


'''simple docstring'''

import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase :List[str]             =     logging.get_logger(__name__)





def lowerCamelCase     (     lowerCAmelCase							:      Tuple						,						lowerCAmelCase							:      Any=False						):





	"""simple docstring"""







	__magic_name__			:       Tuple	      =	OrderedDict()
	for key, value in state_dict.items():
		if encoder_only and not key.startswith('head'						):
			__magic_name__			:       Optional[Any]	      =	'segformer.encoder.' + key
		if key.startswith('backbone'						):
			__magic_name__			:       str	      =	key.replace('backbone'						,						'segformer.encoder'						)
		if "patch_embed" in key:
			# replace for example patch_embed1 by patch_embeddings.0
			__magic_name__			:       int	      =	key[key.find('patch_embed'						) + len('patch_embed'						)]
			__magic_name__			:       List[Any]	      =	key.replace(f'patch_embed{idx}'						,						f'patch_embeddings.{int(_UpperCAmelCase						)-1}'						)
		if "norm" in key:
			__magic_name__			:       Any	      =	key.replace('norm'						,						'layer_norm'						)
		if "segformer.encoder.layer_norm" in key:
			# replace for example layer_norm1 by layer_norm.0
			__magic_name__			:       Union[str, Any]	      =	key[key.find('segformer.encoder.layer_norm'						) + len('segformer.encoder.layer_norm'						)]
			__magic_name__			:       Tuple	      =	key.replace(f'layer_norm{idx}'						,						f'layer_norm.{int(_UpperCAmelCase						)-1}'						)
		if "layer_norm1" in key:
			__magic_name__			:       Dict	      =	key.replace('layer_norm1'						,						'layer_norm_1'						)
		if "layer_norm2" in key:
			__magic_name__			:       List[Any]	      =	key.replace('layer_norm2'						,						'layer_norm_2'						)
		if "block" in key:
			# replace for example block1 by block.0
			__magic_name__			:       List[str]	      =	key[key.find('block'						) + len('block'						)]
			__magic_name__			:       Optional[int]	      =	key.replace(f'block{idx}'						,						f'block.{int(_UpperCAmelCase						)-1}'						)
		if "attn.q" in key:
			__magic_name__			:       Any	      =	key.replace('attn.q'						,						'attention.self.query'						)
		if "attn.proj" in key:
			__magic_name__			:       Optional[Any]	      =	key.replace('attn.proj'						,						'attention.output.dense'						)
		if "attn" in key:
			__magic_name__			:       Dict	      =	key.replace('attn'						,						'attention.self'						)
		if "fc1" in key:
			__magic_name__			:       int	      =	key.replace('fc1'						,						'dense1'						)
		if "fc2" in key:
			__magic_name__			:       List[Any]	      =	key.replace('fc2'						,						'dense2'						)
		if "linear_pred" in key:
			__magic_name__			:       List[Any]	      =	key.replace('linear_pred'						,						'classifier'						)
		if "linear_fuse" in key:
			__magic_name__			:       Any	      =	key.replace('linear_fuse.conv'						,						'linear_fuse'						)
			__magic_name__			:       Any	      =	key.replace('linear_fuse.bn'						,						'batch_norm'						)
		if "linear_c" in key:
			# replace for example linear_c4 by linear_c.3
			__magic_name__			:       int	      =	key[key.find('linear_c'						) + len('linear_c'						)]
			__magic_name__			:       Optional[int]	      =	key.replace(f'linear_c{idx}'						,						f'linear_c.{int(_UpperCAmelCase						)-1}'						)
		if key.startswith('head'						):
			__magic_name__			:       int	      =	key.replace('head'						,						'classifier'						)
		__magic_name__			:       str	      =	value

	return new_state_dict





def lowerCamelCase     (     lowerCAmelCase							:      Union[str, Any]						,						lowerCAmelCase							:      Optional[int]						):





