





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

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
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_         =						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""")
@dataclass
class 							SCREAMING_SNAKE_CASE  :
   _UpperCamelCase       :						Optional[str]					   =			field(
       default='cifar10'     ,      metadata={'help': 'Name of a dataset from the datasets package'} )
   _UpperCamelCase       :						Optional[str]					   =			field(
       default=UpperCAmelCase_     ,      metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} )
   _UpperCamelCase       :						Optional[str]					   =			field(
       default=UpperCAmelCase_     ,      metadata={'help': 'The column name of the images in the files.'} )
   _UpperCamelCase       :						Optional[str]					   =			field(default=UpperCAmelCase_     ,      metadata={'help': 'A folder containing the training data.'} )
   _UpperCamelCase       :						Optional[str]					   =			field(default=UpperCAmelCase_     ,      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=UpperCAmelCase_     ,      metadata={
           'help': (
               'For debugging purposes or quicker training, truncate the number of training examples to this '
               'value if set.'
           )
       }     ,      )
   _UpperCamelCase       :						Optional[int]					   =			field(
       default=UpperCAmelCase_     ,      metadata={
           'help': (
               'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
               'value if set.'
           )
       }     ,      )


   def 			SCREAMING_SNAKE_CASE_				(       self     :							str			)->  str:





     """simple docstring"""







     lowercase__           =    {}
     if self.train_dir is not None:
       lowercase__           =    self.train_dir
     if self.validation_dir is not None:
       lowercase__           =    self.validation_dir
     lowercase__           =    data_files if data_files else None






@dataclass
class 							SCREAMING_SNAKE_CASE  :
   _UpperCamelCase       :						str					   =			field(
       default=UpperCAmelCase_     ,      metadata={
           'help': (
               'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
           )
       }     ,      )
   _UpperCamelCase       :						Optional[str]					   =			field(
       default=UpperCAmelCase_     ,      metadata={'help': 'Pretrained config name or path if not the same as model_name_or_path'} )
   _UpperCamelCase       :						Optional[str]					   =			field(
       default=UpperCAmelCase_     ,      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'
           )
       }     ,      )
   _UpperCamelCase       :						Optional[str]					   =			field(
       default=UpperCAmelCase_     ,      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=UpperCAmelCase_     ,      metadata={'help': 'Name or path of preprocessor config.'} )
   _UpperCamelCase       :						bool					   =			field(
       default=UpperCAmelCase_     ,      metadata={
           'help': (
               'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
               'with private models).'
           )
       }     ,      )
   _UpperCamelCase       :						float					   =			field(
       default=0.75     ,      metadata={'help': 'The ratio of the number of masked tokens in the input sequence.'} )
   _UpperCamelCase       :						bool					   =			field(
       default=UpperCAmelCase_     ,      metadata={'help': 'Whether or not to train with normalized pixel values as target.'} )






@dataclass
class 							SCREAMING_SNAKE_CASE  (UpperCAmelCase_ ):
   _UpperCamelCase       :						float					   =			field(
       default=1E-3     ,      metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'} )






def      __UpperCamelCase    (_SCREAMING_SNAKE_CASE						)							->  List[Any]:
  lowercase__           =    torch.stack([example['pixel_values'] for example in examples]						)
  return {"pixel_values": pixel_values}




def      __UpperCamelCase    ()							->  List[Any]:
  # See all possible arguments in src/transformers/training_args.py
  # or by passing the --help flag to this script.
  # We now keep distinct sets of args, for a cleaner separation of concerns.

  lowercase__           =    HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)						)
  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__           =    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
  else:
    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_mae'						,		__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.'						)

    # Initialize our dataset.
  lowercase__           =    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.
  lowercase__           =    None if """validation""" in ds.keys() else data_args.train_val_split
  if isinstance(data_args.train_val_split						,		__lowerCAmelCase						) and data_args.train_val_split > 0.0:
    lowercase__           =    ds["""train"""].train_test_split(data_args.train_val_split						)
    lowercase__           =    split["""train"""]
    lowercase__           =    split["""test"""]

  # Load pretrained model and image processor
  #
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.
  lowercase__           =    {
      """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:
    lowercase__           =    ViTMAEConfig.from_pretrained(model_args.config_name						,		**__lowerCAmelCase						)
  elif model_args.model_name_or_path:
    lowercase__           =    ViTMAEConfig.from_pretrained(model_args.model_name_or_path						,		**__lowerCAmelCase						)
  else:
    lowercase__           =    ViTMAEConfig()
    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}"""						)

