





'''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 							_lowerCamelCase(  UpperCamelCase__    : Optional[int]		)		->  Dict:
    A       :       Union[str, Any]							=						384
    if "tiny" in model_name:
        A       :       List[Any]							=						[3, 3, 9, 3]
        A       :       Optional[Any]							=						[96, 192, 384, 768]
    if "small" in model_name:
        A       :       Union[str, Any]							=						[3, 3, 27, 3]
        A       :       Dict							=						[96, 192, 384, 768]
    if "base" in model_name:
        A       :       List[Any]							=						[3, 3, 27, 3]
        A       :       Tuple							=						[128, 256, 512, 1_024]
        A       :       List[str]							=						512
    if "large" in model_name:
        A       :       Tuple							=						[3, 3, 27, 3]
        A       :       str							=						[192, 384, 768, 1_536]
        A       :       int							=						768
    if "xlarge" in model_name:
        A       :       int							=						[3, 3, 27, 3]
        A       :       str							=						[256, 512, 1_024, 2_048]
        A       :       str							=						1_024

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

    A       :       str							=						ConvNextConfig(
        depths=_snake_case    ,       hidden_sizes=_snake_case    ,       out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']		)
    A       :       Union[str, Any]							=						UperNetConfig(
        backbone_config=_snake_case    ,       auxiliary_in_channels=_snake_case    ,       num_labels=_snake_case    ,       idalabel=_snake_case    ,       labelaid=_snake_case    ,       )

    return config
def 							_lowerCamelCase(  UpperCamelCase__    : str		)		->  Any:
    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 							_lowerCamelCase(  UpperCamelCase__    : Tuple    ,       UpperCamelCase__    : Any    ,       UpperCamelCase__    : Optional[Any]		)		->  Any:
    A       :       Tuple							=						dct.pop(_snake_case		)
    A       :       str							=						val
def 							_lowerCamelCase(  UpperCamelCase__    : int    ,       UpperCamelCase__    : Any    ,       UpperCamelCase__    : Optional[int]		)		->  int:
    A       :       List[Any]							=						{
        '''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       :       Optional[int]							=						model_name_to_url[model_name]
    A       :       Union[str, Any]							=						torch.hub.load_state_dict_from_url(_snake_case    ,       map_location='''cpu'''		)['''state_dict''']

    A       :       Union[str, Any]							=						get_upernet_config(_snake_case		)
    A       :       Optional[Any]							=						UperNetForSemanticSegmentation(_snake_case		)
    model.eval()

    # replace "bn" => "batch_norm"
    for key in state_dict.copy().keys():
        A       :       Optional[int]							=						state_dict.pop(_snake_case		)
        if "bn" in key:
            A       :       Union[str, Any]							=						key.replace('''bn'''    ,       '''batch_norm'''		)
        A       :       Any							=						val

    # rename keys
    A       :       Dict							=						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       :       Dict							=						'''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
    A       :       List[str]							=						Image.open(requests.get(_snake_case    ,       stream=_snake_case		).raw		).convert('''RGB'''		)

    A       :       List[Any]							=						SegformerImageProcessor()
    A       :       Tuple							=						processor(_snake_case    ,       return_tensors='''pt'''		).pixel_values

    with torch.no_grad():
        A       :       Union[str, Any]							=						model(_snake_case		)

    if model_name == "upernet-convnext-tiny":
        A       :       Tuple							=						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":
        A       :       Tuple							=						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":
        A       :       List[Any]							=						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":
        A       :       str							=						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":
        A       :       Optional[Any]							=						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]    ,       _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__":
     snake_case_     =	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."""
     )

     snake_case_     =	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 transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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


if is_vision_available():
     import PIL

snake_case_     =	logging.get_logger(__name__)
def 							_lowerCamelCase(  UpperCamelCase__    : int		)		->  List[List[ImageInput]]:
    if isinstance(UpperCamelCase__    ,       (list, tuple)		) and isinstance(videos[0]    ,       (list, tuple)		) and is_valid_image(videos[0][0]		):
        return videos

    elif isinstance(UpperCamelCase__    ,       (list, tuple)		) and is_valid_image(videos[0]		):
        return [videos]

    elif is_valid_image(UpperCamelCase__		):
        return [[videos]]

    raise ValueError(F'''Could not make batched video from {videos}'''		)





class   _lowercase						(							a				):
   _UpperCamelCase       		=   ["""pixel_values"""]


   def __init__( self    ,   _UpperCAmelCase = True    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = PILImageResampling.BILINEAR    ,   _UpperCAmelCase = True    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = True    ,   _UpperCAmelCase = 1 / 255    ,   _UpperCAmelCase = True    ,   _UpperCAmelCase = True    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   **_UpperCAmelCase    ,   ):
       super().__init__(**_UpperCAmelCase					)
       A       :       int							=						size if size is not None else {'''shortest_edge''': 256}
       A       :       str							=						get_size_dict(_UpperCAmelCase    ,   default_to_square=_UpperCAmelCase					)
       A       :       List[Any]							=						crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
       A       :       List[Any]							=						get_size_dict(_UpperCAmelCase    ,   param_name='''crop_size'''					)