	"""simple docstring"""







	for i in range(config.num_encoder_blocks						):
		for j in range(config.depths[i]						):
			# read in weights + bias of keys and values (which is a single matrix in the original implementation)
			__magic_name__			:       Tuple	      =	state_dict.pop(f'segformer.encoder.block.{i}.{j}.attention.self.kv.weight'						)
			__magic_name__			:       Tuple	      =	state_dict.pop(f'segformer.encoder.block.{i}.{j}.attention.self.kv.bias'						)
			# next, add keys and values (in that order) to the state dict
			__magic_name__			:       Dict	      =	kv_weight[
			    : config.hidden_sizes[i], :
			]
			__magic_name__			:       Optional[Any]	      =	kv_bias[: config.hidden_sizes[i]]
			__magic_name__			:       Union[str, Any]	      =	kv_weight[
			    config.hidden_sizes[i] :, :
			]
			__magic_name__			:       List[Any]	      =	kv_bias[
			    config.hidden_sizes[i] :
			]





def lowerCamelCase     (     ):





	"""simple docstring"""







	__magic_name__			:       int	      =	'http://images.cocodataset.org/val2017/000000039769.jpg'
	__magic_name__			:       str	      =	Image.open(requests.get(_UpperCAmelCase						,						stream=_UpperCAmelCase						).raw						)

	return image





@torch.no_grad()
def lowerCamelCase     (     lowerCAmelCase							:      int						,						lowerCAmelCase							:      Tuple						,						lowerCAmelCase							:      Optional[int]						):





	"""simple docstring"""







	__magic_name__			:       List[Any]	      =	SegformerConfig()
	__magic_name__			:       Any	      =	False

	# set attributes based on model_name
	__magic_name__			:       List[str]	      =	'huggingface/label-files'
	if "segformer" in model_name:
		__magic_name__			:       str	      =	model_name[len('segformer.'						) : len('segformer.'						) + 2]
		if "ade" in model_name:
			__magic_name__			:       str	      =	150
			__magic_name__			:       Dict	      =	'ade20k-id2label.json'
			__magic_name__			:       Optional[Any]	      =	(1, 150, 128, 128)
		elif "city" in model_name:
			__magic_name__			:       int	      =	19
			__magic_name__			:       str	      =	'cityscapes-id2label.json'
			__magic_name__			:       List[Any]	      =	(1, 19, 128, 128)
		else:
			raise ValueError(f'Model {model_name} not supported'						)
	elif "mit" in model_name:
		__magic_name__			:       Union[str, Any]	      =	True
		__magic_name__			:       str	      =	model_name[4:6]
		__magic_name__			:       List[str]	      =	1000
		__magic_name__			:       List[str]	      =	'imagenet-1k-id2label.json'
		__magic_name__			:       Tuple	      =	(1, 1000)
	else:
		raise ValueError(f'Model {model_name} not supported'						)

	# set config attributes
	__magic_name__			:       List[Any]	      =	json.load(open(hf_hub_download(_UpperCAmelCase						,						_UpperCAmelCase						,						repo_type='dataset'						)						,						'r'						)						)
	__magic_name__			:       Optional[int]	      =	{int(_UpperCAmelCase						): v for k, v in idalabel.items()}
	__magic_name__			:       List[Any]	      =	idalabel
	__magic_name__			:       List[str]	      =	{v: k for k, v in idalabel.items()}
	if size == "b0":
		pass
	elif size == "b1":
		__magic_name__			:       List[Any]	      =	[64, 128, 320, 512]
		__magic_name__			:       Any	      =	256
	elif size == "b2":
		__magic_name__			:       Any	      =	[64, 128, 320, 512]
		__magic_name__			:       Any	      =	768
		__magic_name__			:       Union[str, Any]	      =	[3, 4, 6, 3]
	elif size == "b3":
		__magic_name__			:       int	      =	[64, 128, 320, 512]
		__magic_name__			:       Dict	      =	768
		__magic_name__			:       Optional[int]	      =	[3, 4, 18, 3]
	elif size == "b4":
		__magic_name__			:       Any	      =	[64, 128, 320, 512]
		__magic_name__			:       Optional[Any]	      =	768
		__magic_name__			:       Union[str, Any]	      =	[3, 8, 27, 3]
	elif size == "b5":
		__magic_name__			:       Optional[Any]	      =	[64, 128, 320, 512]
		__magic_name__			:       List[str]	      =	768
		__magic_name__			:       Union[str, Any]	      =	[3, 6, 40, 3]
	else:
		raise ValueError(f'Size {size} not supported'						)

