






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    __magic_name__    (	SCREAMING_SNAKE_CASE				)     ->    List[Any]:
							_lowercase							:   int 							=   384
							if "tiny" in model_name:
														_lowercase							:   Tuple 							=   [3, 3, 9, 3]
														_lowercase							:   List[str] 							=   [96, 192, 384, 768]
							if "small" in model_name:
														_lowercase							:   List[str] 							=   [3, 3, 27, 3]
														_lowercase							:   Union[str, Any] 							=   [96, 192, 384, 768]
							if "base" in model_name:
														_lowercase							:   List[Any] 							=   [3, 3, 27, 3]
														_lowercase							:   Dict 							=   [128, 256, 512, 1_024]
														_lowercase							:   Optional[int] 							=   512
							if "large" in model_name:
														_lowercase							:   List[str] 							=   [3, 3, 27, 3]
														_lowercase							:   List[Any] 							=   [192, 384, 768, 1_536]
														_lowercase							:   Tuple 							=   768
							if "xlarge" in model_name:
														_lowercase							:   str 							=   [3, 3, 27, 3]
														_lowercase							:   List[str] 							=   [256, 512, 1_024, 2_048]
														_lowercase							:   Tuple 							=   1_024

							# set label information
							_lowercase							:   Dict 							=   150
							_lowercase							:   Union[str, Any] 							=   'huggingface/label-files'
							_lowercase							:   str 							=   'ade20k-id2label.json'
							_lowercase							:   List[Any] 							=   json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     repo_type='dataset'				)     ,     'r'				)				)
							_lowercase							:   Dict 							=   {int(SCREAMING_SNAKE_CASE				): v for k, v in idalabel.items()}
							_lowercase							:   Tuple 							=   {v: k for k, v in idalabel.items()}

							_lowercase							:   List[str] 							=   ConvNextConfig(
							    depths=SCREAMING_SNAKE_CASE     ,     hidden_sizes=SCREAMING_SNAKE_CASE     ,     out_features=['stage1', 'stage2', 'stage3', 'stage4']				)
							_lowercase							:   Union[str, Any] 							=   UperNetConfig(
							    backbone_config=SCREAMING_SNAKE_CASE     ,     auxiliary_in_channels=SCREAMING_SNAKE_CASE     ,     num_labels=SCREAMING_SNAKE_CASE     ,     idalabel=SCREAMING_SNAKE_CASE     ,     labelaid=SCREAMING_SNAKE_CASE     ,     )

							return config






def    __magic_name__    (	SCREAMING_SNAKE_CASE				)     ->    int:
							_lowercase							:   Any 							=   []

							# 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    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    Optional[int]:
							_lowercase							:   Any 							=   dct.pop(SCREAMING_SNAKE_CASE				)
							_lowercase							:   Any 							=   val






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    Any:
							_lowercase							:   List[Any] 							=   {
							    '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',
							}
							_lowercase							:   Optional[int] 							=   model_name_to_url[model_name]
							_lowercase							:   str 							=   torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE     ,     map_location='cpu'				)['state_dict']

							_lowercase							:   Optional[int] 							=   get_upernet_config(SCREAMING_SNAKE_CASE				)
							_lowercase							:   Tuple 							=   UperNetForSemanticSegmentation(SCREAMING_SNAKE_CASE				)
							model.eval()

							# replace "bn" => "batch_norm"
							for key in state_dict.copy().keys():
														_lowercase							:   List[Any] 							=   state_dict.pop(SCREAMING_SNAKE_CASE				)
														if "bn" in key:
																					_lowercase							:   Any 							=   key.replace('bn'     ,     'batch_norm'				)
														_lowercase							:   Any 							=   val

							# rename keys
							_lowercase							:   int 							=   create_rename_keys(SCREAMING_SNAKE_CASE				)
							for src, dest in rename_keys:
														rename_key(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)

							model.load_state_dict(SCREAMING_SNAKE_CASE				)

							# verify on image
							_lowercase							:   Union[str, Any] 							=   'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
							_lowercase							:   Any 							=   Image.open(requests.get(SCREAMING_SNAKE_CASE     ,     stream=SCREAMING_SNAKE_CASE				).raw				).convert('RGB'				)

