"""simple docstring"""



from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig


_UpperCamelCase								=		logging.get_logger(__name__)

# General docstring
_UpperCamelCase								=		"RegNetConfig"

# Base docstring
_UpperCamelCase								=		"facebook/regnet-y-040"
_UpperCamelCase								=		[1, 10_88, 7, 7]

# Image classification docstring
_UpperCamelCase								=		"facebook/regnet-y-040"
_UpperCamelCase								=		"tabby, tabby cat"

_UpperCamelCase								=		[
    "facebook/regnet-y-040",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]
class 						__UpperCAmelCase    (tf.keras.layers.Layer     ):







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      snake_case_ = 3   ,      snake_case_ = 1   ,      snake_case_ = 1   ,      snake_case_ = "relu"   ,      **snake_case_   ,      ):







										'''simple docstring'''


										super().__init__(**snake_case_							)
										# The padding and conv has been verified in
										# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
										A__       :     Dict								=			tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2							)
										A__       :     Tuple								=			tf.keras.layers.ConvaD(
										    filters=snake_case_   ,      kernel_size=snake_case_   ,      strides=snake_case_   ,      padding="""VALID"""   ,      groups=snake_case_   ,      use_bias=snake_case_   ,      name="""convolution"""   ,      )
										A__       :     Any								=			tf.keras.layers.BatchNormalization(epsilon=1E-5   ,      momentum=0.9   ,      name="""normalization"""							)
										A__       :     List[Any]								=			ACTaFN[activation] if activation is not None else tf.identity





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Optional[int]								=			self.convolution(self.padding(snake_case_							)							)
										A__       :     Optional[int]								=			self.normalization(snake_case_							)
										A__       :     Dict								=			self.activation(snake_case_							)
										return hidden_state





class 						__UpperCAmelCase    (tf.keras.layers.Layer     ):







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      **snake_case_							):







										'''simple docstring'''


										super().__init__(**snake_case_							)
										A__       :     Optional[int]								=			config.num_channels
										A__       :     List[str]								=			TFRegNetConvLayer(
										    out_channels=config.embedding_size   ,      kernel_size=3   ,      stride=2   ,      activation=config.hidden_act   ,      name="""embedder"""   ,      )





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			shape_list(snake_case_							)[1]
										if tf.executing_eagerly() and num_channels != self.num_channels:
																	raise ValueError(
																	    """Make sure that the channel dimension of the pixel values match with the one set in the configuration."""							)

										# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
										# So change the input format from `NCHW` to `NHWC`.
										# shape = (batch_size, in_height, in_width, in_channels=num_channels)
										A__       :     int								=			tf.transpose(snake_case_   ,      perm=(0, 2, 3, 1)							)
										A__       :     Optional[Any]								=			self.embedder(snake_case_							)
										return hidden_state





class 						__UpperCAmelCase    (tf.keras.layers.Layer     ):







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      snake_case_ = 2   ,      **snake_case_							):







										'''simple docstring'''


										super().__init__(**snake_case_							)
										A__       :     int								=			tf.keras.layers.ConvaD(
										    filters=snake_case_   ,      kernel_size=1   ,      strides=snake_case_   ,      use_bias=snake_case_   ,      name="""convolution"""							)
										A__       :     List[str]								=			tf.keras.layers.BatchNormalization(epsilon=1E-5   ,      momentum=0.9   ,      name="""normalization"""							)





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_ = False							):







										'''simple docstring'''


										return self.normalization(self.convolution(snake_case_							)   ,      training=snake_case_							)





class 						__UpperCAmelCase    (tf.keras.layers.Layer     ):







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      snake_case_   ,      **snake_case_							):







										'''simple docstring'''


										super().__init__(**snake_case_							)
										A__       :     str								=			tf.keras.layers.GlobalAveragePoolingaD(keepdims=snake_case_   ,      name="""pooler"""							)
										A__       :     Dict								=			[
										    tf.keras.layers.ConvaD(filters=snake_case_   ,      kernel_size=1   ,      activation="""relu"""   ,      name="""attention.0"""							),
										    tf.keras.layers.ConvaD(filters=snake_case_   ,      kernel_size=1   ,      activation="""sigmoid"""   ,      name="""attention.2"""							),
										]





