"""simple docstring"""




import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Tuple	)       ->      Union[str, Any]:
  _lowerCAmelCase						:       Union[str, Any]  			=     [
      """encoder.version""",
      """decoder.version""",
      """model.encoder.version""",
      """model.decoder.version""",
      """decoder.output_projection.weight""",
      """_float_tensor""",
      """encoder.embed_positions._float_tensor""",
      """decoder.embed_positions._float_tensor""",
  ]
  for k in ignore_keys:
    state_dict.pop(_lowerCamelCase			,_lowerCamelCase	)





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[str]	)       ->      int:
  _lowerCAmelCase			,	_lowerCAmelCase						:       str  			=     emb.weight.shape
  _lowerCAmelCase						:       List[str]  			=     nn.Linear(_lowerCamelCase			,_lowerCamelCase			,bias=_lowerCamelCase	)
  _lowerCAmelCase						:       Tuple  			=     emb.weight.data
  return lin_layer





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Any			,_lowerCamelCase						:       Union[str, Any]=None	)       ->      List[Any]:
  _lowerCAmelCase						:       Any  			=     {}
  for old_key in state_dict.keys():
    _lowerCAmelCase						:       Optional[int]  			=     old_key
    if "moe_layer.experts." in key:
      if expert_idx is not None:
        _lowerCAmelCase						:       Optional[int]  			=     key.replace("""moe_layer.experts.0"""			,f"ffn.experts.expert_{expert_idx}"	)
      else:
        _lowerCAmelCase						:       Any  			=     key.replace("""moe_layer.experts."""			,"""ffn.experts.expert_"""	)
    if "gate" in key:
      _lowerCAmelCase						:       int  			=     key.replace(""".moe_layer.gate.wg"""			,""".ffn.router.classifier"""	)
    if "fc2" and "experts" not in key:
      _lowerCAmelCase						:       List[Any]  			=     key.replace(""".fc2."""			,""".ffn.fc2."""	)
    if "fc1" and "experts" not in key:
      _lowerCAmelCase						:       Optional[int]  			=     key.replace(""".fc1."""			,""".ffn.fc1."""	)
    if ".encoder_attn." in key:
      _lowerCAmelCase						:       List[str]  			=     key.replace(""".encoder_attn."""			,""".cross_attention."""	)
    if "encoder_attn_layer_norm" in key:
      _lowerCAmelCase						:       Union[str, Any]  			=     key.replace("""encoder_attn_layer_norm"""			,"""cross_attention_layer_norm"""	)
    if "final_layer_norm" in key:
      _lowerCAmelCase						:       Optional[int]  			=     key.replace("""final_layer_norm"""			,"""ff_layer_norm"""	)
    _lowerCAmelCase						:       Union[str, Any]  			=     state_dict[old_key]
  return new_dict





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       Dict			,_lowerCamelCase						:       List[str]			,_lowerCamelCase						:       Optional[Any]			,_lowerCamelCase						:       str = WEIGHTS_NAME	)       ->      int:
  _lowerCAmelCase						:       Optional[int]  			=     []
  _lowerCAmelCase						:       Optional[Any]  			=     0
  os.makedirs(_lowerCamelCase			,exist_ok=_lowerCamelCase	)

  for expert in range(_lowerCamelCase	):
    _lowerCAmelCase						:       Optional[int]  			=     switch_checkpoint_path + f"-rank-{expert}.pt"
    if os.path.isfile(_lowerCamelCase	):
      _lowerCAmelCase						:       List[str]  			=     torch.load(_lowerCamelCase	)["""model"""]
      remove_ignore_keys_(_lowerCamelCase	)
      _lowerCAmelCase						:       List[str]  			=     rename_fairseq_keys(_lowerCamelCase			,_lowerCamelCase	)
      _lowerCAmelCase						:       Tuple  			=     os.path.join(
          _lowerCamelCase			,weights_name.replace(""".bin"""			,f"-{len(_lowerCamelCase	)+1:05d}-of-???.bin"	)	)
      torch.save(_lowerCamelCase			,_lowerCamelCase	)
      sharded_state_dicts.append(expert_state.keys()	)
      total_size += sum([value.numel() for key, value in expert_state.items()]	) * dtype_byte_size(
          expert_state[list(_lowerCamelCase	)[0]].dtype	)

    # Add the last block
  _lowerCAmelCase						:       Dict  			=     os.path.join(_lowerCamelCase			,weights_name.replace(""".bin"""			,f"-{len(_lowerCamelCase	)+1:05d}-of-???.bin"	)	)
  _lowerCAmelCase						:       Tuple  			=     torch.load(switch_checkpoint_path + """-shared.pt"""	)["""model"""]
  remove_ignore_keys_(_lowerCamelCase	)
  _lowerCAmelCase						:       Union[str, Any]  			=     rename_fairseq_keys(_lowerCamelCase			,_lowerCamelCase	)
  _lowerCAmelCase						:       int  			=     shared_weights["""decoder.embed_tokens.weight"""]
  sharded_state_dicts.append(shared_weights.keys()	)

