
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


_snake_case      :							str     	=    16
_snake_case      :							str     	=    32

def  __snake_case							(	__magic_name__		,    __magic_name__ = 16		,    __magic_name__ = "bert-base-cased"						):







				'''simple docstring'''


				lowercase         =      AutoTokenizer.from_pretrained(__magic_name__						)
				lowercase         =      load_dataset("glue"		,    "mrpc"						)

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

				# Apply the method we just defined to all the examples in all the splits of the dataset
				lowercase         =      datasets.map(
				    __magic_name__		,    batched=__magic_name__		,    remove_columns=["idx", "sentence1", "sentence2"]		,    load_from_cache_file=__magic_name__						)

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

				def collate_fn(__magic_name__						):
								# 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(__magic_name__		,    padding="max_length"		,    max_length=128		,    return_tensors="pt"						)
								return tokenizer.pad(__magic_name__		,    padding="longest"		,    return_tensors="pt"						)

				# Instantiate dataloaders.
				lowercase         =      DataLoader(
				    tokenized_datasets["train"]		,    shuffle=__magic_name__		,    collate_fn=__magic_name__		,    batch_size=__magic_name__						)
				lowercase         =      DataLoader(
				    tokenized_datasets["validation"]		,    shuffle=__magic_name__		,    collate_fn=__magic_name__		,    batch_size=__magic_name__						)

				return train_dataloader, eval_dataloader

def  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__		,    __magic_name__						):







				'''simple docstring'''


				model.eval()
				lowercase         =      0
				for step, batch in enumerate(__magic_name__						):
								# We could avoid this line since we set the accelerator with `device_placement=True`.
								batch.to(accelerator.device						)
								with torch.no_grad():
												lowercase         =      model(**__magic_name__						)
								lowercase         =      outputs.logits.argmax(dim=-1						)
								# It is slightly faster to call this once, than multiple times
								lowercase     ,	lowercase         =      accelerator.gather(
								    (predictions, batch["labels"])						)  # If we are in a multiprocess environment, the last batch has duplicates
								if accelerator.use_distributed:
												if step == len(__magic_name__						) - 1:
																lowercase         =      predictions[: len(eval_dataloader.dataset						) - samples_seen]
																lowercase         =      references[: len(eval_dataloader.dataset						) - samples_seen]
												else:
																samples_seen += references.shape[0]
								metric.add_batch(
								    predictions=__magic_name__		,    references=__magic_name__		,    )

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

def  __snake_case							(	__magic_name__		,    __magic_name__						):







				'''simple docstring'''


				lowercase         =      Accelerator()

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

				set_seed(__magic_name__						)
				lowercase     ,	lowercase         =      get_dataloaders(__magic_name__		,    __magic_name__		,    __magic_name__						)

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

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

				if accelerator.state.deepspeed_plugin is not None:
								lowercase         =      accelerator.state.deepspeed_plugin.deepspeed_config[
								    "gradient_accumulation_steps"
								]
				else:
								lowercase         =      1
				lowercase         =      (len(__magic_name__						) * num_epochs) // gradient_accumulation_steps

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

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

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

				if args.resume_from_checkpoint:
								accelerator.load_state(args.resume_from_checkpoint						)
								lowercase         =      args.resume_from_checkpoint.split("epoch_"						)[1]
								lowercase         =      ""
								for char in epoch_string:
												if char.isdigit():
																state_epoch_num += char
												else:
																break
								lowercase         =      int(__magic_name__						) + 1
								lowercase         =      evaluation_loop(__magic_name__		,    __magic_name__		,    __magic_name__		,    __magic_name__						)
								accelerator.print("resumed checkpoint performance:"		,    __magic_name__						)
								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:
												lowercase         =      json.load(__magic_name__						)
												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
				lowercase         =      {}
				for epoch in range(__magic_name__		,    __magic_name__						):
								model.train()
								for step, batch in enumerate(__magic_name__						):
												lowercase         =      model(**__magic_name__						)
												lowercase         =      outputs.loss
												lowercase         =      loss / gradient_accumulation_steps
												accelerator.backward(__magic_name__						)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

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

								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(__magic_name__		,    __magic_name__						)

def  __snake_case							(	):







				'''simple docstring'''


				lowercase         =      argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."						)
				parser.add_argument(
				    "--model_name_or_path"		,    type=__magic_name__		,    default="bert-base-cased"		,    help="Path to pretrained model or model identifier from huggingface.co/models."		,    required=__magic_name__		,    )
				parser.add_argument(
				    "--output_dir"		,    type=__magic_name__		,    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=__magic_name__		,    default=__magic_name__		,    help="If the training should continue from a checkpoint folder."		,    )
				parser.add_argument(
				    "--partial_train_epoch"		,    type=__magic_name__		,    default=__magic_name__		,    help="If passed, the training will stop after this number of epochs."		,    )
				parser.add_argument(
				    "--num_epochs"		,    type=__magic_name__		,    default=2		,    help="Number of train epochs."		,    )
				lowercase         =      parser.parse_args()
				lowercase         =      {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

				training_function(__magic_name__		,    __magic_name__						)


if __name__ == "__main__":
						main()


import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
						raise RuntimeError("At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training")

