





'''simple docstring'''

from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
			import PIL

if is_torch_available():
			import torch


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

class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	str 			=		['''pixel_values''']




		def __init__(self      :	Dict							,			A__      :	bool = True							,			A__      :	Dict[str, int] = None							,			A__      :	PILImageResampling = PILImageResampling.BILINEAR							,			A__      :	bool = True							,			A__      :	Union[int, float] = 1 / 2_5_5							,			A__      :	bool = True							,			A__      :	Dict[str, int] = None							,			A__      :	bool = True							,			**A__      :	List[str]							,			)					-> None:
						super().__init__(**A__				)
						lowercase         = size if size is not None else {"shortest_edge": 2_2_4}
						lowercase         = get_size_dict(A__							,			default_to_square=A__				)
						lowercase         = crop_size if crop_size is not None else {"height": 2_5_6, "width": 2_5_6}
						lowercase         = get_size_dict(A__							,			param_name="crop_size"				)

						lowercase         = do_resize
						lowercase         = size
						lowercase         = resample
						lowercase         = do_rescale
						lowercase         = rescale_factor
						lowercase         = do_center_crop
						lowercase         = crop_size
						lowercase         = do_flip_channel_order




		def 				UpperCAmelCase__   (self      :	int							,			A__      :	np.ndarray							,			A__      :	Dict[str, int]							,			A__      :	PILImageResampling = PIL.Image.BILINEAR							,			A__      :	Optional[Union[str, ChannelDimension]] = None							,			**A__      :	Optional[Any]							,			)					-> np.ndarray:
						lowercase         = get_size_dict(A__							,			default_to_square=A__				)
						if "shortest_edge" not in size:
										raise ValueError(f'The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'				)
						lowercase         = get_resize_output_image_size(A__							,			size=size["shortest_edge"]							,			default_to_square=A__				)
						return resize(A__							,			size=A__							,			resample=A__							,			data_format=A__							,			**A__				)




		def 				UpperCAmelCase__   (self      :	Optional[int]							,			A__      :	np.ndarray							,			A__      :	Dict[str, int]							,			A__      :	Optional[Union[str, ChannelDimension]] = None							,			**A__      :	List[str]							,			)					-> np.ndarray:
						lowercase         = get_size_dict(A__				)
						if "height" not in size or "width" not in size:
										raise ValueError(f'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'				)
						return center_crop(A__							,			size=(size["height"], size["width"])							,			data_format=A__							,			**A__				)




		def 				UpperCAmelCase__   (self      :	Dict							,			A__      :	np.ndarray							,			A__      :	Union[int, float]							,			A__      :	Optional[Union[str, ChannelDimension]] = None							,			**A__      :	List[Any]							,			)					-> Tuple:
						return rescale(A__							,			scale=A__							,			data_format=A__							,			**A__				)




		def 				UpperCAmelCase__   (self      :	str							,			A__      :	np.ndarray							,			A__      :	Optional[Union[str, ChannelDimension]] = None				)					-> np.ndarray:
						return flip_channel_order(A__							,			data_format=A__				)




		def 				UpperCAmelCase__   (self      :	Optional[Any]							,			A__      :	ImageInput							,			A__      :	bool = None							,			A__      :	Dict[str, int] = None							,			A__      :	PILImageResampling = None							,			A__      :	bool = None							,			A__      :	float = None							,			A__      :	bool = None							,			A__      :	Dict[str, int] = None							,			A__      :	bool = None							,			A__      :	Optional[Union[str, TensorType]] = None							,			A__      :	ChannelDimension = ChannelDimension.FIRST							,			**A__      :	Any							,			)					-> PIL.Image.Image:
						lowercase         = do_resize if do_resize is not None else self.do_resize
						lowercase         = resample if resample is not None else self.resample
						lowercase         = do_rescale if do_rescale is not None else self.do_rescale
						lowercase         = rescale_factor if rescale_factor is not None else self.rescale_factor
						lowercase         = do_center_crop if do_center_crop is not None else self.do_center_crop
						lowercase         = (
						    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
						)

						lowercase         = size if size is not None else self.size
						lowercase         = get_size_dict(A__							,			default_to_square=A__				)
						lowercase         = crop_size if crop_size is not None else self.crop_size
						lowercase         = get_size_dict(A__							,			param_name="crop_size"				)

						lowercase         = make_list_of_images(A__				)

						if not valid_images(A__				):
										raise ValueError(
										    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
										    "torch.Tensor, tf.Tensor or jax.ndarray."				)

						if do_resize and size is None:
										raise ValueError("Size must be specified if do_resize is True."				)

						if do_rescale and rescale_factor is None:
										raise ValueError("Rescale factor must be specified if do_rescale is True."				)

						if do_center_crop and crop_size is None:
										raise ValueError("Crop size must be specified if do_center_crop is True."				)

						# All transformations expect numpy arrays.
						lowercase         = [to_numpy_array(A__				) for image in images]

						if do_resize:
										lowercase         = [self.resize(image=A__							,			size=A__							,			resample=A__				) for image in images]

						if do_center_crop:
										lowercase         = [self.center_crop(image=A__							,			size=A__				) for image in images]

						if do_rescale:
										lowercase         = [self.rescale(image=A__							,			scale=A__				) for image in images]

						# the pretrained checkpoints assume images are BGR, not RGB
						if do_flip_channel_order:
										lowercase         = [self.flip_channel_order(image=A__				) for image in images]

						lowercase         = [to_channel_dimension_format(A__							,			A__				) for image in images]

						lowercase         = {"pixel_values": images}
						return BatchFeature(data=A__							,			tensor_type=A__				)





		def 				UpperCAmelCase__   (self      :	List[str]							,			A__      :	str							,			A__      :	List[Tuple] = None				)					-> Union[str, Any]:
						lowercase         = outputs.logits

						# Resize logits and compute semantic segmentation maps
						if target_sizes is not None:
										if len(A__				) != len(A__				):
														raise ValueError(
														    "Make sure that you pass in as many target sizes as the batch dimension of the logits"				)

										if is_torch_tensor(A__				):
														lowercase         = target_sizes.numpy()

										lowercase         = []

										for idx in range(len(A__				)				):
														lowercase         = torch.nn.functional.interpolate(
														    logits[idx].unsqueeze(dim=0				)							,			size=target_sizes[idx]							,			mode="bilinear"							,			align_corners=A__				)
														lowercase         = resized_logits[0].argmax(dim=0				)
														semantic_segmentation.append(A__				)
						else:
										lowercase         = logits.argmax(dim=1				)
										lowercase         = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]				)]

						return semantic_segmentation











'''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)




