

'''simple docstring'''





import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


A_	= 16
A_	= 32







def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase = 16    ,__UpperCamelCase = "bert-base-cased"	)       ->			List[Any]:
					lowerCamelCase_  				=      AutoTokenizer.from_pretrained(__UpperCamelCase	)
					lowerCamelCase_  				=      load_dataset('glue'    ,'mrpc'	)

					def tokenize_function(__UpperCamelCase	):
										# max_length=None => use the model max length (it's actually the default)
										lowerCamelCase_  				=      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
					lowerCamelCase_  				=      datasets.map(
					    __UpperCamelCase    ,batched=__UpperCamelCase    ,remove_columns=['idx', 'sentence1', 'sentence2']    ,load_from_cache_file=__UpperCamelCase	)

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

					def collate_fn(__UpperCamelCase	):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										if accelerator.distributed_type == DistributedType.TPU:
															return tokenizer.pad(__UpperCamelCase    ,padding='max_length'    ,max_length=1_28    ,return_tensors='pt'	)
										return tokenizer.pad(__UpperCamelCase    ,padding='longest'    ,return_tensors='pt'	)

					# Instantiate dataloaders.
					lowerCamelCase_  				=      DataLoader(
					    tokenized_datasets['train']    ,shuffle=__UpperCamelCase    ,collate_fn=__UpperCamelCase    ,batch_size=__UpperCamelCase	)
					lowerCamelCase_  				=      DataLoader(
					    tokenized_datasets['validation']    ,shuffle=__UpperCamelCase    ,collate_fn=__UpperCamelCase    ,batch_size=__UpperCamelCase	)

					return train_dataloader, eval_dataloader







def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase	)       ->			Union[str, Any]:
					model.eval()
					lowerCamelCase_  				=      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():
															lowerCamelCase_  				=      model(**__UpperCamelCase	)
										lowerCamelCase_  				=      outputs.logits.argmax(dim=-1	)
										# It is slightly faster to call this once, than multiple times
										lowerCamelCase_				,lowerCamelCase_  				=      accelerator.gather(
										    (predictions, batch['labels'])	)  # If we are in a multiprocess environment, the last batch has duplicates
										if accelerator.use_distributed:
															if step == len(__UpperCamelCase	) - 1:
																				lowerCamelCase_  				=      predictions[: len(eval_dataloader.dataset	) - samples_seen]
																				lowerCamelCase_  				=      references[: len(eval_dataloader.dataset	) - samples_seen]
															else:
																				samples_seen += references.shape[0]
										metric.add_batch(
										    predictions=__UpperCamelCase    ,references=__UpperCamelCase    ,)

					lowerCamelCase_  				=      metric.compute()
					return eval_metric["accuracy"]







def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase	)       ->			List[str]:
					# Initialize accelerator
					lowerCamelCase_  				=      Accelerator()

					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					lowerCamelCase_  				=      config['lr']
					lowerCamelCase_  				=      int(config['num_epochs']	)
					lowerCamelCase_  				=      int(config['seed']	)
					lowerCamelCase_  				=      int(config['batch_size']	)
					lowerCamelCase_  				=      args.model_name_or_path

					set_seed(__UpperCamelCase	)
					lowerCamelCase_				,lowerCamelCase_  				=      get_dataloaders(__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase	)

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					lowerCamelCase_  				=      AutoModelForSequenceClassification.from_pretrained(__UpperCamelCase    ,return_dict=__UpperCamelCase	)

					# Instantiate optimizer
					lowerCamelCase_  				=      (
					    AdamW
					    if accelerator.state.deepspeed_plugin is None
					    or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
					    else DummyOptim
					)
					lowerCamelCase_  				=      optimizer_cls(params=model.parameters()    ,lr=__UpperCamelCase	)

					if accelerator.state.deepspeed_plugin is not None:
										lowerCamelCase_  				=      accelerator.state.deepspeed_plugin.deepspeed_config[
										    'gradient_accumulation_steps'
										]
					else:
										lowerCamelCase_  				=      1
					lowerCamelCase_  				=      (len(__UpperCamelCase	) * num_epochs) // gradient_accumulation_steps

					# Instantiate scheduler
					if (
					    accelerator.state.deepspeed_plugin is None
					    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
					):
										lowerCamelCase_  				=      get_linear_schedule_with_warmup(
										    optimizer=__UpperCamelCase    ,num_warmup_steps=0    ,num_training_steps=__UpperCamelCase    ,)
					else:
										lowerCamelCase_  				=      DummyScheduler(__UpperCamelCase    ,total_num_steps=__UpperCamelCase    ,warmup_num_steps=0	)

					# 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.
					lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_  				=      accelerator.prepare(
					    __UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase	)