	# load image processor (only resize + normalize)
	__magic_name__			:       str	      =	SegformerImageProcessor(
	    image_scale=(512, 512)						,						keep_ratio=_UpperCAmelCase						,						align=_UpperCAmelCase						,						do_random_crop=_UpperCAmelCase						)

	# prepare image
	__magic_name__			:       Optional[Any]	      =	prepare_img()
	__magic_name__			:       int	      =	image_processor(images=_UpperCAmelCase						,						return_tensors='pt'						).pixel_values

	logger.info(f'Converting model {model_name}...'						)

	# load original state dict
	if encoder_only:
		__magic_name__			:       int	      =	torch.load(_UpperCAmelCase						,						map_location=torch.device('cpu'						)						)
	else:
		__magic_name__			:       Any	      =	torch.load(_UpperCAmelCase						,						map_location=torch.device('cpu'						)						)['state_dict']

	# rename keys
	__magic_name__			:       List[Any]	      =	rename_keys(_UpperCAmelCase						,						encoder_only=_UpperCAmelCase						)
	if not encoder_only:
		del state_dict["decode_head.conv_seg.weight"]
		del state_dict["decode_head.conv_seg.bias"]

	# key and value matrices need special treatment
	read_in_k_v(_UpperCAmelCase						,						_UpperCAmelCase						)

	# create HuggingFace model and load state dict
	if encoder_only:
		__magic_name__			:       int	      =	False
		__magic_name__			:       Any	      =	SegformerForImageClassification(_UpperCAmelCase						)
	else:
		__magic_name__			:       List[str]	      =	SegformerForSemanticSegmentation(_UpperCAmelCase						)
	model.load_state_dict(_UpperCAmelCase						)
	model.eval()

	# forward pass
	__magic_name__			:       int	      =	model(_UpperCAmelCase						)
	__magic_name__			:       Union[str, Any]	      =	outputs.logits