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										A__       :     str								=			self.pooler(snake_case_							)
										for layer_module in self.attention:
																	A__       :     Any								=			layer_module(snake_case_							)
										A__       :     Union[str, Any]								=			hidden_state * pooled
										return hidden_state





class 						__UpperCAmelCase    (tf.keras.layers.Layer     ):







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_ = 1   ,      **snake_case_							):







										'''simple docstring'''


										super().__init__(**snake_case_							)
										A__       :     Optional[Any]								=			in_channels != out_channels or stride != 1
										A__       :     Optional[Any]								=			max(1   ,      out_channels // config.groups_width							)
										A__       :     Optional[Any]								=			(
										    TFRegNetShortCut(snake_case_   ,      stride=snake_case_   ,      name="""shortcut"""							)
										    if should_apply_shortcut
										    else tf.keras.layers.Activation("""linear"""   ,      name="""shortcut"""							)
										)
										# `self.layers` instead of `self.layer` because that is a reserved argument.
										A__       :     Optional[int]								=			[
										    TFRegNetConvLayer(snake_case_   ,      kernel_size=1   ,      activation=config.hidden_act   ,      name="""layer.0"""							),
										    TFRegNetConvLayer(
										        snake_case_   ,      stride=snake_case_   ,      groups=snake_case_   ,      activation=config.hidden_act   ,      name="""layer.1"""							),
										    TFRegNetConvLayer(snake_case_   ,      kernel_size=1   ,      activation=snake_case_   ,      name="""layer.2"""							),
										]
										A__       :     int								=			ACTaFN[config.hidden_act]





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										A__       :     List[Any]								=			hidden_state
										for layer_module in self.layers:
																	A__       :     Optional[int]								=			layer_module(snake_case_							)
										A__       :     Any								=			self.shortcut(snake_case_							)
										hidden_state += residual
										A__       :     Optional[Any]								=			self.activation(snake_case_							)
										return hidden_state





class 						__UpperCAmelCase    (tf.keras.layers.Layer     ):







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_ = 1   ,      **snake_case_							):







										'''simple docstring'''


										super().__init__(**snake_case_							)
										A__       :     List[str]								=			in_channels != out_channels or stride != 1
										A__       :     Optional[Any]								=			max(1   ,      out_channels // config.groups_width							)
										A__       :     str								=			(
										    TFRegNetShortCut(snake_case_   ,      stride=snake_case_   ,      name="""shortcut"""							)
										    if should_apply_shortcut
										    else tf.keras.layers.Activation("""linear"""   ,      name="""shortcut"""							)
										)
										A__       :     str								=			[
										    TFRegNetConvLayer(snake_case_   ,      kernel_size=1   ,      activation=config.hidden_act   ,      name="""layer.0"""							),
										    TFRegNetConvLayer(
										        snake_case_   ,      stride=snake_case_   ,      groups=snake_case_   ,      activation=config.hidden_act   ,      name="""layer.1"""							),
										    TFRegNetSELayer(snake_case_   ,      reduced_channels=int(round(in_channels / 4							)							)   ,      name="""layer.2"""							),
										    TFRegNetConvLayer(snake_case_   ,      kernel_size=1   ,      activation=snake_case_   ,      name="""layer.3"""							),
										]
										A__       :     str								=			ACTaFN[config.hidden_act]





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Optional[int]								=			hidden_state
										for layer_module in self.layers:
																	A__       :     List[Any]								=			layer_module(snake_case_							)
										A__       :     Optional[int]								=			self.shortcut(snake_case_							)
										hidden_state += residual
										A__       :     Union[str, Any]								=			self.activation(snake_case_							)
										return hidden_state





class 						__UpperCAmelCase    (tf.keras.layers.Layer     ):







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_ = 2   ,      snake_case_ = 2   ,      **snake_case_							):







										'''simple docstring'''


										super().__init__(**snake_case_							)