       A       :       Dict							=						do_resize
       A       :       Union[str, Any]							=						size
       A       :       int							=						do_center_crop
       A       :       Union[str, Any]							=						crop_size
       A       :       int							=						resample
       A       :       Any							=						do_rescale
       A       :       Optional[Any]							=						rescale_factor
       A       :       Any							=						offset
       A       :       int							=						do_normalize
       A       :       Optional[Any]							=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       A       :       str							=						image_std if image_std is not None else IMAGENET_STANDARD_STD


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase = PILImageResampling.BILINEAR    ,   _UpperCAmelCase = None    ,   **_UpperCAmelCase    ,   ):
       A       :       str							=						get_size_dict(_UpperCAmelCase    ,   default_to_square=_UpperCAmelCase					)
       if "shortest_edge" in size:
           A       :       Union[str, Any]							=						get_resize_output_image_size(_UpperCAmelCase    ,   size['''shortest_edge''']    ,   default_to_square=_UpperCAmelCase					)
       elif "height" in size and "width" in size:
           A       :       Optional[Any]							=						(size['''height'''], size['''width'''])
       else:
           raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''					)
       return resize(_UpperCAmelCase    ,   size=_UpperCAmelCase    ,   resample=_UpperCAmelCase    ,   data_format=_UpperCAmelCase    ,   **_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None    ,   **_UpperCAmelCase    ,   ):
       A       :       Optional[int]							=						get_size_dict(_UpperCAmelCase					)
       if "height" not in size or "width" not in size:
           raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''					)
       return center_crop(_UpperCAmelCase    ,   size=(size['''height'''], size['''width'''])    ,   data_format=_UpperCAmelCase    ,   **_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase = True    ,   _UpperCAmelCase = None    ,   **_UpperCAmelCase    ,   ):
       A       :       Dict							=						image.astype(np.floataa					)
       if offset:
           A       :       List[str]							=						image - (scale / 2)
       return rescale(_UpperCAmelCase    ,   scale=_UpperCAmelCase    ,   data_format=_UpperCAmelCase    ,   **_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None    ,   **_UpperCAmelCase    ,   ):
       return normalize(_UpperCAmelCase    ,   mean=_UpperCAmelCase    ,   std=_UpperCAmelCase    ,   data_format=_UpperCAmelCase    ,   **_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = ChannelDimension.FIRST    ,   ):
       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.'''					)

       if offset and not do_rescale:
           raise ValueError('''For offset, do_rescale must also be set to True.'''					)

       # All transformations expect numpy arrays.
       A       :       Dict							=						to_numpy_array(_UpperCAmelCase					)

       if do_resize:
           A       :       int							=						self.resize(image=_UpperCAmelCase    ,   size=_UpperCAmelCase    ,   resample=_UpperCAmelCase					)

       if do_center_crop:
           A       :       Optional[int]							=						self.center_crop(_UpperCAmelCase    ,   size=_UpperCAmelCase					)

       if do_rescale:
           A       :       int							=						self.rescale(image=_UpperCAmelCase    ,   scale=_UpperCAmelCase    ,   offset=_UpperCAmelCase					)

       if do_normalize:
           A       :       List[Any]							=						self.normalize(image=_UpperCAmelCase    ,   mean=_UpperCAmelCase    ,   std=_UpperCAmelCase					)

       A       :       Optional[int]							=						to_channel_dimension_format(_UpperCAmelCase    ,   _UpperCAmelCase					)
       return image


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = ChannelDimension.FIRST    ,   **_UpperCAmelCase    ,   ):
       A       :       Union[str, Any]							=						do_resize if do_resize is not None else self.do_resize
       A       :       Optional[Any]							=						resample if resample is not None else self.resample
       A       :       List[str]							=						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       :       Dict							=						rescale_factor if rescale_factor is not None else self.rescale_factor
       A       :       List[Any]							=						offset if offset is not None else self.offset
       A       :       str							=						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       :       Optional[Any]							=						image_std if image_std is not None else self.image_std

       A       :       List[Any]							=						size if size is not None else self.size
       A       :       Any							=						get_size_dict(_UpperCAmelCase    ,   default_to_square=_UpperCAmelCase					)
       A       :       Optional[int]							=						crop_size if crop_size is not None else self.crop_size
       A       :       int							=						get_size_dict(_UpperCAmelCase    ,   param_name='''crop_size'''					)

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

       A       :       List[Any]							=						make_batched(_UpperCAmelCase					)

       A       :       List[Any]							=						[
           [
               self._preprocess_image(
                   image=_UpperCAmelCase    ,   do_resize=_UpperCAmelCase    ,   size=_UpperCAmelCase    ,   resample=_UpperCAmelCase    ,   do_center_crop=_UpperCAmelCase    ,   crop_size=_UpperCAmelCase    ,   do_rescale=_UpperCAmelCase    ,   rescale_factor=_UpperCAmelCase    ,   offset=_UpperCAmelCase    ,   do_normalize=_UpperCAmelCase    ,   image_mean=_UpperCAmelCase    ,   image_std=_UpperCAmelCase    ,   data_format=_UpperCAmelCase    ,   )
               for img in video
           ]
           for video in videos
       ]

       A       :       Any							=						{'''pixel_values''': videos}
       return BatchFeature(data=_UpperCAmelCase    ,   tensor_type=_UpperCAmelCase					)
