






"""simple docstring"""





import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

__lowerCamelCase							=  16
__lowerCamelCase							=  32



def   a    (     __UpperCAmelCase      :    Accelerator      ,   __UpperCAmelCase      :    int = 1_6  )    ->							int:
						__magic_name__:	Optional[int]							=       AutoTokenizer.from_pretrained("""bert-base-cased"""  )
						__magic_name__:	Any							=       load_dataset("""glue"""      ,   """mrpc"""  )

						def tokenize_function(__UpperCAmelCase      :    Optional[Any]  ):
												# max_length=None => use the model max length (it's actually the default)
												__magic_name__:	Dict							=       tokenizer(examples["""sentence1"""]      ,   examples["""sentence2"""]      ,   truncation=__UpperCAmelCase      ,   max_length=__UpperCAmelCase  )
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						# starting with the main process first:
						with accelerator.main_process_first():
												__magic_name__:	int							=       datasets.map(
												    __UpperCAmelCase      ,   batched=__UpperCAmelCase      ,   remove_columns=["""idx""", """sentence1""", """sentence2"""]      ,   )

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						__magic_name__:	Tuple							=       tokenized_datasets.rename_column("""label"""      ,   """labels"""  )

						def collate_fn(__UpperCAmelCase      :    Union[str, Any]  ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												__magic_name__:	str							=       1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		__magic_name__:	Union[str, Any]							=       1_6
												elif accelerator.mixed_precision != "no":
																		__magic_name__:	List[str]							=       8
												else:
																		__magic_name__:	int							=       None

												return tokenizer.pad(
												    __UpperCAmelCase      ,   padding="""longest"""      ,   max_length=__UpperCAmelCase      ,   pad_to_multiple_of=__UpperCAmelCase      ,   return_tensors="""pt"""      ,   )

						# Instantiate dataloaders.
						__magic_name__:	List[Any]							=       DataLoader(
						    tokenized_datasets["""train"""]      ,   shuffle=__UpperCAmelCase      ,   collate_fn=__UpperCAmelCase      ,   batch_size=__UpperCAmelCase  )
						__magic_name__:	str							=       DataLoader(
						    tokenized_datasets["""validation"""]      ,   shuffle=__UpperCAmelCase      ,   collate_fn=__UpperCAmelCase      ,   batch_size=__UpperCAmelCase  )

						return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
							from accelerate.test_utils.training import mocked_dataloaders

							__lowerCamelCase							=  mocked_dataloaders  # noqa: F811



def   a    (     __UpperCAmelCase      :    Tuple      ,   __UpperCAmelCase      :    Union[str, Any]  )    ->							Optional[int]:
						# For testing only
						if os.environ.get("""TESTING_MOCKED_DATALOADERS"""      ,   __UpperCAmelCase  ) == "1":
												__magic_name__:	int							=       2
						# Initialize Accelerator

						# New Code #
						# We pass in "all" to `log_with` to grab all available trackers in the environment
						# Note: If using a custom `Tracker` class, should be passed in here such as:
						# >>> log_with = ["all", MyCustomTrackerClassInstance()]
						if args.with_tracking:
												__magic_name__:	Union[str, Any]							=       Accelerator(
												    cpu=args.cpu      ,   mixed_precision=args.mixed_precision      ,   log_with="""all"""      ,   project_dir=args.project_dir  )
						else:
												__magic_name__:	Tuple							=       Accelerator(cpu=args.cpu      ,   mixed_precision=args.mixed_precision  )
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						__magic_name__:	str							=       config["""lr"""]
						__magic_name__:	Union[str, Any]							=       int(config["""num_epochs"""]  )
						__magic_name__:	List[str]							=       int(config["""seed"""]  )
						__magic_name__:	Union[str, Any]							=       int(config["""batch_size"""]  )
						set_seed(__UpperCAmelCase  )

						__magic_name__,			__magic_name__:	Tuple							=       get_dataloaders(__UpperCAmelCase      ,   __UpperCAmelCase  )
						__magic_name__:	str							=       evaluate.load("""glue"""      ,   """mrpc"""  )

						# If the batch size is too big we use gradient accumulation
						__magic_name__:	Optional[int]							=       1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												__magic_name__:	Optional[Any]							=       batch_size // MAX_GPU_BATCH_SIZE
												__magic_name__:	Dict							=       MAX_GPU_BATCH_SIZE

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						__magic_name__:	Any							=       AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""      ,   return_dict=__UpperCAmelCase  )

						# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
						# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
						# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
						__magic_name__:	Dict							=       model.to(accelerator.device  )

						# Instantiate optimizer
						__magic_name__:	List[str]							=       AdamW(params=model.parameters()      ,   lr=__UpperCAmelCase  )

