'''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 _lowercase	(      lowerCamelCase__						)					->       int:





       """simple docstring"""


       __UpperCAmelCase  :				Union[str, Any]       				=		384
       if "tiny" in model_name:
              __UpperCAmelCase  :				Any       				=		[3, 3, 9, 3]
              __UpperCAmelCase  :				List[Any]       				=		[96, 192, 384, 768]
       if "small" in model_name:
              __UpperCAmelCase  :				str       				=		[3, 3, 27, 3]
              __UpperCAmelCase  :				Any       				=		[96, 192, 384, 768]
       if "base" in model_name:
              __UpperCAmelCase  :				Union[str, Any]       				=		[3, 3, 27, 3]
              __UpperCAmelCase  :				Union[str, Any]       				=		[128, 256, 512, 1024]
              __UpperCAmelCase  :				List[Any]       				=		512
       if "large" in model_name:
              __UpperCAmelCase  :				List[str]       				=		[3, 3, 27, 3]
              __UpperCAmelCase  :				Union[str, Any]       				=		[192, 384, 768, 1536]
              __UpperCAmelCase  :				Tuple       				=		768
       if "xlarge" in model_name:
              __UpperCAmelCase  :				Optional[Any]       				=		[3, 3, 27, 3]
              __UpperCAmelCase  :				int       				=		[256, 512, 1024, 2048]
              __UpperCAmelCase  :				int       				=		1024

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

       __UpperCAmelCase  :				Optional[Any]       				=		ConvNextConfig(
           depths=lowerCamelCase__							,    hidden_sizes=lowerCamelCase__							,    out_features=["stage1", "stage2", "stage3", "stage4"]						)
       __UpperCAmelCase  :				Optional[Any]       				=		UperNetConfig(
           backbone_config=lowerCamelCase__							,    auxiliary_in_channels=lowerCamelCase__							,    num_labels=lowerCamelCase__							,    idalabel=lowerCamelCase__							,    labelaid=lowerCamelCase__							,    )

       return config

def _lowercase	(      lowerCamelCase__						)					->       Optional[int]:





       """simple docstring"""


       __UpperCAmelCase  :				Dict       				=		[]

       # 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 _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       Any:





       """simple docstring"""


       __UpperCAmelCase  :				List[Any]       				=		dct.pop(lowerCamelCase__						)
       __UpperCAmelCase  :				int       				=		val

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       Dict:





       """simple docstring"""


       __UpperCAmelCase  :				str       				=		{
           """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""",
       }
       __UpperCAmelCase  :				Tuple       				=		model_name_to_url[model_name]
       __UpperCAmelCase  :				str       				=		torch.hub.load_state_dict_from_url(lowerCamelCase__							,    map_location="cpu"						)["""state_dict"""]

       __UpperCAmelCase  :				Optional[Any]       				=		get_upernet_config(lowerCamelCase__						)
       __UpperCAmelCase  :				str       				=		UperNetForSemanticSegmentation(lowerCamelCase__						)
       model.eval()

       # replace "bn" => "batch_norm"
       for key in state_dict.copy().keys():
              __UpperCAmelCase  :				Any       				=		state_dict.pop(lowerCamelCase__						)
              if "bn" in key:
                     __UpperCAmelCase  :				Dict       				=		key.replace("bn"							,    "batch_norm"						)
              __UpperCAmelCase  :				Dict       				=		val

       # rename keys
       __UpperCAmelCase  :				Dict       				=		create_rename_keys(lowerCamelCase__						)
       for src, dest in rename_keys:
              rename_key(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)

       model.load_state_dict(lowerCamelCase__						)

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

       __UpperCAmelCase  :				List[str]       				=		SegformerImageProcessor()
       __UpperCAmelCase  :				List[str]       				=		processor(lowerCamelCase__							,    return_tensors="pt"						).pixel_values

       with torch.no_grad():
              __UpperCAmelCase  :				Tuple       				=		model(lowerCamelCase__						)

       if model_name == "upernet-convnext-tiny":
              __UpperCAmelCase  :				List[Any]       				=		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":
              __UpperCAmelCase  :				Any       				=		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":
              __UpperCAmelCase  :				str       				=		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":
              __UpperCAmelCase  :				str       				=		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":
              __UpperCAmelCase  :				Tuple       				=		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]							,    lowerCamelCase__							,    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(lowerCamelCase__						)
              print(f"""Saving processor to {pytorch_dump_folder_path}"""						)
              processor.save_pretrained(lowerCamelCase__						)

       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   :	Optional[int] 							=   parser.parse_args()
   convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''




from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    )					->       list[float]:





       """simple docstring"""


       __UpperCAmelCase				, __UpperCAmelCase  :				Union[str, Any]       				=		coefficient_matrix.shape
       __UpperCAmelCase				, __UpperCAmelCase  :				Any       				=		constant_matrix.shape

       if rowsa != colsa:
              __UpperCAmelCase  :				str       				=		f"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
              raise ValueError(lowerCamelCase__						)

       if colsa != 1:
              __UpperCAmelCase  :				Optional[Any]       				=		f"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
              raise ValueError(lowerCamelCase__						)

       if rowsa != rowsa:
              __UpperCAmelCase  :				Optional[int]       				=		(
                  "Coefficient and constant matrices dimensions must be nxn and nx1 but "
                  f"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
              )
              raise ValueError(lowerCamelCase__						)

       if len(lowerCamelCase__						) != rowsa:
              __UpperCAmelCase  :				List[str]       				=		(
                  "Number of initial values must be equal to number of rows in coefficient "
                  f"""matrix but received {len(lowerCamelCase__						)} and {rowsa}"""
              )
              raise ValueError(lowerCamelCase__						)

       if iterations <= 0:
              raise ValueError("Iterations must be at least 1"						)

       __UpperCAmelCase  :				NDArray[floataa]       				=		np.concatenate(
           (coefficient_matrix, constant_matrix)							,    axis=1						)

       __UpperCAmelCase				, __UpperCAmelCase  :				Tuple       				=		table.shape

       strictly_diagonally_dominant(lowerCamelCase__						)

       # Iterates the whole matrix for given number of times
       for _ in range(lowerCamelCase__						):
              __UpperCAmelCase  :				int       				=		[]
              for row in range(lowerCamelCase__						):
                     __UpperCAmelCase  :				List[str]       				=		0
                     for col in range(lowerCamelCase__						):
                            if col == row:
                                   __UpperCAmelCase  :				int       				=		table[row][col]
                            elif col == cols - 1:
                                   __UpperCAmelCase  :				Any       				=		table[row][col]
                            else:
                                   temp += (-1) * table[row][col] * init_val[col]
                     __UpperCAmelCase  :				List[Any]       				=		(temp + val) / denom
                     new_val.append(lowerCamelCase__						)
              __UpperCAmelCase  :				str       				=		new_val

       return [float(lowerCamelCase__						) for i in new_val]

def _lowercase	(      lowerCamelCase__						)					->       bool:





       """simple docstring"""


       __UpperCAmelCase				, __UpperCAmelCase  :				Optional[int]       				=		table.shape

       __UpperCAmelCase  :				str       				=		True

       for i in range(0							,    lowerCamelCase__						):
              __UpperCAmelCase  :				Union[str, Any]       				=		0
              for j in range(0							,    cols - 1						):
                     if i == j:
                            continue
                     else:
                            total += table[i][j]

              if table[i][i] <= total:
                     raise ValueError("Coefficient matrix is not strictly diagonally dominant"						)

       return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
   import doctest

   doctest.testmod()