    # adapt config
  config.update(
      {
          'mask_ratio': model_args.mask_ratio,
          'norm_pix_loss': model_args.norm_pix_loss,
      }						)

  # create image processor
  if model_args.image_processor_name:
    lowercase__           =    ViTImageProcessor.from_pretrained(model_args.image_processor_name						,		**__lowerCAmelCase						)
  elif model_args.model_name_or_path:
    lowercase__           =    ViTImageProcessor.from_pretrained(model_args.model_name_or_path						,		**__lowerCAmelCase						)
  else:
    lowercase__           =    ViTImageProcessor()

  # create model
  if model_args.model_name_or_path:
    lowercase__           =    ViTMAEForPreTraining.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						,		)
  else:
    logger.info('Training new model from scratch'						)
    lowercase__           =    ViTMAEForPreTraining(__lowerCAmelCase						)

  if training_args.do_train:
    lowercase__           =    ds["""train"""].column_names
  else:
    lowercase__           =    ds["""validation"""].column_names

  if data_args.image_column_name is not None:
    lowercase__           =    data_args.image_column_name
  elif "image" in column_names:
    lowercase__           =    """image"""
  elif "img" in column_names:
    lowercase__           =    """img"""
  else:
    lowercase__           =    column_names[0]

  # transformations as done in original MAE paper
  # source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
  if "shortest_edge" in image_processor.size:
    lowercase__           =    image_processor.size["""shortest_edge"""]
  else:
    lowercase__           =    (image_processor.size["""height"""], image_processor.size["""width"""])
  lowercase__           =    Compose(
      [
          Lambda(lambda _SCREAMING_SNAKE_CASE						: img.convert('RGB'						) if img.mode != "RGB" else img						),
          RandomResizedCrop(__lowerCAmelCase						,		scale=(0.2, 1.0)						,		interpolation=InterpolationMode.BICUBIC						),
          RandomHorizontalFlip(),
          ToTensor(),
          Normalize(mean=image_processor.image_mean						,		std=image_processor.image_std						),
      ]						)

  def preprocess_images(_SCREAMING_SNAKE_CASE						):
    lowercase__           =    [transforms(__lowerCAmelCase						) for image in 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:
      lowercase__           =    ds["""train"""].shuffle(seed=training_args.seed						).select(range(data_args.max_train_samples						)						)
    # Set the training transforms
    ds["train"].set_transform(__lowerCAmelCase						)

  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:
      lowercase__           =    (
          ds["""validation"""].shuffle(seed=training_args.seed						).select(range(data_args.max_eval_samples						)						)
      )
    # Set the validation transforms
    ds["validation"].set_transform(__lowerCAmelCase						)

  # Compute absolute learning rate
  lowercase__           =    (
      training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
  )
  if training_args.base_learning_rate is not None:
    lowercase__           =    training_args.base_learning_rate * total_train_batch_size / 256

  # Initialize our trainer
  lowercase__           =    Trainer(
      model=__lowerCAmelCase						,		args=__lowerCAmelCase						,		train_dataset=ds['train'] if training_args.do_train else None						,		eval_dataset=ds['validation'] if training_args.do_eval else None						,		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__           =    {
      """tasks""": """masked-auto-encoding""",
      """dataset""": data_args.dataset_name,
      """tags""": ["""masked-auto-encoding"""],
  }
  if training_args.push_to_hub:
    trainer.push_to_hub(**__lowerCAmelCase						)
  else:
    trainer.create_model_card(**__lowerCAmelCase						)




def      __UpperCamelCase    (_SCREAMING_SNAKE_CASE						)							->  Tuple:
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
      main()










import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple

import torch
from audiocraft.models import MusicGen

from transformers import (
    AutoFeatureExtractor,
    AutoTokenizer,
    EncodecModel,
    MusicgenDecoderConfig,
    MusicgenForConditionalGeneration,
    MusicgenProcessor,
    TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase    							=      logging.get_logger(__name__)


UpperCamelCase    							=      ['model.decoder.embed_positions.weights']


