


"""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  __snake_case       (   UpperCamelCase__	)      ->		Tuple:







		"""simple docstring"""





		A					       = 384
		if "tiny" in model_name:
				A					       = [3, 3, 9, 3]
				A					       = [96, 192, 384, 768]
		if "small" in model_name:
				A					       = [3, 3, 27, 3]
				A					       = [96, 192, 384, 768]
		if "base" in model_name:
				A					       = [3, 3, 27, 3]
				A					       = [128, 256, 512, 1024]
				A					       = 512
		if "large" in model_name:
				A					       = [3, 3, 27, 3]
				A					       = [192, 384, 768, 1536]
				A					       = 768
		if "xlarge" in model_name:
				A					       = [3, 3, 27, 3]
				A					       = [256, 512, 1024, 2048]
				A					       = 1024

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

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

		return config







def  __snake_case       (   UpperCamelCase__	)      ->		Tuple:







		"""simple docstring"""





		A					       = []

		# 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  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)      ->		str:







		"""simple docstring"""





		A					       = dct.pop(UpperCamelCase__	)
		A					       = val







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)      ->		List[Any]:







		"""simple docstring"""





		A					       = {
		    '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',
		}
		A					       = model_name_to_url[model_name]
		A					       = torch.hub.load_state_dict_from_url(UpperCamelCase__ ,			map_location='cpu'	)['state_dict']

		A					       = get_upernet_config(UpperCamelCase__	)
		A					       = UperNetForSemanticSegmentation(UpperCamelCase__	)
		model.eval()

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

		# rename keys
		A					       = create_rename_keys(UpperCamelCase__	)
		for src, dest in rename_keys:
				rename_key(UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)

		model.load_state_dict(UpperCamelCase__	)

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

		A					       = SegformerImageProcessor()
		A					       = processor(UpperCamelCase__ ,			return_tensors='pt'	).pixel_values

		with torch.no_grad():
				A					       = model(UpperCamelCase__	)

		if model_name == "upernet-convnext-tiny":
				A					       = 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":
				A					       = 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":
				A					       = 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":
				A					       = 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":
				A					       = 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						:				Optional[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."
	)

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









"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase						:				Dict             =      {
    "configuration_megatron_bert": ["MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegatronBertConfig"],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCamelCase						:				Any             =      [
	    "MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "MegatronBertForCausalLM",
	    "MegatronBertForMaskedLM",
	    "MegatronBertForMultipleChoice",
	    "MegatronBertForNextSentencePrediction",
	    "MegatronBertForPreTraining",
	    "MegatronBertForQuestionAnswering",
	    "MegatronBertForSequenceClassification",
	    "MegatronBertForTokenClassification",
	    "MegatronBertModel",
	    "MegatronBertPreTrainedModel",
	]

if TYPE_CHECKING:
	from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_megatron_bert import (
		    MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MegatronBertForCausalLM,
		    MegatronBertForMaskedLM,
		    MegatronBertForMultipleChoice,
		    MegatronBertForNextSentencePrediction,
		    MegatronBertForPreTraining,
		    MegatronBertForQuestionAnswering,
		    MegatronBertForSequenceClassification,
		    MegatronBertForTokenClassification,
		    MegatronBertModel,
		    MegatronBertPreTrainedModel,
		)

else:
	import sys

	UpperCamelCase						:				Dict             =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





