





'''simple docstring'''

import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__lowerCamelCase : int											=			logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-classification/requirements.txt")

__lowerCamelCase : str											=			list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
__lowerCamelCase : int											=			tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				with open(lowerCAmelCase_		,							"rb"							) as f:
								lowercase         = Image.open(lowerCAmelCase_							)
								return im.convert("RGB"							)

@dataclass
class       UpperCAmelCase :
		UpperCAmelCase					:	Optional[str] 			=		field(
		    default=_lowercase			,       metadata={
		        '''help''': '''Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'''
		    }			,       )
		UpperCAmelCase					:	Optional[str] 			=		field(
		    default=_lowercase			,       metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}  )
		UpperCAmelCase					:	Optional[str] 			=		field(default=_lowercase			,       metadata={'''help''': '''A folder containing the training data.'''}  )
		UpperCAmelCase					:	Optional[str] 			=		field(default=_lowercase			,       metadata={'''help''': '''A folder containing the validation data.'''}  )
		UpperCAmelCase					:	Optional[float] 			=		field(
		    default=0.15			,       metadata={'''help''': '''Percent to split off of train for validation.'''}  )
		UpperCAmelCase					:	Optional[int] 			=		field(
		    default=_lowercase			,       metadata={
		        '''help''': (
		            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
		            '''value if set.'''
		        )
		    }			,       )
		UpperCAmelCase					:	Optional[int] 			=		field(
		    default=_lowercase			,       metadata={
		        '''help''': (
		            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
		            '''value if set.'''
		        )
		    }			,       )




		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> int:
						if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
										raise ValueError(
										    "You must specify either a dataset name from the hub or a train and/or validation directory."				)




@dataclass
class       UpperCAmelCase :
		UpperCAmelCase					:	str 			=		field(
		    default='''google/vit-base-patch16-224-in21k'''			,       metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}			,       )
		UpperCAmelCase					:	Optional[str] 			=		field(
		    default=_lowercase			,       metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(_lowercase  )}			,       )
		UpperCAmelCase					:	Optional[str] 			=		field(
		    default=_lowercase			,       metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}  )
		UpperCAmelCase					:	Optional[str] 			=		field(
		    default=_lowercase			,       metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''}  )
		UpperCAmelCase					:	str 			=		field(
		    default='''main'''			,       metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}			,       )
		UpperCAmelCase					:	str 			=		field(default=_lowercase			,       metadata={'''help''': '''Name or path of preprocessor config.'''}  )
		UpperCAmelCase					:	bool 			=		field(
		    default=_lowercase			,       metadata={
		        '''help''': (
		            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
		            '''with private models).'''
		        )
		    }			,       )
		UpperCAmelCase					:	bool 			=		field(
		    default=_lowercase			,       metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''}			,       )







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = torch.stack([example["pixel_values"] for example in examples]							)
				lowercase         = torch.tensor([example["labels"] for example in examples]							)
				return {"pixel_values": pixel_values, "labels": labels}







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






				lowercase         = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)							)
				if len(sys.argv							) == 2 and sys.argv[1].endswith(".json"							):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								lowercase   ,							lowercase   ,							lowercase         = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
				else:
								lowercase   ,							lowercase   ,							lowercase         = parser.parse_args_into_dataclasses()

				# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
				# information sent is the one passed as arguments along with your Python/PyTorch versions.
				send_example_telemetry("run_image_classification"		,							lowerCAmelCase_		,							lowerCAmelCase_							)

				# Setup logging
				logging.basicConfig(
				    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"		,							datefmt="%m/%d/%Y %H:%M:%S"		,							handlers=[logging.StreamHandler(sys.stdout							)]		,							)

				if training_args.should_log:
								# The default of training_args.log_level is passive, so we set log level at info here to have that default.
								transformers.utils.logging.set_verbosity_info()

				lowercase         = training_args.get_process_log_level()
				logger.setLevel(lowerCAmelCase_							)
				transformers.utils.logging.set_verbosity(lowerCAmelCase_							)
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()

				# Log on each process the small summary:
				logger.warning(
				    f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
				    + f'distributed training: {bool(training_args.local_rank != -1							)}, 16-bits training: {training_args.fpaa}'							)
				logger.info(f'Training/evaluation parameters {training_args}'							)

				# Detecting last checkpoint.
				lowercase         = None
				if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
								lowercase         = get_last_checkpoint(training_args.output_dir							)
								if last_checkpoint is None and len(os.listdir(training_args.output_dir							)							) > 0:
												raise ValueError(
												    f'Output directory ({training_args.output_dir}) already exists and is not empty. '
												    "Use --overwrite_output_dir to overcome."							)
								elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
												logger.info(
												    f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
												    "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."							)

    # Set seed before initializing model.
				set_seed(training_args.seed							)

				# Initialize our dataset and prepare it for the 'image-classification' task.
				if data_args.dataset_name is not None:
								lowercase         = load_dataset(
								    data_args.dataset_name		,							data_args.dataset_config_name		,							cache_dir=model_args.cache_dir		,							task="image-classification"		,							use_auth_token=True if model_args.use_auth_token else None		,							)
				else:
								lowercase         = {}
								if data_args.train_dir is not None:
												lowercase         = os.path.join(data_args.train_dir		,							"**"							)
								if data_args.validation_dir is not None:
												lowercase         = os.path.join(data_args.validation_dir		,							"**"							)
								lowercase         = load_dataset(
								    "imagefolder"		,							data_files=lowerCAmelCase_		,							cache_dir=model_args.cache_dir		,							task="image-classification"		,							)