						# Instantiate scheduler
						__magic_name__:	Optional[Any]							=       get_linear_schedule_with_warmup(
						    optimizer=__UpperCAmelCase      ,   num_warmup_steps=1_0_0      ,   num_training_steps=(len(__UpperCAmelCase  ) * num_epochs) // gradient_accumulation_steps      ,   )

						# Prepare everything
						# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
						# prepare method.
						__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__:	List[str]							=       accelerator.prepare(
						    __UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase  )

						# New Code #
						# We need to initialize the trackers we use. Overall configurations can also be stored
						if args.with_tracking:
												__magic_name__:	List[str]							=       os.path.split(__UpperCAmelCase  )[-1].split("""."""  )[0]
												accelerator.init_trackers(__UpperCAmelCase      ,   __UpperCAmelCase  )

						# Now we train the model
						for epoch in range(__UpperCAmelCase  ):
												model.train()
												# New Code #
												# For our tracking example, we will log the total loss of each epoch
												if args.with_tracking:
																		__magic_name__:	int							=       0
												for step, batch in enumerate(__UpperCAmelCase  ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device  )
																		__magic_name__:	Any							=       model(**__UpperCAmelCase  )
																		__magic_name__:	int							=       outputs.loss
																		# New Code #
																		if args.with_tracking:
																								total_loss += loss.detach().float()
																		__magic_name__:	Tuple							=       loss / gradient_accumulation_steps
																		accelerator.backward(__UpperCAmelCase  )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

												model.eval()
												for step, batch in enumerate(__UpperCAmelCase  ):
																		# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
																		batch.to(accelerator.device  )
																		with torch.no_grad():
																								__magic_name__:	int							=       model(**__UpperCAmelCase  )
																		__magic_name__:	int							=       outputs.logits.argmax(dim=-1  )
																		__magic_name__,			__magic_name__:	List[str]							=       accelerator.gather_for_metrics((predictions, batch["""labels"""])  )
																		metric.add_batch(
																		    predictions=__UpperCAmelCase      ,   references=__UpperCAmelCase      ,   )

												__magic_name__:	Union[str, Any]							=       metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f'epoch {epoch}:'      ,   __UpperCAmelCase  )

												# New Code #
												# To actually log, we call `Accelerator.log`
												# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        """accuracy""": eval_metric["""accuracy"""],
																		        """f1""": eval_metric["""f1"""],
																		        """train_loss""": total_loss.item() / len(__UpperCAmelCase  ),
																		        """epoch""": epoch,
																		    }      ,   step=__UpperCAmelCase      ,   )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
						if args.with_tracking:
												accelerator.end_training()



def   a    (     )    ->							str:
						__magic_name__:	Optional[int]							=       argparse.ArgumentParser(description="""Simple example of training script."""  )
						parser.add_argument(
						    """--mixed_precision"""      ,   type=__UpperCAmelCase      ,   default=__UpperCAmelCase      ,   choices=["""no""", """fp16""", """bf16""", """fp8"""]      ,   help="""Whether to use mixed precision. Choose"""
						    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
						    """and an Nvidia Ampere GPU."""      ,   )
						parser.add_argument("""--cpu"""      ,   action="""store_true"""      ,   help="""If passed, will train on the CPU."""  )
						parser.add_argument(
						    """--with_tracking"""      ,   action="""store_true"""      ,   help="""Whether to load in all available experiment trackers from the environment and use them for logging."""      ,   )
						parser.add_argument(
						    """--project_dir"""      ,   type=__UpperCAmelCase      ,   default="""logs"""      ,   help="""Location on where to store experiment tracking logs` and relevent project information"""      ,   )
						__magic_name__:	Union[str, Any]							=       parser.parse_args()
						__magic_name__:	int							=       {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
						training_function(__UpperCAmelCase      ,   __UpperCAmelCase  )


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()
SCREAMING_SNAKE_CASE_   :      Optional[Any]			=   logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_   :      Any			=   ['''model.decoder.embed_positions.weights''']



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    int:
		if "emb" in name:
				__lowercase											=     name.replace('emb'						,   'model.decoder.embed_tokens'							)
		if "transformer" in name:
				__lowercase											=     name.replace('transformer'						,   'model.decoder'							)
		if "cross_attention" in name:
				__lowercase											=     name.replace('cross_attention'						,   'encoder_attn'							)
		if "linear1" in name:
				__lowercase											=     name.replace('linear1'						,   'fc1'							)
		if "linear2" in name:
				__lowercase											=     name.replace('linear2'						,   'fc2'							)
		if "norm1" in name:
				__lowercase											=     name.replace('norm1'						,   'self_attn_layer_norm'							)
		if "norm_cross" in name:
				__lowercase											=     name.replace('norm_cross'						,   'encoder_attn_layer_norm'							)
		if "norm2" in name:
				__lowercase											=     name.replace('norm2'						,   'final_layer_norm'							)
		if "out_norm" in name:
				__lowercase											=     name.replace('out_norm'						,   'model.decoder.layer_norm'							)
		if "linears" in name:
				__lowercase											=     name.replace('linears'						,   'lm_heads'							)
		if "condition_provider.conditioners.description.output_proj" in name:
				__lowercase											=     name.replace('condition_provider.conditioners.description.output_proj'						,   'enc_to_dec_proj'							)
		return name



