

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

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

	A__							:    List[str]        =		ConvNextConfig(
	    depths=lowercase_    ,      hidden_sizes=lowercase_    ,      out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]					)
	A__							:    Optional[int]        =		UperNetConfig(
	    backbone_config=lowercase_    ,      auxiliary_in_channels=lowercase_    ,      num_labels=lowercase_    ,      idalabel=lowercase_    ,      labelaid=lowercase_    ,      )

	return config

def 						UpperCamelCase					(lowercase_: Tuple					)     ->      Dict:
	A__							:    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 						UpperCamelCase					(lowercase_: Optional[int]    ,      lowercase_: Any    ,      lowercase_: int					)     ->      str:
	A__							:    Dict        =		dct.pop(lowercase_					)
	A__							:    int        =		val

def 						UpperCamelCase					(lowercase_: Optional[Any]    ,      lowercase_: List[str]    ,      lowercase_: Union[str, Any]					)     ->      str:
	A__							:    Optional[int]        =		{
	    """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__							:    int        =		model_name_to_url[model_name]
	A__							:    Dict        =		torch.hub.load_state_dict_from_url(lowercase_    ,      map_location="""cpu"""					)["""state_dict"""]

	A__							:    str        =		get_upernet_config(lowercase_					)
	A__							:    int        =		UperNetForSemanticSegmentation(lowercase_					)
	model.eval()

	# replace "bn" => "batch_norm"
	for key in state_dict.copy().keys():
		A__							:    Any        =		state_dict.pop(lowercase_					)
		if "bn" in key:
			A__							:    str        =		key.replace("""bn"""    ,      """batch_norm"""					)
		A__							:    List[Any]        =		val

	# rename keys
	A__							:    Any        =		create_rename_keys(lowercase_					)
	for src, dest in rename_keys:
		rename_key(lowercase_    ,      lowercase_    ,      lowercase_					)

	model.load_state_dict(lowercase_					)

	# verify on image
	A__							:    Union[str, Any]        =		"""https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
	A__							:    int        =		Image.open(requests.get(lowercase_    ,      stream=lowercase_					).raw					).convert("""RGB"""					)

	A__							:    Optional[int]        =		SegformerImageProcessor()
	A__							:    Dict        =		processor(lowercase_    ,      return_tensors="""pt"""					).pixel_values

	with torch.no_grad():
		A__							:    List[Any]        =		model(lowercase_					)

	if model_name == "upernet-convnext-tiny":
		A__							:    Dict        =		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":
		A__							:    int        =		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":
		A__							:    Dict        =		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":
		A__							:    Dict        =		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":
		A__							:    int        =		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]    ,      lowercase_    ,      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(lowercase_					)
		print(f"""Saving processor to {pytorch_dump_folder_path}"""					)
		processor.save_pretrained(lowercase_					)

	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__":
	A_       : str         =			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.'
	)

	A_       : int         =			parser.parse_args()
	convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()

def 						UpperCamelCase					(lowercase_: Optional[int]    ,      lowercase_: Union[str, Any]    ,      lowercase_: Optional[Any]					)     ->      Tuple:
	# Initialise PyTorch model
	A__							:    str        =		AlbertConfig.from_json_file(lowercase_					)
	print(f"""Building PyTorch model from configuration: {config}"""					)
	A__							:    List[Any]        =		AlbertForPreTraining(lowercase_					)

	# Load weights from tf checkpoint
	load_tf_weights_in_albert(lowercase_    ,      lowercase_    ,      lowercase_					)

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


if __name__ == "__main__":
	A_       : int         =			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(
	    '--albert_config_file',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        'The config json file corresponding to the pre-trained ALBERT model. \n'
	        'This specifies the model architecture.'
	    ),
	)
	parser.add_argument(
	    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	A_       : int         =			parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)



