[0]
														if isinstance(UpperCAmelCase_		,       UpperCAmelCase_       ):
																					snake_case_           =		torch.cat(UpperCAmelCase_		,       dim=0       )

														if do_classifier_free_guidance:
																					snake_case_           =		image_embeds.repeat_interleave(UpperCAmelCase_		,       dim=0       )
																					snake_case_           =		negative_image_embeds.repeat_interleave(UpperCAmelCase_		,       dim=0       )

														snake_case_           =		torch.cat([negative_image_embeds, image_embeds]		,       dim=0       ).to(dtype=self.unet.dtype		,       device=UpperCAmelCase_       )

														if not isinstance(UpperCAmelCase_		,       UpperCAmelCase_       ):
																					snake_case_           =		[image]
														if not all(isinstance(UpperCAmelCase_		,       (PIL.Image.Image, torch.Tensor)       ) for i in image       ):
																					raise ValueError(
																					    F"""Input is in incorrect format: {[type(UpperCAmelCase_       ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""       )

														snake_case_           =		torch.cat([prepare_image(UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_       ) for i in image]		,       dim=0       )
														snake_case_           =		image.to(dtype=image_embeds.dtype		,       device=UpperCAmelCase_       )

														snake_case_           =		self.movq.encode(UpperCAmelCase_       )["""latents"""]
														snake_case_           =		latents.repeat_interleave(UpperCAmelCase_		,       dim=0       )
														self.scheduler.set_timesteps(UpperCAmelCase_		,       device=UpperCAmelCase_       )
														snake_case_       ,						snake_case_           =		self.get_timesteps(UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_       )
														snake_case_           =		timesteps[:1].repeat(batch_size * num_images_per_prompt       )
														snake_case_       ,						snake_case_           =		downscale_height_and_width(UpperCAmelCase_		,       UpperCAmelCase_		,       self.movq_scale_factor       )
														snake_case_           =		self.prepare_latents(
														    UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       image_embeds.dtype		,       UpperCAmelCase_		,       UpperCAmelCase_       )
														for i, t in enumerate(self.progress_bar(UpperCAmelCase_       )       ):
																					# expand the latents if we are doing classifier free guidance
																					snake_case_           =		torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents

																					snake_case_           =		{"""image_embeds""": image_embeds}
																					snake_case_           =		self.unet(
																					    sample=UpperCAmelCase_		,       timestep=UpperCAmelCase_		,       encoder_hidden_states=UpperCAmelCase_		,       added_cond_kwargs=UpperCAmelCase_		,       return_dict=UpperCAmelCase_		,       )[0]

																					if do_classifier_free_guidance:
																												snake_case_       ,						snake_case_           =		noise_pred.split(latents.shape[1]		,       dim=1       )
																												snake_case_       ,						snake_case_           =		noise_pred.chunk(2       )
																												snake_case_       ,						snake_case_           =		variance_pred.chunk(2       )
																												snake_case_           =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																												snake_case_           =		torch.cat([noise_pred, variance_pred_text]		,       dim=1       )

																					if not (
																					    hasattr(self.scheduler.config		,       """variance_type"""       )
																					    and self.scheduler.config.variance_type in ["learned", "learned_range"]
																					):
																												snake_case_       ,						snake_case_           =		noise_pred.split(latents.shape[1]		,       dim=1       )

																					# compute the previous noisy sample x_t -> x_t-1
																					snake_case_           =		self.scheduler.step(
																					    UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       generator=UpperCAmelCase_		,       )[0]

														# post-processing
														snake_case_           =		self.movq.decode(UpperCAmelCase_		,       force_not_quantize=UpperCAmelCase_       )["""sample"""]

														if output_type not in ["pt", "np", "pil"]:
																					raise ValueError(F"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""       )

														if output_type in ["np", "pil"]:
																					snake_case_           =		image * 0.5 + 0.5
																					snake_case_           =		image.clamp(0		,       1       )
																					snake_case_           =		image.cpu().permute(0		,       2		,       3		,       1       ).float().numpy()

														if output_type == "pil":
																					snake_case_           =		self.numpy_to_pil(UpperCAmelCase_       )

														if not return_dict:
																					return (image,)

														return ImagePipelineOutput(images=UpperCAmelCase_       )







"""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()
__SCREAMING_SNAKE_CASE							:    str		=    logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE							:    Optional[int]		=    'https://openaipublic.azureedge.net/jukebox/models/'
__SCREAMING_SNAKE_CASE							:    List[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				(      _SCREAMING_SNAKE_CASE				)    ->				int:
							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 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Union[str, Any]:
							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(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_encoder_block_conv_in.match(_SCREAMING_SNAKE_CASE				)
																					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(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														elif re_encoder_block_resnet.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_encoder_block_resnet.match(_SCREAMING_SNAKE_CASE				)
																					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(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														elif re_encoder_block_proj_out.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_encoder_block_proj_out.match(_SCREAMING_SNAKE_CASE				)
																					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(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														# rename vqvae.decoder keys
														elif re_decoder_block_conv_out.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_decoder_block_conv_out.match(_SCREAMING_SNAKE_CASE				)
																					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(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														elif re_decoder_block_resnet.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_decoder_block_resnet.match(_SCREAMING_SNAKE_CASE				)
																					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(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														elif re_decoder_block_proj_in.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_decoder_block_proj_in.match(_SCREAMING_SNAKE_CASE				)
																					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(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

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

														elif re_prior_cond_resnet.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_prior_cond_resnet.match(_SCREAMING_SNAKE_CASE				)
																					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(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														elif re_prior_cond_proj_in.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_prior_cond_proj_in.match(_SCREAMING_SNAKE_CASE				)
																					snake_case_           =		regex_match.groups()
																					snake_case_           =		f"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
																					snake_case_           =		re_prior_cond_proj_in.sub(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														# keep original key
														else:
																					snake_case_           =		original_key

														snake_case_           =		replace_key(_SCREAMING_SNAKE_CASE				)

														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 						_a				(      _SCREAMING_SNAKE_CASE=None				, _SCREAMING_SNAKE_CASE=None				)    ->				Optional[int]:
							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=_SCREAMING_SNAKE_CASE				)
																					os.makedirs(f"""{pytorch_dump_folder_path}/"""				, exist_ok=_SCREAMING_SNAKE_CASE				)
																					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(_SCREAMING_SNAKE_CASE				)
							snake_case_           =		JukeboxModel(_SCREAMING_SNAKE_CASE				)

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

							snake_case_           =		weight_dict.pop(0				)
							model.vqvae.load_state_dict(_SCREAMING_SNAKE_CASE				)
							for i in range(len(_SCREAMING_SNAKE_CASE				)				):
														model.priors[i].load_state_dict(weight_dict[2 - i]				)

							Path(_SCREAMING_SNAKE_CASE				).mkdir(exist_ok=_SCREAMING_SNAKE_CASE				)
							with open(f"""{pytorch_dump_folder_path}/mapping.json"""				, """w"""				) as txtfile:
														json.dump(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

							print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""				)
							model.save_pretrained(_SCREAMING_SNAKE_CASE				)

							return weight_dict


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:    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.',
				)
				__SCREAMING_SNAKE_CASE							:    str		=    parser.parse_args()
				convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)