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    Tuple[Dict, Dict]:
		__lowercase											=     list(state_dict.keys()							)
		__lowercase											=     {}
		for key in keys:
				__lowercase											=     state_dict.pop(snake_case							)
				__lowercase											=     rename_keys(snake_case							)
				if "in_proj_weight" in key:
						# split fused qkv proj
						__lowercase											=     val[:hidden_size, :]
						__lowercase											=     val[hidden_size : 2 * hidden_size, :]
						__lowercase											=     val[-hidden_size:, :]
				elif "enc_to_dec_proj" in key:
						__lowercase											=     val
				else:
						__lowercase											=     val
		return state_dict, enc_dec_proj_state_dict



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    MusicgenDecoderConfig:
		if checkpoint == "small":
				# default config values
				__lowercase											=     1_024
				__lowercase											=     24
				__lowercase											=     16
		elif checkpoint == "medium":
				__lowercase											=     1_536
				__lowercase											=     48
				__lowercase											=     24
		elif checkpoint == "large":
				__lowercase											=     2_048
				__lowercase											=     48
				__lowercase											=     32
		else:
				raise ValueError(F"Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."							)
		__lowercase											=     MusicgenDecoderConfig(
		    hidden_size=snake_case						,   ffn_dim=hidden_size * 4						,   num_hidden_layers=snake_case						,   num_attention_heads=snake_case						,   )
		return config



@torch.no_grad()
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case=None						,   snake_case=None						,   snake_case="cpu"							)		->    Optional[int]:
		__lowercase											=     MusicGen.get_pretrained(snake_case						,   device=snake_case							)
		__lowercase											=     decoder_config_from_checkpoint(snake_case							)

		__lowercase											=     fairseq_model.lm.state_dict()
		__lowercase						,      __lowercase											=     rename_state_dict(
		    snake_case						,   hidden_size=decoder_config.hidden_size							)

		__lowercase											=     TaEncoderModel.from_pretrained('t5-base'							)
		__lowercase											=     EncodecModel.from_pretrained('facebook/encodec_32khz'							)
		__lowercase											=     MusicgenForCausalLM(snake_case							).eval()

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

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

		if len(snake_case							) > 0:
				raise ValueError(F"Missing key(s) in state_dict: {missing_keys}"							)

		if len(snake_case							) > 0:
				raise ValueError(F"Unexpected key(s) in state_dict: {unexpected_keys}"							)

		# init the composite model
		__lowercase											=     MusicgenForConditionalGeneration(text_encoder=snake_case						,   audio_encoder=snake_case						,   decoder=snake_case							)

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

		# check we can do a forward pass
		__lowercase											=     torch.arange(0						,   8						,   dtype=torch.long							).reshape(2						,   -1							)
		__lowercase											=     input_ids.reshape(2 * 4						,   -1							)

		with torch.no_grad():
				__lowercase											=     model(input_ids=snake_case						,   decoder_input_ids=snake_case							).logits

		if logits.shape != (8, 1, 2_048):
				raise ValueError('Incorrect shape for logits'							)

		# now construct the processor
		__lowercase											=     AutoTokenizer.from_pretrained('t5-base'							)
		__lowercase											=     AutoFeatureExtractor.from_pretrained('facebook/encodec_32khz'						,   padding_side='left'							)

		__lowercase											=     MusicgenProcessor(feature_extractor=snake_case						,   tokenizer=snake_case							)

		# set the appropriate bos/pad token ids
		__lowercase											=     2_048
		__lowercase											=     2_048

		# set other default generation config params
		__lowercase											=     int(30 * audio_encoder.config.frame_rate							)
		__lowercase											=     True
		__lowercase											=     3.0

		if pytorch_dump_folder is not None:
				Path(snake_case							).mkdir(exist_ok=snake_case							)
				logger.info(F"Saving model {checkpoint} to {pytorch_dump_folder}"							)
				model.save_pretrained(snake_case							)
				processor.save_pretrained(snake_case							)

		if repo_id:
				logger.info(F"Pushing model {checkpoint} to {repo_id}"							)
				model.push_to_hub(snake_case							)
				processor.push_to_hub(snake_case							)


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_   :      Optional[int]			=   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.'''
		)

		SCREAMING_SNAKE_CASE_   :      Any			=   parser.parse_args()
		convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)




