



import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Optional[Any]			,       UpperCAmelCase__			:str			,       UpperCAmelCase__			:str       ):






 '''simple docstring'''

 a							=		TaConfig.from_json_file(UpperCAmelCase__       )
 print(F"""Building PyTorch model from configuration: {config}"""       )
 a							=		TaForConditionalGeneration(UpperCAmelCase__       )

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(UpperCAmelCase__			,       UpperCAmelCase__			,       UpperCAmelCase__       )

 # Save pytorch-model
 print(F"""Save PyTorch model to {pytorch_dump_path}"""       )
 model.save_pretrained(UpperCAmelCase__       )


if __name__ == "__main__":
     A_		:  List[Any]										=  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
     )
     parser.add_argument(
         '''--config_file''',
         default=None,
         type=str,
         required=True,
         help=(
             '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
         ),
     )
     parser.add_argument(
         '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )
     A_		:  Any										=  parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






'''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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


UpperCamelCase__    :     Union[str, Any]			  =   16
UpperCamelCase__    :     Optional[int]			  =   32



def     UpperCAmelCase     (    a_      ,		a_		)   ->       List[Any]:



   """simple docstring"""






   if args.with_tracking:
      A_  :		Union[str, Any]						=					Accelerator(
          cpu=args.cpu      ,		mixed_precision=args.mixed_precision      ,		log_with="""all"""      ,		project_dir=args.project_dir		)
   else:
      A_  :		Optional[Any]						=					Accelerator(cpu=args.cpu      ,		mixed_precision=args.mixed_precision		)

   if hasattr(args.checkpointing_steps      ,		"""isdigit"""		):
      if args.checkpointing_steps == "epoch":
         A_  :		Tuple						=					args.checkpointing_steps
      elif args.checkpointing_steps.isdigit():
         A_  :		List[str]						=					int(args.checkpointing_steps		)
      else:
         raise ValueError(
             F"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."		)
   else:
      A_  :		Optional[Any]						=					None
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   A_  :		str						=					config["""lr"""]
   A_  :		List[str]						=					int(config["""num_epochs"""]		)
   A_  :		Dict						=					int(config["""seed"""]		)
   A_  :		str						=					int(config["""batch_size"""]		)

   # We need to initialize the trackers we use, and also store our configuration
   if args.with_tracking:
      A_  :		str						=					os.path.split(a_		)[-1].split("""."""		)[0]
      accelerator.init_trackers(a_      ,		a_		)

   A_  :		Union[str, Any]						=					AutoTokenizer.from_pretrained("""bert-base-cased"""		)
   A_  :		Optional[int]						=					load_dataset("""glue"""      ,		"""mrpc"""		)
   A_  :		List[str]						=					evaluate.load("""glue"""      ,		"""mrpc"""		)

   def tokenize_function(a_		):
      # max_length=None => use the model max length (it's actually the default)
      A_  :		Tuple						=					tokenizer(examples["""sentence1"""]      ,		examples["""sentence2"""]      ,		truncation=a_      ,		max_length=a_		)
      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():
      A_  :		Dict						=					datasets.map(
          a_      ,		batched=a_      ,		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
   A_  :		int						=					tokenized_datasets.rename_column("""label"""      ,		"""labels"""		)

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

   def collate_fn(a_		):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      A_  :		List[Any]						=					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":
         A_  :		Any						=					1_6
      elif accelerator.mixed_precision != "no":
         A_  :		Optional[int]						=					8
      else:
         A_  :		Optional[Any]						=					None

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

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

   set_seed(a_		)

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

   # 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).
   A_  :		Union[str, Any]						=					model.to(accelerator.device		)

   # Instantiate optimizer
   A_  :		Any						=					AdamW(params=model.parameters()      ,		lr=a_		)

   # Instantiate scheduler
   A_  :		Dict						=					get_linear_schedule_with_warmup(
       optimizer=a_      ,		num_warmup_steps=1_0_0      ,		num_training_steps=(len(a_		) * 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.
   A_						,  A_						,  A_						,  A_						,  A_  :		List[str]						=					accelerator.prepare(
       a_      ,		a_      ,		a_      ,		a_      ,		a_		)

