
'''simple docstring'''




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

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
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
lowercase     :							List[Any]       						=     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-pretraining/requirements.txt')

lowercase     :							Optional[int]       						=     list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
lowercase     :							List[Any]       						=     tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





@dataclass
class 					A							:
     __magic_name__          =						field(
         default='''cifar10'''       ,     metadata={'''help''': '''Name of a dataset from the datasets package'''}		)
     __magic_name__          =						field(
         default=__snake_case       ,     metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}		)
     __magic_name__          =						field(
         default=__snake_case       ,     metadata={'''help''': '''The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'''}       ,     )
     __magic_name__          =						field(default=__snake_case       ,     metadata={'''help''': '''A folder containing the training data.'''}		)
     __magic_name__          =						field(default=__snake_case       ,     metadata={'''help''': '''A folder containing the validation data.'''}		)
     __magic_name__          =						field(
         default=0.15       ,     metadata={'''help''': '''Percent to split off of train for validation.'''}		)
     __magic_name__          =						field(default=32       ,     metadata={'''help''': '''The size of the square patches to use for masking.'''}		)
     __magic_name__          =						field(
         default=0.6       ,     metadata={'''help''': '''Percentage of patches to mask.'''}       ,     )
     __magic_name__          =						field(
         default=__snake_case       ,     metadata={
             '''help''': (
                 '''For debugging purposes or quicker training, truncate the number of training examples to this '''
                 '''value if set.'''
             )
         }       ,     )
     __magic_name__          =						field(
         default=__snake_case       ,     metadata={
             '''help''': (
                 '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
                 '''value if set.'''
             )
         }       ,     )





     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






         A			:  Dict   =		{}
         if self.train_dir is not None:
             A			:  List[str]   =		self.train_dir
         if self.validation_dir is not None:
             A			:  Optional[int]   =		self.validation_dir
         A			:  Dict   =		data_files if data_files else None





@dataclass
class 					A							:
     __magic_name__          =						field(
         default=__snake_case       ,     metadata={
             '''help''': (
                 '''The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a '''
                 '''checkpoint identifier on the hub. '''
                 '''Don\'t set if you want to train a model from scratch.'''
             )
         }       ,     )
     __magic_name__          =						field(
         default=__snake_case       ,     metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(__snake_case		)}       ,     )
     __magic_name__          =						field(
         default=__snake_case       ,     metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}		)
     __magic_name__          =						field(
         default=__snake_case       ,     metadata={
             '''help''': (
                 '''Override some existing default config settings when a model is trained from scratch. Example: '''
                 '''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
             )
         }       ,     )
     __magic_name__          =						field(
         default=__snake_case       ,     metadata={'''help''': '''Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'''}       ,     )
     __magic_name__          =						field(
         default='''main'''       ,     metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}       ,     )
     __magic_name__          =						field(default=__snake_case       ,     metadata={'''help''': '''Name or path of preprocessor config.'''}		)
     __magic_name__          =						field(
         default=__snake_case       ,     metadata={
             '''help''': (
                 '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
                 '''with private models).'''
             )
         }       ,     )
     __magic_name__          =						field(
         default=__snake_case       ,     metadata={
             '''help''': (
                 '''The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'''
             )
         }       ,     )
     __magic_name__          =						field(
         default=__snake_case       ,     metadata={
             '''help''': (
                 '''The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'''
             )
         }       ,     )
     __magic_name__          =						field(
         default=__snake_case       ,     metadata={'''help''': '''Stride to use for the encoder.'''}       ,     )





class 					A							:





     def __init__(				self		,     SCREAMING_SNAKE_CASE=192		,     SCREAMING_SNAKE_CASE=32		,     SCREAMING_SNAKE_CASE=4		,     SCREAMING_SNAKE_CASE=0.6      )					->							List[Any]:






         """simple docstring"""