	# set expected_slice based on model name
	# ADE20k checkpoints
	if model_name == "segformer.b0.512x512.ade.160k":
		__magic_name__			:       int	      =	torch.tensor(
		    [
		        [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
		        [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
		        [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
		    ]						)
	elif model_name == "segformer.b1.512x512.ade.160k":
		__magic_name__			:       Optional[int]	      =	torch.tensor(
		    [
		        [[-7.5820, -8.7231, -8.3215], [-8.0600, -10.3529, -10.0304], [-7.5208, -9.4103, -9.6239]],
		        [[-12.6918, -13.8994, -13.7137], [-13.3196, -15.7523, -15.4789], [-12.9343, -14.8757, -14.9689]],
		        [[-11.1911, -11.9421, -11.3243], [-11.3342, -13.6839, -13.3581], [-10.3909, -12.1832, -12.4858]],
		    ]						)
	elif model_name == "segformer.b2.512x512.ade.160k":
		__magic_name__			:       Any	      =	torch.tensor(
		    [
		        [[-11.8173, -14.3850, -16.3128], [-14.5648, -16.5804, -18.6568], [-14.7223, -15.7387, -18.4218]],
		        [[-15.7290, -17.9171, -19.4423], [-18.3105, -19.9448, -21.4661], [-17.9296, -18.6497, -20.7910]],
		        [[-15.0783, -17.0336, -18.2789], [-16.8771, -18.6870, -20.1612], [-16.2454, -17.1426, -19.5055]],
		    ]						)
	elif model_name == "segformer.b3.512x512.ade.160k":
		__magic_name__			:       List[Any]	      =	torch.tensor(
		    [
		        [[-9.0878, -10.2081, -10.1891], [-9.3144, -10.7941, -10.9843], [-9.2294, -10.3855, -10.5704]],
		        [[-12.2316, -13.9068, -13.6102], [-12.9161, -14.3702, -14.3235], [-12.5233, -13.7174, -13.7932]],
		        [[-14.6275, -15.2490, -14.9727], [-14.3400, -15.9687, -16.2827], [-14.1484, -15.4033, -15.8937]],
		    ]						)
	elif model_name == "segformer.b4.512x512.ade.160k":
		__magic_name__			:       int	      =	torch.tensor(
		    [
		        [[-12.3144, -13.2447, -14.0802], [-13.3614, -14.5816, -15.6117], [-13.3340, -14.4433, -16.2219]],
		        [[-19.2781, -20.4128, -20.7506], [-20.6153, -21.6566, -22.0998], [-19.9800, -21.0430, -22.1494]],
		        [[-18.8739, -19.7804, -21.1834], [-20.1233, -21.6765, -23.2944], [-20.0315, -21.2641, -23.6944]],
		    ]						)
	elif model_name == "segformer.b5.640x640.ade.160k":
		__magic_name__			:       List[str]	      =	torch.tensor(
		    [
		        [[-9.5524, -12.0835, -11.7348], [-10.5229, -13.6446, -14.5662], [-9.5842, -12.8851, -13.9414]],
		        [[-15.3432, -17.5323, -17.0818], [-16.3330, -18.9255, -19.2101], [-15.1340, -17.7848, -18.3971]],
		        [[-12.6072, -14.9486, -14.6631], [-13.7629, -17.0907, -17.7745], [-12.7899, -16.1695, -17.1671]],
		    ]						)
	# Cityscapes checkpoints
	elif model_name == "segformer.b0.1024x1024.city.160k":
		__magic_name__			:       Any	      =	torch.tensor(
		    [
		        [[-11.9295, -13.4057, -14.8106], [-13.3431, -14.8179, -15.3781], [-14.2836, -15.5942, -16.1588]],
		        [[-11.4906, -12.8067, -13.6564], [-13.1189, -14.0500, -14.1543], [-13.8748, -14.5136, -14.8789]],
		        [[0.5374, 0.1067, -0.4742], [0.1141, -0.2255, -0.7099], [-0.3000, -0.5924, -1.3105]],
		    ]						)
	elif model_name == "segformer.b0.512x1024.city.160k":
		__magic_name__			:       Any	      =	torch.tensor(
		    [
		        [[-7.8217, -9.8767, -10.1717], [-9.4438, -10.9058, -11.4047], [-9.7939, -12.3495, -12.1079]],
		        [[-7.1514, -9.5336, -10.0860], [-9.7776, -11.6822, -11.8439], [-10.1411, -12.7655, -12.8972]],
