"""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   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							3_8_4
				if "tiny" in model_name:
								UpperCAmelCase__             =							[3, 3, 9, 3]
								UpperCAmelCase__             =							[9_6, 1_9_2, 3_8_4, 7_6_8]
				if "small" in model_name:
								UpperCAmelCase__             =							[3, 3, 2_7, 3]
								UpperCAmelCase__             =							[9_6, 1_9_2, 3_8_4, 7_6_8]
				if "base" in model_name:
								UpperCAmelCase__             =							[3, 3, 2_7, 3]
								UpperCAmelCase__             =							[1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
								UpperCAmelCase__             =							5_1_2
				if "large" in model_name:
								UpperCAmelCase__             =							[3, 3, 2_7, 3]
								UpperCAmelCase__             =							[1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
								UpperCAmelCase__             =							7_6_8
				if "xlarge" in model_name:
								UpperCAmelCase__             =							[3, 3, 2_7, 3]
								UpperCAmelCase__             =							[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]
								UpperCAmelCase__             =							1_0_2_4

				# set label information
				UpperCAmelCase__             =							1_5_0
				UpperCAmelCase__             =							'huggingface/label-files'
				UpperCAmelCase__             =							'ade20k-id2label.json'
				UpperCAmelCase__             =							json.load(open(hf_hub_download(lowerCamelCase					,			lowerCamelCase					,			repo_type='dataset'       )					,			'r'       )       )
				UpperCAmelCase__             =							{int(lowerCamelCase       ): v for k, v in idalabel.items()}
				UpperCAmelCase__             =							{v: k for k, v in idalabel.items()}

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

				return config



def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							[]

				# 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   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							dct.pop(lowerCamelCase       )
				UpperCAmelCase__             =							val



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							{
				    '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',
				}
				UpperCAmelCase__             =							model_name_to_url[model_name]
				UpperCAmelCase__             =							torch.hub.load_state_dict_from_url(lowerCamelCase					,			map_location='cpu'       )['state_dict']

				UpperCAmelCase__             =							get_upernet_config(lowerCamelCase       )
				UpperCAmelCase__             =							UperNetForSemanticSegmentation(lowerCamelCase       )
				model.eval()

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

				# rename keys
				UpperCAmelCase__             =							create_rename_keys(lowerCamelCase       )
				for src, dest in rename_keys:
								rename_key(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )

				model.load_state_dict(lowerCamelCase       )

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

				UpperCAmelCase__             =							SegformerImageProcessor()
				UpperCAmelCase__             =							processor(lowerCamelCase					,			return_tensors='pt'       ).pixel_values

				with torch.no_grad():
								UpperCAmelCase__             =							model(lowerCamelCase       )

				if model_name == "upernet-convnext-tiny":
								UpperCAmelCase__             =							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":
								UpperCAmelCase__             =							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":
								UpperCAmelCase__             =							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":
								UpperCAmelCase__             =							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":
								UpperCAmelCase__             =							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]					,			lowerCamelCase					,			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(lowerCamelCase       )
								print(f'''Saving processor to {pytorch_dump_folder_path}'''       )
								processor.save_pretrained(lowerCamelCase       )

				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__":
						lowerCAmelCase__  :  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.'
						)

						lowerCAmelCase__  :  Any						     =	parser.parse_args()
						convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    # Initialise PyTorch model
    __a						       =				LxmertConfig.from_json_file(_UpperCAmelCase				)
    print(f'Building PyTorch model from configuration: {config}'				)
    __a						       =				LxmertForPreTraining(_UpperCAmelCase				)

    # Load weights from tf checkpoint
    load_tf_weights_in_lxmert(_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				)

    # Save pytorch-model
    print(f'Save PyTorch model to {pytorch_dump_path}'				)
    torch.save(model.state_dict()   , _UpperCAmelCase				)


if __name__ == "__main__":
 __snake_case						:List[Any]      		=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
 )
 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 __snake_case						:Optional[Any]      		=     parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