										A__       :     Optional[Any]								=			TFRegNetXLayer if config.layer_type == """x""" else TFRegNetYLayer
										A__       :     Optional[int]								=			[
										    # downsampling is done in the first layer with stride of 2
										    layer(snake_case_   ,      snake_case_   ,      snake_case_   ,      stride=snake_case_   ,      name="""layers.0"""							),
										    *[layer(snake_case_   ,      snake_case_   ,      snake_case_   ,      name=F'''layers.{i+1}'''							) for i in range(depth - 1							)],
										]





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										for layer_module in self.layers:
																	A__       :     Optional[Any]								=			layer_module(snake_case_							)
										return hidden_state





class 						__UpperCAmelCase    (tf.keras.layers.Layer     ):







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      **snake_case_							):







										'''simple docstring'''


										super().__init__(**snake_case_							)
										A__       :     Optional[Any]								=			[]
										# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
										self.stages.append(
										    TFRegNetStage(
										        snake_case_   ,      config.embedding_size   ,      config.hidden_sizes[0]   ,      stride=2 if config.downsample_in_first_stage else 1   ,      depth=config.depths[0]   ,      name="""stages.0"""   ,      )							)
										A__       :     int								=			zip(config.hidden_sizes   ,      config.hidden_sizes[1:]							)
										for i, ((in_channels, out_channels), depth) in enumerate(zip(snake_case_   ,      config.depths[1:]							)							):
																	self.stages.append(TFRegNetStage(snake_case_   ,      snake_case_   ,      snake_case_   ,      depth=snake_case_   ,      name=F'''stages.{i+1}'''							)							)





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_ = False   ,      snake_case_ = True							):







										'''simple docstring'''


										A__       :     Tuple								=			() if output_hidden_states else None

										for stage_module in self.stages:
																	if output_hidden_states:
																								A__       :     Optional[int]								=			hidden_states + (hidden_state,)

																	A__       :     List[Any]								=			stage_module(snake_case_							)

										if output_hidden_states:
																	A__       :     Optional[Any]								=			hidden_states + (hidden_state,)

										if not return_dict:
																	return tuple(v for v in [hidden_state, hidden_states] if v is not None							)

										return TFBaseModelOutputWithNoAttention(last_hidden_state=snake_case_   ,      hidden_states=snake_case_							)





@keras_serializable
class 						__UpperCAmelCase    (tf.keras.layers.Layer     ):







			'''simple docstring'''
			_UpperCamelCase  :						Any					     =				RegNetConfig





			def __init__(			self   ,      snake_case_   ,      **snake_case_							):







										'''simple docstring'''


										super().__init__(**snake_case_							)
										A__       :     List[str]								=			config
										A__       :     Optional[Any]								=			TFRegNetEmbeddings(snake_case_   ,      name="""embedder"""							)
										A__       :     Dict								=			TFRegNetEncoder(snake_case_   ,      name="""encoder"""							)
										A__       :     int								=			tf.keras.layers.GlobalAveragePoolingaD(keepdims=snake_case_   ,      name="""pooler"""							)





			@unpack_inputs
			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = False   ,      ):







										'''simple docstring'''


										A__       :     List[str]								=			(
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										A__       :     Any								=			return_dict if return_dict is not None else self.config.use_return_dict

										A__       :     Optional[Any]								=			self.embedder(snake_case_   ,      training=snake_case_							)

										A__       :     Tuple								=			self.encoder(
										    snake_case_   ,      output_hidden_states=snake_case_   ,      return_dict=snake_case_   ,      training=snake_case_							)

										A__       :     Dict								=			encoder_outputs[0]
										A__       :     Optional[int]								=			self.pooler(snake_case_							)

										# Change to NCHW output format have uniformity in the modules
										A__       :     Tuple								=			tf.transpose(snake_case_   ,      perm=(0, 3, 1, 2)							)
										A__       :     List[str]								=			tf.transpose(snake_case_   ,      perm=(0, 3, 1, 2)							)