         A			:  List[str]   =		input_size
         A			:  Any   =		mask_patch_size
         A			:  Dict   =		model_patch_size
         A			:  Dict   =		mask_ratio

         if self.input_size % self.mask_patch_size != 0:
             raise ValueError('''Input size must be divisible by mask patch size'''      )
         if self.mask_patch_size % self.model_patch_size != 0:
             raise ValueError('''Mask patch size must be divisible by model patch size'''      )

         A			:  Any   =		self.input_size // self.mask_patch_size
         A			:  Union[str, Any]   =		self.mask_patch_size // self.model_patch_size

         A			:  int   =		self.rand_size**2
         A			:  List[str]   =		int(np.ceil(self.token_count * self.mask_ratio      )      )





     def __call__(				self      )					->							List[Any]:






         """simple docstring"""






         A			:  str   =		np.random.permutation(self.token_count      )[: self.mask_count]
         A			:  str   =		np.zeros(self.token_count		,     dtype=SCREAMING_SNAKE_CASE      )
         A			:  List[Any]   =		1

         A			:  Optional[int]   =		mask.reshape((self.rand_size, self.rand_size)      )
         A			:  List[Any]   =		mask.repeat(self.scale		,     axis=0      ).repeat(self.scale		,     axis=1      )

         return torch.tensor(mask.flatten()      )







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    A			:  Dict   =		torch.stack([example['''pixel_values'''] for example in examples]		)
    A			:  List[str]   =		torch.stack([example['''mask'''] for example in examples]		)
    return {"pixel_values": pixel_values, "bool_masked_pos": mask}







def 						lowerCAmelCase_							(  ):




    '''simple docstring'''



    A			:  Tuple   =		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.
        A,    A,    A			:  Optional[int]   =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
    else:
        A,    A,    A			:  str   =		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_mim'''						,							snake_case__						,							snake_case__		)

    # 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()

    A			:  int   =		training_args.get_process_log_level()
    logger.setLevel(snake_case__		)
    transformers.utils.logging.set_verbosity(snake_case__		)
    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.
    A			:  int   =		None
    if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
        A			:  List[Any]   =		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.'''		)

    # Initialize our dataset.
    A			:  Union[str, Any]   =		load_dataset(
        data_args.dataset_name						,							data_args.dataset_config_name						,							data_files=data_args.data_files						,							cache_dir=model_args.cache_dir						,							use_auth_token=True if model_args.use_auth_token else None						,							)

    # If we don't have a validation split, split off a percentage of train as validation.
    A			:  Optional[Any]   =		None if '''validation''' in ds.keys() else data_args.train_val_split
    if isinstance(data_args.train_val_split						,							snake_case__		) and data_args.train_val_split > 0.0:
        A			:  Union[str, Any]   =		ds['''train'''].train_test_split(data_args.train_val_split		)
        A			:  List[Any]   =		split['''train''']
        A			:  List[Any]   =		split['''test''']

    # Create config
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    A			:  str   =		{
        '''cache_dir''': model_args.cache_dir,
        '''revision''': model_args.model_revision,
        '''use_auth_token''': True if model_args.use_auth_token else None,
    }
    if model_args.config_name_or_path:
        A			:  Optional[Any]   =		AutoConfig.from_pretrained(model_args.config_name_or_path						,							**snake_case__		)
    elif model_args.model_name_or_path:
        A			:  Tuple   =		AutoConfig.from_pretrained(model_args.model_name_or_path						,							**snake_case__		)
    else:
        A			:  Dict   =		CONFIG_MAPPING[model_args.model_type]()
        logger.warning('''You are instantiating a new config instance from scratch.'''		)
        if model_args.config_overrides is not None:
            logger.info(F'Overriding config: {model_args.config_overrides}'		)
            config.update_from_string(model_args.config_overrides		)
            logger.info(F'New config: {config}'		)

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
    if hasattr(snake_case__						,							'''decoder_type'''		):
        A			:  Dict   =		'''simmim'''