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Optional[int]							)     ->  Union[str, Any]:
   if "emb" in name:
      __UpperCamelCase	:			Any					=					name.replace("""emb"""					,							"""model.decoder.embed_tokens"""							)
   if "transformer" in name:
      __UpperCamelCase	:			str					=					name.replace("""transformer"""					,							"""model.decoder"""							)
   if "cross_attention" in name:
      __UpperCamelCase	:			List[str]					=					name.replace("""cross_attention"""					,							"""encoder_attn"""							)
   if "linear1" in name:
      __UpperCamelCase	:			Optional[int]					=					name.replace("""linear1"""					,							"""fc1"""							)
   if "linear2" in name:
      __UpperCamelCase	:			Optional[Any]					=					name.replace("""linear2"""					,							"""fc2"""							)
   if "norm1" in name:
      __UpperCamelCase	:			Tuple					=					name.replace("""norm1"""					,							"""self_attn_layer_norm"""							)
   if "norm_cross" in name:
      __UpperCamelCase	:			List[str]					=					name.replace("""norm_cross"""					,							"""encoder_attn_layer_norm"""							)
   if "norm2" in name:
      __UpperCamelCase	:			Tuple					=					name.replace("""norm2"""					,							"""final_layer_norm"""							)
   if "out_norm" in name:
      __UpperCamelCase	:			Union[str, Any]					=					name.replace("""out_norm"""					,							"""model.decoder.layer_norm"""							)
   if "linears" in name:
      __UpperCamelCase	:			Optional[int]					=					name.replace("""linears"""					,							"""lm_heads"""							)
   if "condition_provider.conditioners.description.output_proj" in name:
      __UpperCamelCase	:			Tuple					=					name.replace("""condition_provider.conditioners.description.output_proj"""					,							"""enc_to_dec_proj"""							)
   return name


def 				__lowerCamelCase     (							__lowerCAmelCase							:					OrderedDict					,							__lowerCAmelCase							:					int							)     ->  Tuple[Dict, Dict]:
   __UpperCamelCase	:			Tuple					=					list(state_dict.keys()							)
   __UpperCamelCase	:			List[Any]					=					{}
   for key in keys:
      __UpperCamelCase	:			Optional[Any]					=					state_dict.pop(__lowerCAmelCase							)
      __UpperCamelCase	:			Dict					=					rename_keys(__lowerCAmelCase							)
      if "in_proj_weight" in key:
         # split fused qkv proj
         __UpperCamelCase	:			Optional[Any]					=					val[:hidden_size, :]
         __UpperCamelCase	:			Optional[Any]					=					val[hidden_size : 2 * hidden_size, :]
         __UpperCamelCase	:			List[str]					=					val[-hidden_size:, :]
      elif "enc_to_dec_proj" in key:
         __UpperCamelCase	:			Dict					=					val
      else:
         __UpperCamelCase	:			Any					=					val
   return state_dict, enc_dec_proj_state_dict


def 				__lowerCamelCase     (							__lowerCAmelCase							:					str							)     ->  MusicgenDecoderConfig:
   if checkpoint == "small":
      # default config values
      __UpperCamelCase	:			int					=					1024
      __UpperCamelCase	:			Union[str, Any]					=					24
      __UpperCamelCase	:			int					=					16
   elif checkpoint == "medium":
      __UpperCamelCase	:			List[Any]					=					1536
      __UpperCamelCase	:			Dict					=					48
      __UpperCamelCase	:			Dict					=					24
   elif checkpoint == "large":
      __UpperCamelCase	:			List[Any]					=					2048
      __UpperCamelCase	:			str					=					48
      __UpperCamelCase	:			Optional[int]					=					32
   else:
      raise ValueError(f'Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'							)
   __UpperCamelCase	:			Any					=					MusicgenDecoderConfig(
       hidden_size=__lowerCAmelCase					,							ffn_dim=hidden_size * 4					,							num_hidden_layers=__lowerCAmelCase					,							num_attention_heads=__lowerCAmelCase					,							)
   return config


@torch.no_grad()
def 				__lowerCamelCase     (							__lowerCAmelCase							:					List[str]					,							__lowerCAmelCase							:					Optional[Any]=None					,							__lowerCAmelCase							:					Tuple=None					,							__lowerCAmelCase							:					str="cpu"							)     ->  Optional[int]:
   __UpperCamelCase	:			str					=					MusicGen.get_pretrained(__lowerCAmelCase					,							device=__lowerCAmelCase							)
   __UpperCamelCase	:			int					=					decoder_config_from_checkpoint(__lowerCAmelCase							)