		        [[0.3021, 0.0805, -0.2310], [-0.0328, -0.1605, -0.2714], [-0.1408, -0.5477, -0.6976]],
		    ]						)
	elif model_name == "segformer.b0.640x1280.city.160k":
		__magic_name__			:       Any	      =	torch.tensor(
		    [
		        [
		            [-1.1372e01, -1.2787e01, -1.3477e01],
		            [-1.2536e01, -1.4194e01, -1.4409e01],
		            [-1.3217e01, -1.4888e01, -1.5327e01],
		        ],
		        [
		            [-1.4791e01, -1.7122e01, -1.8277e01],
		            [-1.7163e01, -1.9192e01, -1.9533e01],
		            [-1.7897e01, -1.9991e01, -2.0315e01],
		        ],
		        [
		            [7.6723e-01, 4.1921e-01, -7.7878e-02],
		            [4.7772e-01, 9.5557e-03, -2.8082e-01],
		            [3.6032e-01, -2.4826e-01, -5.1168e-01],
		        ],
		    ]						)
	elif model_name == "segformer.b0.768x768.city.160k":
		__magic_name__			:       Dict	      =	torch.tensor(
		    [
		        [[-9.4959, -11.3087, -11.7479], [-11.0025, -12.6540, -12.3319], [-11.4064, -13.0487, -12.9905]],
		        [[-9.8905, -11.3084, -12.0854], [-11.1726, -12.7698, -12.9583], [-11.5985, -13.3278, -14.1774]],
		        [[0.2213, 0.0192, -0.2466], [-0.1731, -0.4213, -0.4874], [-0.3126, -0.6541, -1.1389]],
		    ]						)
	elif model_name == "segformer.b1.1024x1024.city.160k":
		__magic_name__			:       Optional[Any]	      =	torch.tensor(
		    [
		        [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
		        [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
		        [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
		    ]						)
	elif model_name == "segformer.b2.1024x1024.city.160k":
		__magic_name__			:       Union[str, Any]	      =	torch.tensor(
		    [
		        [[-16.0976, -16.4856, -17.3962], [-16.6234, -19.0342, -19.7685], [-16.0900, -18.0661, -19.1180]],
		        [[-18.4750, -18.8488, -19.5074], [-19.4030, -22.1570, -22.5977], [-19.1191, -20.8486, -22.3783]],
		        [[-4.5178, -5.5037, -6.5109], [-5.0884, -7.2174, -8.0334], [-4.4156, -5.8117, -7.2970]],
		    ]						)
	elif model_name == "segformer.b3.1024x1024.city.160k":
		__magic_name__			:       int	      =	torch.tensor(
		    [
		        [[-14.2081, -14.4732, -14.1977], [-14.5867, -16.4423, -16.6356], [-13.4441, -14.9685, -16.8696]],
		        [[-14.4576, -14.7073, -15.0451], [-15.0816, -17.6237, -17.9873], [-14.4213, -16.0199, -18.5992]],
		        [[-4.7349, -4.9588, -5.0966], [-4.3210, -6.9325, -7.2591], [-3.4312, -4.7484, -7.1917]],
		    ]						)
	elif model_name == "segformer.b4.1024x1024.city.160k":
		__magic_name__			:       Union[str, Any]	      =	torch.tensor(
		    [
		        [[-11.7737, -11.9526, -11.3273], [-13.6692, -14.4574, -13.8878], [-13.8937, -14.6924, -15.9345]],
		        [[-14.6706, -14.5330, -14.1306], [-16.1502, -16.8180, -16.4269], [-16.8338, -17.8939, -20.1746]],
		        [[1.0491, 0.8289, 1.0310], [1.1044, 0.5219, 0.8055], [1.0899, 0.6926, 0.5590]],
		    ]						)
	elif model_name == "segformer.b5.1024x1024.city.160k":
		__magic_name__			:       List[Any]	      =	torch.tensor(
		    [
		        [[-12.5641, -13.4777, -13.0684], [-13.9587, -15.8983, -16.6557], [-13.3109, -15.7350, -16.3141]],
		        [[-14.7074, -15.4352, -14.5944], [-16.6353, -18.1663, -18.6120], [-15.1702, -18.0329, -18.1547]],
		        [[-1.7990, -2.0951, -1.7784], [-2.6397, -3.8245, -3.9686], [-1.5264, -2.8126, -2.9316]],
		    ]						)
	else:
		__magic_name__			:       str	      =	logits.argmax(-1						).item()
		print('Predicted class:'						,						model.config.idalabel[predicted_class_idx]						)

