





'''simple docstring'''




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


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


if is_vision_available():
			import PIL






class 				__snake_case     (					__magic_name__							):
	__lowerCAmelCase       						=  ['''pixel_values''']





	def __init__(		self      ,       UpperCamelCase_ = True      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = PILImageResampling.BICUBIC      ,       UpperCamelCase_ = True      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = True      ,       UpperCamelCase_ = 1 / 255      ,       UpperCamelCase_ = True      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = True      ,       **UpperCamelCase_      ,       )      ->		None:
		super().__init__(**UpperCamelCase_		)
		snake_case__	       =   size if size is not None else {'shortest_edge': 224}
		snake_case__	       =   get_size_dict(UpperCamelCase_      ,       default_to_square=UpperCamelCase_		)
		snake_case__	       =   crop_size if crop_size is not None else {'height': 224, 'width': 224}
		snake_case__	       =   get_size_dict(UpperCamelCase_      ,       default_to_square=UpperCamelCase_      ,       param_name='crop_size'		)

		snake_case__	       =   do_resize
		snake_case__	       =   size
		snake_case__	       =   resample
		snake_case__	       =   do_center_crop
		snake_case__	       =   crop_size
		snake_case__	       =   do_rescale
		snake_case__	       =   rescale_factor
		snake_case__	       =   do_normalize
		snake_case__	       =   image_mean if image_mean is not None else OPENAI_CLIP_MEAN
		snake_case__	       =   image_std if image_std is not None else OPENAI_CLIP_STD
		snake_case__	       =   do_convert_rgb





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_ = PILImageResampling.BICUBIC      ,       UpperCamelCase_ = None      ,       **UpperCamelCase_      ,       )      ->		np.ndarray:
		snake_case__	       =   get_size_dict(UpperCamelCase_      ,       default_to_square=UpperCamelCase_		)
		if "shortest_edge" not in size:
			raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''		)
		snake_case__	       =   get_resize_output_image_size(UpperCamelCase_      ,       size=size['shortest_edge']      ,       default_to_square=UpperCamelCase_		)
		return resize(UpperCamelCase_      ,       size=UpperCamelCase_      ,       resample=UpperCamelCase_      ,       data_format=UpperCamelCase_      ,       **UpperCamelCase_		)





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_ = None      ,       **UpperCamelCase_      ,       )      ->		np.ndarray:
		snake_case__	       =   get_size_dict(UpperCamelCase_		)
		if "height" not in size or "width" not in size:
			raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''		)
		return center_crop(UpperCamelCase_      ,       size=(size['height'], size['width'])      ,       data_format=UpperCamelCase_      ,       **UpperCamelCase_		)





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_ = None      ,       **UpperCamelCase_      ,       )      ->		str:
		return rescale(UpperCamelCase_      ,       scale=UpperCamelCase_      ,       data_format=UpperCamelCase_      ,       **UpperCamelCase_		)





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_ = None      ,       **UpperCamelCase_      ,       )      ->		np.ndarray:
		return normalize(UpperCamelCase_      ,       mean=UpperCamelCase_      ,       std=UpperCamelCase_      ,       data_format=UpperCamelCase_      ,       **UpperCamelCase_		)





	def   _snake_case   (		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_ = None      ,       UpperCamelCase_ = ChannelDimension.FIRST      ,       **UpperCamelCase_      ,       )      ->		PIL.Image.Image:
		snake_case__	       =   do_resize if do_resize is not None else self.do_resize
		snake_case__	       =   size if size is not None else self.size
		snake_case__	       =   get_size_dict(UpperCamelCase_      ,       param_name='size'      ,       default_to_square=UpperCamelCase_		)
		snake_case__	       =   resample if resample is not None else self.resample
		snake_case__	       =   do_center_crop if do_center_crop is not None else self.do_center_crop
		snake_case__	       =   crop_size if crop_size is not None else self.crop_size
		snake_case__	       =   get_size_dict(UpperCamelCase_      ,       param_name='crop_size'      ,       default_to_square=UpperCamelCase_		)
		snake_case__	       =   do_rescale if do_rescale is not None else self.do_rescale
		snake_case__	       =   rescale_factor if rescale_factor is not None else self.rescale_factor
		snake_case__	       =   do_normalize if do_normalize is not None else self.do_normalize
		snake_case__	       =   image_mean if image_mean is not None else self.image_mean
		snake_case__	       =   image_std if image_std is not None else self.image_std
		snake_case__	       =   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

		snake_case__	       =   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:
			raise ValueError('Size must be specified if do_resize is True.'		)

		if do_center_crop and crop_size is None:
			raise ValueError('Crop size must be specified if do_center_crop 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.'		)

		# PIL RGBA images are converted to RGB
		if do_convert_rgb:
			snake_case__	       =   [convert_to_rgb(UpperCamelCase_		) for image in images]

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

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

		if do_center_crop:
			snake_case__	       =   [self.center_crop(image=UpperCamelCase_      ,       size=UpperCamelCase_		) for image in images]

