




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      UpperCAmelCase_				(						SCREAMING_SNAKE_CASE_							:    Union[str, Any]					):
					__lowerCAmelCase           =	3_84
					if "tiny" in model_name:
										__lowerCAmelCase           =	[3, 3, 9, 3]
										__lowerCAmelCase           =	[96, 1_92, 3_84, 7_68]
					if "small" in model_name:
										__lowerCAmelCase           =	[3, 3, 27, 3]
										__lowerCAmelCase           =	[96, 1_92, 3_84, 7_68]
					if "base" in model_name:
										__lowerCAmelCase           =	[3, 3, 27, 3]
										__lowerCAmelCase           =	[1_28, 2_56, 5_12, 10_24]
										__lowerCAmelCase           =	5_12
					if "large" in model_name:
										__lowerCAmelCase           =	[3, 3, 27, 3]
										__lowerCAmelCase           =	[1_92, 3_84, 7_68, 15_36]
										__lowerCAmelCase           =	7_68
					if "xlarge" in model_name:
										__lowerCAmelCase           =	[3, 3, 27, 3]
										__lowerCAmelCase           =	[2_56, 5_12, 10_24, 20_48]
										__lowerCAmelCase           =	10_24

					# set label information
					__lowerCAmelCase           =	1_50
					__lowerCAmelCase           =	'''huggingface/label-files'''
					__lowerCAmelCase           =	'''ade20k-id2label.json'''
					__lowerCAmelCase           =	json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__				,				repo_type="dataset"					)				,				"r"					)					)
					__lowerCAmelCase           =	{int(SCREAMING_SNAKE_CASE__					): v for k, v in idalabel.items()}
					__lowerCAmelCase           =	{v: k for k, v in idalabel.items()}

					__lowerCAmelCase           =	ConvNextConfig(
					    depths=SCREAMING_SNAKE_CASE__				,				hidden_sizes=SCREAMING_SNAKE_CASE__				,				out_features=["stage1", "stage2", "stage3", "stage4"]					)
					__lowerCAmelCase           =	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      UpperCAmelCase_				(						SCREAMING_SNAKE_CASE_							:    List[Any]					):
					__lowerCAmelCase           =	[]

					# 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      UpperCAmelCase_				(						SCREAMING_SNAKE_CASE_							:    Union[str, Any]				,				SCREAMING_SNAKE_CASE_							:    Optional[int]				,				SCREAMING_SNAKE_CASE_							:    Tuple					):
					__lowerCAmelCase           =	dct.pop(SCREAMING_SNAKE_CASE__					)
					__lowerCAmelCase           =	val







def      UpperCAmelCase_				(						SCREAMING_SNAKE_CASE_							:    Any				,				SCREAMING_SNAKE_CASE_							:    str				,				SCREAMING_SNAKE_CASE_							:    List[str]					):
					__lowerCAmelCase           =	{
					    '''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''',
					}
					__lowerCAmelCase           =	model_name_to_url[model_name]
					__lowerCAmelCase           =	torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__				,				map_location="cpu"					)['''state_dict''']

					__lowerCAmelCase           =	get_upernet_config(SCREAMING_SNAKE_CASE__					)
					__lowerCAmelCase           =	UperNetForSemanticSegmentation(SCREAMING_SNAKE_CASE__					)
					model.eval()

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

					# rename keys
					__lowerCAmelCase           =	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
					__lowerCAmelCase           =	'''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
					__lowerCAmelCase           =	Image.open(requests.get(SCREAMING_SNAKE_CASE__				,				stream=SCREAMING_SNAKE_CASE__					).raw					).convert("RGB"					)

					__lowerCAmelCase           =	SegformerImageProcessor()
					__lowerCAmelCase           =	processor(SCREAMING_SNAKE_CASE__				,				return_tensors="pt"					).pixel_values

					with torch.no_grad():
										__lowerCAmelCase           =	model(SCREAMING_SNAKE_CASE__					)

					if model_name == "upernet-convnext-tiny":
										__lowerCAmelCase           =	torch.tensor(
										    [[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]]					)
					elif model_name == "upernet-convnext-small":
										__lowerCAmelCase           =	torch.tensor(
										    [[-8.82_36, -8.82_36, -8.67_71], [-8.82_36, -8.82_36, -8.67_71], [-8.76_38, -8.76_38, -8.62_40]]					)
					elif model_name == "upernet-convnext-base":
										__lowerCAmelCase           =	torch.tensor(
										    [[-8.85_58, -8.85_58, -8.69_05], [-8.85_58, -8.85_58, -8.69_05], [-8.76_69, -8.76_69, -8.60_21]]					)
					elif model_name == "upernet-convnext-large":
										__lowerCAmelCase           =	torch.tensor(
										    [[-8.66_60, -8.66_60, -8.62_10], [-8.66_60, -8.66_60, -8.62_10], [-8.63_10, -8.63_10, -8.59_64]]					)
					elif model_name == "upernet-convnext-xlarge":
										__lowerCAmelCase           =	torch.tensor(
										    [[-8.49_80, -8.49_80, -8.39_77], [-8.49_80, -8.49_80, -8.39_77], [-8.43_79, -8.43_79, -8.34_12]]					)
					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)