										# Change the other hidden state outputs to NCHW as well
										if output_hidden_states:
																	A__       :     Optional[Any]								=			tuple([tf.transpose(snake_case_   ,      perm=(0, 3, 1, 2)							) for h in encoder_outputs[1]]							)

										if not return_dict:
																	return (last_hidden_state, pooled_output) + encoder_outputs[1:]

										return TFBaseModelOutputWithPoolingAndNoAttention(
										    last_hidden_state=snake_case_   ,      pooler_output=snake_case_   ,      hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states   ,      )





class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Any					     =				RegNetConfig
			_UpperCamelCase  :						List[Any]					     =				'regnet'
			_UpperCamelCase  :						List[Any]					     =				'pixel_values'





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)   ,      dtype=tf.floataa							)}





_UpperCamelCase								=		R"\n    Parameters:\n    This model is a Tensorflow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n    behavior.\n        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n"

_UpperCamelCase								=		R"\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConveNextImageProcessor.__call__`] for details.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"
@add_start_docstrings(
    'The bare RegNet model outputting raw features without any specific head on top.'							,   __A							,   )
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      *snake_case_   ,      **snake_case_							):







										'''simple docstring'''


										super().__init__(snake_case_   ,      *snake_case_   ,      **snake_case_							)
										A__       :     Optional[Any]								=			TFRegNetMainLayer(snake_case_   ,      name="""regnet"""							)





			@unpack_inputs
			@add_start_docstrings_to_model_forward(snake_case_							)
			@add_code_sample_docstrings(
			    checkpoint=_CHECKPOINT_FOR_DOC   ,      output_type=snake_case_   ,      config_class=_CONFIG_FOR_DOC   ,      modality="""vision"""   ,      expected_output=_EXPECTED_OUTPUT_SHAPE   ,      )
			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_=False   ,      ):







										'''simple docstring'''


										A__       :     List[str]								=			(
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										A__       :     Optional[Any]								=			return_dict if return_dict is not None else self.config.use_return_dict

										A__       :     Optional[Any]								=			self.regnet(
										    pixel_values=snake_case_   ,      output_hidden_states=snake_case_   ,      return_dict=snake_case_   ,      training=snake_case_   ,      )
										if not return_dict:
																	return (outputs[0],) + outputs[1:]

										return TFBaseModelOutputWithPoolingAndNoAttention(
										    last_hidden_state=outputs.last_hidden_state   ,      pooler_output=outputs.pooler_output   ,      hidden_states=outputs.hidden_states   ,      )





@add_start_docstrings(
    '\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    '							,   __A							,   )
class 						__UpperCAmelCase    (__A							,   __A     ):







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      *snake_case_   ,      **snake_case_							):







										'''simple docstring'''


										super().__init__(snake_case_   ,      *snake_case_   ,      **snake_case_							)
										A__       :     Tuple								=			config.num_labels
										A__       :     str								=			TFRegNetMainLayer(snake_case_   ,      name="""regnet"""							)
										# classification head
										A__       :     List[Any]								=			[
										    tf.keras.layers.Flatten(),
										    tf.keras.layers.Dense(config.num_labels   ,      name="""classifier.1"""							) if config.num_labels > 0 else tf.identity,
										]





			@unpack_inputs
			@add_start_docstrings_to_model_forward(snake_case_							)
			@add_code_sample_docstrings(
			    checkpoint=_IMAGE_CLASS_CHECKPOINT   ,      output_type=snake_case_   ,      config_class=_CONFIG_FOR_DOC   ,      expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT   ,      )
			def 		lowerCamelCase					(			self   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_=False   ,      ):







										'''simple docstring'''


										A__       :     int								=			(
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										A__       :     List[Any]								=			return_dict if return_dict is not None else self.config.use_return_dict

										A__       :     List[str]								=			self.regnet(
										    snake_case_   ,      output_hidden_states=snake_case_   ,      return_dict=snake_case_   ,      training=snake_case_							)

										A__       :     List[str]								=			outputs.pooler_output if return_dict else outputs[1]

										A__       :     Optional[Any]								=			self.classifier[0](snake_case_							)
										A__       :     Union[str, Any]								=			self.classifier[1](snake_case_							)