   # We need to keep track of how many total steps we have iterated over
   A_  :		Optional[int]						=					0
   # We also need to keep track of the stating epoch so files are named properly
   A_  :		Union[str, Any]						=					0

   # Potentially load in the weights and states from a previous save
   if args.resume_from_checkpoint:
      if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
         accelerator.print(F"Resumed from checkpoint: {args.resume_from_checkpoint}"		)
         accelerator.load_state(args.resume_from_checkpoint		)
         A_  :		int						=					os.path.basename(args.resume_from_checkpoint		)
      else:
         # Get the most recent checkpoint
         A_  :		int						=					[f.name for f in os.scandir(os.getcwd()		) if f.is_dir()]
         dirs.sort(key=os.path.getctime		)
         A_  :		Optional[Any]						=					dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
      # Extract `epoch_{i}` or `step_{i}`
      A_  :		Optional[Any]						=					os.path.splitext(a_		)[0]

      if "epoch" in training_difference:
         A_  :		List[Any]						=					int(training_difference.replace("""epoch_"""      ,		""""""		)		) + 1
         A_  :		List[str]						=					None
      else:
         A_  :		Optional[int]						=					int(training_difference.replace("""step_"""      ,		""""""		)		)
         A_  :		Optional[int]						=					resume_step // len(a_		)
         resume_step -= starting_epoch * len(a_		)

    # Now we train the model
   for epoch in range(a_      ,		a_		):
      model.train()
      if args.with_tracking:
         A_  :		List[str]						=					0
      if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
         # We need to skip steps until we reach the resumed step
         A_  :		List[Any]						=					accelerator.skip_first_batches(a_      ,		a_		)
         overall_step += resume_step
      else:
         # After the first iteration though, we need to go back to the original dataloader
         A_  :		Tuple						=					train_dataloader
      for step, batch in enumerate(a_		):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device		)
         A_  :		List[Any]						=					model(**a_		)
         A_  :		str						=					outputs.loss
         A_  :		Tuple						=					loss / gradient_accumulation_steps
         # We keep track of the loss at each epoch
         if args.with_tracking:
            total_loss += loss.detach().float()
         accelerator.backward(a_		)
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         overall_step += 1

         if isinstance(a_      ,		a_		):
            A_  :		str						=					F"step_{overall_step}"
            if overall_step % checkpointing_steps == 0:
               if args.output_dir is not None:
                  A_  :		Dict						=					os.path.join(args.output_dir      ,		a_		)
               accelerator.save_state(a_		)

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

      A_  :		Union[str, Any]						=					metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"epoch {epoch}:"      ,		a_		)
      if args.with_tracking:
         accelerator.log(
             {
                 """accuracy""": eval_metric["""accuracy"""],
                 """f1""": eval_metric["""f1"""],
                 """train_loss""": total_loss.item() / len(a_		),
                 """epoch""": epoch,
             }      ,		step=a_      ,		)

      if checkpointing_steps == "epoch":
         A_  :		str						=					F"epoch_{epoch}"
         if args.output_dir is not None:
            A_  :		Dict						=					os.path.join(args.output_dir      ,		a_		)
         accelerator.save_state(a_		)

   if args.with_tracking:
      accelerator.end_training()



def     UpperCAmelCase     (    )   ->       str:



   """simple docstring"""






   A_  :		List[Any]						=					argparse.ArgumentParser(description="""Simple example of training script."""		)
   parser.add_argument(
       """--mixed_precision"""      ,		type=a_      ,		default=a_      ,		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(
       """--checkpointing_steps"""      ,		type=a_      ,		default=a_      ,		help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""      ,		)
   parser.add_argument(
       """--resume_from_checkpoint"""      ,		type=a_      ,		default=a_      ,		help="""If the training should continue from a checkpoint folder."""      ,		)
   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(
       """--output_dir"""      ,		type=a_      ,		default="""."""      ,		help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""      ,		)
   parser.add_argument(
       """--project_dir"""      ,		type=a_      ,		default="""logs"""      ,		help="""Location on where to store experiment tracking logs` and relevent project information"""      ,		)
   A_  :		Tuple						=					parser.parse_args()
   A_  :		Union[str, Any]						=					{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
   training_function(a_      ,		a_		)


if __name__ == "__main__":
  main()