	# verify logits
	if not encoder_only:
		assert logits.shape == expected_shape
		assert torch.allclose(logits[0, :3, :3, :3]						,						_UpperCAmelCase						,						atol=1e-2						)

	# finally, save model and image processor
	logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'						)
	Path(_UpperCAmelCase						).mkdir(exist_ok=_UpperCAmelCase						)
	model.save_pretrained(_UpperCAmelCase						)
	image_processor.save_pretrained(_UpperCAmelCase						)


if __name__ == "__main__":
							lowerCAmelCase :Dict             =     argparse.ArgumentParser()

							parser.add_argument(
							    '''--model_name''',
							    default='''segformer.b0.512x512.ade.160k''',
							    type=str,
							    help='''Name of the model you\'d like to convert.''',
							)
							parser.add_argument(
							    '''--checkpoint_path''', default=None, type=str, help='''Path to the original PyTorch checkpoint (.pth file).'''
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
							)
							lowerCAmelCase :List[str]             =     parser.parse_args()
							convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
a__      :     Dict         =		logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')





@dataclass
class UpperCAmelCase_				:
  __lowerCAmelCase   :						Optional[str]							=   field(
      default="""cifar10"""     ,		metadata={"""help""": """Name of a dataset from the datasets package"""} )
  __lowerCAmelCase   :						Optional[str]							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
  __lowerCAmelCase   :						Optional[str]							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		metadata={"""help""": """The column name of the images in the files."""} )
  __lowerCAmelCase   :						Optional[str]							=   field(default=_SCREAMING_SNAKE_CASE     ,		metadata={"""help""": """A folder containing the training data."""} )
  __lowerCAmelCase   :						Optional[str]							=   field(default=_SCREAMING_SNAKE_CASE     ,		metadata={"""help""": """A folder containing the validation data."""} )
  __lowerCAmelCase   :						Optional[float]							=   field(
      default=0.15     ,		metadata={"""help""": """Percent to split off of train for validation."""} )
  __lowerCAmelCase   :						Optional[int]							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		metadata={
          """help""": (
              """For debugging purposes or quicker training, truncate the number of training examples to this """
              """value if set."""
          )
      }     ,		)
  __lowerCAmelCase   :						Optional[int]							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		metadata={
          """help""": (
              """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
              """value if set."""
          )
      }     ,		)


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       {}
       if self.train_dir is not None:
            A_          =       self.train_dir
       if self.validation_dir is not None:
            A_          =       self.validation_dir
       A_          =       data_files if data_files else None





@dataclass
class UpperCAmelCase_				:
  __lowerCAmelCase   :						str							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		metadata={
          """help""": (
              """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
          )
      }     ,		)
  __lowerCAmelCase   :						Optional[str]							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""} )
  __lowerCAmelCase   :						Optional[str]							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		metadata={
          """help""": (
              """Override some existing default config settings when a model is trained from scratch. Example: """
              """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
          )
      }     ,		)
  __lowerCAmelCase   :						Optional[str]							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""} )
  __lowerCAmelCase   :						str							=   field(
      default="""main"""     ,		metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}     ,		)
  __lowerCAmelCase   :						str							=   field(default=_SCREAMING_SNAKE_CASE     ,		metadata={"""help""": """Name or path of preprocessor config."""} )
  __lowerCAmelCase   :						bool							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		metadata={
          """help""": (
              """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
              """with private models)."""
          )
      }     ,		)
  __lowerCAmelCase   :						float							=   field(
      default=0.75     ,		metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""} )
  __lowerCAmelCase   :						bool							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		metadata={"""help""": """Whether or not to train with normalized pixel values as target."""} )





@dataclass
class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):
  __lowerCAmelCase   :						float							=   field(
      default=1E-3     ,		metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""} )




def      UpperCAmelCase_    (    _UpperCAmelCase							:Any				)	->				List[str]:
     '''simple docstring'''


     A_          =       torch.stack([example['''pixel_values'''] for example in examples]				)
     return {"pixel_values": pixel_values}

def      UpperCAmelCase_    (    )	->				str:
     '''simple docstring'''


     A_          =       HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)				)
     if len(sys.argv				) == 2 and sys.argv[1].endswith('''.json'''				):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          A_   ,    A_   ,    A_          =       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
     else:
          A_   ,    A_   ,    A_          =       parser.parse_args_into_dataclasses()

     # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
     # information sent is the one passed as arguments along with your Python/PyTorch versions.
     send_example_telemetry('''run_mae'''  ,						_UpperCAmelCase  ,						_UpperCAmelCase				)