import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint







def      _a				(						SCREAMING_SNAKE_CASE_							:    Any					):
					# encoder.embeddings are double copied in original FLAVA
					return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()					)







def      _a				(						SCREAMING_SNAKE_CASE_							:    Dict				,				SCREAMING_SNAKE_CASE_							:    Dict					):
					__lowerCAmelCase           =	{}

					for key, value in state_dict.items():
										if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
															continue

										__lowerCAmelCase           =	key.replace("heads.cmd.mim_head.cls.predictions"				,				"mmm_image_head"					)
										__lowerCAmelCase           =	key.replace("heads.cmd.mlm_head.cls.predictions"				,				"mmm_text_head"					)
										__lowerCAmelCase           =	key.replace("heads.cmd.itm_head.cls"				,				"itm_head"					)
										__lowerCAmelCase           =	key.replace("heads.cmd.itm_head.pooler"				,				"itm_head.pooler"					)
										__lowerCAmelCase           =	key.replace("heads.cmd.clip_head.logit_scale"				,				"flava.logit_scale"					)
										__lowerCAmelCase           =	key.replace("heads.fairseq_mlm.cls.predictions"				,				"mlm_head"					)
										__lowerCAmelCase           =	key.replace("heads.imagenet.mim_head.cls.predictions"				,				"mim_head"					)
										__lowerCAmelCase           =	key.replace("mm_text_projection"				,				"flava.text_to_mm_projection"					)
										__lowerCAmelCase           =	key.replace("mm_image_projection"				,				"flava.image_to_mm_projection"					)
										__lowerCAmelCase           =	key.replace("image_encoder.module"				,				"flava.image_model"					)
										__lowerCAmelCase           =	key.replace("text_encoder.module"				,				"flava.text_model"					)
										__lowerCAmelCase           =	key.replace("mm_encoder.module.encoder.cls_token"				,				"flava.multimodal_model.cls_token"					)
										__lowerCAmelCase           =	key.replace("mm_encoder.module"				,				"flava.multimodal_model"					)
										__lowerCAmelCase           =	key.replace("text_projection"				,				"flava.text_projection"					)
										__lowerCAmelCase           =	key.replace("image_projection"				,				"flava.image_projection"					)

										__lowerCAmelCase           =	value.float()

					for key, value in codebook_state_dict.items():
										__lowerCAmelCase           =	value

					return upgrade







@torch.no_grad()
def      _a				(						SCREAMING_SNAKE_CASE_							:    List[Any]				,				SCREAMING_SNAKE_CASE_							:    Optional[int]				,				SCREAMING_SNAKE_CASE_							:    List[str]				,				SCREAMING_SNAKE_CASE_							:    int=None					):
					if config_path is not None:
										__lowerCAmelCase           =	FlavaConfig.from_pretrained(SCREAMING_SNAKE_CASE_					)
					else:
										__lowerCAmelCase           =	FlavaConfig()

					__lowerCAmelCase           =	FlavaForPreTraining(SCREAMING_SNAKE_CASE_					).eval()

					__lowerCAmelCase           =	convert_dalle_checkpoint(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				save_checkpoint=SCREAMING_SNAKE_CASE_					)

					if os.path.exists(SCREAMING_SNAKE_CASE_					):
										__lowerCAmelCase           =	torch.load(SCREAMING_SNAKE_CASE_				,				map_location="cpu"					)
					else:
										__lowerCAmelCase           =	torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_				,				map_location="cpu"					)

					__lowerCAmelCase           =	upgrade_state_dict(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)
					hf_model.load_state_dict(SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase           =	hf_model.state_dict()
					__lowerCAmelCase           =	count_parameters(SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase           =	count_parameters(SCREAMING_SNAKE_CASE_					) + count_parameters(SCREAMING_SNAKE_CASE_					)

					assert torch.allclose(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				atol=1E-3					)

					hf_model.save_pretrained(SCREAMING_SNAKE_CASE_					)


if __name__ == "__main__":
					UpperCamelCase__         =      argparse.ArgumentParser()
					parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
					parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to flava checkpoint""")
					parser.add_argument("""--codebook_path""", default=None, type=str, help="""Path to flava codebook checkpoint""")
					parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
					UpperCamelCase__         =      parser.parse_args()

					convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)




