


'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case							   =  logging.get_logger()


def     a   (    __a				,      __a				,      __a				,      __a				,      __a = True     )						->			Tuple:



	'''simple docstring'''






	print(f'''Converting {name}...'''     )

	with torch.no_grad():
		if hidden_sizes == 128:
			if name[-1] == "S":
				UpperCamelCase__     :int							      =				timm.create_model('''levit_128s'''				,      pretrained=__a     )
			else:
				UpperCamelCase__     :int							      =				timm.create_model('''levit_128'''				,      pretrained=__a     )
		if hidden_sizes == 192:
			UpperCamelCase__     :Union[str, Any]							      =				timm.create_model('''levit_192'''				,      pretrained=__a     )
		if hidden_sizes == 256:
			UpperCamelCase__     :int							      =				timm.create_model('''levit_256'''				,      pretrained=__a     )
		if hidden_sizes == 384:
			UpperCamelCase__     :Dict							      =				timm.create_model('''levit_384'''				,      pretrained=__a     )

		from_model.eval()
		UpperCamelCase__     :Any							      =				LevitForImageClassificationWithTeacher(__a     ).eval()
		UpperCamelCase__     :Any							      =				OrderedDict()

		UpperCamelCase__     :List[str]							      =				from_model.state_dict()
		UpperCamelCase__     :Dict							      =				list(from_model.state_dict().keys()     )
		UpperCamelCase__     :str							      =				list(our_model.state_dict().keys()     )
		print(len(__a     )				,      len(__a     )     )
		for i in range(len(__a     )     ):
			UpperCamelCase__     :str							      =				weights[og_keys[i]]
		our_model.load_state_dict(__a     )

		UpperCamelCase__     :Optional[int]							      =				torch.randn((2, 3, 224, 224)     )
		UpperCamelCase__     :Tuple							      =				from_model(__a     )
		UpperCamelCase__     :str							      =				our_model(__a     ).logits

	assert torch.allclose(__a				,      __a     ), "The model logits don't match the original one."

	UpperCamelCase__     :Tuple							      =				name
	print(__a     )

	if push_to_hub:
		our_model.save_pretrained(save_directory / checkpoint_name     )
		UpperCamelCase__     :Union[str, Any]							      =				LevitImageProcessor()
		image_processor.save_pretrained(save_directory / checkpoint_name     )

		print(f'''Pushed {checkpoint_name}'''     )


def     a   (    __a				,      __a = None				,      __a = True     )						->			Tuple:



	'''simple docstring'''






	UpperCamelCase__     :List[Any]							      =				'''imagenet-1k-id2label.json'''
	UpperCamelCase__     :Optional[Any]							      =				1000
	UpperCamelCase__     :List[Any]							      =				(1, num_labels)

	UpperCamelCase__     :Dict							      =				'''huggingface/label-files'''
	UpperCamelCase__     :int							      =				num_labels
	UpperCamelCase__     :Optional[Any]							      =				json.load(open(hf_hub_download(__a				,      __a				,      repo_type='''dataset'''     )				,      '''r'''     )     )
	UpperCamelCase__     :Optional[Any]							      =				{int(__a     ): v for k, v in idalabel.items()}

	UpperCamelCase__     :Dict							      =				idalabel
	UpperCamelCase__     :Tuple							      =				{v: k for k, v in idalabel.items()}

	UpperCamelCase__     :Tuple							      =				partial(__a				,      num_labels=__a				,      idalabel=__a				,      labelaid=__a     )

	UpperCamelCase__     :int							      =				{
	    '''levit-128S''': 128,
	    '''levit-128''': 128,
	    '''levit-192''': 192,
	    '''levit-256''': 256,
	    '''levit-384''': 384,
	}

	UpperCamelCase__     :List[str]							      =				{
	    '''levit-128S''': ImageNetPreTrainedConfig(
	        hidden_sizes=[128, 256, 384]				,      num_attention_heads=[4, 6, 8]				,      depths=[2, 3, 4]				,      key_dim=[16, 16, 16]				,      drop_path_rate=0				,      ),
	    '''levit-128''': ImageNetPreTrainedConfig(
	        hidden_sizes=[128, 256, 384]				,      num_attention_heads=[4, 8, 12]				,      depths=[4, 4, 4]				,      key_dim=[16, 16, 16]				,      drop_path_rate=0				,      ),
	    '''levit-192''': ImageNetPreTrainedConfig(
	        hidden_sizes=[192, 288, 384]				,      num_attention_heads=[3, 5, 6]				,      depths=[4, 4, 4]				,      key_dim=[32, 32, 32]				,      drop_path_rate=0				,      ),
	    '''levit-256''': ImageNetPreTrainedConfig(
	        hidden_sizes=[256, 384, 512]				,      num_attention_heads=[4, 6, 8]				,      depths=[4, 4, 4]				,      key_dim=[32, 32, 32]				,      drop_path_rate=0				,      ),
	    '''levit-384''': ImageNetPreTrainedConfig(
	        hidden_sizes=[384, 512, 768]				,      num_attention_heads=[6, 9, 12]				,      depths=[4, 4, 4]				,      key_dim=[32, 32, 32]				,      drop_path_rate=0.1				,      ),
	}

