

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__				(						SCREAMING_SNAKE_CASE_:				str   )      ->   List[str]:




				'''simple docstring'''





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

class        a__    (       snake_case      ):
			"""simple docstring"""







			def __init__(     self   ,   lowercase   ,   lowercase=None   ,   lowercase=None							)	->    List[str]:
							'''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   ,   lowercase							)	->    List[str]:
							'''simple docstring'''






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



def        lowerCAmelCase__				(						SCREAMING_SNAKE_CASE_:				List[Any]						,							SCREAMING_SNAKE_CASE_:				Dict   )      ->   Optional[Any]:




				'''simple docstring'''





				if args.with_tracking:
								A__          =      Accelerator(
								    cpu=args.cpu						,							mixed_precision=args.mixed_precision						,							log_with="all"						,							project_dir=args.project_dir   )
				else:
								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)

				# Parse out whether we are saving every epoch or after a certain number of batches
				if hasattr(args.checkpointing_steps						,							"isdigit"   ):
								if args.checkpointing_steps == "epoch":
												A__          =      args.checkpointing_steps
								elif args.checkpointing_steps.isdigit():
												A__          =      int(args.checkpointing_steps   )
								else:
												raise ValueError(
												    F'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'   )
				else:
								A__          =      None

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

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

								if "epoch" in training_difference:
												A__          =      int(training_difference.replace("epoch_"						,							""   )   ) + 1
												A__          =      None
								else:
												A__          =      int(training_difference.replace("step_"						,							""   )   )
												A__          =      resume_step // len(UpperCamelCase_   )
												resume_step -= starting_epoch * len(UpperCamelCase_   )

    # Now we train the model
				for epoch in range(UpperCamelCase_						,							UpperCamelCase_   ):
								model.train()
								if args.with_tracking:
												A__          =      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
												A__          =      accelerator.skip_first_batches(UpperCamelCase_						,							UpperCamelCase_   )
												overall_step += resume_step
								else:
												# After the first iteration though, we need to go back to the original dataloader
												A__          =      train_dataloader
								for batch in active_dataloader:
												# 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"]   )
												# We keep track of the loss at each epoch
												if args.with_tracking:
																total_loss += loss.detach().float()
												accelerator.backward(UpperCamelCase_   )
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()
												overall_step += 1
												if isinstance(UpperCamelCase_						,							UpperCamelCase_   ):
																A__          =      F'step_{overall_step}'
																if overall_step % checkpointing_steps == 0:
																				if args.output_dir is not None:
																								A__          =      os.path.join(args.output_dir						,							UpperCamelCase_   )
																				accelerator.save_state(UpperCamelCase_   )
								model.eval()
								A__          =      0
								A__          =      0
								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
												with torch.no_grad():
																A__          =      model(UpperCamelCase_   )
												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}: {1_0_0 * eval_metric:.2f}'   )
								if args.with_tracking:
												accelerator.log(
												    {
												        "accuracy": 1_0_0 * eval_metric,
												        "train_loss": total_loss.item() / len(UpperCamelCase_   ),
												        "epoch": epoch,
												    }						,							step=UpperCamelCase_						,							)
								if checkpointing_steps == "epoch":
												A__          =      F'epoch_{epoch}'
												if args.output_dir is not None:
																A__          =      os.path.join(args.output_dir						,							UpperCamelCase_   )
												accelerator.save_state(UpperCamelCase_   )

				if args.with_tracking:
								accelerator.end_training()



def        lowerCAmelCase__				(						)      ->   Optional[Any]:




				'''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("--fp16"						,							action="store_true"						,							help="If passed, will use FP16 training."   )
				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("--cpu"						,							action="store_true"						,							help="If passed, will train on the CPU."   )
				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(
				    "--output_dir"						,							type=UpperCamelCase_						,							default="."						,							help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."						,							)
				parser.add_argument(
				    "--resume_from_checkpoint"						,							type=UpperCamelCase_						,							default=UpperCamelCase_						,							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(
				    "--project_dir"						,							type=UpperCamelCase_						,							default="logs"						,							help="Location on where to store experiment tracking logs` and relevent project information"						,							)
				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()





'''simple docstring'''
from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


__SCREAMING_SNAKE_CASE					:				Tuple									=		logging.get_logger(__name__)







def       a_				(	UpperCamelCase_ ):
							if isinstance(UpperCamelCase_						,  np.ndarray ):
														return list(tensor.shape )

							A_			    =		tf.shape(UpperCamelCase_ )

							if tensor.shape == tf.TensorShape(UpperCamelCase_ ):
														return dynamic

							A_			    =		tensor.shape.as_list()

							return [dynamic[i] if s is None else s for i, s in enumerate(UpperCamelCase_ )]







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ = None						,  UpperCamelCase_ = None ):
							return tf.nn.softmax(logits=logits + 1e-9						,  axis=UpperCamelCase_						,  name=UpperCamelCase_ )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_=1e-5						,  UpperCamelCase_=-1 ):
							# This is a very simplified functional layernorm, designed to duplicate
							# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
							# models in Transformers.