# TF training parameters
_snake_case      :							Any     	=    False
_snake_case      :							int     	=    False

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				return TrainCommand(__magic_name__						)





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						@staticmethod
						def        SCREAMING_SNAKE_CASE(			lowerCAmelCase__							:ArgumentParser     )  ->str:
										lowercase         =      parser.add_parser("train"		,    help="CLI tool to train a model on a task."     )

										train_parser.add_argument(
										    "--train_data"		,    type=lowerCAmelCase__		,    required=lowerCAmelCase__		,    help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."		,    )
										train_parser.add_argument(
										    "--column_label"		,    type=lowerCAmelCase__		,    default=0		,    help="Column of the dataset csv file with example labels."     )
										train_parser.add_argument(
										    "--column_text"		,    type=lowerCAmelCase__		,    default=1		,    help="Column of the dataset csv file with example texts."     )
										train_parser.add_argument(
										    "--column_id"		,    type=lowerCAmelCase__		,    default=2		,    help="Column of the dataset csv file with example ids."     )
										train_parser.add_argument(
										    "--skip_first_row"		,    action="store_true"		,    help="Skip the first row of the csv file (headers)."     )

										train_parser.add_argument("--validation_data"		,    type=lowerCAmelCase__		,    default=""		,    help="path to validation dataset."     )
										train_parser.add_argument(
										    "--validation_split"		,    type=lowerCAmelCase__		,    default=0.1		,    help="if validation dataset is not provided, fraction of train dataset to use as validation dataset."		,    )

										train_parser.add_argument("--output"		,    type=lowerCAmelCase__		,    default="./"		,    help="path to saved the trained model."     )

										train_parser.add_argument(
										    "--task"		,    type=lowerCAmelCase__		,    default="text_classification"		,    help="Task to train the model on."     )
										train_parser.add_argument(
										    "--model"		,    type=lowerCAmelCase__		,    default="bert-base-uncased"		,    help="Model's name or path to stored model."     )
										train_parser.add_argument("--train_batch_size"		,    type=lowerCAmelCase__		,    default=32		,    help="Batch size for training."     )
										train_parser.add_argument("--valid_batch_size"		,    type=lowerCAmelCase__		,    default=64		,    help="Batch size for validation."     )
										train_parser.add_argument("--learning_rate"		,    type=lowerCAmelCase__		,    default=3E-5		,    help="Learning rate."     )
										train_parser.add_argument("--adam_epsilon"		,    type=lowerCAmelCase__		,    default=1E-0_8		,    help="Epsilon for Adam optimizer."     )
										train_parser.set_defaults(func=lowerCAmelCase__     )





						def __init__(			self							:Optional[int]		,    lowerCAmelCase__							:Namespace     )  ->str:
										lowercase         =      logging.get_logger("transformers-cli/training"     )

										lowercase         =      "tf" if is_tf_available() else "torch"

										os.makedirs(args.output		,    exist_ok=lowerCAmelCase__     )
										lowercase         =      args.output

										lowercase         =      args.column_label
										lowercase         =      args.column_text
										lowercase         =      args.column_id

										self.logger.info(F'''Loading {args.task} pipeline for {args.model}'''     )
										if args.task == "text_classification":
														lowercase         =      TextClassificationPipeline.from_pretrained(args.model     )
										elif args.task == "token_classification":
														raise NotImplementedError
										elif args.task == "question_answering":
														raise NotImplementedError

										self.logger.info(F'''Loading dataset from {args.train_data}'''     )
										lowercase         =      Processor.create_from_csv(
										    args.train_data		,    column_label=args.column_label		,    column_text=args.column_text		,    column_id=args.column_id		,    skip_first_row=args.skip_first_row		,    )
										lowercase         =      None
										if args.validation_data:
														self.logger.info(F'''Loading validation dataset from {args.validation_data}'''     )
														lowercase         =      Processor.create_from_csv(
														    args.validation_data		,    column_label=args.column_label		,    column_text=args.column_text		,    column_id=args.column_id		,    skip_first_row=args.skip_first_row		,    )

										lowercase         =      args.validation_split
										lowercase         =      args.train_batch_size
										lowercase         =      args.valid_batch_size
										lowercase         =      args.learning_rate
										lowercase         =      args.adam_epsilon





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->List[str]:
										if self.framework == "tf":
														return self.run_tf()
										return self.run_torch()





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->Dict:
										raise NotImplementedError





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->str:
										self.pipeline.fit(
										    self.train_dataset		,    validation_data=self.valid_dataset		,    validation_split=self.validation_split		,    learning_rate=self.learning_rate		,    adam_epsilon=self.adam_epsilon		,    train_batch_size=self.train_batch_size		,    valid_batch_size=self.valid_batch_size		,    )

										# Save trained pipeline
										self.pipeline.save_pretrained(self.output     )