    # adapt config
    A			:  Tuple   =		model_args.image_size if model_args.image_size is not None else config.image_size
    A			:  Optional[int]   =		model_args.patch_size if model_args.patch_size is not None else config.patch_size
    A			:  List[Any]   =		(
        model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
    )

    config.update(
        {
            '''image_size''': model_args.image_size,
            '''patch_size''': model_args.patch_size,
            '''encoder_stride''': model_args.encoder_stride,
        }		)

    # create image processor
    if model_args.image_processor_name:
        A			:  Optional[Any]   =		AutoImageProcessor.from_pretrained(model_args.image_processor_name						,							**snake_case__		)
    elif model_args.model_name_or_path:
        A			:  Optional[Any]   =		AutoImageProcessor.from_pretrained(model_args.model_name_or_path						,							**snake_case__		)
    else:
        A			:  List[str]   =		{
            conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
        }
        A			:  Optional[int]   =		IMAGE_PROCESSOR_TYPES[model_args.model_type]()

    # create model
    if model_args.model_name_or_path:
        A			:  List[str]   =		AutoModelForMaskedImageModeling.from_pretrained(
            model_args.model_name_or_path						,							from_tf=bool('''.ckpt''' in model_args.model_name_or_path		)						,							config=snake_case__						,							cache_dir=model_args.cache_dir						,							revision=model_args.model_revision						,							use_auth_token=True if model_args.use_auth_token else None						,							)
    else:
        logger.info('''Training new model from scratch'''		)
        A			:  Optional[Any]   =		AutoModelForMaskedImageModeling.from_config(snake_case__		)

    if training_args.do_train:
        A			:  Dict   =		ds['''train'''].column_names
    else:
        A			:  Dict   =		ds['''validation'''].column_names

    if data_args.image_column_name is not None:
        A			:  Any   =		data_args.image_column_name
    elif "image" in column_names:
        A			:  int   =		'''image'''
    elif "img" in column_names:
        A			:  int   =		'''img'''
    else:
        A			:  Optional[Any]   =		column_names[0]

    # transformations as done in original SimMIM paper
    # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
    A			:  Tuple   =		Compose(
        [
            Lambda(lambda snake_case__		: img.convert('''RGB'''		) if img.mode != "RGB" else img		),
            RandomResizedCrop(model_args.image_size						,							scale=(0.67, 1.0)						,							ratio=(3.0 / 4.0, 4.0 / 3.0)		),
            RandomHorizontalFlip(),
            ToTensor(),
            Normalize(mean=image_processor.image_mean						,							std=image_processor.image_std		),
        ]		)

    # create mask generator
    A			:  int   =		MaskGenerator(
        input_size=model_args.image_size						,							mask_patch_size=data_args.mask_patch_size						,							model_patch_size=model_args.patch_size						,							mask_ratio=data_args.mask_ratio						,							)

    def preprocess_images(snake_case__		):
        A			:  int   =		[transforms(snake_case__		) for image in examples[image_column_name]]
        A			:  Union[str, Any]   =		[mask_generator() for i in range(len(examples[image_column_name]		)		)]

        return examples

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

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

    # Initialize our trainer
    A			:  Dict   =		Trainer(
        model=snake_case__						,							args=snake_case__						,							train_dataset=ds['''train'''] if training_args.do_train else None						,							eval_dataset=ds['''validation'''] if training_args.do_eval else None						,							tokenizer=snake_case__						,							data_collator=snake_case__						,							)

    # Training
    if training_args.do_train:
        A			:  str   =		None
        if training_args.resume_from_checkpoint is not None:
            A			:  List[str]   =		training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            A			:  Optional[int]   =		last_checkpoint
        A			:  Optional[Any]   =		trainer.train(resume_from_checkpoint=snake_case__		)
        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:
        A			:  Tuple   =		trainer.evaluate()
        trainer.log_metrics('''eval'''						,							snake_case__		)
        trainer.save_metrics('''eval'''						,							snake_case__		)