										A__       :     Tuple								=			None if labels is None else self.hf_compute_loss(labels=snake_case_   ,      logits=snake_case_							)

										if not return_dict:
																	A__       :     Tuple								=			(logits,) + outputs[2:]
																	return ((loss,) + output) if loss is not None else output

										return TFSequenceClassifierOutput(loss=snake_case_   ,      logits=snake_case_   ,      hidden_states=outputs.hidden_states							)






"""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  _A(		lowerCAmelCase							):
							A__       :     int								=			fname.split(os.path.sep							)[-1]
							return re.search(r"""^(.*)_\d+\.jpg$"""			,  lowerCAmelCase							).groups()[0]
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      snake_case_=None   ,      snake_case_=None							):







										'''simple docstring'''


										A__       :     Dict								=			file_names
										A__       :     str								=			image_transform
										A__       :     Dict								=			label_to_id





			def __len__(			self							):







										'''simple docstring'''


										return len(self.file_names							)





			def __getitem__(			self   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			self.file_names[idx]
										A__       :     Optional[Any]								=			PIL.Image.open(snake_case_							)
										A__       :     str								=			raw_image.convert("""RGB"""							)
										if self.image_transform is not None:
																	A__       :     Optional[int]								=			self.image_transform(snake_case_							)
										A__       :     Dict								=			extract_label(snake_case_							)
										if self.label_to_id is not None:
																	A__       :     List[Any]								=			self.label_to_id[label]
										return {"image": image, "label": label}





def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							# Initialize accelerator
							if args.with_tracking:
														A__       :     List[Any]								=			Accelerator(
														    cpu=args.cpu			,  mixed_precision=args.mixed_precision			,  log_with="""all"""			,  project_dir=args.project_dir							)
							else:
														A__       :     Optional[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__       :     List[Any]								=			config["""lr"""]
							A__       :     Any								=			int(config["""num_epochs"""]							)
							A__       :     List[Any]								=			int(config["""seed"""]							)
							A__       :     Tuple								=			int(config["""batch_size"""]							)
							A__       :     List[str]								=			config["""image_size"""]
							if not isinstance(lowerCAmelCase			,  (list, tuple)							):
														A__       :     List[str]								=			(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__       :     List[Any]								=			args.checkpointing_steps
														elif args.checkpointing_steps.isdigit():
																					A__       :     int								=			int(args.checkpointing_steps							)
														else:
																					raise ValueError(
																					    F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''							)
							else:
														A__       :     Any								=			None

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

							# Grab all the image filenames
							A__       :     Union[str, 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__       :     int								=			[extract_label(lowerCAmelCase							) for fname in file_names]
							A__       :     Dict								=			list(set(lowerCAmelCase							)							)
							id_to_label.sort()
							A__       :     int								=			{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__       :     str								=			np.random.permutation(len(lowerCAmelCase							)							)
							A__       :     Optional[int]								=			int(0.8 * len(lowerCAmelCase							)							)
							A__       :     Union[str, Any]								=			random_perm[:cut]
							A__       :     Optional[int]								=			random_perm[cut:]

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

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

							# Instantiate dataloaders.
							A__       :     List[Any]								=			DataLoader(lowerCAmelCase			,  shuffle=lowerCAmelCase			,  batch_size=lowerCAmelCase			,  num_workers=4							)
							A__       :     str								=			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__       :     Dict								=			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__       :     Any								=			False
							for param in model.get_classifier().parameters():
														A__       :     Dict								=			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__       :     str								=			torch.optim.Adam(params=model.parameters()			,  lr=lr / 25							)

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

														if "epoch" in training_difference:
																					A__       :     Optional[Any]								=			int(training_difference.replace("""epoch_"""			,  """"""							)							) + 1
																					A__       :     int								=			None
														else:
																					A__       :     Optional[Any]								=			int(training_difference.replace("""step_"""			,  """"""							)							)
																					A__       :     Union[str, Any]								=			resume_step // len(lowerCAmelCase							)
																					resume_step -= starting_epoch * len(lowerCAmelCase							)

