




'''simple docstring'''







import sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


if TYPE_CHECKING:
						import sqlitea

						import sqlalchemy


_lowerCamelCase							:						Any								=  datasets.utils.logging.get_logger(__name__)





@dataclass
class      lowerCamelCase_							(       datasets.BuilderConfig							):




					'''simple docstring'''






					lowercase__    = None
					lowercase__    = None
					lowercase__    = None
					lowercase__    = True
					lowercase__    = None
					lowercase__    = None
					lowercase__    = None
					lowercase__    = 1_0_0_0_0
					lowercase__    = None






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							if self.sql is None:
									raise ValueError("sql must be specified" )
							if self.con is None:
									raise ValueError("con must be specified" )






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase = None,  ):
							_snake_case	       =       config_kwargs.copy()
							# We need to stringify the Selectable object to make its hash deterministic

							# The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
							_snake_case	       =       config_kwargs["sql"]
							if not isinstance(__UpperCamelCase,  __UpperCamelCase ):
									if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
											import sqlalchemy

											if isinstance(__UpperCamelCase,  sqlalchemy.sql.Selectable ):
													_snake_case	       =       sqlalchemy.create_engine(config_kwargs["con"].split("://" )[0] + "://" )
													_snake_case	       =       str(sql.compile(dialect=engine.dialect ) )
													_snake_case	       =       sql_str
											else:
													raise TypeError(
													    f'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(__UpperCamelCase )}: {sql}''' )
									else:
											raise TypeError(
											    f'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(__UpperCamelCase )}: {sql}''' )
							_snake_case	       =       config_kwargs["con"]
							if not isinstance(__UpperCamelCase,  __UpperCamelCase ):
									_snake_case	       =       id(__UpperCamelCase )
									logger.info(
									    f'''SQL connection \'con\' of type {type(__UpperCamelCase )} couldn\'t be hashed properly. To enable hashing, specify \'con\' as URI string instead.''' )

							return super().create_config_id(__UpperCamelCase,  custom_features=__UpperCamelCase )






					@property
					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       {
							    "index_col": self.index_col,
							    "columns": self.columns,
							    "params": self.params,
							    "coerce_float": self.coerce_float,
							    "parse_dates": self.parse_dates,
							}
							return pd_read_sql_kwargs





class      lowerCamelCase_							(       datasets.ArrowBasedBuilder							):




					'''simple docstring'''






					lowercase__    = SqlConfig






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							return datasets.DatasetInfo(features=self.config.features )






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase ):
							return [datasets.SplitGenerator(name=datasets.Split.TRAIN,  gen_kwargs={} )]






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase ):
							if self.config.features is not None:
									_snake_case	       =       self.config.features.arrow_schema
									if all(not require_storage_cast(__UpperCamelCase ) for feature in self.config.features.values() ):
											# cheaper cast
											_snake_case	       =       pa.Table.from_arrays([pa_table[field.name] for field in schema],  schema=__UpperCamelCase )
									else:
											# more expensive cast; allows str <-> int/float or str to Audio for example
											_snake_case	       =       table_cast(__UpperCamelCase,  __UpperCamelCase )
							return pa_table






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       self.config.chunksize
							_snake_case	       =       pd.read_sql(
							    self.config.sql,  self.config.con,  chunksize=__UpperCamelCase,  **self.config.pd_read_sql_kwargs )
							_snake_case	       =       [sql_reader] if chunksize is None else sql_reader
							for chunk_idx, df in enumerate(__UpperCamelCase ):
									_snake_case	       =       pa.Table.from_pandas(__UpperCamelCase )
									yield chunk_idx, self._cast_table(__UpperCamelCase )











'''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		(	lowercase_  ):



		'''simple docstring'''







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





class      lowerCamelCase_							(       UpperCAmelCase_							):




					'''simple docstring'''






					def __init__(    self,  __UpperCamelCase,  __UpperCamelCase=None,  __UpperCamelCase=None ):
							_snake_case	       =       file_names
							_snake_case	       =       image_transform
							_snake_case	       =       label_to_id






					def __len__(    self ):
							return len(self.file_names )






					def __getitem__(    self,  __UpperCamelCase ):
							_snake_case	       =       self.file_names[idx]
							_snake_case	       =       PIL.Image.open(__UpperCamelCase )
							_snake_case	       =       raw_image.convert("RGB" )
							if self.image_transform is not None:
									_snake_case	       =       self.image_transform(__UpperCamelCase )
							_snake_case	       =       extract_label(__UpperCamelCase )
							if self.label_to_id is not None:
									_snake_case	       =       self.label_to_id[label]
							return {"image": image, "label": label}






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







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

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

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

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

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

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

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

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

		# Instantiate dataloaders.
		_snake_case	       =       DataLoader(lowercase_						,							shuffle=lowercase_						,							batch_size=lowercase_						,							num_workers=4  )
		_snake_case	       =       DataLoader(lowercase_						,							shuffle=lowercase_						,							batch_size=lowercase_						,							num_workers=4  )

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		_snake_case	       =       create_model("resnet50d"						,							pretrained=lowercase_						,							num_classes=len(lowercase_  )  )

		# 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).
		_snake_case	       =       model.to(accelerator.device  )

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

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

		# Instantiate optimizer
		_snake_case	       =       torch.optim.Adam(params=model.parameters()						,							lr=lr / 25  )

		# Instantiate learning rate scheduler
		_snake_case	       =       OneCycleLR(optimizer=lowercase_						,							max_lr=lowercase_						,							epochs=lowercase_						,							steps_per_epoch=len(lowercase_  )  )

		# 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.
		_snake_case       ,    _snake_case       ,    _snake_case       ,    _snake_case       ,    _snake_case	       =       accelerator.prepare(
		    lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							lowercase_  )

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

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

				_snake_case	       =       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'''







		_snake_case	       =       argparse.ArgumentParser(description="Simple example of training script."  )
		parser.add_argument("--data_dir"						,							required=lowercase_						,							help="The data folder on disk."  )
		parser.add_argument(
		    "--mixed_precision"						,							type=lowercase_						,							default=lowercase_						,							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=lowercase_						,							default=lowercase_						,							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."  )
		_snake_case	       =       parser.parse_args()
		_snake_case	       =       {"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
		training_function(lowercase_						,							lowercase_  )


if __name__ == "__main__":
						main()