	if model_name:
		convert_weight_and_push(
		    names_to_hidden_sizes[model_name]				,      __a				,      names_to_config[model_name]				,      __a				,      __a     )
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(names_to_hidden_sizes[model_name]				,      __a				,      __a				,      __a				,      __a     )
	return config, expected_shape


if __name__ == "__main__":
		__snake_case							   =  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--model_name''',
		    default=None,
		    type=str,
		    help='''The name of the model you wish to convert, it must be one of the supported Levit* architecture,''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''',
		    default='''levit-dump-folder/''',
		    type=Path,
		    required=False,
		    help='''Path to the output PyTorch model directory.''',
		)
		parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
		parser.add_argument(
		    '''--no-push_to_hub''',
		    dest='''push_to_hub''',
		    action='''store_false''',
		    help='''Do not push model and image processor to the hub''',
		)

		__snake_case							   =  parser.parse_args()
		__snake_case							   =  args.pytorch_dump_folder_path
		pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
		convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)



'''simple docstring'''
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
		import torch

if is_vision_available():
		import PIL


__snake_case							   =  logging.get_logger(__name__)


def     a   (    __a				,      __a				,      __a				,      __a     )						->			Tuple[int, int]:



					'''simple docstring'''






					def constraint_to_multiple_of(__a				,      __a				,      __a=0				,      __a=None     ):
										UpperCamelCase__     :Union[str, Any]							      =				round(val / multiple     ) * multiple

										if max_val is not None and x > max_val:
															UpperCamelCase__     :List[str]							      =				math.floor(val / multiple     ) * multiple

										if x < min_val:
															UpperCamelCase__     :Union[str, Any]							      =				math.ceil(val / multiple     ) * multiple

										return x

					UpperCamelCase__     :str							      =				(output_size, output_size) if isinstance(__a				,      __a     ) else output_size

					UpperCamelCase__      ,       UpperCamelCase__     :int							      =				get_image_size(__a     )
					UpperCamelCase__      ,       UpperCamelCase__     :Optional[int]							      =				output_size

					# determine new height and width
					UpperCamelCase__     :List[str]							      =				output_height / input_height
					UpperCamelCase__     :str							      =				output_width / input_width

					if keep_aspect_ratio:
										# scale as little as possible
										if abs(1 - scale_width     ) < abs(1 - scale_height     ):
															# fit width
															UpperCamelCase__     :str							      =				scale_width
										else:
															# fit height
															UpperCamelCase__     :int							      =				scale_height

					UpperCamelCase__     :Optional[int]							      =				constraint_to_multiple_of(scale_height * input_height				,      multiple=__a     )
					UpperCamelCase__     :List[Any]							      =				constraint_to_multiple_of(scale_width * input_width				,      multiple=__a     )

					return (new_height, new_width)







class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       ['pixel_values']




		def __init__(	self					,     UpperCamelCase_ = True					,     UpperCamelCase_ = None					,     UpperCamelCase_ = PILImageResampling.BILINEAR					,     UpperCamelCase_ = False					,     UpperCamelCase_ = 1					,     UpperCamelCase_ = True					,     UpperCamelCase_ = 1 / 255					,     UpperCamelCase_ = True					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							super().__init__(**UpperCamelCase_							)
							UpperCamelCase__     :List[Any]							      =				size if size is not None else {'''height''': 384, '''width''': 384}
							UpperCamelCase__     :Dict							      =				get_size_dict(UpperCamelCase_							)
							UpperCamelCase__     :Dict							      =				do_resize
							UpperCamelCase__     :Union[str, Any]							      =				size
							UpperCamelCase__     :List[Any]							      =				keep_aspect_ratio
							UpperCamelCase__     :Optional[int]							      =				ensure_multiple_of
							UpperCamelCase__     :Union[str, Any]							      =				resample
							UpperCamelCase__     :str							      =				do_rescale
							UpperCamelCase__     :Union[str, Any]							      =				rescale_factor
							UpperCamelCase__     :List[str]							      =				do_normalize
							UpperCamelCase__     :List[str]							      =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							UpperCamelCase__     :Dict							      =				image_std if image_std is not None else IMAGENET_STANDARD_STD




