
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  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		old_name

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

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

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

															lowercase					=		'''last_stage.''' + trimmed_name

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

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

					return new_name



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					for key in checkpoint.copy().keys():
										lowercase					=		checkpoint.pop(lowerCAmelCase__	)
										lowercase					=		val

					return checkpoint



def  UpperCamelCase					(							):
					'''simple docstring'''
					lowercase					=		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					lowercase					=		Image.open(requests.get(lowerCAmelCase__				,    stream=lowerCAmelCase__	).raw	)

					return image



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		torch.load(lowerCAmelCase__				,    map_location='''cpu'''	)['''model''']
					lowercase					=		EfficientFormerConfig.from_json_file(lowerCAmelCase__	)
					lowercase					=		EfficientFormerForImageClassificationWithTeacher(lowerCAmelCase__	)
					lowercase					=		'''_'''.join(checkpoint_path.split('''/'''	)[-1].split('''.'''	)[0].split('''_'''	)[:-1]	)

					lowercase					=		config.depths[-1] - config.num_metaad_blocks + 1
					lowercase					=		convert_torch_checkpoint(lowerCAmelCase__				,    lowerCAmelCase__	)

					model.load_state_dict(lowerCAmelCase__	)
					model.eval()

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

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

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

					assert torch.allclose(lowerCAmelCase__				,    lowerCAmelCase__	)

					lowercase					=		model(lowerCAmelCase__	)
					lowercase					=		outputs.logits

					lowercase					=		(1, 1000)

					if "l1" in model_name:
										lowercase					=		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]				,    lowerCAmelCase__				,    atol=1E-3	)
										assert logits.shape == expected_shape
					elif "l3" in model_name:
										lowercase					=		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]				,    lowerCAmelCase__				,    atol=1E-3	)
										assert logits.shape == expected_shape
					elif "l7" in model_name:
										lowercase					=		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(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__":
							lowercase__	:Optional[Any]										=					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)

							lowercase__	:Optional[Any]										=					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

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__ = 10**-10	):
					'''simple docstring'''
					lowercase					=		a
					while True:
										lowercase					=		Decimal(lowerCAmelCase__	) - (
										    Decimal(eval(lowerCAmelCase__	)	) / Decimal(eval(str(diff(lowerCAmelCase__	)	)	)	)  # noqa: S307
										)
										# This number dictates the accuracy of the answer
										if abs(eval(lowerCAmelCase__	)	) < precision:  # noqa: S307
															return float(lowerCAmelCase__	)


# Let's Execute
if __name__ == "__main__":
							# Find root of trigonometric function
							# Find value of pi
							print(F'The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}')
							# Find root of polynomial
							print(F'The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}')
							# Find Square Root of 5
							print(F'The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}')
							# Exponential Roots
							print(F'The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}')




