

'''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 							_lowerCAmelCase (      __magic_name__	:    Tuple	)							->     Tuple:
						lowercase   :       List[str]								=384
						if "tiny" in model_name:
												lowercase   :       List[str]								=[3, 3, 9, 3]
												lowercase   :       Dict								=[96, 192, 384, 768]
						if "small" in model_name:
												lowercase   :       Optional[int]								=[3, 3, 27, 3]
												lowercase   :       Dict								=[96, 192, 384, 768]
						if "base" in model_name:
												lowercase   :       Optional[Any]								=[3, 3, 27, 3]
												lowercase   :       Optional[int]								=[128, 256, 512, 1024]
												lowercase   :       Tuple								=512
						if "large" in model_name:
												lowercase   :       Dict								=[3, 3, 27, 3]
												lowercase   :       str								=[192, 384, 768, 1536]
												lowercase   :       Optional[Any]								=768
						if "xlarge" in model_name:
												lowercase   :       str								=[3, 3, 27, 3]
												lowercase   :       List[Any]								=[256, 512, 1024, 2048]
												lowercase   :       Any								=1024

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

						lowercase   :       Tuple								=ConvNextConfig(
						    depths=UpperCamelCase__      ,   hidden_sizes=UpperCamelCase__      ,   out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']	)
						lowercase   :       Optional[int]								=UperNetConfig(
						    backbone_config=UpperCamelCase__      ,   auxiliary_in_channels=UpperCamelCase__      ,   num_labels=UpperCamelCase__      ,   idalabel=UpperCamelCase__      ,   labelaid=UpperCamelCase__      ,   )

						return config






def 							_lowerCAmelCase (      __magic_name__	:    Optional[Any]	)							->     Tuple:
						lowercase   :       List[str]								=[]

						# 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 (      __magic_name__	:    List[Any]      ,   __magic_name__	:    Optional[int]      ,   __magic_name__	:    Tuple	)							->     str:
						lowercase   :       Union[str, Any]								=dct.pop(UpperCamelCase__	)
						lowercase   :       Optional[int]								=val






def 							_lowerCAmelCase (      __magic_name__	:    Any      ,   __magic_name__	:    List[str]      ,   __magic_name__	:    List[Any]	)							->     List[Any]:
						lowercase   :       str								={
						    '''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[Any]								=model_name_to_url[model_name]
						lowercase   :       Tuple								=torch.hub.load_state_dict_from_url(UpperCamelCase__      ,   map_location='''cpu'''	)['''state_dict''']

						lowercase   :       Optional[Any]								=get_upernet_config(UpperCamelCase__	)
						lowercase   :       int								=UperNetForSemanticSegmentation(UpperCamelCase__	)
						model.eval()

						# replace "bn" => "batch_norm"
						for key in state_dict.copy().keys():
												lowercase   :       Dict								=state_dict.pop(UpperCamelCase__	)
												if "bn" in key:
																		lowercase   :       Any								=key.replace('''bn'''      ,   '''batch_norm'''	)
												lowercase   :       Optional[int]								=val

						# rename keys
						lowercase   :       List[str]								=create_rename_keys(UpperCamelCase__	)
						for src, dest in rename_keys:
												rename_key(UpperCamelCase__      ,   UpperCamelCase__      ,   UpperCamelCase__	)

						model.load_state_dict(UpperCamelCase__	)

						# verify on image
						lowercase   :       Optional[int]								='''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
						lowercase   :       Tuple								=Image.open(requests.get(UpperCamelCase__      ,   stream=UpperCamelCase__	).raw	).convert('''RGB'''	)

						lowercase   :       List[Any]								=SegformerImageProcessor()
						lowercase   :       Dict								=processor(UpperCamelCase__      ,   return_tensors='''pt'''	).pixel_values

						with torch.no_grad():
												lowercase   :       List[str]								=model(UpperCamelCase__	)

						if model_name == "upernet-convnext-tiny":
												lowercase   :       int								=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":
												lowercase   :       List[Any]								=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":
												lowercase   :       Union[str, Any]								=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":
												lowercase   :       Union[str, Any]								=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":
												lowercase   :       Optional[int]								=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__":
							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'''





from collections import defaultdict






def 							_lowerCAmelCase (      __magic_name__	:    str      ,   __magic_name__	:    str	)							->     bool:
						lowercase   :       Optional[int]								=first_str.lower().strip()
						lowercase   :       Union[str, Any]								=second_str.lower().strip()

						# Remove whitespace
						lowercase   :       Optional[int]								=first_str.replace(''' '''      ,   ''''''	)
						lowercase   :       Optional[Any]								=second_str.replace(''' '''      ,   ''''''	)

						# Strings of different lengths are not anagrams
						if len(__magic_name__	) != len(__magic_name__	):
												return False

						# Default values for count should be 0
						lowercase   :       defaultdict[str, int]								=defaultdict(__magic_name__	)

						# For each character in input strings,
						# increment count in the corresponding
						for i in range(len(__magic_name__	)	):
												count[first_str[i]] += 1
												count[second_str[i]] -= 1

						return all(_count == 0 for _count in count.values()	)


if __name__ == "__main__":
							from doctest import testmod

							testmod()
							UpperCamelCase_							=					input("""Enter the first string """).strip()
							UpperCamelCase_							=					input("""Enter the second string """).strip()

							UpperCamelCase_							=					check_anagrams(input_a, input_b)
							print(f'''{input_a} and {input_b} are {'' if status else 'not '}anagrams.''')





