






"""simple docstring"""


import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator



def    lowerCAmelCase							(       __UpperCamelCase					):




  """simple docstring"""







  __A		     =					fname.split(os.path.sep					)[-1]
  return re.search(r'''^(.*)_\d+\.jpg$'''						,			UpperCAmelCase_					).groups()[0]


class    snake_case						(				_lowercase		):


   '''simple docstring'''

   def __init__(      self					:	List[str],			_lowerCamelCase					:	Tuple,			_lowerCamelCase					:	List[Any]=None,			_lowerCamelCase					:	str=None    ):

     '''simple docstring'''
     __A		     =					file_names
     __A		     =					image_transform
     __A		     =					label_to_id


   def __len__(      self					:	List[Any]    ):

     '''simple docstring'''
     return len(self.file_names    )






   def __getitem__(      self					:	List[Any],			_lowerCamelCase					:	str    ):

     '''simple docstring'''
     __A		     =					self.file_names[idx]
     __A		     =					PIL.Image.open(__snake_case    )
     __A		     =					raw_image.convert('''RGB'''    )
     if self.image_transform is not None:
       __A		     =					self.image_transform(__snake_case    )
     __A		     =					extract_label(__snake_case    )
     if self.label_to_id is not None:
       __A		     =					self.label_to_id[label]
     return {"image": image, "label": label}





def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  __A		     =					Accelerator(cpu=args.cpu						,			mixed_precision=args.mixed_precision					)

  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  __A		     =					config['lr']
  __A		     =					int(config['''num_epochs''']					)
  __A		     =					int(config['''seed''']					)
  __A		     =					int(config['''batch_size''']					)
  __A		     =					config['image_size']
  if not isinstance(UpperCAmelCase_						,			(list, tuple)					):
    __A		     =					(image_size, image_size)

  # Grab all the image filenames
  __A		     =					[os.path.join(args.data_dir						,			UpperCAmelCase_					) for fname in os.listdir(args.data_dir					) if fname.endswith('''.jpg'''					)]

  # Build the label correspondences
  __A		     =					[extract_label(UpperCAmelCase_					) for fname in file_names]
  __A		     =					list(set(UpperCAmelCase_					)					)
  id_to_label.sort()
  __A		     =					{lbl: i for i, lbl in enumerate(UpperCAmelCase_					)}

  # Set the seed before splitting the data.
  np.random.seed(UpperCAmelCase_					)
  torch.manual_seed(UpperCAmelCase_					)
  torch.cuda.manual_seed_all(UpperCAmelCase_					)

  # Split our filenames between train and validation
  __A		     =					np.random.permutation(len(UpperCAmelCase_					)					)
  __A		     =					int(0.8 * len(UpperCAmelCase_					)					)
  __A		     =					random_perm[:cut]
  __A		     =					random_perm[cut:]

  # For training we use a simple RandomResizedCrop
  __A		     =					Compose([RandomResizedCrop(UpperCAmelCase_						,			scale=(0.5, 1.0)					), ToTensor()]					)
  __A		     =					PetsDataset(
      [file_names[i] for i in train_split]						,			image_transform=UpperCAmelCase_						,			label_to_id=UpperCAmelCase_					)

  # For evaluation, we use a deterministic Resize
  __A		     =					Compose([Resize(UpperCAmelCase_					), ToTensor()]					)
  __A		     =					PetsDataset([file_names[i] for i in eval_split]						,			image_transform=UpperCAmelCase_						,			label_to_id=UpperCAmelCase_					)

  # Instantiate dataloaders.
  __A		     =					DataLoader(UpperCAmelCase_						,			shuffle=UpperCAmelCase_						,			batch_size=UpperCAmelCase_						,			num_workers=4					)
  __A		     =					DataLoader(UpperCAmelCase_						,			shuffle=UpperCAmelCase_						,			batch_size=UpperCAmelCase_						,			num_workers=4					)

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  __A		     =					create_model('''resnet50d'''						,			pretrained=UpperCAmelCase_						,			num_classes=len(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).
  __A		     =					model.to(accelerator.device					)

  # Freezing the base model
  for param in model.parameters():
    __A		     =					False
  for param in model.get_classifier().parameters():
    __A		     =					True

