



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   lowerCamelCase__       (       UpperCamelCase__     :    Tuple  )     ->	Dict:



					'''simple docstring'''




					_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   lowerCamelCase__       (       UpperCamelCase__     :    Optional[int]  )     ->	str:



					'''simple docstring'''




					_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   lowerCamelCase__       (       UpperCamelCase__     :    Optional[int]    ,      UpperCamelCase__     :    Union[str, Any]    ,      UpperCamelCase__     :    Union[str, Any]  )     ->	Optional[Any]:



					'''simple docstring'''




					_snake_case													=			dct.pop(UpperCamelCase__  )
					_snake_case													=			val





def   lowerCamelCase__       (       UpperCamelCase__     :    Optional[Any]    ,      UpperCamelCase__     :    str    ,      UpperCamelCase__     :    Optional[Any]  )     ->	Dict:



					'''simple docstring'''




					_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(
										    [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]  )
					elif model_name == "upernet-convnext-small":
										_snake_case													=			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":
										_snake_case													=			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":
										_snake_case													=			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":
										_snake_case													=			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]    ,      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__":
					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)










def   lowerCamelCase__       (       UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    Optional[int]  )     ->	Tuple:



					'''simple docstring'''




					_snake_case													=			[0 for i in range(r + 1  )]
					# nc0 = 1
					_snake_case													=			1
					for i in range(1    ,      n + 1  ):
										# to compute current row from previous row.
										_snake_case													=			min(UpperCamelCase__    ,      UpperCamelCase__  )
										while j > 0:
															c[j] += c[j - 1]
															j -= 1
					return c[r]




print(binomial_coefficient(n=10, r=5))