  # If we only have the shared weights (dummy model/experts saved on the same file)
  if len(_lowerCamelCase	) == 1:
    _lowerCAmelCase						:       Any  			=     os.path.join(_lowerCamelCase			,_lowerCamelCase	)
    torch.save(_lowerCamelCase			,_lowerCamelCase	)
    return {weights_name: sharded_state_dicts[0]}, None
  else:
    torch.save(_lowerCamelCase			,_lowerCamelCase	)
  # Otherwise, let's build the index
  _lowerCAmelCase						:       int  			=     {}
  for idx, shard in enumerate(_lowerCamelCase	):
    _lowerCAmelCase						:       Optional[int]  			=     weights_name.replace(""".bin"""			,f"-{idx+1:05d}-of-{len(_lowerCamelCase	):05d}.bin"	)
    _lowerCAmelCase						:       int  			=     os.path.join(_lowerCamelCase			,weights_name.replace(""".bin"""			,f"-{idx+1:05d}-of-???.bin"	)	)
    os.rename(_lowerCamelCase			,os.path.join(_lowerCamelCase			,_lowerCamelCase	)	)
    for key in shard:
      _lowerCAmelCase						:       Tuple  			=     shard_file

    # Add the metadata
  _lowerCAmelCase						:       Any  			=     {"""total_size""": total_size}
  _lowerCAmelCase						:       List[str]  			=     {"""metadata""": metadata, """weight_map""": weight_map}

  with open(os.path.join(_lowerCamelCase			,_lowerCamelCase	)			,"""w"""			,encoding="""utf-8"""	) as f:
    _lowerCAmelCase						:       List[str]  			=     json.dumps(_lowerCamelCase			,indent=2			,sort_keys=_lowerCamelCase	) + """\n"""
    f.write(_lowerCamelCase	)

  return metadata, index


if __name__ == "__main__":
     _a      :		Tuple           =   argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--nllb_moe_checkpoint_path',
         default='/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000',
         type=str,
         required=False,
         help='Path to a directory containing a folder per layer. Follows the original Google format.',
     )
     parser.add_argument('--dtype', default='float32', type=str, required=False, help='dtype of the saved model')
     parser.add_argument(
         '--pytorch_dump_folder_path',
         default='/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b',
         type=str,
         required=False,
         help='Path to the output pytorch model.',
     )
     _a      :		List[Any]           =   parser.parse_args()
     _a    ,   _a      :		Tuple           =   shard_on_the_fly(
         args.nllb_moe_checkpoint_path,
         args.pytorch_dump_folder_path,
         128,
         args.dtype,
     )

     _a      :		Any           =   NllbMoeConfig.from_pretrained(
         'facebook/nllb-200-3.3B', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
     )
     config.save_pretrained(args.pytorch_dump_folder_path)
     _a      :		List[str]           =   NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
     print('Done')
     model.save_pretrained(args.pytorch_dump_folder_path)









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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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
#
########################################################################


__A   			=1_6
__A   			=3_2

def        a						(      _UpperCAmelCase							:					Accelerator ,   _UpperCAmelCase							:					int = 16						):







		'''simple docstring'''





		__UpperCAmelCase    :     Union[str, Any]													=	AutoTokenizer.from_pretrained('''bert-base-cased'''						)
		__UpperCAmelCase    :     Any													=	load_dataset('''glue''' ,   '''mrpc'''						)

		def tokenize_function(_UpperCAmelCase							:					List[Any]						):
				# max_length=None => use the model max length (it's actually the default)
				__UpperCAmelCase    :     str													=	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():
				__UpperCAmelCase    :     Optional[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
		__UpperCAmelCase    :     Optional[Any]													=	tokenized_datasets.rename_column('''label''' ,   '''labels'''						)

		def collate_fn(_UpperCAmelCase							:					str						):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				__UpperCAmelCase    :     List[Any]													=	1_28 if accelerator.distributed_type == DistributedType.TPU else None
				# When using mixed precision we want round multiples of 8/16
				if accelerator.mixed_precision == "fp8":
						__UpperCAmelCase    :     Any													=	16
				elif accelerator.mixed_precision != "no":
						__UpperCAmelCase    :     Tuple													=	8
				else:
						__UpperCAmelCase    :     Optional[int]													=	None

				return tokenizer.pad(
				    _UpperCAmelCase ,   padding='''longest''' ,   max_length=_UpperCAmelCase ,   pad_to_multiple_of=_UpperCAmelCase ,   return_tensors='''pt''' ,   )