   __UpperCamelCase	:			Optional[int]					=					fairseq_model.lm.state_dict()
   __UpperCamelCase	,     __UpperCamelCase	:			Union[str, Any]					=					rename_state_dict(
       __lowerCAmelCase					,							hidden_size=decoder_config.hidden_size							)

   __UpperCamelCase	:			List[Any]					=					TaEncoderModel.from_pretrained("""t5-base"""							)
   __UpperCamelCase	:			Dict					=					EncodecModel.from_pretrained("""facebook/encodec_32khz"""							)
   __UpperCamelCase	:			List[str]					=					MusicgenForCausalLM(__lowerCAmelCase							).eval()

   # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
   __UpperCamelCase	,     __UpperCamelCase	:			Tuple					=					decoder.load_state_dict(__lowerCAmelCase					,							strict=__lowerCAmelCase							)

   for key in missing_keys.copy():
      if key.startswith(("""text_encoder""", """audio_encoder""")							) or key in EXPECTED_MISSING_KEYS:
         missing_keys.remove(__lowerCAmelCase							)

   if len(__lowerCAmelCase							) > 0:
      raise ValueError(f'Missing key(s) in state_dict: {missing_keys}'							)

   if len(__lowerCAmelCase							) > 0:
      raise ValueError(f'Unexpected key(s) in state_dict: {unexpected_keys}'							)

   # init the composite model
   __UpperCamelCase	:			Tuple					=					MusicgenForConditionalGeneration(text_encoder=__lowerCAmelCase					,							audio_encoder=__lowerCAmelCase					,							decoder=__lowerCAmelCase							)

   # load the pre-trained enc-dec projection (from the decoder state dict)
   model.enc_to_dec_proj.load_state_dict(__lowerCAmelCase							)

   # check we can do a forward pass
   __UpperCamelCase	:			int					=					torch.arange(0					,							8					,							dtype=torch.long							).reshape(2					,							-1							)
   __UpperCamelCase	:			List[Any]					=					input_ids.reshape(2 * 4					,							-1							)

   with torch.no_grad():
      __UpperCamelCase	:			int					=					model(input_ids=__lowerCAmelCase					,							decoder_input_ids=__lowerCAmelCase							).logits

   if logits.shape != (8, 1, 2048):
      raise ValueError("""Incorrect shape for logits"""							)

   # now construct the processor
   __UpperCamelCase	:			Union[str, Any]					=					AutoTokenizer.from_pretrained("""t5-base"""							)
   __UpperCamelCase	:			List[Any]					=					AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz"""					,							padding_side="""left"""							)

   __UpperCamelCase	:			Any					=					MusicgenProcessor(feature_extractor=__lowerCAmelCase					,							tokenizer=__lowerCAmelCase							)

   # set the appropriate bos/pad token ids
   __UpperCamelCase	:			Tuple					=					2048
   __UpperCamelCase	:			int					=					2048

   # set other default generation config params
   __UpperCamelCase	:			str					=					int(30 * audio_encoder.config.frame_rate							)
   __UpperCamelCase	:			List[str]					=					True
   __UpperCamelCase	:			Tuple					=					3.0

   if pytorch_dump_folder is not None:
      Path(__lowerCAmelCase							).mkdir(exist_ok=__lowerCAmelCase							)
      logger.info(f'Saving model {checkpoint} to {pytorch_dump_folder}'							)
      model.save_pretrained(__lowerCAmelCase							)
      processor.save_pretrained(__lowerCAmelCase							)

   if repo_id:
      logger.info(f'Pushing model {checkpoint} to {repo_id}'							)
      model.push_to_hub(__lowerCAmelCase							)
      processor.push_to_hub(__lowerCAmelCase							)


if __name__ == "__main__":
       UpperCamelCase    							=      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--checkpoint',
           default='small',
           type=str,
           help='Checkpoint size of the MusicGen model you\'d like to convert. Can be one of: `[\'small\', \'medium\', \'large\']`.',
       )
       parser.add_argument(
           '--pytorch_dump_folder',
           required=True,
           default=None,
           type=str,
           help='Path to the output PyTorch model directory.',
       )
       parser.add_argument(
           '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
       )
       parser.add_argument(
           '--device', default='cpu', type=str, help='Torch device to run the conversion, either cpu or cuda.'
       )

       UpperCamelCase    							=      parser.parse_args()
       convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)