  # We normalize the batches of images to be a bit faster.
  __A		     =					torch.tensor(model.default_cfg['''mean''']					)[None, :, None, None].to(accelerator.device					)
  __A		     =					torch.tensor(model.default_cfg['''std''']					)[None, :, None, None].to(accelerator.device					)

  # Instantiate optimizer
  __A		     =					torch.optim.Adam(params=model.parameters()						,			lr=lr / 2_5					)

  # Instantiate learning rate scheduler
  __A		     =					OneCycleLR(optimizer=UpperCAmelCase_						,			max_lr=UpperCAmelCase_						,			epochs=UpperCAmelCase_						,			steps_per_epoch=len(UpperCAmelCase_					)					)

  # 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		     =					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`.
      __A		     =					{k: v.to(accelerator.device					) for k, v in batch.items()}
      __A		     =					(batch['image'] - mean) / std
      __A		     =					model(UpperCAmelCase_					)
      __A		     =					torch.nn.functional.cross_entropy(UpperCAmelCase_						,			batch['''label''']					)
      accelerator.backward(UpperCAmelCase_					)
      optimizer.step()
      lr_scheduler.step()
      optimizer.zero_grad()

    model.eval()
    __A		     =					0
    __A		     =					0
    for _, batch in enumerate(UpperCAmelCase_					):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      __A		     =					{k: v.to(accelerator.device					) for k, v in batch.items()}
      __A		     =					(batch['image'] - mean) / std
      with torch.no_grad():
        __A		     =					model(UpperCAmelCase_					)
      __A		     =					outputs.argmax(dim=-1					)
      __A		     =					accelerator.gather_for_metrics((predictions, batch['''label'''])					)
      __A		     =					predictions == references
      num_elems += accurate_preds.shape[0]
      accurate += accurate_preds.long().sum()

    __A		     =					accurate.item() / num_elems
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'epoch {epoch}: {1_0_0 * eval_metric:.2f}'					)



def    lowerCAmelCase							(       ):




  """simple docstring"""







  __A		     =					argparse.ArgumentParser(description='''Simple example of training script.'''					)
  parser.add_argument('''--data_dir'''						,			required=UpperCAmelCase_						,			help='''The data folder on disk.'''					)
  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(
      '''--checkpointing_steps'''						,			type=UpperCAmelCase_						,			default=UpperCAmelCase_						,			help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''						,			)
  parser.add_argument('''--cpu'''						,			action='''store_true'''						,			help='''If passed, will train on the CPU.'''					)
  __A		     =					parser.parse_args()
  __A		     =					{'lr': 3e-2, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 6_4, 'image_size': 2_2_4}
  training_function(UpperCAmelCase_						,			UpperCAmelCase_					)


if __name__ == "__main__":
       main()







import argparse
import itertools
import operator
import sys
from collections import OrderedDict

from run_eval import datetime_now, run_generate

from utils import ROUGE_KEYS


# A table of supported tasks and the list of scores in the order of importance to be sorted by.
# To add a new task, simply list the score names that `run_eval.run_generate()` returns
lowerCAmelCase__             =		{
    """translation""": ["""bleu"""],
    """summarization""": ROUGE_KEYS,
}






def        lowerCamelCase_  (				UpperCAmelCase_				:  Optional[int]						)    ->					Any:




	'''simple docstring'''




	_UpperCamelCase :		str								=    search.split()
	_UpperCamelCase :		str								=    dict((g.split('='						) for g in groups)						)
	_UpperCamelCase :		List[Any]								=    list(entries.keys()						)
	_UpperCamelCase :		Dict								=    [[F'''--{k} {v}''' for v in vs.split(':'						)] for k, vs in entries.items()]
	_UpperCamelCase :		Dict								=    [list(UpperCAmelCase_						) for x in itertools.product(*UpperCAmelCase_						)]
	return matrix, entry_names






def        lowerCamelCase_  (				)    ->					int:




	'''simple docstring'''




	_UpperCamelCase :		Optional[Any]								=    sys.argv[0]