     # Setup logging
     logging.basicConfig(
         format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''  ,						datefmt='''%m/%d/%Y %H:%M:%S'''  ,						handlers=[logging.StreamHandler(sys.stdout				)]  ,						)

     if training_args.should_log:
          # The default of training_args.log_level is passive, so we set log level at info here to have that default.
          transformers.utils.logging.set_verbosity_info()

     A_          =       training_args.get_process_log_level()
     logger.setLevel(_UpperCAmelCase				)
     transformers.utils.logging.set_verbosity(_UpperCAmelCase				)
     transformers.utils.logging.enable_default_handler()
     transformers.utils.logging.enable_explicit_format()

     # Log on each process the small summary:
     logger.warning(
         f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
         + f'distributed training: {bool(training_args.local_rank != -1				)}, 16-bits training: {training_args.fpaa}'				)
     logger.info(f'Training/evaluation parameters {training_args}'				)

     # Detecting last checkpoint.
     A_          =       None
     if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
          A_          =       get_last_checkpoint(training_args.output_dir				)
          if last_checkpoint is None and len(os.listdir(training_args.output_dir				)				) > 0:
               raise ValueError(
                   f'Output directory ({training_args.output_dir}) already exists and is not empty. '
                   '''Use --overwrite_output_dir to overcome.'''				)
          elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
               logger.info(
                   f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
                   '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''				)

    # Initialize our dataset.
     A_          =       load_dataset(
         data_args.dataset_name  ,						data_args.dataset_config_name  ,						data_files=data_args.data_files  ,						cache_dir=model_args.cache_dir  ,						use_auth_token=True if model_args.use_auth_token else None  ,						)

     # If we don't have a validation split, split off a percentage of train as validation.
     A_          =       None if '''validation''' in ds.keys() else data_args.train_val_split
     if isinstance(data_args.train_val_split  ,						_UpperCAmelCase				) and data_args.train_val_split > 0.0:
          A_          =       ds['''train'''].train_test_split(data_args.train_val_split				)
          A_          =       split['''train''']
          A_          =       split['''test''']

     # Load pretrained model and image processor
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     A_          =       {
         '''cache_dir''': model_args.cache_dir,
         '''revision''': model_args.model_revision,
         '''use_auth_token''': True if model_args.use_auth_token else None,
     }
     if model_args.config_name:
          A_          =       ViTMAEConfig.from_pretrained(model_args.config_name  ,						**_UpperCAmelCase				)
     elif model_args.model_name_or_path:
          A_          =       ViTMAEConfig.from_pretrained(model_args.model_name_or_path  ,						**_UpperCAmelCase				)
     else:
          A_          =       ViTMAEConfig()
          logger.warning('''You are instantiating a new config instance from scratch.'''				)
          if model_args.config_overrides is not None:
               logger.info(f'Overriding config: {model_args.config_overrides}'				)
               config.update_from_string(model_args.config_overrides				)
               logger.info(f'New config: {config}'				)

    # adapt config
     config.update(
         {
             '''mask_ratio''': model_args.mask_ratio,
             '''norm_pix_loss''': model_args.norm_pix_loss,
         }				)

     # create image processor
     if model_args.image_processor_name:
          A_          =       ViTImageProcessor.from_pretrained(model_args.image_processor_name  ,						**_UpperCAmelCase				)
     elif model_args.model_name_or_path:
          A_          =       ViTImageProcessor.from_pretrained(model_args.model_name_or_path  ,						**_UpperCAmelCase				)
     else:
          A_          =       ViTImageProcessor()

     # create model
     if model_args.model_name_or_path:
          A_          =       ViTMAEForPreTraining.from_pretrained(
              model_args.model_name_or_path  ,						from_tf=bool('''.ckpt''' in model_args.model_name_or_path				)  ,						config=_UpperCAmelCase  ,						cache_dir=model_args.cache_dir  ,						revision=model_args.model_revision  ,						use_auth_token=True if model_args.use_auth_token else None  ,						)
     else:
          logger.info('''Training new model from scratch'''				)
          A_          =       ViTMAEForPreTraining(_UpperCAmelCase				)

     if training_args.do_train:
          A_          =       ds['''train'''].column_names
     else:
          A_          =       ds['''validation'''].column_names

     if data_args.image_column_name is not None:
          A_          =       data_args.image_column_name
     elif "image" in column_names:
          A_          =       '''image'''
     elif "img" in column_names:
          A_          =       '''img'''
     else:
          A_          =       column_names[0]

