




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_		(       UpperCamelCase__				:      Any ,     UpperCamelCase__				:      Any      )     ->       Union[str, Any]:





				"""simple docstring"""


				__lowerCamelCase		       =    old_name

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

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

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

												__lowerCamelCase		       =    'last_stage.' + trimmed_name

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

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

				return new_name







def 			lowerCamelCase_		(       UpperCamelCase__				:      Optional[Any] ,     UpperCamelCase__				:      Tuple      )     ->       int:





				"""simple docstring"""


				for key in checkpoint.copy().keys():
								__lowerCamelCase		       =    checkpoint.pop(UpperCamelCase__      )
								__lowerCamelCase		       =    val

				return checkpoint







def 			lowerCamelCase_		(       )     ->       Dict:





				"""simple docstring"""


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

				return image







def 			lowerCamelCase_		(       UpperCamelCase__				:      Path ,     UpperCamelCase__				:      Path ,     UpperCamelCase__				:      Path ,     UpperCamelCase__				:      bool      )     ->       Optional[Any]:





				"""simple docstring"""


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

				__lowerCamelCase		       =    config.depths[-1] - config.num_metaad_blocks + 1
				__lowerCamelCase		       =    convert_torch_checkpoint(UpperCamelCase__ ,     UpperCamelCase__      )

				model.load_state_dict(UpperCamelCase__      )
				model.eval()

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

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

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

				assert torch.allclose(UpperCamelCase__ ,     UpperCamelCase__      )

				__lowerCamelCase		       =    model(UpperCamelCase__      )
				__lowerCamelCase		       =    outputs.logits

				__lowerCamelCase		       =    (1, 1000)

				if "l1" in model_name:
								__lowerCamelCase		       =    torch.Tensor(
								    [-0.13_12, 0.43_53, -1.04_99, -0.51_24, 0.41_83, -0.67_93, -1.37_77, -0.08_93, -0.73_58, -2.43_28]      )
								assert torch.allclose(logits[0, :10] ,     UpperCamelCase__ ,     atol=1E-3      )
								assert logits.shape == expected_shape
				elif "l3" in model_name:
								__lowerCamelCase		       =    torch.Tensor(
								    [-1.31_50, -1.54_56, -1.25_56, -0.84_96, -0.71_27, -0.78_97, -0.97_28, -0.30_52, 0.37_51, -0.31_27]      )
								assert torch.allclose(logits[0, :10] ,     UpperCamelCase__ ,     atol=1E-3      )
								assert logits.shape == expected_shape
				elif "l7" in model_name:
								__lowerCamelCase		       =    torch.Tensor(
								    [-1.02_83, -1.41_31, -0.56_44, -1.31_15, -0.57_85, -1.20_49, -0.75_28, 0.19_92, -0.38_22, -0.08_78]      )
								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(UpperCamelCase__      ).mkdir(exist_ok=UpperCamelCase__      )
				model.save_pretrained(UpperCamelCase__      )
				print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""      )
				processor.save_pretrained(UpperCamelCase__      )
				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=UpperCamelCase__ ,     )
								processor.push_to_hub(
								    repo_id=F"""Bearnardd/{pytorch_dump_path}""" ,     commit_message='Add image processor' ,     use_temp_dir=UpperCamelCase__ ,     )


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








from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
__A        =    10







def 			lowerCamelCase_		(       UpperCamelCase__				:      int ,     UpperCamelCase__				:      int ,     UpperCamelCase__				:      list[int] ,     UpperCamelCase__				:      int      )     ->       int:





				"""simple docstring"""


				for i in range(UpperCamelCase__ ,     UpperCamelCase__      ):
								if array[i] == target:
												return i
				return -1







def 			lowerCamelCase_		(       UpperCamelCase__				:      list[int] ,     UpperCamelCase__				:      int      )     ->       int:





				"""simple docstring"""


				__lowerCamelCase		       =    0
				__lowerCamelCase		       =    len(UpperCamelCase__      )
				while left <= right:
								if right - left < precision:
												return lin_search(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )

								__lowerCamelCase		       =    (left + right) // 3 + 1
								__lowerCamelCase		       =    2 * (left + right) // 3 + 1

								if array[one_third] == target:
												return one_third
								elif array[two_third] == target:
												return two_third

								elif target < array[one_third]:
												__lowerCamelCase		       =    one_third - 1
								elif array[two_third] < target:
												__lowerCamelCase		       =    two_third + 1

								else:
												__lowerCamelCase		       =    one_third + 1
												__lowerCamelCase		       =    two_third - 1
				else:
								return -1







def 			lowerCamelCase_		(       UpperCamelCase__				:      int ,     UpperCamelCase__				:      int ,     UpperCamelCase__				:      list[int] ,     UpperCamelCase__				:      int      )     ->       int:





				"""simple docstring"""


				if left < right:
								if right - left < precision:
												return lin_search(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
								__lowerCamelCase		       =    (left + right) // 3 + 1
								__lowerCamelCase		       =    2 * (left + right) // 3 + 1

								if array[one_third] == target:
												return one_third
								elif array[two_third] == target:
												return two_third

								elif target < array[one_third]:
												return rec_ternary_search(UpperCamelCase__ ,     one_third - 1 ,     UpperCamelCase__ ,     UpperCamelCase__      )
								elif array[two_third] < target:
												return rec_ternary_search(two_third + 1 ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
								else:
												return rec_ternary_search(one_third + 1 ,     two_third - 1 ,     UpperCamelCase__ ,     UpperCamelCase__      )
				else:
								return -1


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					__A        =    input("Enter numbers separated by comma:\n").strip()
					__A        =    [int(item.strip()) for item in user_input.split(",")]
					assert collection == sorted(collection), f"List must be ordered.\n{collection}."
					__A        =    int(input("Enter the number to be found in the list:\n").strip())
					__A        =    ite_ternary_search(collection, target)
					__A        =    rec_ternary_search(0, len(collection) - 1, collection, target)
					if resulta != -1:
										print(f'''Iterative search: {target} found at positions: {resulta}''')
										print(f'''Recursive search: {target} found at positions: {resulta}''')
					else:
										print("Not found")