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

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

		snake_case__	       =   [to_channel_dimension_format(UpperCamelCase_      ,       UpperCamelCase_		) for image in images]

		snake_case__	       =   {'pixel_values': images}
		return BatchFeature(data=UpperCamelCase_      ,       tensor_type=UpperCamelCase_		)










'''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()
a__					:			Any          =     logging.get_logger()




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_ = True							)   ->Dict:
	print(f'''Converting {name}...'''							)

	with torch.no_grad():
		if hidden_sizes == 1_28:
			if name[-1] == "S":
				snake_case__	       =   timm.create_model('levit_128s'					,				pretrained=UpperCAmelCase_							)
			else:
				snake_case__	       =   timm.create_model('levit_128'					,				pretrained=UpperCAmelCase_							)
		if hidden_sizes == 1_92:
			snake_case__	       =   timm.create_model('levit_192'					,				pretrained=UpperCAmelCase_							)
		if hidden_sizes == 2_56:
			snake_case__	       =   timm.create_model('levit_256'					,				pretrained=UpperCAmelCase_							)
		if hidden_sizes == 3_84:
			snake_case__	       =   timm.create_model('levit_384'					,				pretrained=UpperCAmelCase_							)

		from_model.eval()
		snake_case__	       =   LevitForImageClassificationWithTeacher(UpperCAmelCase_							).eval()
		snake_case__	       =   OrderedDict()

		snake_case__	       =   from_model.state_dict()
		snake_case__	       =   list(from_model.state_dict().keys()							)
		snake_case__	       =   list(our_model.state_dict().keys()							)
		print(len(UpperCAmelCase_							)					,				len(UpperCAmelCase_							)							)
		for i in range(len(UpperCAmelCase_							)							):
			snake_case__	       =   weights[og_keys[i]]
		our_model.load_state_dict(UpperCAmelCase_							)

		snake_case__	       =   torch.randn((2, 3, 2_24, 2_24)							)
		snake_case__	       =   from_model(UpperCAmelCase_							)
		snake_case__	       =   our_model(UpperCAmelCase_							).logits

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

	snake_case__	       =   name
	print(UpperCAmelCase_							)

	if push_to_hub:
		our_model.save_pretrained(save_directory / checkpoint_name							)
		snake_case__	       =   LevitImageProcessor()
		image_processor.save_pretrained(save_directory / checkpoint_name							)

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




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_ = None					,				UpperCAmelCase_ = True							)   ->Any:
	snake_case__	       =   'imagenet-1k-id2label.json'
	snake_case__	       =   10_00
	snake_case__	       =   (1, num_labels)

	snake_case__	       =   'huggingface/label-files'
	snake_case__	       =   num_labels
	snake_case__	       =   json.load(open(hf_hub_download(UpperCAmelCase_					,				UpperCAmelCase_					,				repo_type='dataset'							)					,				'r'							)							)
	snake_case__	       =   {int(UpperCAmelCase_							): v for k, v in idalabel.items()}

	snake_case__	       =   idalabel
	snake_case__	       =   {v: k for k, v in idalabel.items()}

	snake_case__	       =   partial(UpperCAmelCase_					,				num_labels=UpperCAmelCase_					,				idalabel=UpperCAmelCase_					,				labelaid=UpperCAmelCase_							)

	snake_case__	       =   {
	    'levit-128S': 1_28,
	    'levit-128': 1_28,
	    'levit-192': 1_92,
	    'levit-256': 2_56,
	    'levit-384': 3_84,
	}

	snake_case__	       =   {
	    'levit-128S': ImageNetPreTrainedConfig(
	        hidden_sizes=[1_28, 2_56, 3_84]					,				num_attention_heads=[4, 6, 8]					,				depths=[2, 3, 4]					,				key_dim=[16, 16, 16]					,				drop_path_rate=0					,				),
	    'levit-128': ImageNetPreTrainedConfig(
	        hidden_sizes=[1_28, 2_56, 3_84]					,				num_attention_heads=[4, 8, 12]					,				depths=[4, 4, 4]					,				key_dim=[16, 16, 16]					,				drop_path_rate=0					,				),
	    'levit-192': ImageNetPreTrainedConfig(
	        hidden_sizes=[1_92, 2_88, 3_84]					,				num_attention_heads=[3, 5, 6]					,				depths=[4, 4, 4]					,				key_dim=[32, 32, 32]					,				drop_path_rate=0					,				),
	    'levit-256': ImageNetPreTrainedConfig(
	        hidden_sizes=[2_56, 3_84, 5_12]					,				num_attention_heads=[4, 6, 8]					,				depths=[4, 4, 4]					,				key_dim=[32, 32, 32]					,				drop_path_rate=0					,				),
	    'levit-384': ImageNetPreTrainedConfig(
	        hidden_sizes=[3_84, 5_12, 7_68]					,				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]					,				UpperCAmelCase_					,				names_to_config[model_name]					,				UpperCAmelCase_					,				UpperCAmelCase_							)
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(names_to_hidden_sizes[model_name]					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)
	return config, expected_shape


if __name__ == "__main__":
			a__					:			Optional[int]          =     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''',
			)

			a__					:			Optional[Any]          =     parser.parse_args()
			a__					:			Path          =     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)