							if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(UpperCamelCase_						,  UpperCamelCase_ ):
														raise NotImplementedError("Only 1D weight and bias tensors are supported for now, with only a single axis." )

							# Get mean and variance on the axis to be normalized
							A_    ,					A_			    =		tf.nn.moments(UpperCamelCase_						,  axes=[axis]						,  keepdims=UpperCamelCase_ )

							if axis != -1:
														# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
														# on every dimension except axis
														A_			    =		[1] * inputs.shape.rank
														A_			    =		shape_list(UpperCamelCase_ )[axis]
														A_			    =		tf.reshape(UpperCamelCase_						,  UpperCamelCase_ )
														A_			    =		tf.reshape(UpperCamelCase_						,  UpperCamelCase_ )

							# Compute layer normalization using the batch_normalization
							# function.
							A_			    =		tf.nn.batch_normalization(
							    UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  offset=UpperCamelCase_						,  scale=UpperCamelCase_						,  variance_epsilon=UpperCamelCase_						,  )
							return outputs







def       a_				(	UpperCamelCase_						,  UpperCamelCase_=0						,  UpperCamelCase_=-1 ):
							# Replicates the behavior of torch.flatten in TF

							# If end_dim or start_dim is negative, count them from the end
							if end_dim < 0:
														end_dim += input.shape.rank
							if start_dim < 0:
														start_dim += input.shape.rank

							if start_dim == end_dim:
														return input

							A_			    =		tf.shape(UpperCamelCase_ )
							A_			    =		tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
							A_			    =		tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]						,  axis=0 )
							return tf.reshape(UpperCamelCase_						,  UpperCamelCase_ )







def       a_				(	UpperCamelCase_ ):
							if not isinstance(UpperCamelCase_						,  tf.Tensor ):
														A_			    =		tf.convert_to_tensor(UpperCamelCase_ )  # Catches stray NumPy inputs
							if encoder_attention_mask.shape.rank == 3:
														A_			    =		encoder_attention_mask[:, None, :, :]
							if encoder_attention_mask.shape.rank == 2:
														A_			    =		encoder_attention_mask[:, None, None, :]
							# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
							# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
							# /transformer/transformer_layers.py#L270
							# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
							# encoder_extended_attention_mask.transpose(-1, -2))
							A_			    =		(
							    tf.cast(1						,  encoder_attention_mask.dtype ) - encoder_extended_attention_mask
							) * encoder_extended_attention_mask.dtype.min

							return encoder_extended_attention_mask







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ = "input_ids" ):
							tf.debugging.assert_less(
							    UpperCamelCase_						,  tf.cast(UpperCamelCase_						,  dtype=tensor.dtype )						,  message=(
							        f"The maximum value of {tensor_name} ({tf.math.reduce_max(UpperCamelCase_ )}) must be smaller than the embedding "
							        f"layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."
							    )						,  )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		6_4_5_1_2
							# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
							# because in that case even chunking the array would not make the saving
							# possible.
							A_			    =		[x for x in data if len(UpperCamelCase_ ) > HDF5_OBJECT_HEADER_LIMIT]

							# Expecting this to never be true.
							if bad_attributes:
														raise RuntimeError(
														    "The following attributes cannot be saved to HDF5 file because "
														    f"they are larger than {HDF5_OBJECT_HEADER_LIMIT} "
														    f"bytes: {bad_attributes}" )

							A_			    =		np.asarray(UpperCamelCase_ )

							A_			    =		1
							A_			    =		np.array_split(UpperCamelCase_						,  UpperCamelCase_ )

							# This will never loop forever thanks to the test above.
							while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
														num_chunks += 1
														A_			    =		np.array_split(UpperCamelCase_						,  UpperCamelCase_ )

							if num_chunks > 1:
														for chunk_id, chunk_data in enumerate(UpperCamelCase_ ):
																					A_			    =		chunk_data
							else:
														A_			    =		data







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							if name in group.attrs:
														A_			    =		[n.decode("utf8" ) if hasattr(UpperCamelCase_						,  "decode" ) else n for n in group.attrs[name]]
							else:
														A_			    =		[]
														A_			    =		0
														while "%s%d" % (name, chunk_id) in group.attrs:
																					data.extend(
																					    [n.decode("utf8" ) if hasattr(UpperCamelCase_						,  "decode" ) else n for n in group.attrs["%s%d" % (name, chunk_id)]] )
																					chunk_id += 1
							return data







def       a_				(	UpperCamelCase_ ):

							def _expand_single_ad_tensor(UpperCamelCase_ ):
														if isinstance(UpperCamelCase_						,  tf.Tensor ) and t.shape.rank == 1:
																					return tf.expand_dims(UpperCamelCase_						,  axis=-1 )
														return t

							return tf.nest.map_structure(_expand_single_ad_tensor						,  UpperCamelCase_ )





