
'''simple docstring'''







import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			int:
	_snake_case     		=	384
	if "tiny" in model_name:
		_snake_case     		=	[3, 3, 9, 3]
		_snake_case     		=	[96, 192, 384, 768]
	if "small" in model_name:
		_snake_case     		=	[3, 3, 27, 3]
		_snake_case     		=	[96, 192, 384, 768]
	if "base" in model_name:
		_snake_case     		=	[3, 3, 27, 3]
		_snake_case     		=	[128, 256, 512, 1_024]
		_snake_case     		=	512
	if "large" in model_name:
		_snake_case     		=	[3, 3, 27, 3]
		_snake_case     		=	[192, 384, 768, 1_536]
		_snake_case     		=	768
	if "xlarge" in model_name:
		_snake_case     		=	[3, 3, 27, 3]
		_snake_case     		=	[256, 512, 1_024, 2_048]
		_snake_case     		=	1_024

	# set label information
	_snake_case     		=	150
	_snake_case     		=	'huggingface/label-files'
	_snake_case     		=	'ade20k-id2label.json'
	_snake_case     		=	json.load(open(hf_hub_download(__UpperCamelCase    ,       __UpperCamelCase    ,       repo_type='dataset'			)    ,       'r'			)			)
	_snake_case     		=	{int(__UpperCamelCase			): v for k, v in idalabel.items()}
	_snake_case     		=	{v: k for k, v in idalabel.items()}

	_snake_case     		=	ConvNextConfig(
	    depths=__UpperCamelCase    ,       hidden_sizes=__UpperCamelCase    ,       out_features=['stage1', 'stage2', 'stage3', 'stage4']			)
	_snake_case     		=	UperNetConfig(
	    backbone_config=__UpperCamelCase    ,       auxiliary_in_channels=__UpperCamelCase    ,       num_labels=__UpperCamelCase    ,       idalabel=__UpperCamelCase    ,       labelaid=__UpperCamelCase    ,       )

	return config







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			Union[str, Any]:
	_snake_case     		=	[]

	# fmt: off
	# stem
	rename_keys.append(('backbone.downsample_layers.0.0.weight', 'backbone.embeddings.patch_embeddings.weight')			)
	rename_keys.append(('backbone.downsample_layers.0.0.bias', 'backbone.embeddings.patch_embeddings.bias')			)
	rename_keys.append(('backbone.downsample_layers.0.1.weight', 'backbone.embeddings.layernorm.weight')			)
	rename_keys.append(('backbone.downsample_layers.0.1.bias', 'backbone.embeddings.layernorm.bias')			)
	# stages
	for i in range(len(config.backbone_config.depths			)			):
		for j in range(config.backbone_config.depths[i]			):
			rename_keys.append((F'backbone.stages.{i}.{j}.gamma', F'backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter')			)
			rename_keys.append((F'backbone.stages.{i}.{j}.depthwise_conv.weight', F'backbone.encoder.stages.{i}.layers.{j}.dwconv.weight')			)
			rename_keys.append((F'backbone.stages.{i}.{j}.depthwise_conv.bias', F'backbone.encoder.stages.{i}.layers.{j}.dwconv.bias')			)
			rename_keys.append((F'backbone.stages.{i}.{j}.norm.weight', F'backbone.encoder.stages.{i}.layers.{j}.layernorm.weight')			)
			rename_keys.append((F'backbone.stages.{i}.{j}.norm.bias', F'backbone.encoder.stages.{i}.layers.{j}.layernorm.bias')			)
			rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv1.weight', F'backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight')			)
			rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv1.bias', F'backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias')			)
			rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv2.weight', F'backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight')			)
			rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv2.bias', F'backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias')			)
		if i > 0:
			rename_keys.append((F'backbone.downsample_layers.{i}.0.weight', F'backbone.encoder.stages.{i}.downsampling_layer.0.weight')			)
			rename_keys.append((F'backbone.downsample_layers.{i}.0.bias', F'backbone.encoder.stages.{i}.downsampling_layer.0.bias')			)
			rename_keys.append((F'backbone.downsample_layers.{i}.1.weight', F'backbone.encoder.stages.{i}.downsampling_layer.1.weight')			)
			rename_keys.append((F'backbone.downsample_layers.{i}.1.bias', F'backbone.encoder.stages.{i}.downsampling_layer.1.bias')			)

		rename_keys.append((F'backbone.norm{i}.weight', F'backbone.hidden_states_norms.stage{i+1}.weight')			)
		rename_keys.append((F'backbone.norm{i}.bias', F'backbone.hidden_states_norms.stage{i+1}.bias')			)

	# decode head
	rename_keys.extend(
	    [
	        ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
	        ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
	        ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
	        ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
	    ]			)
	# fmt: on

	return rename_keys







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A			) ->			Optional[Any]:
	_snake_case     		=	dct.pop(__UpperCamelCase			)
	_snake_case     		=	val