		# Instantiate dataloaders.
		__UpperCAmelCase    :     Optional[Any]													=	DataLoader(
		    tokenized_datasets['''train'''] ,   shuffle=_UpperCAmelCase ,   collate_fn=_UpperCAmelCase ,   batch_size=_UpperCAmelCase						)
		__UpperCAmelCase    :     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

				__A   			=mocked_dataloaders  # noqa: F811

def        a						(      _UpperCAmelCase							:					List[str] ,   _UpperCAmelCase							:					Any						):







		'''simple docstring'''





		if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,   _UpperCAmelCase						) == "1":
				__UpperCAmelCase    :     Dict													=	2
		# Initialize accelerator
		__UpperCAmelCase    :     Dict													=	Accelerator(cpu=args.cpu ,   mixed_precision=args.mixed_precision						)
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		__UpperCAmelCase    :     List[Any]													=	config['''lr''']
		__UpperCAmelCase    :     Optional[Any]													=	int(config['''num_epochs''']						)
		__UpperCAmelCase    :     Optional[int]													=	int(config['''seed''']						)
		__UpperCAmelCase    :     Any													=	int(config['''batch_size''']						)

		__UpperCAmelCase    :     Optional[Any]													=	evaluate.load('''glue''' ,   '''mrpc'''						)

		# If the batch size is too big we use gradient accumulation
		__UpperCAmelCase    :     List[str]													=	1
		if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
				__UpperCAmelCase    :     Tuple													=	batch_size // MAX_GPU_BATCH_SIZE
				__UpperCAmelCase    :     List[str]													=	MAX_GPU_BATCH_SIZE

		set_seed(_UpperCAmelCase						)
		__UpperCAmelCase							,					__UpperCAmelCase    :     Union[str, Any]													=	get_dataloaders(_UpperCAmelCase ,   _UpperCAmelCase						)
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		__UpperCAmelCase    :     List[str]													=	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).
		__UpperCAmelCase    :     Tuple													=	model.to(accelerator.device						)

		# Instantiate optimizer
		__UpperCAmelCase    :     Any													=	AdamW(params=model.parameters() ,   lr=_UpperCAmelCase						)

		# Instantiate scheduler
		__UpperCAmelCase    :     str													=	get_linear_schedule_with_warmup(
		    optimizer=_UpperCAmelCase ,   num_warmup_steps=1_00 ,   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.
		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase    :     List[str]													=	accelerator.prepare(
		    _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						)

		# Now we train the model
		for epoch in range(_UpperCAmelCase						):
				model.train()
				for step, batch in enumerate(_UpperCAmelCase						):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device						)
						__UpperCAmelCase    :     int													=	model(**_UpperCAmelCase						)
						__UpperCAmelCase    :     str													=	outputs.loss
						__UpperCAmelCase    :     str													=	loss / gradient_accumulation_steps
						accelerator.backward(_UpperCAmelCase						)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

				model.eval()
				__UpperCAmelCase    :     Tuple													=	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						)
						with torch.no_grad():
								__UpperCAmelCase    :     Dict													=	model(**_UpperCAmelCase						)
						__UpperCAmelCase    :     str													=	outputs.logits.argmax(dim=-1						)
						__UpperCAmelCase							,					__UpperCAmelCase    :     List[Any]													=	accelerator.gather((predictions, batch['''labels'''])						)
						# New Code #
						# First we check if it's a distributed system
						if accelerator.use_distributed:
								# Then see if we're on the last batch of our eval dataloader
								if step == len(_UpperCAmelCase						) - 1:
										# Last batch needs to be truncated on distributed systems as it contains additional samples
										__UpperCAmelCase    :     Dict													=	predictions[: len(eval_dataloader.dataset						) - samples_seen]
										__UpperCAmelCase    :     List[str]													=	references[: len(eval_dataloader.dataset						) - samples_seen]
								else:
										# Otherwise we add the number of samples seen
										samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
						metric.add_batch(
						    predictions=_UpperCAmelCase ,   references=_UpperCAmelCase ,   )

				__UpperCAmelCase    :     Tuple													=	metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(f'epoch {epoch}:' ,   _UpperCAmelCase						)

def        a						(      ):







		'''simple docstring'''





		__UpperCAmelCase    :     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.'''						)
		__UpperCAmelCase    :     int													=	parser.parse_args()
		__UpperCAmelCase    :     Union[str, Any]													=	{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
		training_function(_UpperCAmelCase ,   _UpperCAmelCase						)


if __name__ == "__main__":
				main()





