



"""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  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[Any]					,     _SCREAMING_SNAKE_CASE   :						List[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'''					,     _SCREAMING_SNAKE_CASE				):
								_UpperCAmelCase					=    r'''\b\d{2}\b'''
								if bool(re.search(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)				):
												_UpperCAmelCase					=    re.search(r'''\d\.\d\d.'''					,     _SCREAMING_SNAKE_CASE				).group()
								else:
												_UpperCAmelCase					=    re.search(r'''\d\.\d.'''					,     _SCREAMING_SNAKE_CASE				).group()
								if int(match[0]				) < 6:
												_UpperCAmelCase					=    old_name.replace(_SCREAMING_SNAKE_CASE					,     ''''''				)
												_UpperCAmelCase					=    trimmed_name.replace('''network'''					,     match[0] + '''.meta4D_layers.blocks.''' + match[2:-1]				)
												_UpperCAmelCase					=    '''intermediate_stages.''' + trimmed_name
								else:
												_UpperCAmelCase					=    old_name.replace(_SCREAMING_SNAKE_CASE					,     ''''''				)
												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.'''					,     _SCREAMING_SNAKE_CASE				):
								_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   :						Optional[Any]					,     _SCREAMING_SNAKE_CASE   :						Union[str, Any]				):






				'''simple docstring'''



				for key in checkpoint.copy().keys():
								_UpperCAmelCase					=    checkpoint.pop(_SCREAMING_SNAKE_CASE				)
								_UpperCAmelCase					=    val

				return checkpoint







def  lowercase	(      ):






				'''simple docstring'''



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

				return image







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Path					,     _SCREAMING_SNAKE_CASE   :						Path					,     _SCREAMING_SNAKE_CASE   :						Path					,     _SCREAMING_SNAKE_CASE   :						bool				):






				'''simple docstring'''



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

				_UpperCAmelCase					=    config.depths[-1] - config.num_metaad_blocks + 1
				_UpperCAmelCase					=    convert_torch_checkpoint(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)

				model.load_state_dict(_SCREAMING_SNAKE_CASE				)
				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=_SCREAMING_SNAKE_CASE					,     return_tensors='''pt'''				).pixel_values

				# original processing pipeline
				_UpperCAmelCase					=    Compose(
				    [
				        Resize(_SCREAMING_SNAKE_CASE					,     interpolation=pillow_resamplings['''bicubic''']				),
				        CenterCrop(_SCREAMING_SNAKE_CASE				),
				        ToTensor(),
				        Normalize(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				),
				    ]				)
				_UpperCAmelCase					=    image_transforms(_SCREAMING_SNAKE_CASE				).unsqueeze(0				)

				assert torch.allclose(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)

				_UpperCAmelCase					=    model(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    outputs.logits

				_UpperCAmelCase					=    (1, 1000)

				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]					,     _SCREAMING_SNAKE_CASE					,     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]					,     _SCREAMING_SNAKE_CASE					,     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(_SCREAMING_SNAKE_CASE				).mkdir(exist_ok=_SCREAMING_SNAKE_CASE				)
				model.save_pretrained(_SCREAMING_SNAKE_CASE				)
				print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'				)
				processor.save_pretrained(_SCREAMING_SNAKE_CASE				)
				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=_SCREAMING_SNAKE_CASE					,     )
								processor.push_to_hub(
								    repo_id=f'Bearnardd/{pytorch_dump_path}'					,     commit_message='''Add image processor'''					,     use_temp_dir=_SCREAMING_SNAKE_CASE					,     )


if __name__ == "__main__":
					__A   :							Dict										=   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)

					__A   :							Dict										=   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"""


import operator







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						list					,     _SCREAMING_SNAKE_CASE   :						bool = False					,     _SCREAMING_SNAKE_CASE   :						list | None = None				):






				'''simple docstring'''



				_UpperCAmelCase					=    operator.lt if reverse else operator.gt
				_UpperCAmelCase					=    solution or []

				if not arr:
								return solution

				_UpperCAmelCase					=    [arr.pop(0				)]
				for i, item in enumerate(_SCREAMING_SNAKE_CASE				):
								if _operator(_SCREAMING_SNAKE_CASE					,     sublist[-1]				):
												sublist.append(_SCREAMING_SNAKE_CASE				)
												arr.pop(_SCREAMING_SNAKE_CASE				)

    #  merging sublist into solution list
				if not solution:
								solution.extend(_SCREAMING_SNAKE_CASE				)
				else:
								while sublist:
												_UpperCAmelCase					=    sublist.pop(0				)
												for i, xx in enumerate(_SCREAMING_SNAKE_CASE				):
																if not _operator(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				):
																				solution.insert(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
																				break
												else:
																solution.append(_SCREAMING_SNAKE_CASE				)

				strand_sort(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
				return solution


if __name__ == "__main__":
					assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
					assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]



