


import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin


class 	a__						(    lowerCAmelCase_    ,							unittest.TestCase     ):
       lowerCamelCase__:  List[Any]				     =  BertweetTokenizer
       lowerCamelCase__:  str				     =  False

       def     UpperCAmelCase( self   :  Tuple     ):
            super().setUp()

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            a_     :  Any				      =     ["""I""", """m""", """V@@""", """R@@""", """r""", """e@@"""]
            a_     :  Any				      =     dict(zip(lowerCamelCase_   ,				range(len(lowerCamelCase_     )     )     )     )
            a_     :  str				      =     ["""#version: 0.2""", """a m</w>"""]
            a_     :  int				      =     {"""unk_token""": """<unk>"""}

            a_     :  Optional[Any]				      =     os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES["""vocab_file"""]     )
            a_     :  List[str]				      =     os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES["""merges_file"""]     )
            with open(self.vocab_file   ,				"""w"""   ,				encoding="""utf-8"""     ) as fp:
                 for token in vocab_tokens:
                      fp.write(F'''{token} {vocab_tokens[token]}\n'''     )
            with open(self.merges_file   ,				"""w"""   ,				encoding="""utf-8"""     ) as fp:
                 fp.write("""\n""".join(lowerCamelCase_     )     )

       def     UpperCAmelCase( self   :  List[str]   ,				**lowerCamelCase_   :  List[str]     ):
            kwargs.update(self.special_tokens_map     )
            return BertweetTokenizer.from_pretrained(self.tmpdirname   ,				**lowerCamelCase_     )

       def     UpperCAmelCase( self   :  Any   ,				lowerCamelCase_   :  int     ):
            a_     :  Dict				      =     """I am VinAI Research"""
            a_     :  Tuple				      =     """I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"""
            return input_text, output_text

       def     UpperCAmelCase( self   :  List[Any]     ):
            a_     :  Optional[Any]				      =     BertweetTokenizer(self.vocab_file   ,				self.merges_file   ,				**self.special_tokens_map     )
            a_     :  str				      =     """I am VinAI Research"""
            a_     :  Tuple				      =     """I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h""".split()
            a_     :  Any				      =     tokenizer.tokenize(lowerCamelCase_     )
            self.assertListEqual(lowerCamelCase_   ,				lowerCamelCase_     )

            a_     :  Optional[Any]				      =     tokens + [tokenizer.unk_token]

            a_     :  Union[str, Any]				      =     [4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_     )   ,				lowerCamelCase_     )










'''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      lowercase  (  lowerCAmelCase :						Any):



				"""simple docstring"""


				_A :				Union[str, Any]				=  fname.split(os.path.sep)[-1]
				return re.search(R'''^(.*)_\d+\.jpg$'''   , lowerCAmelCase).groups()[0]



class  lowerCamelCase__				(						snake_case_						):


	"""simple docstring"""





	def __init__(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__=None   ,     UpperCAmelCase__=None			)       ->				int:
					_A :				Tuple				=  file_names
					_A :				Any				=  image_transform
					_A :				str				=  label_to_id




	def __len__(					self			)       ->				Dict:
					return len(self.file_names			)




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





def      lowercase  (  lowerCAmelCase :						str   , lowerCAmelCase :						List[Any]):



				"""simple docstring"""


				_A :				Union[str, Any]				=  Accelerator(cpu=args.cpu   , mixed_precision=args.mixed_precision)

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

				# Grab all the image filenames
				_A :				Optional[Any]				=  [os.path.join(args.data_dir   , lowerCAmelCase) for fname in os.listdir(args.data_dir) if fname.endswith('''.jpg''')]

				# Build the label correspondences
				_A :				str				=  [extract_label(lowerCAmelCase) for fname in file_names]
				_A :				List[Any]				=  list(set(lowerCAmelCase))
				id_to_label.sort()
				_A :				Dict				=  {lbl: i for i, lbl in enumerate(lowerCAmelCase)}

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

				# Split our filenames between train and validation
				_A :				Dict				=  np.random.permutation(len(lowerCAmelCase))
				_A :				List[str]				=  int(0.8 * len(lowerCAmelCase))
				_A :				Tuple				=  random_perm[:cut]
				_A :				Union[str, Any]				=  random_perm[cut:]

				# For training we use a simple RandomResizedCrop
				_A :				str				=  Compose([RandomResizedCrop(lowerCAmelCase   , scale=(0.5, 1.0)), ToTensor()])
				_A :				Tuple				=  PetsDataset(
				    [file_names[i] for i in train_split]   , image_transform=lowerCAmelCase   , label_to_id=lowerCAmelCase)

				# For evaluation, we use a deterministic Resize
				_A :				List[Any]				=  Compose([Resize(lowerCAmelCase), ToTensor()])
				_A :				str				=  PetsDataset([file_names[i] for i in eval_split]   , image_transform=lowerCAmelCase   , label_to_id=lowerCAmelCase)

				# Instantiate dataloaders.
				_A :				str				=  DataLoader(lowerCAmelCase   , shuffle=lowerCAmelCase   , batch_size=lowerCAmelCase   , num_workers=4)
				_A :				Optional[int]				=  DataLoader(lowerCAmelCase   , shuffle=lowerCAmelCase   , batch_size=lowerCAmelCase   , num_workers=4)

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				_A :				Tuple				=  create_model('''resnet50d'''   , pretrained=lowerCAmelCase   , num_classes=len(lowerCAmelCase))

				# 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 :				Tuple				=  model.to(accelerator.device)

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

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

				# Instantiate optimizer
				_A :				Optional[Any]				=  torch.optim.Adam(params=model.parameters()   , lr=lr / 25)

				# Instantiate learning rate scheduler
				_A :				List[str]				=  OneCycleLR(optimizer=lowerCAmelCase   , max_lr=lowerCAmelCase   , epochs=lowerCAmelCase   , steps_per_epoch=len(lowerCAmelCase))

				# 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 :				List[str]				=  accelerator.prepare(
				    lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase)

				# Now we train the model
				for epoch in range(lowerCAmelCase):
								model.train()
								for step, batch in enumerate(lowerCAmelCase):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												_A :				Optional[Any]				=  {k: v.to(accelerator.device) for k, v in batch.items()}
												_A :				Tuple				=  (batch['''image'''] - mean) / std
												_A :				Any				=  model(lowerCAmelCase)
												_A :				Optional[Any]				=  torch.nn.functional.cross_entropy(lowerCAmelCase   , batch['''label'''])
												accelerator.backward(lowerCAmelCase)
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

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

								_A :				int				=  accurate.item() / num_elems
								# Use accelerator.print to print only on the main process.
								accelerator.print(f"""epoch {epoch}: {100 * eval_metric:.2f}""")





def      lowercase  (  ):



				"""simple docstring"""


				_A :				Any				=  argparse.ArgumentParser(description='''Simple example of training script.''')
				parser.add_argument('''--data_dir'''   , required=lowerCAmelCase   , help='''The data folder on disk.''')
				parser.add_argument(
				    '''--mixed_precision'''   , type=lowerCAmelCase   , default=lowerCAmelCase   , 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=lowerCAmelCase   , default=lowerCAmelCase   , 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 :				Optional[int]				=  parser.parse_args()
				_A :				Optional[Any]				=  {'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
				training_function(lowerCAmelCase   , lowerCAmelCase)


if __name__ == "__main__":
	main()