    # Now we train the model
							for epoch in range(lowerCAmelCase			,  lowerCAmelCase							):
														model.train()
														if args.with_tracking:
																					A__       :     List[Any]								=			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__       :     int								=			accelerator.skip_first_batches(lowerCAmelCase			,  lowerCAmelCase							)
																					overall_step += resume_step
														else:
																					# After the first iteration though, we need to go back to the original dataloader
																					A__       :     Optional[int]								=			train_dataloader
														for batch in active_dataloader:
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					A__       :     Union[str, Any]								=			{k: v.to(accelerator.device							) for k, v in batch.items()}
																					A__       :     Any								=			(batch["""image"""] - mean) / std
																					A__       :     Tuple								=			model(lowerCAmelCase							)
																					A__       :     int								=			torch.nn.functional.cross_entropy(lowerCAmelCase			,  batch["""label"""]							)
																					# We keep track of the loss at each epoch
																					if args.with_tracking:
																												total_loss += loss.detach().float()
																					accelerator.backward(lowerCAmelCase							)
																					optimizer.step()
																					lr_scheduler.step()
																					optimizer.zero_grad()
																					overall_step += 1
																					if isinstance(lowerCAmelCase			,  lowerCAmelCase							):
																												A__       :     Union[str, Any]								=			F'''step_{overall_step}'''
																												if overall_step % checkpointing_steps == 0:
																																			if args.output_dir is not None:
																																										A__       :     List[str]								=			os.path.join(args.output_dir			,  lowerCAmelCase							)
																																			accelerator.save_state(lowerCAmelCase							)
														model.eval()
														A__       :     Any								=			0
														A__       :     Tuple								=			0
														for step, batch in enumerate(lowerCAmelCase							):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					A__       :     List[Any]								=			{k: v.to(accelerator.device							) for k, v in batch.items()}
																					A__       :     Any								=			(batch["""image"""] - mean) / std
																					with torch.no_grad():
																												A__       :     Optional[int]								=			model(lowerCAmelCase							)
																					A__       :     Tuple								=			outputs.argmax(dim=-1							)
																					A__				,							A__       :     Tuple								=			accelerator.gather_for_metrics((predictions, batch["""label"""])							)
																					A__       :     int								=			predictions == references
																					num_elems += accurate_preds.shape[0]
																					accurate += accurate_preds.long().sum()

														A__       :     List[Any]								=			accurate.item() / num_elems
														# Use accelerator.print to print only on the main process.
														accelerator.print(F'''epoch {epoch}: {100 * eval_metric:.2f}'''							)
														if args.with_tracking:
																					accelerator.log(
																					    {
																					        """accuracy""": 100 * eval_metric,
																					        """train_loss""": total_loss.item() / len(lowerCAmelCase							),
																					        """epoch""": epoch,
																					    }			,  step=lowerCAmelCase			,  )
														if checkpointing_steps == "epoch":
																					A__       :     Any								=			F'''epoch_{epoch}'''
																					if args.output_dir is not None:
																												A__       :     Any								=			os.path.join(args.output_dir			,  lowerCAmelCase							)
																					accelerator.save_state(lowerCAmelCase							)

							if args.with_tracking:
														accelerator.end_training()

def  _A(		):
							A__       :     Union[str, 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("""--fp16"""			,  action="""store_true"""			,  help="""If passed, will use FP16 training."""							)
							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("""--cpu"""			,  action="""store_true"""			,  help="""If passed, will train on the CPU."""							)
							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(
							    """--output_dir"""			,  type=lowerCAmelCase			,  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=lowerCAmelCase			,  default=lowerCAmelCase			,  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=lowerCAmelCase			,  default="""logs"""			,  help="""Location on where to store experiment tracking logs` and relevent project information"""			,  )
							A__       :     Optional[Any]								=			parser.parse_args()
							A__       :     Any								=			{"""lr""": 3E-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 224}
							training_function(lowerCAmelCase			,  lowerCAmelCase							)


if __name__ == "__main__":
				main()