    # Write model card and (optionally) push to hub
    A			:  Union[str, Any]   =		{
        '''finetuned_from''': model_args.model_name_or_path,
        '''tasks''': '''masked-image-modeling''',
        '''dataset''': data_args.dataset_name,
        '''tags''': ['''masked-image-modeling'''],
    }
    if training_args.push_to_hub:
        trainer.push_to_hub(**snake_case__		)
    else:
        trainer.create_model_card(**snake_case__		)


if __name__ == "__main__":
      main()






'''simple docstring'''




import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
lowercase     :							List[str]       						=     logging.get_logger('transformers.models.encodec')

lowercase     :							str       						=     {
    'quantizer.vq.layers.*._codebook.inited': 'quantizer.layers.*.codebook.inited',
    'quantizer.vq.layers.*._codebook.cluster_size': 'quantizer.layers.*.codebook.cluster_size',
    'quantizer.vq.layers.*._codebook.embed': 'quantizer.layers.*.codebook.embed',
    'quantizer.vq.layers.*._codebook.embed_avg': 'quantizer.layers.*.codebook.embed_avg',
}
lowercase     :							Optional[int]       						=     {
    'encoder.model.0.conv.conv': 'encoder.layers.0.conv',
    'encoder.model.1.block.1.conv.conv': 'encoder.layers.1.block.1.conv',
    'encoder.model.1.block.3.conv.conv': 'encoder.layers.1.block.3.conv',
    'encoder.model.1.shortcut.conv.conv': 'encoder.layers.1.shortcut.conv',
    'encoder.model.3.conv.conv': 'encoder.layers.3.conv',
    'encoder.model.4.block.1.conv.conv': 'encoder.layers.4.block.1.conv',
    'encoder.model.4.block.3.conv.conv': 'encoder.layers.4.block.3.conv',
    'encoder.model.4.shortcut.conv.conv': 'encoder.layers.4.shortcut.conv',
    'encoder.model.6.conv.conv': 'encoder.layers.6.conv',
    'encoder.model.7.block.1.conv.conv': 'encoder.layers.7.block.1.conv',
    'encoder.model.7.block.3.conv.conv': 'encoder.layers.7.block.3.conv',
    'encoder.model.7.shortcut.conv.conv': 'encoder.layers.7.shortcut.conv',
    'encoder.model.9.conv.conv': 'encoder.layers.9.conv',
    'encoder.model.10.block.1.conv.conv': 'encoder.layers.10.block.1.conv',
    'encoder.model.10.block.3.conv.conv': 'encoder.layers.10.block.3.conv',
    'encoder.model.10.shortcut.conv.conv': 'encoder.layers.10.shortcut.conv',
    'encoder.model.12.conv.conv': 'encoder.layers.12.conv',
    'encoder.model.13.lstm': 'encoder.layers.13.lstm',
    'encoder.model.15.conv.conv': 'encoder.layers.15.conv',
}
lowercase     :							int       						=     {
    'encoder.model.0.conv.norm': 'encoder.layers.0.norm',
    'encoder.model.1.block.1.conv.norm': 'encoder.layers.1.block.1.norm',
    'encoder.model.1.block.3.conv.norm': 'encoder.layers.1.block.3.norm',
    'encoder.model.1.shortcut.conv.norm': 'encoder.layers.1.shortcut.norm',
    'encoder.model.3.conv.norm': 'encoder.layers.3.norm',
    'encoder.model.4.block.1.conv.norm': 'encoder.layers.4.block.1.norm',
    'encoder.model.4.block.3.conv.norm': 'encoder.layers.4.block.3.norm',
    'encoder.model.4.shortcut.conv.norm': 'encoder.layers.4.shortcut.norm',
    'encoder.model.6.conv.norm': 'encoder.layers.6.norm',
    'encoder.model.7.block.1.conv.norm': 'encoder.layers.7.block.1.norm',
    'encoder.model.7.block.3.conv.norm': 'encoder.layers.7.block.3.norm',
    'encoder.model.7.shortcut.conv.norm': 'encoder.layers.7.shortcut.norm',
    'encoder.model.9.conv.norm': 'encoder.layers.9.norm',