							_lowercase							:   Tuple 							=   SegformerImageProcessor()
							_lowercase							:   Tuple 							=   processor(SCREAMING_SNAKE_CASE     ,     return_tensors='pt'				).pixel_values

							with torch.no_grad():
														_lowercase							:   Dict 							=   model(SCREAMING_SNAKE_CASE				)

							if model_name == "upernet-convnext-tiny":
														_lowercase							:   Dict 							=   torch.tensor(
														    [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]				)
							elif model_name == "upernet-convnext-small":
														_lowercase							:   Union[str, Any] 							=   torch.tensor(
														    [[-8.8236, -8.8236, -8.6771], [-8.8236, -8.8236, -8.6771], [-8.7638, -8.7638, -8.6240]]				)
							elif model_name == "upernet-convnext-base":
														_lowercase							:   Dict 							=   torch.tensor(
														    [[-8.8558, -8.8558, -8.6905], [-8.8558, -8.8558, -8.6905], [-8.7669, -8.7669, -8.6021]]				)
							elif model_name == "upernet-convnext-large":
														_lowercase							:   Optional[int] 							=   torch.tensor(
														    [[-8.6660, -8.6660, -8.6210], [-8.6660, -8.6660, -8.6210], [-8.6310, -8.6310, -8.5964]]				)
							elif model_name == "upernet-convnext-xlarge":
														_lowercase							:   str 							=   torch.tensor(
														    [[-8.4980, -8.4980, -8.3977], [-8.4980, -8.4980, -8.3977], [-8.4379, -8.4379, -8.3412]]				)
							print('Logits:'     ,     outputs.logits[0, 0, :3, :3]				)
							assert torch.allclose(outputs.logits[0, 0, :3, :3]     ,     SCREAMING_SNAKE_CASE     ,     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(SCREAMING_SNAKE_CASE				)
														print(F"""Saving processor to {pytorch_dump_folder_path}"""				)
														processor.save_pretrained(SCREAMING_SNAKE_CASE				)

							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__":
							UpperCamelCase								=		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."
							)

							UpperCamelCase								=		parser.parse_args()
							convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










'''simple docstring'''


import numpy
class 						a_				:




	def __init__(       self     :  Any       ,		__lowerCAmelCase     :  numpy.ndarray       ,		__lowerCAmelCase     :  numpy.ndarray      ):
							__snake_case						 =   input_array

							# Random initial weights are assigned where first argument is the
							# number of nodes in previous layer and second argument is the
							# number of nodes in the next layer.

							# Random initial weights are assigned.
							# self.input_array.shape[1] is used to represent number of nodes in input layer.
							# First hidden layer consists of 4 nodes.
							__snake_case						 =   numpy.random.rand(
							    self.input_array.shape[1]       ,		4      )

							# Random initial values for the first hidden layer.
							# First hidden layer has 4 nodes.
							# Second hidden layer has 3 nodes.
							__snake_case						 =   numpy.random.rand(
							    4       ,		3      )

							# Random initial values for the second hidden layer.
							# Second hidden layer has 3 nodes.
							# Output layer has 1 node.
							__snake_case						 =   numpy.random.rand(3       ,		1      )

							# Real output values provided.
							__snake_case						 =   output_array

							# Predicted output values by the neural network.
							# Predicted_output array initially consists of zeroes.
							__snake_case						 =   numpy.zeros(output_array.shape      )




	def   lowercase__   (       self     :  Optional[Any]      ):
							__snake_case						 =   sigmoid(
							    numpy.dot(self.input_array       ,		self.input_layer_and_first_hidden_layer_weights      )      )

							# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
							# connecting the first hidden set of nodes with the second hidden set of nodes.
							__snake_case						 =   sigmoid(
							    numpy.dot(
							        self.layer_between_input_and_first_hidden_layer       ,		self.first_hidden_layer_and_second_hidden_layer_weights       ,		)      )

