





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			:     List[str]							):




	'''simple docstring'''







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

	# set label information
	lowerCamelCase__										=			150
	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     snake_case							(UpperCamelCase			:     Optional[int]							):




	'''simple docstring'''







	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     snake_case							(UpperCamelCase			:     Any			,    UpperCamelCase			:     Optional[int]			,    UpperCamelCase			:     Dict							):




	'''simple docstring'''







	lowerCamelCase__										=			dct.pop(_SCREAMING_SNAKE_CASE							)
	lowerCamelCase__										=			val

def     snake_case							(UpperCamelCase			:     Dict			,    UpperCamelCase			:     str			,    UpperCamelCase			:     int							):




	'''simple docstring'''







	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(
		    [[-8.8_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":
		lowerCamelCase__										=			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":
		lowerCamelCase__										=			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":
		lowerCamelCase__										=			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":
		lowerCamelCase__										=			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]			,    _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__":
					a__    :		Optional[Any]      						=     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__    :		List[Any]      						=     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


__A											=						{
    """configuration_x_clip""": [
        """XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XCLIPConfig""",
        """XCLIPTextConfig""",
        """XCLIPVisionConfig""",
    ],
    """processing_x_clip""": ["""XCLIPProcessor"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=						[
      """XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """XCLIPModel""",
      """XCLIPPreTrainedModel""",
      """XCLIPTextModel""",
      """XCLIPVisionModel""",
  ]

if TYPE_CHECKING:
  from .configuration_x_clip import (
      XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
      XCLIPConfig,
      XCLIPTextConfig,
      XCLIPVisionConfig,
  )
  from .processing_x_clip import XCLIPProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_x_clip import (
        XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
        XCLIPModel,
        XCLIPPreTrainedModel,
        XCLIPTextModel,
        XCLIPVisionModel,
    )

else:
  import sys

  __A											=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