    'encoder.model.10.block.1.conv.norm': 'encoder.layers.10.block.1.norm',
    'encoder.model.10.block.3.conv.norm': 'encoder.layers.10.block.3.norm',
    'encoder.model.10.shortcut.conv.norm': 'encoder.layers.10.shortcut.norm',
    'encoder.model.12.conv.norm': 'encoder.layers.12.norm',
    'encoder.model.15.conv.norm': 'encoder.layers.15.norm',
}
lowercase     :							int       						=     {
    'decoder.model.0.conv.conv': 'decoder.layers.0.conv',
    'decoder.model.1.lstm': 'decoder.layers.1.lstm',
    'decoder.model.3.convtr.convtr': 'decoder.layers.3.conv',
    'decoder.model.4.block.1.conv.conv': 'decoder.layers.4.block.1.conv',
    'decoder.model.4.block.3.conv.conv': 'decoder.layers.4.block.3.conv',
    'decoder.model.4.shortcut.conv.conv': 'decoder.layers.4.shortcut.conv',
    'decoder.model.6.convtr.convtr': 'decoder.layers.6.conv',
    'decoder.model.7.block.1.conv.conv': 'decoder.layers.7.block.1.conv',
    'decoder.model.7.block.3.conv.conv': 'decoder.layers.7.block.3.conv',
    'decoder.model.7.shortcut.conv.conv': 'decoder.layers.7.shortcut.conv',
    'decoder.model.9.convtr.convtr': 'decoder.layers.9.conv',
    'decoder.model.10.block.1.conv.conv': 'decoder.layers.10.block.1.conv',
    'decoder.model.10.block.3.conv.conv': 'decoder.layers.10.block.3.conv',
    'decoder.model.10.shortcut.conv.conv': 'decoder.layers.10.shortcut.conv',
    'decoder.model.12.convtr.convtr': 'decoder.layers.12.conv',
    'decoder.model.13.block.1.conv.conv': 'decoder.layers.13.block.1.conv',
    'decoder.model.13.block.3.conv.conv': 'decoder.layers.13.block.3.conv',
    'decoder.model.13.shortcut.conv.conv': 'decoder.layers.13.shortcut.conv',
    'decoder.model.15.conv.conv': 'decoder.layers.15.conv',
}
lowercase     :							List[Any]       						=     {
    'decoder.model.0.conv.norm': 'decoder.layers.0.norm',
    'decoder.model.3.convtr.norm': 'decoder.layers.3.norm',
    'decoder.model.4.block.1.conv.norm': 'decoder.layers.4.block.1.norm',
    'decoder.model.4.block.3.conv.norm': 'decoder.layers.4.block.3.norm',
    'decoder.model.4.shortcut.conv.norm': 'decoder.layers.4.shortcut.norm',
    'decoder.model.6.convtr.norm': 'decoder.layers.6.norm',
    'decoder.model.7.block.1.conv.norm': 'decoder.layers.7.block.1.norm',
    'decoder.model.7.block.3.conv.norm': 'decoder.layers.7.block.3.norm',
    'decoder.model.7.shortcut.conv.norm': 'decoder.layers.7.shortcut.norm',
    'decoder.model.9.convtr.norm': 'decoder.layers.9.norm',
    'decoder.model.10.block.1.conv.norm': 'decoder.layers.10.block.1.norm',
    'decoder.model.10.block.3.conv.norm': 'decoder.layers.10.block.3.norm',
    'decoder.model.10.shortcut.conv.norm': 'decoder.layers.10.shortcut.norm',
    'decoder.model.12.convtr.norm': 'decoder.layers.12.norm',
    'decoder.model.13.block.1.conv.norm': 'decoder.layers.13.block.1.norm',
    'decoder.model.13.block.3.conv.norm': 'decoder.layers.13.block.3.norm',
    'decoder.model.13.shortcut.conv.norm': 'decoder.layers.13.shortcut.norm',
    'decoder.model.15.conv.norm': 'decoder.layers.15.norm',
}
lowercase     :							Dict       						=     {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
lowercase     :							List[str]       						=     {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
lowercase     :							Union[str, Any]       						=     []
lowercase     :							int       						=     []







