
"""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  _snake_case			(						snake_case__				:       List[str]       ):
	A  				=				fname.split(os.path.sep       )[-1]
	return re.search(r'^(.*)_\d+\.jpg$'		, snake_case__       ).groups()[0]







class 			lowerCAmelCase_			( _lowercase      ):



						'''simple docstring'''


						def __init__(	self       : List[Any]					,A_       : Optional[Any]					,A_       : Dict=None					,A_       : List[str]=None )       ->				Tuple:
							A  				=				file_names
							A  				=				image_transform
							A  				=				label_to_id


						def __len__(	self       : int )       ->				Optional[Any]:
							return len(self.file_names )




						def __getitem__(	self       : Union[str, Any]					,A_       : List[str] )       ->				Optional[int]:
							A  				=				self.file_names[idx]
							A  				=				PIL.Image.open(A_ )
							A  				=				raw_image.convert('RGB' )
							if self.image_transform is not None:
								A  				=				self.image_transform(A_ )
							A  				=				extract_label(A_ )
							if self.label_to_id is not None:
								A  				=				self.label_to_id[label]
							return {"image": image, "label": label}






def  _snake_case			(						snake_case__				:       List[Any]		, snake_case__				:       Optional[Any]       ):
	# Initialize accelerator
	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(snake_case__		, (list, tuple)       ):
		A  				=				(image_size, image_size)

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

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

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

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

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

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

	# Instantiate dataloaders.
	A  				=				DataLoader(snake_case__		, shuffle=snake_case__		, batch_size=snake_case__		, num_workers=4       )
	A  				=				DataLoader(snake_case__		, shuffle=snake_case__		, batch_size=snake_case__		, 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=snake_case__		, num_classes=len(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).
	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 / 25       )

	# Instantiate learning rate scheduler
	A  				=				OneCycleLR(optimizer=snake_case__		, max_lr=snake_case__		, epochs=snake_case__		, steps_per_epoch=len(snake_case__       )       )

	# 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  				=				accelerator.prepare(
	    snake_case__		, snake_case__		, snake_case__		, snake_case__		, snake_case__       )

	# Now we train the model
	for epoch in range(snake_case__       ):
		model.train()
		for step, batch in enumerate(snake_case__       ):
			# 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(snake_case__       )
			A  				=				torch.nn.functional.cross_entropy(snake_case__		, batch['label']       )
			accelerator.backward(snake_case__       )
			optimizer.step()
			lr_scheduler.step()
			optimizer.zero_grad()

		model.eval()
		A  				=				0
		A  				=				0
		for _, batch in enumerate(snake_case__       ):
			# 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(snake_case__       )
			A  				=				outputs.argmax(dim=-1       )
			A				, 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}: {100 * eval_metric:.2f}'       )






def  _snake_case			(						):
	A  				=				argparse.ArgumentParser(description='Simple example of training script.'       )
	parser.add_argument('--data_dir'		, required=snake_case__		, help='The data folder on disk.'       )
	parser.add_argument(
	    '--mixed_precision'		, type=snake_case__		, default=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(
	    '--checkpointing_steps'		, type=snake_case__		, default=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('--cpu'		, action='store_true'		, help='If passed, will train on the CPU.'       )
	A  				=				parser.parse_args()
	A  				=				{'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
	training_function(snake_case__		, snake_case__       )


if __name__ == "__main__":
		main()

"""simple docstring"""


import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 			lowerCAmelCase_			( _lowercase						,   unittest.TestCase      ):



						'''simple docstring'''


						_lowerCamelCase:      List[str] =      HerbertTokenizer
						_lowerCamelCase:      Any =      HerbertTokenizerFast
						_lowerCamelCase:      Optional[int] =      True


						def   _SCREAMING_SNAKE_CASE (	self       : Dict )       ->				Tuple:
							super().setUp()

							# Use a simpler test file without japanese/chinese characters
							with open(F'{get_tests_dir()}/fixtures/sample_text_no_unicode.txt'					,encoding='utf-8' ) as f_data:
								A  				=				f_data.read().replace('\n\n'					,'\n' ).strip()

							A  				=				[
							    '<s>',
							    '</s>',
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    'w</w>',
							    'r</w>',
							    't</w>',
							    'lo',
							    'low',
							    'er</w>',
							    'low</w>',
							    'lowest</w>',
							    'newer</w>',
							    'wider</w>',
							    ',</w>',
							    '<unk>',
							]
							A  				=				dict(zip(A_					,range(len(A_ ) ) ) )
							A  				=				['l o 123', 'lo w 1456', 'e r</w> 1789', '']

							A  				=				os.path.join(self.tmpdirname					,VOCAB_FILES_NAMES['vocab_file'] )
							A  				=				os.path.join(self.tmpdirname					,VOCAB_FILES_NAMES['merges_file'] )
							with open(self.vocab_file					,'w' ) as fp:
								fp.write(json.dumps(A_ ) )
							with open(self.merges_file					,'w' ) as fp:
								fp.write('\n'.join(A_ ) )


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple					,A_       : List[str] )       ->				int:
							A  				=				'lower newer'
							A  				=				'lower newer'
							return input_text, output_text


						def   _SCREAMING_SNAKE_CASE (	self       : List[Any] )       ->				int:
							A  				=				self.tokenizer_class(vocab_file=self.vocab_file					,merges_file=self.merges_file )

							A  				=				'lower'
							A  				=				['low', 'er</w>']
							A  				=				tokenizer.tokenize(A_ )
							self.assertListEqual(A_					,A_ )

							A  				=				tokens + ['<unk>']
							A  				=				[16, 17, 23]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(A_ )					,A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : Dict )       ->				Optional[int]:
							if not self.test_rust_tokenizer:
								return

							A  				=				self.get_tokenizer()
							A  				=				self.get_rust_tokenizer()

							A  				=				'lower,newer'

							A  				=				tokenizer.tokenize(A_ )
							A  				=				rust_tokenizer.tokenize(A_ )
							self.assertListEqual(A_					,A_ )

							A  				=				tokenizer.encode(A_					,add_special_tokens=A_ )
							A  				=				rust_tokenizer.encode(A_					,add_special_tokens=A_ )
							self.assertListEqual(A_					,A_ )

							A  				=				self.get_rust_tokenizer()
							A  				=				tokenizer.encode(A_ )
							A  				=				rust_tokenizer.encode(A_ )
							self.assertListEqual(A_					,A_ )




						@slow
						def   _SCREAMING_SNAKE_CASE (	self       : str )       ->				List[Any]:
							A  				=				self.tokenizer_class.from_pretrained('allegro/herbert-base-cased' )

							A  				=				tokenizer.encode('konstruowanie sekwencji'					,add_special_tokens=A_ )
							A  				=				tokenizer.encode('konstruowanie wielu sekwencji'					,add_special_tokens=A_ )

							A  				=				tokenizer.build_inputs_with_special_tokens(A_ )
							A  				=				tokenizer.build_inputs_with_special_tokens(A_					,A_ )

							assert encoded_sentence == [0] + text + [2]
							assert encoded_pair == [0] + text + [2] + text_a + [2]