	_UpperCamelCase :		Tuple								=    argparse.ArgumentParser(
	    usage=(
	        '\n\nImportant: this script accepts all arguments `run_eval.py` accepts and then a few extra, therefore'
	        ' refer to `run_eval.py -h` for the complete list.'
	    )						)
	parser.add_argument(
	    '--search'				,							type=UpperCAmelCase_				,							required=UpperCAmelCase_				,							help='param space to search, e.g. "num_beams=5:10 length_penalty=0.8:1.0:1.2"'				,							)
	parser.add_argument(
	    '--bs'				,							type=UpperCAmelCase_				,							default=8				,							required=UpperCAmelCase_				,							help='initial batch size (may get reduced if it\'s too big)'						)
	parser.add_argument('--task'				,							type=UpperCAmelCase_				,							help='used for task_specific_params + metrics'						)
	parser.add_argument(
	    '--info'				,							nargs='?'				,							type=UpperCAmelCase_				,							const=datetime_now()				,							help=(
	        'add custom notes to be printed before the results table. If no value is passed, the current datetime'
	        ' string will be used.'
	    )				,							)
	_UpperCamelCase						,		_UpperCamelCase :		str								=    parser.parse_known_args()
	# we share some of the args
	args_main.extend(['--task', args.task]						)
	_UpperCamelCase :		str								=    [prog] + args_main

	# to support variations like translation_en_to_de"
	_UpperCamelCase :		Tuple								=    'translation' if 'translation' in args.task else 'summarization'

	_UpperCamelCase						,		_UpperCamelCase :		Optional[int]								=    parse_search_arg(args.search						)
	_UpperCamelCase :		List[Any]								=    task_score_names[task]  # score cols first
	_UpperCamelCase :		Optional[int]								=    {col: len(str(UpperCAmelCase_						)						) for col in col_names}
	_UpperCamelCase :		str								=    []
	for r in matrix:
		_UpperCamelCase :		Union[str, Any]								=    dict((x.replace('--'				,							''						).split() for x in r)						)
		_UpperCamelCase :		List[str]								=    ' '.join(UpperCAmelCase_						).split()
		args_exp.extend(['--bs', str(args.bs						)]						)  # in case we need to reduce its size due to CUDA OOM
		_UpperCamelCase :		Union[str, Any]								=    args_normal + args_exp

		# XXX: need to trap CUDA OOM and lower args.bs if that happens and retry

		_UpperCamelCase :		Any								=    run_generate(verbose=UpperCAmelCase_						)
		# make sure scores are first in the table
		_UpperCamelCase :		Optional[int]								=    OrderedDict()
		for score in task_score_names[task]:
			_UpperCamelCase :		Tuple								=    scores[score]
		result.update(UpperCAmelCase_						)
		results.append(UpperCAmelCase_						)

		# find widest entries
		for k, v in result.items():
			_UpperCamelCase :		Tuple								=    len(str(UpperCAmelCase_						)						)
			if l > col_widths[k]:
				_UpperCamelCase :		str								=    l

	_UpperCamelCase :		Optional[int]								=    sorted(UpperCAmelCase_				,							key=operator.itemgetter(*task_score_names[task]						)				,							reverse=UpperCAmelCase_						)
	print(' | '.join([F'''{col:{col_widths[col]}}''' for col in col_names]						)						)
	print(' | '.join([F'''{"-"*col_widths[col]}''' for col in col_names]						)						)
	for row in results_sorted:
		print(' | '.join([F'''{row[col]:{col_widths[col]}}''' for col in col_names]						)						)

	_UpperCamelCase :		Optional[int]								=    results_sorted[0]
	for score in task_score_names[task]:
		del best[score]
	_UpperCamelCase :		str								=    [F'''--{k} {v}''' for k, v in best.items()]
	_UpperCamelCase :		Optional[Any]								=    ['--bs', str(args.bs						)]
	if args.info:
		print(F'''\nInfo: {args.info}'''						)
	print('\nBest score args:'						)
	print(' '.join(args_main + best_args + dyn_args						)						)

	return results_sorted


if __name__ == "__main__":
							# Usage:
							# [normal-run_eval_search.py cmd plus] \
							# --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
							#
							# Example:
							# PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py $MODEL_NAME \
							# $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target \
							# --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation \
							# --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
							run_search()