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    for attribute in key.split('''.'''		):
        A			:  int   =		getattr(snake_case__						,							snake_case__		)

    if weight_type is not None:
        A			:  int   =		getattr(snake_case__						,							snake_case__		).shape
    else:
        A			:  List[str]   =		hf_pointer.shape

    if hf_shape != value.shape:
        raise ValueError(
            F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
            F' {value.shape} for {full_name}'		)

    if weight_type == "weight":
        A			:  str   =		value
    elif weight_type == "weight_g":
        A			:  Union[str, Any]   =		value
    elif weight_type == "weight_v":
        A			:  List[Any]   =		value
    elif weight_type == "bias":
        A			:  Union[str, Any]   =		value
    elif weight_type == "running_mean":
        A			:  Union[str, Any]   =		value
    elif weight_type == "running_var":
        A			:  List[str]   =		value
    elif weight_type == "num_batches_tracked":
        A			:  List[Any]   =		value
    elif weight_type == "weight_ih_l0":
        A			:  Optional[int]   =		value
    elif weight_type == "weight_hh_l0":
        A			:  List[Any]   =		value
    elif weight_type == "bias_ih_l0":
        A			:  List[Any]   =		value
    elif weight_type == "bias_hh_l0":
        A			:  int   =		value
    elif weight_type == "weight_ih_l1":
        A			:  Tuple   =		value
    elif weight_type == "weight_hh_l1":
        A			:  List[str]   =		value
    elif weight_type == "bias_ih_l1":
        A			:  Any   =		value
    elif weight_type == "bias_hh_l1":
        A			:  Tuple   =		value
    else:
        A			:  str   =		value

    logger.info(F'{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.'		)







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    for key in ignore_keys:
        if key.endswith('''.*'''		):
            if name.startswith(key[:-1]		):
                return True
        elif ".*." in key:
            A,    A			:  List[str]   =		key.split('''.*.'''		)
            if prefix in name and suffix in name:
                return True
        elif key in name:
            return True
    return False







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  List[Any]   =		[]

    if model_name == "encodec_24khz" or "encodec_32khz":
        A			:  str   =		MAPPING_24K
    elif model_name == "encodec_48khz":
        A			:  List[Any]   =		MAPPING_48K
    else:
        raise ValueError(F'Unsupported model: {model_name}'		)

    for name, value in orig_dict.items():
        if should_ignore(snake_case__						,							snake_case__		):
            logger.info(F'{name} was ignored'		)
            continue

        A			:  int   =		False
        for key, mapped_key in MAPPING.items():
            if "*" in key:
                A,    A			:  Optional[int]   =		key.split('''.*.'''		)
                if prefix in name and suffix in name:
                    A			:  Dict   =		suffix

            if key in name:
                # HACK otherwise .embed gets initialized with .embed_avg too
                if key.endswith('''embed'''		) and name.endswith('''embed_avg'''		):
                    continue