					# We need to keep track of how many total steps we have iterated over
					lowerCamelCase_  				=      0
					# We also need to keep track of the stating epoch so files are named properly
					lowerCamelCase_  				=      0
					lowerCamelCase_  				=      evaluate.load('glue'    ,'mrpc'	)
					lowerCamelCase_  				=      num_epochs

					if args.partial_train_epoch is not None:
										lowerCamelCase_  				=      args.partial_train_epoch

					if args.resume_from_checkpoint:
										accelerator.load_state(args.resume_from_checkpoint	)
										lowerCamelCase_  				=      args.resume_from_checkpoint.split('epoch_'	)[1]
										lowerCamelCase_  				=      ''
										for char in epoch_string:
															if char.isdigit():
																				state_epoch_num += char
															else:
																				break
										lowerCamelCase_  				=      int(__UpperCamelCase	) + 1
										lowerCamelCase_  				=      evaluation_loop(__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase	)
										accelerator.print('resumed checkpoint performance:'    ,__UpperCamelCase	)
										accelerator.print('resumed checkpoint\'s scheduler\'s lr:'    ,lr_scheduler.get_lr()[0]	)
										accelerator.print('resumed optimizers\'s lr:'    ,optimizer.param_groups[0]['lr']	)
										with open(os.path.join(args.output_dir    ,f'''state_{starting_epoch-1}.json'''	)    ,'r'	) as f:
															lowerCamelCase_  				=      json.load(__UpperCamelCase	)
															assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
															assert (
															    resumed_state["lr"] == lr_scheduler.get_lr()[0]
															), "Scheduler learning rate mismatch, loading from checkpoint failed"
															assert (
															    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
															), "Optimizer learning rate mismatch, loading from checkpoint failed"
															assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
															return

    # Now we train the model
					lowerCamelCase_  				=      {}
					for epoch in range(__UpperCamelCase    ,__UpperCamelCase	):
										model.train()
										for step, batch in enumerate(__UpperCamelCase	):
															lowerCamelCase_  				=      model(**__UpperCamelCase	)
															lowerCamelCase_  				=      outputs.loss
															lowerCamelCase_  				=      loss / gradient_accumulation_steps
															accelerator.backward(__UpperCamelCase	)
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

															overall_step += 1
										lowerCamelCase_  				=      f'''epoch_{epoch}'''
										lowerCamelCase_  				=      os.path.join(args.output_dir    ,__UpperCamelCase	)
										accelerator.save_state(__UpperCamelCase	)
										lowerCamelCase_  				=      evaluation_loop(__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase	)
										lowerCamelCase_  				=      accuracy
										lowerCamelCase_  				=      lr_scheduler.get_lr()[0]
										lowerCamelCase_  				=      optimizer.param_groups[0]['lr']
										lowerCamelCase_  				=      epoch
										lowerCamelCase_  				=      overall_step
										accelerator.print(f'''epoch {epoch}:'''    ,__UpperCamelCase	)

										accelerator.wait_for_everyone()
										if accelerator.is_main_process:
															with open(os.path.join(args.output_dir    ,f'''state_{epoch}.json'''	)    ,'w'	) as f:
																				json.dump(__UpperCamelCase    ,__UpperCamelCase	)







def   _UpperCamelCase	(		)       ->			str:
					lowerCamelCase_  				=      argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'	)
					parser.add_argument(
					    '--model_name_or_path'    ,type=__UpperCamelCase    ,default='bert-base-cased'    ,help='Path to pretrained model or model identifier from huggingface.co/models.'    ,required=__UpperCamelCase    ,)
					parser.add_argument(
					    '--output_dir'    ,type=__UpperCamelCase    ,default='.'    ,help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'    ,)
					parser.add_argument(
					    '--resume_from_checkpoint'    ,type=__UpperCamelCase    ,default=__UpperCamelCase    ,help='If the training should continue from a checkpoint folder.'    ,)
					parser.add_argument(
					    '--partial_train_epoch'    ,type=__UpperCamelCase    ,default=__UpperCamelCase    ,help='If passed, the training will stop after this number of epochs.'    ,)
					parser.add_argument(
					    '--num_epochs'    ,type=__UpperCamelCase    ,default=2    ,help='Number of train epochs.'    ,)
					lowerCamelCase_  				=      parser.parse_args()
					lowerCamelCase_  				=      {'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}

					training_function(__UpperCamelCase    ,__UpperCamelCase	)


if __name__ == "__main__":
				main()







import argparse

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


logging.set_verbosity_info()



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Optional[int]:
    # Initialise PyTorch model
    _lowercase			:				Optional[int] 						=						TaConfig.from_json_file(lowerCamelCase_					)
    print(F'''Building PyTorch model from configuration: {config}'''					)
    _lowercase			:				Union[str, Any] 						=						TaForConditionalGeneration(lowerCamelCase_					)

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

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


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE			: Optional[int]				   =					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."
 )
 SCREAMING_SNAKE_CASE			: Any				   =					parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


