

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					(	__A :					List[Any]		)				->			Any:





    '''simple docstring'''




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

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

    snake_case							:		int        =		ConvNextConfig(
        depths=__A ,    hidden_sizes=__A ,    out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]		)
    snake_case							:		Tuple        =		UperNetConfig(
        backbone_config=__A ,    auxiliary_in_channels=__A ,    num_labels=__A ,    idalabel=__A ,    labelaid=__A ,    )

    return config







def       lowercase					(	__A :					List[str]		)				->			Any:





    '''simple docstring'''




    snake_case							:		Union[str, Any]        =		[]

    # 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					(	__A :					Union[str, Any] ,    __A :					List[Any] ,    __A :					Union[str, Any]		)				->			Optional[int]:





    '''simple docstring'''




    snake_case							:		Optional[Any]        =		dct.pop(__A		)
    snake_case							:		List[Any]        =		val







def       lowercase					(	__A :					str ,    __A :					Dict ,    __A :					List[str]		)				->			List[Any]:





    '''simple docstring'''




    snake_case							:		Tuple        =		{
        """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""",
    }
    snake_case							:		List[str]        =		model_name_to_url[model_name]
    snake_case							:		Optional[int]        =		torch.hub.load_state_dict_from_url(__A ,    map_location="""cpu"""		)["""state_dict"""]

    snake_case							:		Optional[int]        =		get_upernet_config(__A		)
    snake_case							:		Optional[Any]        =		UperNetForSemanticSegmentation(__A		)
    model.eval()

    # replace "bn" => "batch_norm"
    for key in state_dict.copy().keys():
        snake_case							:		Tuple        =		state_dict.pop(__A		)
        if "bn" in key:
            snake_case							:		Dict        =		key.replace("""bn""" ,    """batch_norm"""		)
        snake_case							:		Union[str, Any]        =		val

    # rename keys
    snake_case							:		str        =		create_rename_keys(__A		)
    for src, dest in rename_keys:
        rename_key(__A ,    __A ,    __A		)

    model.load_state_dict(__A		)

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

    snake_case							:		List[str]        =		SegformerImageProcessor()
    snake_case							:		Optional[int]        =		processor(__A ,    return_tensors="""pt"""		).pixel_values

    with torch.no_grad():
        snake_case							:		Optional[Any]        =		model(__A		)

    if model_name == "upernet-convnext-tiny":
        snake_case							:		Any        =		torch.tensor(
            [[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]]		)
    elif model_name == "upernet-convnext-small":
        snake_case							:		List[Any]        =		torch.tensor(
            [[-8.8_236, -8.8_236, -8.6_771], [-8.8_236, -8.8_236, -8.6_771], [-8.7_638, -8.7_638, -8.6_240]]		)
    elif model_name == "upernet-convnext-base":
        snake_case							:		Union[str, Any]        =		torch.tensor(
            [[-8.8_558, -8.8_558, -8.6_905], [-8.8_558, -8.8_558, -8.6_905], [-8.7_669, -8.7_669, -8.6_021]]		)
    elif model_name == "upernet-convnext-large":
        snake_case							:		Optional[Any]        =		torch.tensor(
            [[-8.6_660, -8.6_660, -8.6_210], [-8.6_660, -8.6_660, -8.6_210], [-8.6_310, -8.6_310, -8.5_964]]		)
    elif model_name == "upernet-convnext-xlarge":
        snake_case							:		Tuple        =		torch.tensor(
            [[-8.4_980, -8.4_980, -8.3_977], [-8.4_980, -8.4_980, -8.3_977], [-8.4_379, -8.4_379, -8.3_412]]		)
    print("""Logits:""" ,    outputs.logits[0, 0, :3, :3]		)
    assert torch.allclose(outputs.logits[0, 0, :3, :3] ,    __A ,    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(__A		)
        print(f"""Saving processor to {pytorch_dump_folder_path}"""		)
        processor.save_pretrained(__A		)

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









from __future__ import annotations

from decimal import Decimal

from numpy import array

def lowercase    (  SCREAMING_SNAKE_CASE )			->							list[list[float]]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						Decimal

			# Check if the provided matrix has 2 rows and 2 columns
			# since this implementation only works for 2x2 matrices
			if len(SCREAMING_SNAKE_CASE ) == 2 and len(matrix[0] ) == 2 and len(matrix[1] ) == 2:
						# Calculate the determinant of the matrix
						SCREAMING_SNAKE_CASE_								=						float(
						    d(matrix[0][0] ) * d(matrix[1][1] ) - d(matrix[1][0] ) * d(matrix[0][1] ) )
						if determinant == 0:
									raise ValueError('This matrix has no inverse.' )

