






'''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 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> List[Any]:
							lowerCamelCase__      :			Optional[int] 						= old_name

							if "patch_embed" in old_name:
														lowerCamelCase__      :			List[str] 						= old_name.split("""."""   )

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

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

																					lowerCamelCase__      :			List[str] 						= """last_stage.""" + trimmed_name

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

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

							return new_name





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> Optional[int]:
							for key in checkpoint.copy().keys():
														lowerCamelCase__      :			List[Any] 						= checkpoint.pop(UpperCamelCase   )
														lowerCamelCase__      :			Optional[Any] 						= val

							return checkpoint





def 				SCREAMING_SNAKE_CASE_ ()   -> str:
							lowerCamelCase__      :			Dict 						= """http://images.cocodataset.org/val2017/000000039769.jpg"""
							lowerCamelCase__      :			Optional[int] 						= Image.open(requests.get(UpperCamelCase       ,    stream=UpperCamelCase   ).raw   )

							return image





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> List[Any]:
							lowerCamelCase__      :			Tuple 						= torch.load(UpperCamelCase       ,    map_location="""cpu"""   )["""model"""]
							lowerCamelCase__      :			Any 						= EfficientFormerConfig.from_json_file(UpperCamelCase   )
							lowerCamelCase__      :			List[str] 						= EfficientFormerForImageClassificationWithTeacher(UpperCamelCase   )
							lowerCamelCase__      :			Union[str, Any] 						= """_""".join(checkpoint_path.split("""/"""   )[-1].split("""."""   )[0].split("""_"""   )[:-1]   )

							lowerCamelCase__      :			Union[str, Any] 						= config.depths[-1] - config.num_metaad_blocks + 1
							lowerCamelCase__      :			str 						= convert_torch_checkpoint(UpperCamelCase       ,    UpperCamelCase   )

							model.load_state_dict(UpperCamelCase   )
							model.eval()

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

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

							# original processing pipeline
							lowerCamelCase__      :			Optional[Any] 						= Compose(
							    [
							        Resize(UpperCamelCase       ,    interpolation=pillow_resamplings["""bicubic"""]   ),
							        CenterCrop(UpperCamelCase   ),
							        ToTensor(),
							        Normalize(UpperCamelCase       ,    UpperCamelCase   ),
							    ]   )
							lowerCamelCase__      :			List[str] 						= image_transforms(UpperCamelCase   ).unsqueeze(0   )

							assert torch.allclose(UpperCamelCase       ,    UpperCamelCase   )

							lowerCamelCase__      :			int 						= model(UpperCamelCase   )
							lowerCamelCase__      :			Optional[Any] 						= outputs.logits

							lowerCamelCase__      :			Any 						= (1, 1000)

							if "l1" in model_name:
														lowerCamelCase__      :			Optional[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]       ,    UpperCamelCase       ,    atol=1E-3   )
														assert logits.shape == expected_shape
							elif "l3" in model_name:
														lowerCamelCase__      :			List[str] 						= 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]       ,    UpperCamelCase       ,    atol=1E-3   )
														assert logits.shape == expected_shape
							elif "l7" in model_name:
														lowerCamelCase__      :			Tuple 						= 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(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			:    int					  =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			:    int					  =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 sys
import turtle





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> tuple[float, float]:
							return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    )   -> None:
							my_pen.up()
							my_pen.goto(vertexa[0]       ,    vertexa[1]   )
							my_pen.down()
							my_pen.goto(vertexa[0]       ,    vertexa[1]   )
							my_pen.goto(vertexa[0]       ,    vertexa[1]   )
							my_pen.goto(vertexa[0]       ,    vertexa[1]   )

							if depth == 0:
														return

							triangle(UpperCamelCase       ,    get_mid(UpperCamelCase       ,    UpperCamelCase   )       ,    get_mid(UpperCamelCase       ,    UpperCamelCase   )       ,    depth - 1   )
							triangle(UpperCamelCase       ,    get_mid(UpperCamelCase       ,    UpperCamelCase   )       ,    get_mid(UpperCamelCase       ,    UpperCamelCase   )       ,    depth - 1   )
							triangle(UpperCamelCase       ,    get_mid(UpperCamelCase       ,    UpperCamelCase   )       ,    get_mid(UpperCamelCase       ,    UpperCamelCase   )       ,    depth - 1   )


if __name__ == "__main__":
			if len(sys.argv) != 2:
						raise ValueError(
						    '''Correct format for using this script: '''
						    '''python fractals.py <int:depth_for_fractal>'''
						)
			_A			:    Any					  =turtle.Turtle()
			my_pen.ht()
			my_pen.speed(5)
			my_pen.pencolor('''red''')

			_A			:    Dict					  =[(-175, -125), (0, 175), (175, -125)]  # vertices of triangle
			triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))

