
"""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					(		snake_case__ :  Dict  ):
	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(snake_case__						,   snake_case__						,   repo_type='dataset'  )						,   'r'  )  )
	A     = {int(snake_case__  ): v for k, v in idalabel.items()}
	A     = {v: k for k, v in idalabel.items()}

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

	return config






def    _snake_case					(		snake_case__ :  int  ):
	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					(		snake_case__ :  int						,   snake_case__ :  Any						,   snake_case__ :  Union[str, Any]  ):
	A     = dct.pop(snake_case__  )
	A     = val






def    _snake_case					(		snake_case__ :  Union[str, Any]						,   snake_case__ :  str						,   snake_case__ :  Optional[Any]  ):
	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(snake_case__						,   map_location='cpu'  )['state_dict']

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

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

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

	model.load_state_dict(snake_case__  )

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

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

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

	if model_name == "upernet-convnext-tiny":
		A     = 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     = 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     = 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     = 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     = 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]						,   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(snake_case__  )
		print(F'Saving processor to {pytorch_dump_folder_path}'  )
		processor.save_pretrained(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__":
		_lowercase				 =					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.'''
		)

		_lowercase				 =					parser.parse_args()
		convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






"""simple docstring"""




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


a		:					int	      =     logging.get_logger(__name__)


class 			__UpperCamelCase							(		a__		):
							lowerCamelCase  :   Optional[int]    =["""pixel_values"""]






							def __init__(  self ,   lowerCAmelCase__ = True ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = PIL.Image.BICUBIC ,   lowerCAmelCase__ = True ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = 1 / 255 ,   lowerCAmelCase__ = True ,   lowerCAmelCase__ = True ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					None:
								super().__init__(**lowerCAmelCase__ )
								a :						Optional[int]      =							size if size is not None else {"height": 256, "width": 256}
								a :						Tuple      =							get_size_dict(lowerCAmelCase__ )
								a :						Any      =							crop_size if crop_size is not None else {"height": 224, "width": 224}
								a :						Dict      =							get_size_dict(lowerCAmelCase__ ,   param_name="crop_size" )

								a :						Tuple      =							do_resize
								a :						int      =							size
								a :						List[str]      =							resample
								a :						List[Any]      =							do_center_crop
								a :						List[Any]      =							crop_size
								a :						Union[str, Any]      =							do_rescale
								a :						List[Any]      =							rescale_factor
								a :						Union[str, Any]      =							do_normalize
								a :						Union[str, Any]      =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								a :						List[str]      =							image_std if image_std is not None else IMAGENET_STANDARD_STD






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ = PIL.Image.BICUBIC ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					np.ndarray:
								a :						Optional[int]      =							get_size_dict(lowerCAmelCase__ )
								if "height" not in size or "width" not in size:
									raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""" )
								return resize(
								    lowerCAmelCase__ ,   size=(size["height"], size["width"]) ,   resample=lowerCAmelCase__ ,   data_format=lowerCAmelCase__ ,   **lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					np.ndarray:
								a :						List[str]      =							get_size_dict(lowerCAmelCase__ )
								if "height" not in size or "width" not in size:
									raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""" )
								return center_crop(lowerCAmelCase__ ,   size=(size["height"], size["width"]) ,   data_format=lowerCAmelCase__ ,   **lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					Union[str, Any]:
								return rescale(lowerCAmelCase__ ,   scale=lowerCAmelCase__ ,   data_format=lowerCAmelCase__ ,   **lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					np.ndarray:
								return normalize(lowerCAmelCase__ ,   mean=lowerCAmelCase__ ,   std=lowerCAmelCase__ ,   data_format=lowerCAmelCase__ ,   **lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__=None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = ChannelDimension.FIRST ,   **lowerCAmelCase__ ,   )      ->					PIL.Image.Image:
								a :						List[Any]      =							do_resize if do_resize is not None else self.do_resize
								a :						str      =							resample if resample is not None else self.resample
								a :						Dict      =							do_center_crop if do_center_crop is not None else self.do_center_crop
								a :						Union[str, Any]      =							do_rescale if do_rescale is not None else self.do_rescale
								a :						str      =							rescale_factor if rescale_factor is not None else self.rescale_factor
								a :						Dict      =							do_normalize if do_normalize is not None else self.do_normalize
								a :						List[str]      =							image_mean if image_mean is not None else self.image_mean
								a :						Union[str, Any]      =							image_std if image_std is not None else self.image_std

								a :						str      =							size if size is not None else self.size
								a :						Any      =							get_size_dict(lowerCAmelCase__ )
								a :						Dict      =							crop_size if crop_size is not None else self.crop_size
								a :						List[str]      =							get_size_dict(lowerCAmelCase__ ,   param_name="crop_size" )

								a :						str      =							make_list_of_images(lowerCAmelCase__ )

								if not valid_images(lowerCAmelCase__ ):
									raise ValueError(
									    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
									    "torch.Tensor, tf.Tensor or jax.ndarray." )

								if do_resize and size is None or resample is None:
									raise ValueError("Size and resample must be specified if do_resize is True." )

								if do_center_crop and crop_size is None:
									raise ValueError("Crop size must be specified if do_center_crop is True." )

								if do_rescale and rescale_factor is None:
									raise ValueError("Rescale factor must be specified if do_rescale is True." )

								if do_normalize and (image_mean is None or image_std is None):
									raise ValueError("Image mean and std must be specified if do_normalize is True." )

								# All transformations expect numpy arrays.
								a :						Optional[Any]      =							[to_numpy_array(lowerCAmelCase__ ) for image in images]

								if do_resize:
									a :						Optional[int]      =							[self.resize(image=lowerCAmelCase__ ,   size=lowerCAmelCase__ ,   resample=lowerCAmelCase__ ) for image in images]

								if do_center_crop:
									a :						Dict      =							[self.center_crop(image=lowerCAmelCase__ ,   size=lowerCAmelCase__ ) for image in images]

								if do_rescale:
									a :						Any      =							[self.rescale(image=lowerCAmelCase__ ,   scale=lowerCAmelCase__ ) for image in images]

								if do_normalize:
									a :						Optional[Any]      =							[self.normalize(image=lowerCAmelCase__ ,   mean=lowerCAmelCase__ ,   std=lowerCAmelCase__ ) for image in images]

								a :						Optional[Any]      =							[to_channel_dimension_format(lowerCAmelCase__ ,   lowerCAmelCase__ ) for image in images]

								a :						Tuple      =							{"pixel_values": images}
								return BatchFeature(data=lowerCAmelCase__ ,   tensor_type=lowerCAmelCase__ )


