
import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    snake_case__    =					384
    snake_case__    =					7
    if "tiny" in model_name:
        snake_case__    =					96
        snake_case__    =					(2, 2, 6, 2)
        snake_case__    =					(3, 6, 12, 24)
    elif "small" in model_name:
        snake_case__    =					96
        snake_case__    =					(2, 2, 18, 2)
        snake_case__    =					(3, 6, 12, 24)
    elif "base" in model_name:
        snake_case__    =					128
        snake_case__    =					(2, 2, 18, 2)
        snake_case__    =					(4, 8, 16, 32)
        snake_case__    =					12
        snake_case__    =					512
    elif "large" in model_name:
        snake_case__    =					192
        snake_case__    =					(2, 2, 18, 2)
        snake_case__    =					(6, 12, 24, 48)
        snake_case__    =					12
        snake_case__    =					768

    # set label information
    snake_case__    =					150
    snake_case__    =					"huggingface/label-files"
    snake_case__    =					"ade20k-id2label.json"
    snake_case__    =					json.load(open(hf_hub_download(__lowerCAmelCase     ,		__lowerCAmelCase     ,		repo_type="dataset"				)     ,		"r"				)				)
    snake_case__    =					{int(__lowerCAmelCase				): v for k, v in idalabel.items()}
    snake_case__    =					{v: k for k, v in idalabel.items()}

    snake_case__    =					SwinConfig(
        embed_dim=__lowerCAmelCase     ,		depths=__lowerCAmelCase     ,		num_heads=__lowerCAmelCase     ,		window_size=__lowerCAmelCase     ,		out_features=["stage1", "stage2", "stage3", "stage4"]     ,		)
    snake_case__    =					UperNetConfig(
        backbone_config=__lowerCAmelCase     ,		auxiliary_in_channels=__lowerCAmelCase     ,		num_labels=__lowerCAmelCase     ,		idalabel=__lowerCAmelCase     ,		labelaid=__lowerCAmelCase     ,		)

    return config





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    snake_case__    =					[]

    # fmt: off
    # stem
    rename_keys.append(("backbone.patch_embed.projection.weight", "backbone.embeddings.patch_embeddings.projection.weight")				)
    rename_keys.append(("backbone.patch_embed.projection.bias", "backbone.embeddings.patch_embeddings.projection.bias")				)
    rename_keys.append(("backbone.patch_embed.norm.weight", "backbone.embeddings.norm.weight")				)
    rename_keys.append(("backbone.patch_embed.norm.bias", "backbone.embeddings.norm.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}.blocks.{j}.norm1.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")				)
            rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm1.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")				)
            rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")				)
            rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")				)
            rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")				)
            rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")				)
            rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm2.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")				)
            rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm2.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")				)
            rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")				)
            rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")				)
            rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight""")				)
            rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias""")				)

        if i < 3:
            rename_keys.append((F"""backbone.stages.{i}.downsample.reduction.weight""", F"""backbone.encoder.layers.{i}.downsample.reduction.weight""")				)
            rename_keys.append((F"""backbone.stages.{i}.downsample.norm.weight""", F"""backbone.encoder.layers.{i}.downsample.norm.weight""")				)
            rename_keys.append((F"""backbone.stages.{i}.downsample.norm.bias""", F"""backbone.encoder.layers.{i}.downsample.norm.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 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    snake_case__    =					dct.pop(__lowerCAmelCase				)
    snake_case__    =					val





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase				):
    snake_case__    =					[int(backbone_config.embed_dim * 2**i				) for i in range(len(backbone_config.depths				)				)]
    for i in range(len(backbone_config.depths				)				):
        snake_case__    =					num_features[i]
        for j in range(backbone_config.depths[i]				):
            # fmt: off
            # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
            snake_case__    =					state_dict.pop(F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"""				)
            snake_case__    =					state_dict.pop(F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias"""				)
            # next, add query, keys and values (in that order) to the state dict
            snake_case__    =					in_proj_weight[:dim, :]
            snake_case__    =					in_proj_bias[: dim]
            snake_case__    =					in_proj_weight[
                dim : dim * 2, :
            ]
            snake_case__    =					in_proj_bias[
                dim : dim * 2
            ]
            snake_case__    =					in_proj_weight[
                -dim :, :
            ]
            snake_case__    =					in_proj_bias[-dim :]
            # fmt: on





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    snake_case__    ,       snake_case__    =					x.shape
    snake_case__    =					x.reshape(__lowerCAmelCase     ,		4     ,		in_channel // 4				)
    snake_case__    =					x[:, [0, 2, 1, 3], :].transpose(1     ,		2				).reshape(__lowerCAmelCase     ,		__lowerCAmelCase				)
    return x





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    snake_case__    ,       snake_case__    =					x.shape
    snake_case__    =					x.reshape(__lowerCAmelCase     ,		in_channel // 4     ,		4				)
    snake_case__    =					x[:, :, [0, 2, 1, 3]].transpose(1     ,		2				).reshape(__lowerCAmelCase     ,		__lowerCAmelCase				)

    return x





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    snake_case__    =					x.shape[0]
    snake_case__    =					x.reshape(4     ,		in_channel // 4				)
    snake_case__    =					x[[0, 2, 1, 3], :].transpose(0     ,		1				).reshape(__lowerCAmelCase				)
    return x