     # transformations as done in original MAE paper
     # source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
     if "shortest_edge" in image_processor.size:
          A_          =       image_processor.size['''shortest_edge''']
     else:
          A_          =       (image_processor.size['''height'''], image_processor.size['''width'''])
     A_          =       Compose(
         [
             Lambda(lambda _UpperCAmelCase				: img.convert('''RGB'''				) if img.mode != "RGB" else img				),
             RandomResizedCrop(_UpperCAmelCase  ,						scale=(0.2, 1.0)  ,						interpolation=InterpolationMode.BICUBIC				),
             RandomHorizontalFlip(),
             ToTensor(),
             Normalize(mean=image_processor.image_mean  ,						std=image_processor.image_std				),
         ]				)

     def preprocess_images(_UpperCAmelCase							:Optional[Any]				):
          A_          =       [transforms(_UpperCAmelCase				) for image in examples[image_column_name]]
          return examples

     if training_args.do_train:
          if "train" not in ds:
               raise ValueError('''--do_train requires a train dataset'''				)
          if data_args.max_train_samples is not None:
               A_          =       ds['''train'''].shuffle(seed=training_args.seed				).select(range(data_args.max_train_samples				)				)
          # Set the training transforms
          ds["train"].set_transform(_UpperCAmelCase				)

     if training_args.do_eval:
          if "validation" not in ds:
               raise ValueError('''--do_eval requires a validation dataset'''				)
          if data_args.max_eval_samples is not None:
               A_          =       (
                   ds['''validation'''].shuffle(seed=training_args.seed				).select(range(data_args.max_eval_samples				)				)
               )
          # Set the validation transforms
          ds["validation"].set_transform(_UpperCAmelCase				)

     # Compute absolute learning rate
     A_          =       (
         training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
     )
     if training_args.base_learning_rate is not None:
          A_          =       training_args.base_learning_rate * total_train_batch_size / 256

     # Initialize our trainer
     A_          =       Trainer(
         model=_UpperCAmelCase  ,						args=_UpperCAmelCase  ,						train_dataset=ds['''train'''] if training_args.do_train else None  ,						eval_dataset=ds['''validation'''] if training_args.do_eval else None  ,						tokenizer=_UpperCAmelCase  ,						data_collator=_UpperCAmelCase  ,						)

     # Training
     if training_args.do_train:
          A_          =       None
          if training_args.resume_from_checkpoint is not None:
               A_          =       training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               A_          =       last_checkpoint
          A_          =       trainer.train(resume_from_checkpoint=_UpperCAmelCase				)
          trainer.save_model()
          trainer.log_metrics('''train'''  ,						train_result.metrics				)
          trainer.save_metrics('''train'''  ,						train_result.metrics				)
          trainer.save_state()

     # Evaluation
     if training_args.do_eval:
          A_          =       trainer.evaluate()
          trainer.log_metrics('''eval'''  ,						_UpperCAmelCase				)
          trainer.save_metrics('''eval'''  ,						_UpperCAmelCase				)

     # Write model card and (optionally) push to hub
     A_          =       {
         '''tasks''': '''masked-auto-encoding''',
         '''dataset''': data_args.dataset_name,
         '''tags''': ['''masked-auto-encoding'''],
     }
     if training_args.push_to_hub:
          trainer.push_to_hub(**_UpperCAmelCase				)
     else:
          trainer.create_model_card(**_UpperCAmelCase				)

def      UpperCAmelCase_    (    _UpperCAmelCase							:Union[str, Any]				)	->				Dict:
     '''simple docstring'''


     main()


if __name__ == "__main__":
      main()