							# layer_between_second_hidden_layer_and_output is the layer connecting
							# second hidden layer with the output node.
							__snake_case						 =   sigmoid(
							    numpy.dot(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer       ,		self.second_hidden_layer_and_output_layer_weights       ,		)      )

							return self.layer_between_second_hidden_layer_and_output




	def   lowercase__   (       self     :  List[str]      ):
							__snake_case						 =   numpy.dot(
							    self.layer_between_first_hidden_layer_and_second_hidden_layer.T       ,		2
							    * (self.output_array - self.predicted_output)
							    * sigmoid_derivative(self.predicted_output      )       ,		)
							__snake_case						 =   numpy.dot(
							    self.layer_between_input_and_first_hidden_layer.T       ,		numpy.dot(
							        2
							        * (self.output_array - self.predicted_output)
							        * sigmoid_derivative(self.predicted_output      )       ,		self.second_hidden_layer_and_output_layer_weights.T       ,		)
							    * sigmoid_derivative(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer      )       ,		)
							__snake_case						 =   numpy.dot(
							    self.input_array.T       ,		numpy.dot(
							        numpy.dot(
							            2
							            * (self.output_array - self.predicted_output)
							            * sigmoid_derivative(self.predicted_output      )       ,		self.second_hidden_layer_and_output_layer_weights.T       ,		)
							        * sigmoid_derivative(
							            self.layer_between_first_hidden_layer_and_second_hidden_layer      )       ,		self.first_hidden_layer_and_second_hidden_layer_weights.T       ,		)
							    * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer      )       ,		)

							self.input_layer_and_first_hidden_layer_weights += (
							    updated_input_layer_and_first_hidden_layer_weights
							)
							self.first_hidden_layer_and_second_hidden_layer_weights += (
							    updated_first_hidden_layer_and_second_hidden_layer_weights
							)
							self.second_hidden_layer_and_output_layer_weights += (
							    updated_second_hidden_layer_and_output_layer_weights
							)




	def   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  numpy.ndarray       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  bool      ):
							for iteration in range(1       ,		iterations + 1      ):
													__snake_case						 =   self.feedforward()
													self.back_propagation()
													if give_loss:
																			__snake_case						 =   numpy.mean(numpy.square(output - self.feedforward()      )      )
																			print(F'Iteration {iteration} Loss: {loss}'      )




	def   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  numpy.ndarray      ):
							__snake_case						 =   input_arr

							__snake_case						 =   sigmoid(
							    numpy.dot(self.array       ,		self.input_layer_and_first_hidden_layer_weights      )      )

							__snake_case						 =   sigmoid(
							    numpy.dot(
							        self.layer_between_input_and_first_hidden_layer       ,		self.first_hidden_layer_and_second_hidden_layer_weights       ,		)      )

							__snake_case						 =   sigmoid(
							    numpy.dot(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer       ,		self.second_hidden_layer_and_output_layer_weights       ,		)      )

							return int(self.layer_between_second_hidden_layer_and_output > 0.6      )





def 						lowerCamelCase__   (				a							):
						return 1 / (1 + numpy.exp(-value							))
def 						lowerCamelCase__   (				a							):
						return (value) * (1 - (value))
def 						lowerCamelCase__   (				):
						__snake_case						 =   numpy.array(
						    (
						        [0, 0, 0],
						        [0, 0, 1],
						        [0, 1, 0],
						        [0, 1, 1],
						        [1, 0, 0],
						        [1, 0, 1],
						        [1, 1, 0],
						        [1, 1, 1],
						    )    ,	dtype=numpy.floataa    ,	)

						# True output values for the given input values.
						__snake_case						 =   numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])    ,	dtype=numpy.floataa							)

						# Calling neural network class.
						__snake_case						 =   TwoHiddenLayerNeuralNetwork(
						    input_array=a    ,	output_array=a							)

						# Calling training function.
						# Set give_loss to True if you want to see loss in every iteration.
						neural_network.train(output=a    ,	iterations=10    ,	give_loss=a							)

						return neural_network.predict(numpy.array(([1, 1, 1])    ,	dtype=numpy.floataa							)							)


if __name__ == "__main__":
	example()

