


'''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_	:     Any							     =	16
UpperCAmelCase_	:     Dict							     =	32







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





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

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

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

				_SCREAMING_SNAKE_CASE				: Tuple  	=   AutoTokenizer.from_pretrained("""bert-base-cased"""  )
				_SCREAMING_SNAKE_CASE				: Dict  	=   load_dataset("""glue"""							,   """mrpc"""  )
				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   evaluate.load("""glue"""							,   """mrpc"""  )

				def tokenize_function(SCREAMING_SNAKE_CASE__  ):
								# max_length=None => use the model max length (it's actually the default)
								_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   tokenizer(examples["""sentence1"""]							,   examples["""sentence2"""]							,   truncation=SCREAMING_SNAKE_CASE__							,   max_length=SCREAMING_SNAKE_CASE__  )
								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():
								_SCREAMING_SNAKE_CASE				: Any  	=   datasets.map(
								    SCREAMING_SNAKE_CASE__							,   batched=SCREAMING_SNAKE_CASE__							,   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
				_SCREAMING_SNAKE_CASE				: List[Any]  	=   tokenized_datasets.rename_column("""label"""							,   """labels"""  )

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

				def collate_fn(SCREAMING_SNAKE_CASE__  ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   128 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												_SCREAMING_SNAKE_CASE				: Dict  	=   16
								elif accelerator.mixed_precision != "no":
												_SCREAMING_SNAKE_CASE				: int  	=   8
								else:
												_SCREAMING_SNAKE_CASE				: str  	=   None

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

				# Instantiate dataloaders.
				_SCREAMING_SNAKE_CASE				: Dict  	=   DataLoader(
				    tokenized_datasets["""train"""]							,   shuffle=SCREAMING_SNAKE_CASE__							,   collate_fn=SCREAMING_SNAKE_CASE__							,   batch_size=SCREAMING_SNAKE_CASE__  )
				_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   DataLoader(
				    tokenized_datasets["""validation"""]							,   shuffle=SCREAMING_SNAKE_CASE__							,   collate_fn=SCREAMING_SNAKE_CASE__							,   batch_size=SCREAMING_SNAKE_CASE__  )

				set_seed(SCREAMING_SNAKE_CASE__  )

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

				# 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).
				_SCREAMING_SNAKE_CASE				: Any  	=   model.to(accelerator.device  )

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

				# Instantiate scheduler
				_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   get_linear_schedule_with_warmup(
				    optimizer=SCREAMING_SNAKE_CASE__							,   num_warmup_steps=100							,   num_training_steps=(len(SCREAMING_SNAKE_CASE__  ) * 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.
				_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: Optional[Any]  	=   accelerator.prepare(
				    SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )

				# We need to keep track of how many total steps we have iterated over
				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   0
				# We also need to keep track of the stating epoch so files are named properly
				_SCREAMING_SNAKE_CASE				: Optional[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  )
												_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   os.path.basename(args.resume_from_checkpoint  )
								else:
												# Get the most recent checkpoint
												_SCREAMING_SNAKE_CASE				: int  	=   [f.name for f in os.scandir(os.getcwd()  ) if f.is_dir()]
												dirs.sort(key=os.path.getctime  )
												_SCREAMING_SNAKE_CASE				: Dict  	=   dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
								# Extract `epoch_{i}` or `step_{i}`
								_SCREAMING_SNAKE_CASE				: str  	=   os.path.splitext(SCREAMING_SNAKE_CASE__  )[0]