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A			) ->			List[str]:
	_snake_case     		=	{
	    'upernet-convnext-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth',
	    'upernet-convnext-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth',
	    'upernet-convnext-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth',
	    'upernet-convnext-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth',
	    'upernet-convnext-xlarge': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth',
	}
	_snake_case     		=	model_name_to_url[model_name]
	_snake_case     		=	torch.hub.load_state_dict_from_url(__UpperCamelCase    ,       map_location='cpu'			)['state_dict']

	_snake_case     		=	get_upernet_config(__UpperCamelCase			)
	_snake_case     		=	UperNetForSemanticSegmentation(__UpperCamelCase			)
	model.eval()

	# replace "bn" => "batch_norm"
	for key in state_dict.copy().keys():
		_snake_case     		=	state_dict.pop(__UpperCamelCase			)
		if "bn" in key:
			_snake_case     		=	key.replace('bn'    ,       'batch_norm'			)
		_snake_case     		=	val

	# rename keys
	_snake_case     		=	create_rename_keys(__UpperCamelCase			)
	for src, dest in rename_keys:
		rename_key(__UpperCamelCase    ,       __UpperCamelCase    ,       __UpperCamelCase			)

	model.load_state_dict(__UpperCamelCase			)

	# verify on image
	_snake_case     		=	'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
	_snake_case     		=	Image.open(requests.get(__UpperCamelCase    ,       stream=__UpperCamelCase			).raw			).convert('RGB'			)

	_snake_case     		=	SegformerImageProcessor()
	_snake_case     		=	processor(__UpperCamelCase    ,       return_tensors='pt'			).pixel_values

	with torch.no_grad():
		_snake_case     		=	model(__UpperCamelCase			)

	if model_name == "upernet-convnext-tiny":
		_snake_case     		=	torch.tensor(
		    [[-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.7_7_4_6, -8.7_7_4_6, -8.6_1_3_0]]			)
	elif model_name == "upernet-convnext-small":
		_snake_case     		=	torch.tensor(
		    [[-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.7_6_3_8, -8.7_6_3_8, -8.6_2_4_0]]			)
	elif model_name == "upernet-convnext-base":
		_snake_case     		=	torch.tensor(
		    [[-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.7_6_6_9, -8.7_6_6_9, -8.6_0_2_1]]			)
	elif model_name == "upernet-convnext-large":
		_snake_case     		=	torch.tensor(
		    [[-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_3_1_0, -8.6_3_1_0, -8.5_9_6_4]]			)
	elif model_name == "upernet-convnext-xlarge":
		_snake_case     		=	torch.tensor(
		    [[-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_3_7_9, -8.4_3_7_9, -8.3_4_1_2]]			)
	print('Logits:'    ,       outputs.logits[0, 0, :3, :3]			)
	assert torch.allclose(outputs.logits[0, 0, :3, :3]    ,       __UpperCamelCase    ,       atol=1e-4			)
	print('Looks ok!'			)

	if pytorch_dump_folder_path is not None:
		print(F'Saving model {model_name} to {pytorch_dump_folder_path}'			)
		model.save_pretrained(__UpperCamelCase			)
		print(F'Saving processor to {pytorch_dump_folder_path}'			)
		processor.save_pretrained(__UpperCamelCase			)

	if push_to_hub:
		print(F'Pushing model and processor for {model_name} to hub'			)
		model.push_to_hub(F'openmmlab/{model_name}'			)
		processor.push_to_hub(F'openmmlab/{model_name}'			)


if __name__ == "__main__":
					lowercase					:							int   =      argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--model_name",
					    default="upernet-convnext-tiny",
					    type=str,
					    choices=[F'''upernet-convnext-{size}''' for size in ["tiny", "small", "base", "large", "xlarge"]],
					    help="Name of the ConvNext UperNet model you\'d like to convert.",
					)
					parser.add_argument(
					    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
					)
					parser.add_argument(
					    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
					)

					lowercase					:							int   =      parser.parse_args()
					convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''







from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A = "x"    ,       __A = 10**-10    ,       __A = 1    ,       ) ->			complex:
	_snake_case     		=	symbols(__A			)
	_snake_case     		=	lambdify(__A    ,       __A			)
	_snake_case     		=	lambdify(__A    ,       diff(__A    ,       __A			)			)

	_snake_case     		=	starting_point

	while True:
		if diff_function(__A			) != 0:
			_snake_case     		=	prev_guess - multiplicity * func(__A			) / diff_function(
			    __A			)
		else:
			raise ZeroDivisionError('Could not find root'			) from None

		# Precision is checked by comparing the difference of consecutive guesses
		if abs(next_guess - prev_guess			) < precision:
			return next_guess

		_snake_case     		=	next_guess


# 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
					# Find fourth Root of 5
					print(F'''The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5J)}''')

					# Find value of e
					print(
					    "The root of log(y) - 1 = 0 is ",
					    F'''{newton_raphson("log(y) - 1", 2, variable="y")}''',
					)

					# Exponential Roots
					print(
					    "The root of exp(x) - 1 = 0 is",
					    F'''{newton_raphson("exp(x) - 1", 10, precision=0.005)}''',
					)

					# Find root of cos(x)
					print(F'''The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}''')