                A			:  Union[str, Any]   =		True
                if "*" in mapped_key:
                    A			:  List[str]   =		name.split(snake_case__		)[0].split('''.'''		)[-2]
                    A			:  Optional[Any]   =		mapped_key.replace('''*'''						,							snake_case__		)
                if "weight_g" in name:
                    A			:  Dict   =		'''weight_g'''
                elif "weight_v" in name:
                    A			:  List[str]   =		'''weight_v'''
                elif "weight_ih_l0" in name:
                    A			:  int   =		'''weight_ih_l0'''
                elif "weight_hh_l0" in name:
                    A			:  Any   =		'''weight_hh_l0'''
                elif "bias_ih_l0" in name:
                    A			:  Dict   =		'''bias_ih_l0'''
                elif "bias_hh_l0" in name:
                    A			:  List[Any]   =		'''bias_hh_l0'''
                elif "weight_ih_l1" in name:
                    A			:  Any   =		'''weight_ih_l1'''
                elif "weight_hh_l1" in name:
                    A			:  Tuple   =		'''weight_hh_l1'''
                elif "bias_ih_l1" in name:
                    A			:  Dict   =		'''bias_ih_l1'''
                elif "bias_hh_l1" in name:
                    A			:  List[str]   =		'''bias_hh_l1'''
                elif "bias" in name:
                    A			:  int   =		'''bias'''
                elif "weight" in name:
                    A			:  Optional[Any]   =		'''weight'''
                elif "running_mean" in name:
                    A			:  Optional[Any]   =		'''running_mean'''
                elif "running_var" in name:
                    A			:  Dict   =		'''running_var'''
                elif "num_batches_tracked" in name:
                    A			:  List[str]   =		'''num_batches_tracked'''
                else:
                    A			:  int   =		None
                set_recursively(snake_case__						,							snake_case__						,							snake_case__						,							snake_case__						,							snake_case__		)
            continue
        if not is_used:
            unused_weights.append(snake_case__		)

    logger.warning(F'Unused weights: {unused_weights}'		)







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




    '''simple docstring'''



    if config_path is not None:
        A			:  Dict   =		EncodecConfig.from_pretrained(snake_case__		)
    else:
        A			:  Dict   =		EncodecConfig()

    if model_name == "encodec_24khz":
        pass  # config is already correct
    elif model_name == "encodec_32khz":
        A			:  Optional[Any]   =		[8, 5, 4, 4]
        A			:  List[Any]   =		[2.2]
        A			:  Tuple   =		64
        A			:  str   =		3_2000
        A			:  Optional[Any]   =		2048
        A			:  Optional[int]   =		False
        A			:  Optional[int]   =		False
        A			:  List[Any]   =		False
    elif model_name == "encodec_48khz":
        A			:  List[str]   =		[8, 5, 4, 2]
        A			:  Any   =		[3.0, 6.0, 12.0, 24.0]
        A			:  Tuple   =		4_8000
        A			:  Optional[int]   =		2
        A			:  Dict   =		False
        A			:  Union[str, Any]   =		'''time_group_norm'''
        A			:  List[Any]   =		True
        A			:  Union[str, Any]   =		1.0
        A			:  Union[str, Any]   =		0.01
    else:
        raise ValueError(F'Unknown model name: {model_name}'		)

    A			:  Optional[Any]   =		EncodecModel(snake_case__		)

    A			:  Optional[Any]   =		EncodecFeatureExtractor(
        feature_size=config.audio_channels						,							sampling_rate=config.sampling_rate						,							chunk_length_s=config.chunk_length_s						,							overlap=config.overlap						,							)
    feature_extractor.save_pretrained(snake_case__		)

    A			:  Optional[int]   =		torch.load(snake_case__		)
    if "best_state" in original_checkpoint:
        # we might have a training state saved, in which case discard the yaml results and just retain the weights
        A			:  Union[str, Any]   =		original_checkpoint['''best_state''']
    recursively_load_weights(snake_case__						,							snake_case__						,							snake_case__		)
    model.save_pretrained(snake_case__		)

    if repo_id:
        print('''Pushing to the hub...'''		)
        feature_extractor.push_to_hub(snake_case__		)
        model.push_to_hub(snake_case__		)


if __name__ == "__main__":
      lowercase     :							str       						=     argparse.ArgumentParser()
      parser.add_argument(
          '--model',
          default='encodec_24khz',
          type=str,
          help='The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.',
      )
      parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
      parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
      parser.add_argument(
          '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
      )
      parser.add_argument(
          '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
      )

      lowercase     :							Union[str, Any]       						=     parser.parse_args()
      convert_checkpoint(
          args.model,
          args.checkpoint_path,
          args.pytorch_dump_folder_path,
          args.config_path,
          args.push_to_hub,
      )