						# Creates a copy of the matrix with swapped positions of the elements
						SCREAMING_SNAKE_CASE_								=						[[0.0, 0.0], [0.0, 0.0]]
						SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						matrix[1][1], matrix[0][0]
						SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						-matrix[1][0], -matrix[0][1]

						# Calculate the inverse of the matrix
						return [
						    [(float(d(SCREAMING_SNAKE_CASE ) ) / determinant) or 0.0 for n in row] for row in swapped_matrix
						]
			elif (
			    len(SCREAMING_SNAKE_CASE ) == 3
			    and len(matrix[0] ) == 3
			    and len(matrix[1] ) == 3
			    and len(matrix[2] ) == 3
			):
						# Calculate the determinant of the matrix using Sarrus rule
						SCREAMING_SNAKE_CASE_								=						float(
						    (
						        (d(matrix[0][0] ) * d(matrix[1][1] ) * d(matrix[2][2] ))
						        + (d(matrix[0][1] ) * d(matrix[1][2] ) * d(matrix[2][0] ))
						        + (d(matrix[0][2] ) * d(matrix[1][0] ) * d(matrix[2][1] ))
						    )
						    - (
						        (d(matrix[0][2] ) * d(matrix[1][1] ) * d(matrix[2][0] ))
						        + (d(matrix[0][1] ) * d(matrix[1][0] ) * d(matrix[2][2] ))
						        + (d(matrix[0][0] ) * d(matrix[1][2] ) * d(matrix[2][1] ))
						    ) )
						if determinant == 0:
									raise ValueError('This matrix has no inverse.' )

						# Creating cofactor matrix
						SCREAMING_SNAKE_CASE_								=						[
						    [d(0.0 ), d(0.0 ), d(0.0 )],
						    [d(0.0 ), d(0.0 ), d(0.0 )],
						    [d(0.0 ), d(0.0 ), d(0.0 )],
						]
						SCREAMING_SNAKE_CASE_								=						(d(matrix[1][1] ) * d(matrix[2][2] )) - (
						    d(matrix[1][2] ) * d(matrix[2][1] )
						)
						SCREAMING_SNAKE_CASE_								=						-(
						    (d(matrix[1][0] ) * d(matrix[2][2] )) - (d(matrix[1][2] ) * d(matrix[2][0] ))
						)
						SCREAMING_SNAKE_CASE_								=						(d(matrix[1][0] ) * d(matrix[2][1] )) - (
						    d(matrix[1][1] ) * d(matrix[2][0] )
						)
						SCREAMING_SNAKE_CASE_								=						-(
						    (d(matrix[0][1] ) * d(matrix[2][2] )) - (d(matrix[0][2] ) * d(matrix[2][1] ))
						)
						SCREAMING_SNAKE_CASE_								=						(d(matrix[0][0] ) * d(matrix[2][2] )) - (
						    d(matrix[0][2] ) * d(matrix[2][0] )
						)
						SCREAMING_SNAKE_CASE_								=						-(
						    (d(matrix[0][0] ) * d(matrix[2][1] )) - (d(matrix[0][1] ) * d(matrix[2][0] ))
						)
						SCREAMING_SNAKE_CASE_								=						(d(matrix[0][1] ) * d(matrix[1][2] )) - (
						    d(matrix[0][2] ) * d(matrix[1][1] )
						)
						SCREAMING_SNAKE_CASE_								=						-(
						    (d(matrix[0][0] ) * d(matrix[1][2] )) - (d(matrix[0][2] ) * d(matrix[1][0] ))
						)
						SCREAMING_SNAKE_CASE_								=						(d(matrix[0][0] ) * d(matrix[1][1] )) - (
						    d(matrix[0][1] ) * d(matrix[1][0] )
						)

						# Transpose the cofactor matrix (Adjoint matrix)
						SCREAMING_SNAKE_CASE_								=						array(SCREAMING_SNAKE_CASE )
						for i in range(3 ):
									for j in range(3 ):
												SCREAMING_SNAKE_CASE_								=						cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
						SCREAMING_SNAKE_CASE_								=						array(SCREAMING_SNAKE_CASE )
						for i in range(3 ):
									for j in range(3 ):
												inverse_matrix[i][j] /= d(SCREAMING_SNAKE_CASE )

        # Calculate the inverse of the matrix
						return [[float(d(SCREAMING_SNAKE_CASE ) ) or 0.0 for n in row] for row in inverse_matrix]
			raise ValueError('Please provide a matrix of size 2x2 or 3x3.' )
