


'''simple docstring'''

import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling





def lowerCamelCase      (      lowerCAmelCase    :     List[str]		,		lowerCAmelCase    :     Tuple		):





	"""simple docstring"""







	__magic_name__	:   List[str]					     =					old_name

	if "patch_embed" in old_name:
		__magic_name__     ,    __magic_name__     ,    __magic_name__	:   Union[str, Any]					     =					old_name.split('.'		)

		if layer == "0":
			__magic_name__	:   Optional[Any]					     =					old_name.replace('0'		,		'convolution1'		)
		elif layer == "1":
			__magic_name__	:   Optional[Any]					     =					old_name.replace('1'		,		'batchnorm_before'		)
		elif layer == "3":
			__magic_name__	:   int					     =					old_name.replace('3'		,		'convolution2'		)
		else:
			__magic_name__	:   List[str]					     =					old_name.replace('4'		,		'batchnorm_after'		)

	if "network" in old_name and re.search(R'\d\.\d'		,		lowerCAmelCase		):
		__magic_name__	:   str					     =					R'\b\d{2}\b'
		if bool(re.search(lowerCAmelCase		,		lowerCAmelCase		)		):
			__magic_name__	:   int					     =					re.search(R'\d\.\d\d.'		,		lowerCAmelCase		).group()
		else:
			__magic_name__	:   Union[str, Any]					     =					re.search(R'\d\.\d.'		,		lowerCAmelCase		).group()
		if int(match[0]		) < 6:
			__magic_name__	:   Dict					     =					old_name.replace(lowerCAmelCase		,		''		)
			__magic_name__	:   List[str]					     =					trimmed_name.replace('network'		,		match[0] + '.meta4D_layers.blocks.' + match[2:-1]		)
			__magic_name__	:   Optional[int]					     =					'intermediate_stages.' + trimmed_name
		else:
			__magic_name__	:   Optional[Any]					     =					old_name.replace(lowerCAmelCase		,		''		)
			if int(match[2]		) < num_meta4D_last_stage:
				__magic_name__	:   Any					     =					trimmed_name.replace('network'		,		'meta4D_layers.blocks.' + match[2]		)
			else:
				__magic_name__	:   Dict					     =					str(int(match[2]		) - num_meta4D_last_stage		)
				__magic_name__	:   Any					     =					trimmed_name.replace('network'		,		'meta3D_layers.blocks.' + layer_index		)
				if "norm1" in old_name:
					__magic_name__	:   Union[str, Any]					     =					trimmed_name.replace('norm1'		,		'layernorm1'		)
				elif "norm2" in old_name:
					__magic_name__	:   Union[str, Any]					     =					trimmed_name.replace('norm2'		,		'layernorm2'		)
				elif "fc1" in old_name:
					__magic_name__	:   Any					     =					trimmed_name.replace('fc1'		,		'linear_in'		)
				elif "fc2" in old_name:
					__magic_name__	:   List[Any]					     =					trimmed_name.replace('fc2'		,		'linear_out'		)

			__magic_name__	:   Optional[int]					     =					'last_stage.' + trimmed_name

	elif "network" in old_name and re.search(R'.\d.'		,		lowerCAmelCase		):
		__magic_name__	:   Dict					     =					old_name.replace('network'		,		'intermediate_stages'		)

	if "fc" in new_name:
		__magic_name__	:   Optional[int]					     =					new_name.replace('fc'		,		'convolution'		)
	elif ("norm1" in new_name) and ("layernorm1" not in new_name):
		__magic_name__	:   Optional[int]					     =					new_name.replace('norm1'		,		'batchnorm_before'		)
	elif ("norm2" in new_name) and ("layernorm2" not in new_name):
		__magic_name__	:   List[Any]					     =					new_name.replace('norm2'		,		'batchnorm_after'		)
	if "proj" in new_name:
		__magic_name__	:   Tuple					     =					new_name.replace('proj'		,		'projection'		)
	if "dist_head" in new_name:
		__magic_name__	:   List[Any]					     =					new_name.replace('dist_head'		,		'distillation_classifier'		)
	elif "head" in new_name:
		__magic_name__	:   str					     =					new_name.replace('head'		,		'classifier'		)
	elif "patch_embed" in new_name:
		__magic_name__	:   str					     =					'efficientformer.' + new_name
	elif new_name == "norm.weight" or new_name == "norm.bias":
		__magic_name__	:   int					     =					new_name.replace('norm'		,		'layernorm'		)
		__magic_name__	:   Union[str, Any]					     =					'efficientformer.' + new_name
	else:
		__magic_name__	:   Any					     =					'efficientformer.encoder.' + new_name

	return new_name





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





	"""simple docstring"""







	for key in checkpoint.copy().keys():
		__magic_name__	:   Optional[Any]					     =					checkpoint.pop(lowerCAmelCase		)
		__magic_name__	:   List[str]					     =					val

	return checkpoint





def lowerCamelCase      (      ):





	"""simple docstring"""







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

	return image





def lowerCamelCase      (      lowerCAmelCase    :     Path		,		lowerCAmelCase    :     Path		,		lowerCAmelCase    :     Path		,		lowerCAmelCase    :     bool		):





	"""simple docstring"""