				# If we don't have a validation split, split off a percentage of train as validation.
				lowercase         = None if "validation" in dataset.keys() else data_args.train_val_split
				if isinstance(data_args.train_val_split		,							lowerCAmelCase_							) and data_args.train_val_split > 0.0:
								lowercase         = dataset["train"].train_test_split(data_args.train_val_split							)
								lowercase         = split["train"]
								lowercase         = split["test"]

				# Prepare label mappings.
				# We'll include these in the model's config to get human readable labels in the Inference API.
				lowercase         = dataset["train"].features["labels"].names
				lowercase   ,							lowercase         = {}, {}
				for i, label in enumerate(lowerCAmelCase_							):
								lowercase         = str(lowerCAmelCase_							)
								lowercase         = label

				# Load the accuracy metric from the datasets package
				lowercase         = evaluate.load("accuracy"							)

				# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
				# predictions and label_ids field) and has to return a dictionary string to float.
				def compute_metrics(lowerCAmelCase_							):
								return metric.compute(predictions=np.argmax(p.predictions		,							axis=1							)		,							references=p.label_ids							)

				lowercase         = AutoConfig.from_pretrained(
				    model_args.config_name or model_args.model_name_or_path		,							num_labels=len(lowerCAmelCase_							)		,							labelaid=lowerCAmelCase_		,							idalabel=lowerCAmelCase_		,							finetuning_task="image-classification"		,							cache_dir=model_args.cache_dir		,							revision=model_args.model_revision		,							use_auth_token=True if model_args.use_auth_token else None		,							)
				lowercase         = AutoModelForImageClassification.from_pretrained(
				    model_args.model_name_or_path		,							from_tf=bool(".ckpt" in model_args.model_name_or_path							)		,							config=lowerCAmelCase_		,							cache_dir=model_args.cache_dir		,							revision=model_args.model_revision		,							use_auth_token=True if model_args.use_auth_token else None		,							ignore_mismatched_sizes=model_args.ignore_mismatched_sizes		,							)
				lowercase         = AutoImageProcessor.from_pretrained(
				    model_args.image_processor_name or model_args.model_name_or_path		,							cache_dir=model_args.cache_dir		,							revision=model_args.model_revision		,							use_auth_token=True if model_args.use_auth_token else None		,							)

				# Define torchvision transforms to be applied to each image.
				if "shortest_edge" in image_processor.size:
								lowercase         = image_processor.size["shortest_edge"]
				else:
								lowercase         = (image_processor.size["height"], image_processor.size["width"])
				lowercase         = Normalize(mean=image_processor.image_mean		,							std=image_processor.image_std							)
				lowercase         = Compose(
				    [
				        RandomResizedCrop(lowerCAmelCase_							),
				        RandomHorizontalFlip(),
				        ToTensor(),
				        normalize,
				    ]							)
				lowercase         = Compose(
				    [
				        Resize(lowerCAmelCase_							),
				        CenterCrop(lowerCAmelCase_							),
				        ToTensor(),
				        normalize,
				    ]							)

				def train_transforms(lowerCAmelCase_							):
								lowercase         = [
								    _train_transforms(pil_img.convert("RGB"							)							) for pil_img in example_batch["image"]
								]
								return example_batch

				def val_transforms(lowerCAmelCase_							):
								lowercase         = [_val_transforms(pil_img.convert("RGB"							)							) for pil_img in example_batch["image"]]
								return example_batch

				if training_args.do_train:
								if "train" not in dataset:
												raise ValueError("--do_train requires a train dataset"							)
								if data_args.max_train_samples is not None:
												lowercase         = (
												    dataset["train"].shuffle(seed=training_args.seed							).select(range(data_args.max_train_samples							)							)
												)
								# Set the training transforms
								dataset["train"].set_transform(lowerCAmelCase_							)

				if training_args.do_eval:
								if "validation" not in dataset:
												raise ValueError("--do_eval requires a validation dataset"							)
								if data_args.max_eval_samples is not None:
												lowercase         = (
												    dataset["validation"].shuffle(seed=training_args.seed							).select(range(data_args.max_eval_samples							)							)
												)
								# Set the validation transforms
								dataset["validation"].set_transform(lowerCAmelCase_							)

				# Initalize our trainer
				lowercase         = Trainer(
				    model=lowerCAmelCase_		,							args=lowerCAmelCase_		,							train_dataset=dataset["train"] if training_args.do_train else None		,							eval_dataset=dataset["validation"] if training_args.do_eval else None		,							compute_metrics=lowerCAmelCase_		,							tokenizer=lowerCAmelCase_		,							data_collator=lowerCAmelCase_		,							)

				# Training
				if training_args.do_train:
								lowercase         = None
								if training_args.resume_from_checkpoint is not None:
												lowercase         = training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												lowercase         = last_checkpoint
								lowercase         = trainer.train(resume_from_checkpoint=lowerCAmelCase_							)
								trainer.save_model()
								trainer.log_metrics("train"		,							train_result.metrics							)
								trainer.save_metrics("train"		,							train_result.metrics							)
								trainer.save_state()

				# Evaluation
				if training_args.do_eval:
								lowercase         = trainer.evaluate()
								trainer.log_metrics("eval"		,							lowerCAmelCase_							)
								trainer.save_metrics("eval"		,							lowerCAmelCase_							)

				# Write model card and (optionally) push to hub
				lowercase         = {
				    "finetuned_from": model_args.model_name_or_path,
				    "tasks": "image-classification",
				    "dataset": data_args.dataset_name,
				    "tags": ["image-classification", "vision"],
				}
				if training_args.push_to_hub:
								trainer.push_to_hub(**lowerCAmelCase_							)
				else:
								trainer.create_model_card(**lowerCAmelCase_							)


if __name__ == "__main__":
			main()











'''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)




