


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 				_lowercase     (		SCREAMING_SNAKE_CASE_ :		Optional[Any]							,	SCREAMING_SNAKE_CASE_ :		Union[str, Any]						):





			"""simple docstring"""




			UpperCamelCase								=       old_name

			if "patch_embed" in old_name:
						UpperCamelCase	,	UpperCamelCase	,	UpperCamelCase								=       old_name.split("""."""						)

						if layer == "0":
									UpperCamelCase								=       old_name.replace("""0"""							,	"""convolution1"""						)
						elif layer == "1":
									UpperCamelCase								=       old_name.replace("""1"""							,	"""batchnorm_before"""						)
						elif layer == "3":
									UpperCamelCase								=       old_name.replace("""3"""							,	"""convolution2"""						)
						else:
									UpperCamelCase								=       old_name.replace("""4"""							,	"""batchnorm_after"""						)

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

									UpperCamelCase								=       """last_stage.""" + trimmed_name

			elif "network" in old_name and re.search(r""".\d."""							,	_lowercase						):
						UpperCamelCase								=       old_name.replace("""network"""							,	"""intermediate_stages"""						)

			if "fc" in new_name:
						UpperCamelCase								=       new_name.replace("""fc"""							,	"""convolution"""						)
			elif ("norm1" in new_name) and ("layernorm1" not in new_name):
						UpperCamelCase								=       new_name.replace("""norm1"""							,	"""batchnorm_before"""						)
			elif ("norm2" in new_name) and ("layernorm2" not in new_name):
						UpperCamelCase								=       new_name.replace("""norm2"""							,	"""batchnorm_after"""						)
			if "proj" in new_name:
						UpperCamelCase								=       new_name.replace("""proj"""							,	"""projection"""						)
			if "dist_head" in new_name:
						UpperCamelCase								=       new_name.replace("""dist_head"""							,	"""distillation_classifier"""						)
			elif "head" in new_name:
						UpperCamelCase								=       new_name.replace("""head"""							,	"""classifier"""						)
			elif "patch_embed" in new_name:
						UpperCamelCase								=       """efficientformer.""" + new_name
			elif new_name == "norm.weight" or new_name == "norm.bias":
						UpperCamelCase								=       new_name.replace("""norm"""							,	"""layernorm"""						)
						UpperCamelCase								=       """efficientformer.""" + new_name
			else:
						UpperCamelCase								=       """efficientformer.encoder.""" + new_name

			return new_name






def 				_lowercase     (		SCREAMING_SNAKE_CASE_ :		List[str]							,	SCREAMING_SNAKE_CASE_ :		str						):





			"""simple docstring"""




			for key in checkpoint.copy().keys():
						UpperCamelCase								=       checkpoint.pop(_lowercase						)
						UpperCamelCase								=       val

			return checkpoint






def 				_lowercase     (		):





			"""simple docstring"""




			UpperCamelCase								=       """http://images.cocodataset.org/val2017/000000039769.jpg"""
			UpperCamelCase								=       Image.open(requests.get(_lowercase							,	stream=_lowercase						).raw						)

			return image






def 				_lowercase     (		SCREAMING_SNAKE_CASE_ :		Optional[int]							,	SCREAMING_SNAKE_CASE_ :		List[Any]							,	SCREAMING_SNAKE_CASE_ :		str							,	SCREAMING_SNAKE_CASE_ :		int						):





			"""simple docstring"""




			UpperCamelCase								=       torch.load(_lowercase							,	map_location="""cpu"""						)["""model"""]
			UpperCamelCase								=       EfficientFormerConfig.from_json_file(_lowercase						)
			UpperCamelCase								=       EfficientFormerForImageClassificationWithTeacher(_lowercase						)
			UpperCamelCase								=       """_""".join(checkpoint_path.split("""/"""						)[-1].split("""."""						)[0].split("""_"""						)[:-1]						)

			UpperCamelCase								=       config.depths[-1] - config.num_metaad_blocks + 1
			UpperCamelCase								=       convert_torch_checkpoint(_lowercase							,	_lowercase						)

			model.load_state_dict(_lowercase						)
			model.eval()

			UpperCamelCase								=       {
			    """bilinear""": PILImageResampling.BILINEAR,
			    """bicubic""": PILImageResampling.BICUBIC,
			    """nearest""": PILImageResampling.NEAREST,
			}

			# prepare image
			UpperCamelCase								=       prepare_img()
			UpperCamelCase								=       256
			UpperCamelCase								=       224
			UpperCamelCase								=       EfficientFormerImageProcessor(
			    size={"""shortest_edge""": image_size}							,	crop_size={"""height""": crop_size, """width""": crop_size}							,	resample=pillow_resamplings["""bicubic"""]							,	)
			UpperCamelCase								=       processor(images=_lowercase							,	return_tensors="""pt"""						).pixel_values

			# original processing pipeline
			UpperCamelCase								=       Compose(
			    [
			        Resize(_lowercase							,	interpolation=pillow_resamplings["""bicubic"""]						),
			        CenterCrop(_lowercase						),
			        ToTensor(),
			        Normalize(_lowercase							,	_lowercase						),
			    ]						)
			UpperCamelCase								=       image_transforms(_lowercase						).unsqueeze(0						)

			assert torch.allclose(_lowercase							,	_lowercase						)

			UpperCamelCase								=       model(_lowercase						)
			UpperCamelCase								=       outputs.logits

			UpperCamelCase								=       (1, 1_000)

			if "l1" in model_name:
						UpperCamelCase								=       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]							,	_lowercase							,	atol=1e-3						)
						assert logits.shape == expected_shape
			elif "l3" in model_name:
						UpperCamelCase								=       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]							,	_lowercase							,	atol=1e-3						)
						assert logits.shape == expected_shape
			elif "l7" in model_name:
						UpperCamelCase								=       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(_lowercase						).mkdir(exist_ok=_lowercase						)
			model.save_pretrained(_lowercase						)
			print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'						)
			processor.save_pretrained(_lowercase						)
			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=_lowercase							,	)
						processor.push_to_hub(
						    repo_id=f'Bearnardd/{pytorch_dump_path}'							,	commit_message="""Add image processor"""							,	use_temp_dir=_lowercase							,	)


if __name__ == "__main__":
			__snake_case      = 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)

			__snake_case      = 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,
			)






def  __lowerCamelCase					( _lowercase			)       ->      list:
     for i in range(len(_lowercase			) - 1						,     0						,     -1			):
          UpperCamelCase						    =					False

          for j in range(_lowercase						,     0						,     -1			):
               if unsorted[j] < unsorted[j - 1]:
                    UpperCamelCase    ,		UpperCamelCase						    =					unsorted[j - 1], unsorted[j]
                    UpperCamelCase						    =					True

          for j in range(_lowercase			):
               if unsorted[j] > unsorted[j + 1]:
                    UpperCamelCase    ,		UpperCamelCase						    =					unsorted[j + 1], unsorted[j]
                    UpperCamelCase						    =					True

          if not swapped:
               break
     return unsorted


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 _snake_case      = input('''Enter numbers separated by a comma:\n''').strip()
 _snake_case      = [int(item) for item in user_input.split(''',''')]
 print(F"{cocktail_shaker_sort(unsorted) = }")