	__magic_name__	:   Any					     =					torch.load(lowerCAmelCase		,		map_location='cpu'		)['model']
	__magic_name__	:   Optional[int]					     =					EfficientFormerConfig.from_json_file(lowerCAmelCase		)
	__magic_name__	:   Optional[Any]					     =					EfficientFormerForImageClassificationWithTeacher(lowerCAmelCase		)
	__magic_name__	:   int					     =					'_'.join(checkpoint_path.split('/'		)[-1].split('.'		)[0].split('_'		)[:-1]		)

	__magic_name__	:   Optional[Any]					     =					config.depths[-1] - config.num_metaad_blocks + 1
	__magic_name__	:   Optional[Any]					     =					convert_torch_checkpoint(lowerCAmelCase		,		lowerCAmelCase		)

	model.load_state_dict(lowerCAmelCase		)
	model.eval()

	__magic_name__	:   str					     =					{
	    'bilinear': PILImageResampling.BILINEAR,
	    'bicubic': PILImageResampling.BICUBIC,
	    'nearest': PILImageResampling.NEAREST,
	}

	# prepare image
	__magic_name__	:   Tuple					     =					prepare_img()
	__magic_name__	:   List[str]					     =					256
	__magic_name__	:   Dict					     =					224
	__magic_name__	:   List[str]					     =					EfficientFormerImageProcessor(
	    size={'shortest_edge': image_size}		,		crop_size={'height': crop_size, 'width': crop_size}		,		resample=pillow_resamplings['bicubic']		,		)
	__magic_name__	:   Optional[Any]					     =					processor(images=lowerCAmelCase		,		return_tensors='pt'		).pixel_values

	# original processing pipeline
	__magic_name__	:   int					     =					Compose(
	    [
	        Resize(lowerCAmelCase		,		interpolation=pillow_resamplings['bicubic']		),
	        CenterCrop(lowerCAmelCase		),
	        ToTensor(),
	        Normalize(lowerCAmelCase		,		lowerCAmelCase		),
	    ]		)
	__magic_name__	:   Union[str, Any]					     =					image_transforms(lowerCAmelCase		).unsqueeze(0		)

	assert torch.allclose(lowerCAmelCase		,		lowerCAmelCase		)

	__magic_name__	:   Optional[int]					     =					model(lowerCAmelCase		)
	__magic_name__	:   Tuple					     =					outputs.logits

	__magic_name__	:   Union[str, Any]					     =					(1, 1000)

	if "l1" in model_name:
		__magic_name__	:   Union[str, Any]					     =					torch.Tensor(
		    [-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328]		)
		assert torch.allclose(logits[0, :10]		,		lowerCAmelCase		,		atol=1e-3		)
		assert logits.shape == expected_shape
	elif "l3" in model_name:
		__magic_name__	:   Optional[Any]					     =					torch.Tensor(
		    [-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127]		)
		assert torch.allclose(logits[0, :10]		,		lowerCAmelCase		,		atol=1e-3		)
		assert logits.shape == expected_shape
	elif "l7" in model_name:
		__magic_name__	:   Optional[Any]					     =					torch.Tensor(
		    [-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878]		)
		assert logits.shape == expected_shape
	else:
		raise ValueError(
		    f'Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7'		)

	# Save Checkpoints
	Path(lowerCAmelCase		).mkdir(exist_ok=lowerCAmelCase		)
	model.save_pretrained(lowerCAmelCase		)
	print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'		)
	processor.save_pretrained(lowerCAmelCase		)
	print(f'Processor successfuly saved at {pytorch_dump_path}'		)

	if push_to_hub:
		print('Pushing model to the hub...'		)

		model.push_to_hub(
		    repo_id=f'Bearnardd/{pytorch_dump_path}'		,		commit_message='Add model'		,		use_temp_dir=lowerCAmelCase		,		)
		processor.push_to_hub(
		    repo_id=f'Bearnardd/{pytorch_dump_path}'		,		commit_message='Add image processor'		,		use_temp_dir=lowerCAmelCase		,		)


if __name__ == "__main__":
							lowerCAmelCase				:List[Any]        =      argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--pytorch_model_path''',
							    default=None,
							    type=str,
							    required=True,
							    help='''Path to EfficientFormer pytorch checkpoint.''',
							)
							parser.add_argument(
							    '''--config_file''',
							    default=None,
							    type=str,
							    required=True,
							    help='''The json file for EfficientFormer model config.''',
							)
							parser.add_argument(
							    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
							)

							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''',
							)
							parser.set_defaults(push_to_hub=True)

							lowerCAmelCase				:Tuple        =      parser.parse_args()
							convert_efficientformer_checkpoint(
							    checkpoint_path=args.pytorch_model_path,
							    efficientformer_config_file=args.config_file,
							    pytorch_dump_path=args.pytorch_dump_path,
							    push_to_hub=args.push_to_hub,
							)



'''simple docstring'''

def lowerCamelCase      (      lowerCAmelCase    :     str		):





	"""simple docstring"""







	if not all(x.isalpha() for x in string		):
		raise ValueError('String must only contain alphabetic characters.'		)

	__magic_name__	:   int					     =					sorted(string.lower()		)
	return len(lowerCAmelCase		) == len(set(lowerCAmelCase		)		)


if __name__ == "__main__":
							lowerCAmelCase				:Any        =      input('''Enter a string ''').strip()

							lowerCAmelCase				:List[Any]        =      is_isogram(input_str)
							print(F'{input_str} is {"an" if isogram else "not an"} isogram.')