






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 					snake_case_						(   lowerCAmelCase_				:  List[Any]				,				lowerCAmelCase_				:  List[str]					):
							__lowercase					:							Optional[Any]			=   old_name

							if "patch_embed" in old_name:
														__lowercase	,	__lowercase	,	__lowercase					:							Optional[int]			=   old_name.split("""."""					)

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

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

																					__lowercase					:							str			=   """last_stage.""" + trimmed_name

							elif "network" in old_name and re.search(r""".\d."""				,				lowerCAmelCase_					):
														__lowercase					:							Any			=   old_name.replace("""network"""				,				"""intermediate_stages"""					)

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

							return new_name
def 					snake_case_						(   lowerCAmelCase_				:  List[str]				,				lowerCAmelCase_				:  Optional[int]					):
							for key in checkpoint.copy().keys():
														__lowercase					:							List[str]			=   checkpoint.pop(lowerCAmelCase_					)
														__lowercase					:							int			=   val

							return checkpoint
def 					snake_case_						(   ):
							__lowercase					:							Dict			=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
							__lowercase					:							Any			=   Image.open(requests.get(lowerCAmelCase_				,				stream=lowerCAmelCase_					).raw					)

							return image
def 					snake_case_						(   lowerCAmelCase_				:  Path				,				lowerCAmelCase_				:  Path				,				lowerCAmelCase_				:  Path				,				lowerCAmelCase_				:  bool					):
							__lowercase					:							str			=   torch.load(lowerCAmelCase_				,				map_location="""cpu"""					)["""model"""]
							__lowercase					:							Tuple			=   EfficientFormerConfig.from_json_file(lowerCAmelCase_					)
							__lowercase					:							Union[str, Any]			=   EfficientFormerForImageClassificationWithTeacher(lowerCAmelCase_					)
							__lowercase					:							List[str]			=   """_""".join(checkpoint_path.split("""/"""					)[-1].split("""."""					)[0].split("""_"""					)[:-1]					)

							__lowercase					:							List[str]			=   config.depths[-1] - config.num_metaad_blocks + 1
							__lowercase					:							str			=   convert_torch_checkpoint(lowerCAmelCase_				,				lowerCAmelCase_					)

							model.load_state_dict(lowerCAmelCase_					)
							model.eval()

							__lowercase					:							List[str]			=   {
							    """bilinear""": PILImageResampling.BILINEAR,
							    """bicubic""": PILImageResampling.BICUBIC,
							    """nearest""": PILImageResampling.NEAREST,
							}

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

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

							assert torch.allclose(lowerCAmelCase_				,				lowerCAmelCase_					)

							__lowercase					:							List[Any]			=   model(lowerCAmelCase_					)
							__lowercase					:							Dict			=   outputs.logits

							__lowercase					:							Optional[Any]			=   (1, 1000)

							if "l1" in model_name:
														__lowercase					:							Optional[int]			=   torch.Tensor(
														    [-0.1_312, 0.4_353, -1.0_499, -0.5_124, 0.4_183, -0.6_793, -1.3_777, -0.0_893, -0.7_358, -2.4_328]					)
														assert torch.allclose(logits[0, :10]				,				lowerCAmelCase_				,				atol=1e-3					)
														assert logits.shape == expected_shape
							elif "l3" in model_name:
														__lowercase					:							List[Any]			=   torch.Tensor(
														    [-1.3_150, -1.5_456, -1.2_556, -0.8_496, -0.7_127, -0.7_897, -0.9_728, -0.3_052, 0.3_751, -0.3_127]					)
														assert torch.allclose(logits[0, :10]				,				lowerCAmelCase_				,				atol=1e-3					)
														assert logits.shape == expected_shape
							elif "l7" in model_name:
														__lowercase					:							List[str]			=   torch.Tensor(
														    [-1.0_283, -1.4_131, -0.5_644, -1.3_115, -0.5_785, -1.2_049, -0.7_528, 0.1_992, -0.3_822, -0.0_878]					)
														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       :       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)

	lowerCamelCase       :       Optional[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,
	)







from maths.prime_check import is_prime
def 					snake_case_						(   lowerCAmelCase_				:  int					):
							if not isinstance(lowerCAmelCase_				,				lowerCAmelCase_					):
														__lowercase					:							Dict			=   F"Input value of [number={number}] must be an integer"
														raise TypeError(lowerCAmelCase_					)
							if is_prime(lowerCAmelCase_					) and is_prime(number + 2					):
														return number + 2
							else:
														return -1


if __name__ == "__main__":
	import doctest

	doctest.testmod()