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    snake_case__    =					x.shape[0]
    snake_case__    =					x.reshape(in_channel // 4     ,		4				)
    snake_case__    =					x[:, [0, 2, 1, 3]].transpose(0     ,		1				).reshape(__lowerCAmelCase				)
    return x





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    snake_case__    =					{
        "upernet-swin-tiny": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth",
        "upernet-swin-small": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth",
        "upernet-swin-base": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth",
        "upernet-swin-large": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth",
    }
    snake_case__    =					model_name_to_url[model_name]
    snake_case__    =					torch.hub.load_state_dict_from_url(__lowerCAmelCase     ,		map_location="cpu"     ,		file_name=__lowerCAmelCase				)[
        "state_dict"
    ]

    for name, param in state_dict.items():
        print(__lowerCAmelCase     ,		param.shape				)

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

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

    # rename keys
    snake_case__    =					create_rename_keys(__lowerCAmelCase				)
    for src, dest in rename_keys:
        rename_key(__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				)
    read_in_q_k_v(__lowerCAmelCase     ,		config.backbone_config				)

    # fix downsample parameters
    for key, value in state_dict.items():
        if "downsample" in key:
            if "reduction" in key:
                snake_case__    =					reverse_correct_unfold_reduction_order(__lowerCAmelCase				)
            if "norm" in key:
                snake_case__    =					reverse_correct_unfold_norm_order(__lowerCAmelCase				)

    model.load_state_dict(__lowerCAmelCase				)

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

    snake_case__    =					SegformerImageProcessor()
    snake_case__    =					processor(__lowerCAmelCase     ,		return_tensors="pt"				).pixel_values

    with torch.no_grad():
        snake_case__    =					model(__lowerCAmelCase				)
        snake_case__    =					outputs.logits

    print(logits.shape				)
    print("First values of logits:"     ,		logits[0, 0, :3, :3]				)
    # assert values
    if model_name == "upernet-swin-tiny":
        snake_case__    =					torch.tensor(
            [[-7.5_958, -7.5_958, -7.4_302], [-7.5_958, -7.5_958, -7.4_302], [-7.4_797, -7.4_797, -7.3_068]]				)
    elif model_name == "upernet-swin-small":
        snake_case__    =					torch.tensor(
            [[-7.1_921, -7.1_921, -6.9_532], [-7.1_921, -7.1_921, -6.9_532], [-7.0_908, -7.0_908, -6.8_534]]				)
    elif model_name == "upernet-swin-base":
        snake_case__    =					torch.tensor(
            [[-6.5_851, -6.5_851, -6.4_330], [-6.5_851, -6.5_851, -6.4_330], [-6.4_763, -6.4_763, -6.3_254]]				)
    elif model_name == "upernet-swin-large":
        snake_case__    =					torch.tensor(
            [[-7.5_297, -7.5_297, -7.3_802], [-7.5_297, -7.5_297, -7.3_802], [-7.4_044, -7.4_044, -7.2_586]]				)
    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__":
     __magic_name__  							=	argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--model_name''',
         default='''upernet-swin-tiny''',
         type=str,
         choices=[F'''upernet-swin-{size}''' for size in ['''tiny''', '''small''', '''base''', '''large''']],
         help='''Name of the Swin + 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.'''
     )

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





from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
__magic_name__  							=	299_792_458

# Symbols
__magic_name__							,   __magic_name__							,   __magic_name__							,   __magic_name__  							=	symbols('''ct x y z''')





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    if velocity > c:
        raise ValueError("Speed must not exceed light speed 299,792,458 [m/s]!"				)
    elif velocity < 1:
        # Usually the speed should be much higher than 1 (c order of magnitude)
        raise ValueError("Speed must be greater than or equal to 1!"				)

    return velocity / c





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    return 1 / sqrt(1 - beta(__lowerCAmelCase				) ** 2				)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    return np.array(
        [
            [gamma(__lowerCAmelCase				), -gamma(__lowerCAmelCase				) * beta(__lowerCAmelCase				), 0, 0],
            [-gamma(__lowerCAmelCase				) * beta(__lowerCAmelCase				), gamma(__lowerCAmelCase				), 0, 0],
            [0, 0, 1, 0],
            [0, 0, 0, 1],
        ]				)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase = None				):
    # Ensure event is not empty
    if event is None:
        snake_case__    =					np.array([ct, x, y, z]				)  # Symbolic four vector
    else:
        event[0] *= c  # x0 is ct (speed of light * time)

    return transformation_matrix(__lowerCAmelCase				) @ event


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     # Example of symbolic vector:
     __magic_name__  							=	transform(29_979_245)
     print('''Example of four vector: ''')
     print(F'''ct\' = {four_vector[0]}''')
     print(F'''x\' = {four_vector[1]}''')
     print(F'''y\' = {four_vector[2]}''')
     print(F'''z\' = {four_vector[3]}''')

     # Substitute symbols with numerical values
     __magic_name__  							=	{ct: c, x: 1, y: 1, z: 1}
     __magic_name__  							=	[four_vector[i].subs(sub_dict) for i in range(4)]

     print(F'''\n{numerical_vector}''')