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_ = False					,     UpperCamelCase_ = 1					,     UpperCamelCase_ = PILImageResampling.BICUBIC					,     UpperCamelCase_ = None					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				get_size_dict(UpperCamelCase_							)
							if "height" not in size or "width" not in size:
												raise ValueError(F'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''							)
							UpperCamelCase__     :int							      =				get_resize_output_image_size(
							    UpperCamelCase_					,     output_size=(size['''height'''], size['''width'''])					,     keep_aspect_ratio=UpperCamelCase_					,     multiple=UpperCamelCase_					,     )
							return resize(UpperCamelCase_					,     size=UpperCamelCase_					,     resample=UpperCamelCase_					,     data_format=UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_ = None					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							return rescale(UpperCamelCase_					,     scale=UpperCamelCase_					,     data_format=UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_ = None					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							return normalize(UpperCamelCase_					,     mean=UpperCamelCase_					,     std=UpperCamelCase_					,     data_format=UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = ChannelDimension.FIRST					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				do_resize if do_resize is not None else self.do_resize
							UpperCamelCase__     :Any							      =				size if size is not None else self.size
							UpperCamelCase__     :List[str]							      =				get_size_dict(UpperCamelCase_							)
							UpperCamelCase__     :Any							      =				keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
							UpperCamelCase__     :str							      =				ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
							UpperCamelCase__     :Union[str, Any]							      =				resample if resample is not None else self.resample
							UpperCamelCase__     :str							      =				do_rescale if do_rescale is not None else self.do_rescale
							UpperCamelCase__     :int							      =				rescale_factor if rescale_factor is not None else self.rescale_factor
							UpperCamelCase__     :Optional[Any]							      =				do_normalize if do_normalize is not None else self.do_normalize
							UpperCamelCase__     :Optional[int]							      =				image_mean if image_mean is not None else self.image_mean
							UpperCamelCase__     :List[str]							      =				image_std if image_std is not None else self.image_std

							UpperCamelCase__     :List[str]							      =				make_list_of_images(UpperCamelCase_							)

							if not valid_images(UpperCamelCase_							):
												raise ValueError(
												    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
												    '''torch.Tensor, tf.Tensor or jax.ndarray.'''							)

							if do_resize and size is None or resample is None:
												raise ValueError('''Size and resample must be specified if do_resize is True.'''							)

							if do_rescale and rescale_factor is None:
												raise ValueError('''Rescale factor must be specified if do_rescale is True.'''							)

							if do_normalize and (image_mean is None or image_std is None):
												raise ValueError('''Image mean and std must be specified if do_normalize is True.'''							)

							# All transformations expect numpy arrays.
							UpperCamelCase__     :str							      =				[to_numpy_array(UpperCamelCase_							) for image in images]

							if do_resize:
												UpperCamelCase__     :str							      =				[self.resize(image=UpperCamelCase_					,     size=UpperCamelCase_					,     resample=UpperCamelCase_							) for image in images]

							if do_rescale:
												UpperCamelCase__     :int							      =				[self.rescale(image=UpperCamelCase_					,     scale=UpperCamelCase_							) for image in images]

							if do_normalize:
												UpperCamelCase__     :str							      =				[self.normalize(image=UpperCamelCase_					,     mean=UpperCamelCase_					,     std=UpperCamelCase_							) for image in images]

							UpperCamelCase__     :Optional[int]							      =				[to_channel_dimension_format(UpperCamelCase_					,     UpperCamelCase_							) for image in images]

							UpperCamelCase__     :List[str]							      =				{'''pixel_values''': images}
							return BatchFeature(data=UpperCamelCase_					,     tensor_type=UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_ = None							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				outputs.logits

							# Resize logits and compute semantic segmentation maps
							if target_sizes is not None:
												if len(UpperCamelCase_							) != len(UpperCamelCase_							):
																	raise ValueError(
																	    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''							)

												if is_torch_tensor(UpperCamelCase_							):
																	UpperCamelCase__     :Union[str, Any]							      =				target_sizes.numpy()

												UpperCamelCase__     :int							      =				[]

												for idx in range(len(UpperCamelCase_							)							):
																	UpperCamelCase__     :str							      =				torch.nn.functional.interpolate(
																	    logits[idx].unsqueeze(dim=0							)					,     size=target_sizes[idx]					,     mode='''bilinear'''					,     align_corners=UpperCamelCase_							)
																	UpperCamelCase__     :List[Any]							      =				resized_logits[0].argmax(dim=0							)
																	semantic_segmentation.append(UpperCamelCase_							)
							else:
												UpperCamelCase__     :Any							      =				logits.argmax(dim=1							)
												UpperCamelCase__     :Optional[Any]							      =				[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]							)]

							return semantic_segmentation