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

    # Now we train the model
				for epoch in range(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):
								model.train()
								if args.with_tracking:
												_SCREAMING_SNAKE_CASE				: 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
												_SCREAMING_SNAKE_CASE				: List[str]  	=   accelerator.skip_first_batches(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
												overall_step += resume_step
								else:
												# After the first iteration though, we need to go back to the original dataloader
												_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   train_dataloader
								for step, batch in enumerate(SCREAMING_SNAKE_CASE__  ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device  )
												_SCREAMING_SNAKE_CASE				: Tuple  	=   model(**SCREAMING_SNAKE_CASE__  )
												_SCREAMING_SNAKE_CASE				: Optional[int]  	=   outputs.loss
												_SCREAMING_SNAKE_CASE				: List[Any]  	=   loss / gradient_accumulation_steps
												# We keep track of the loss at each epoch
												if args.with_tracking:
																total_loss += loss.detach().float()
												accelerator.backward(SCREAMING_SNAKE_CASE__  )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

												overall_step += 1

												if isinstance(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):
																_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   f"""step_{overall_step}"""
																if overall_step % checkpointing_steps == 0:
																				if args.output_dir is not None:
																								_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   os.path.join(args.output_dir							,   SCREAMING_SNAKE_CASE__  )
																				accelerator.save_state(SCREAMING_SNAKE_CASE__  )

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

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

								if checkpointing_steps == "epoch":
												_SCREAMING_SNAKE_CASE				: Optional[int]  	=   f"""epoch_{epoch}"""
												if args.output_dir is not None:
																_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   os.path.join(args.output_dir							,   SCREAMING_SNAKE_CASE__  )
												accelerator.save_state(SCREAMING_SNAKE_CASE__  )

				if args.with_tracking:
								accelerator.end_training()







def     snake_case_     (						):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   argparse.ArgumentParser(description="""Simple example of training script."""  )
				parser.add_argument(
				    """--mixed_precision"""							,   type=SCREAMING_SNAKE_CASE__							,   default=SCREAMING_SNAKE_CASE__							,   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=SCREAMING_SNAKE_CASE__							,   default=SCREAMING_SNAKE_CASE__							,   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=SCREAMING_SNAKE_CASE__							,   default=SCREAMING_SNAKE_CASE__							,   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=SCREAMING_SNAKE_CASE__							,   default="""."""							,   help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""							,   )
				parser.add_argument(
				    """--project_dir"""							,   type=SCREAMING_SNAKE_CASE__							,   default="""logs"""							,   help="""Location on where to store experiment tracking logs` and relevent project information"""							,   )
				_SCREAMING_SNAKE_CASE				: List[str]  	=   parser.parse_args()
				_SCREAMING_SNAKE_CASE				: str  	=   {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
				training_function(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )


if __name__ == "__main__":
				main()








'''simple docstring'''


import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser







def     snake_case_     (						SCREAMING_SNAKE_CASE__=None							,   SCREAMING_SNAKE_CASE__=None  ):







				"""simple docstring"""





				return field(default_factory=lambda: default							,   metadata=SCREAMING_SNAKE_CASE__  )





@dataclass
class 					lowercase__						:





							'''simple docstring'''


							A_							:   str   			=						field(
							    metadata={"""help""": """The csv file to plot."""}  ,  )
							A_							:   bool   			=						field(
							    default=_snake_case  ,  metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}  ,  )
							A_							:   bool   			=						field(
							    default=_snake_case  ,  metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}  ,  )
							A_							:   bool   			=						field(
							    default=_snake_case  ,  metadata={"""help""": """Disable logarithmic scale when plotting"""}  ,  )
							A_							:   bool   			=						field(
							    default=_snake_case  ,  metadata={
							        """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
							    }  ,  )
							A_							:   Optional[str]   			=						field(
							    default=_snake_case  ,  metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}  ,  )
							A_							:   Optional[List[str]]   			=						list_field(
							    default=_snake_case  ,  metadata={"""help""": """List of model names that are used instead of the ones in the csv file."""}      )







def     snake_case_     (						SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				try:
								int(SCREAMING_SNAKE_CASE__  )
								return True
				except ValueError:
								return False







def     snake_case_     (						SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				try:
								float(SCREAMING_SNAKE_CASE__  )
								return True
				except ValueError:
								return False





class 					lowercase__						:





							'''simple docstring'''


							def __init__(					self					,      __snake_case						):
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   args
											_SCREAMING_SNAKE_CASE				: Dict  	=   defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}						)

											with open(self.args.csv_file					,      newline=""""""						) as csv_file:
															_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   csv.DictReader(__snake_case						)
															for row in reader:
																			_SCREAMING_SNAKE_CASE				: int  	=   row["""model"""]
																			self.result_dict[model_name]["bsz"].append(int(row["""batch_size"""]						)						)
																			self.result_dict[model_name]["seq_len"].append(int(row["""sequence_length"""]						)						)
																			if can_convert_to_int(row["""result"""]						):
																							# value is not None
																							_SCREAMING_SNAKE_CASE				: Any  	=   int(row["""result"""]						)
																			elif can_convert_to_float(row["""result"""]						):
																							# value is not None
																							_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   float(row["""result"""]						)






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: List[Any]  	=   plt.subplots()
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   """Time usage""" if self.args.is_time else """Memory usage"""
											_SCREAMING_SNAKE_CASE				: int  	=   title_str + """ for training""" if self.args.is_train else title_str + """ for inference"""

											if not self.args.no_log_scale:
															# set logarithm scales
															ax.set_xscale("""log"""						)
															ax.set_yscale("""log"""						)

											for axis in [ax.xaxis, ax.yaxis]:
															axis.set_major_formatter(ScalarFormatter()						)

											for model_name_idx, model_name in enumerate(self.result_dict.keys()						):
															_SCREAMING_SNAKE_CASE				: str  	=   sorted(set(self.result_dict[model_name]["""bsz"""]						)						)
															_SCREAMING_SNAKE_CASE				: List[str]  	=   sorted(set(self.result_dict[model_name]["""seq_len"""]						)						)
															_SCREAMING_SNAKE_CASE				: Optional[int]  	=   self.result_dict[model_name]["""result"""]

															((_SCREAMING_SNAKE_CASE)	, (_SCREAMING_SNAKE_CASE))				: Tuple  	=   (
															    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
															)

															_SCREAMING_SNAKE_CASE				: Any  	=   (
															    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
															)

															for inner_loop_value in inner_loop_array:
																			if self.args.plot_along_batch:
																							_SCREAMING_SNAKE_CASE				: Any  	=   np.asarray(
																							    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]					,      dtype=__snake_case					,      )
																			else:
																							_SCREAMING_SNAKE_CASE				: Dict  	=   np.asarray(
																							    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]					,      dtype=np.floataa					,      )

																			((_SCREAMING_SNAKE_CASE)	, (_SCREAMING_SNAKE_CASE))				: List[Any]  	=   (
																			    ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
																			)

																			_SCREAMING_SNAKE_CASE				: str  	=   np.asarray(__snake_case					,      __snake_case						)[: len(__snake_case						)]
																			plt.scatter(
																			    __snake_case					,      __snake_case					,      label=f"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""						)
																			plt.plot(__snake_case					,      __snake_case					,      """--"""						)

															title_str += f""" {label_model_name} vs."""

											_SCREAMING_SNAKE_CASE				: str  	=   title_str[:-4]
											_SCREAMING_SNAKE_CASE				: int  	=   """Time in s""" if self.args.is_time else """Memory in MB"""

											# plot
											plt.title(__snake_case						)
											plt.xlabel(__snake_case						)
											plt.ylabel(__snake_case						)
											plt.legend()

											if self.args.figure_png_file is not None:
															plt.savefig(self.args.figure_png_file						)
											else:
															plt.show()







def     snake_case_     (						):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Dict  	=   HfArgumentParser(SCREAMING_SNAKE_CASE__  )
				_SCREAMING_SNAKE_CASE				: str  	=   parser.parse_args_into_dataclasses()[0]
				_SCREAMING_SNAKE_CASE				: int  	=   Plot(args=SCREAMING_SNAKE_CASE__  )
				plot.plot()


if __name__ == "__main__":
				main()




