






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

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

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

							return config






def    __magic_name__    (	SCREAMING_SNAKE_CASE				)     ->    int:
							_lowercase							:   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    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    Optional[int]:
							_lowercase							:   Any 							=   dct.pop(SCREAMING_SNAKE_CASE				)
							_lowercase							:   Any 							=   val






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    Any:
							_lowercase							:   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',
							}
							_lowercase							:   Optional[int] 							=   model_name_to_url[model_name]
							_lowercase							:   str 							=   torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE     ,     map_location='cpu'				)['state_dict']

							_lowercase							:   Optional[int] 							=   get_upernet_config(SCREAMING_SNAKE_CASE				)
							_lowercase							:   Tuple 							=   UperNetForSemanticSegmentation(SCREAMING_SNAKE_CASE				)
							model.eval()

							# replace "bn" => "batch_norm"
							for key in state_dict.copy().keys():
														_lowercase							:   List[Any] 							=   state_dict.pop(SCREAMING_SNAKE_CASE				)
														if "bn" in key:
																					_lowercase							:   Any 							=   key.replace('bn'     ,     'batch_norm'				)
														_lowercase							:   Any 							=   val

							# rename keys
							_lowercase							:   int 							=   create_rename_keys(SCREAMING_SNAKE_CASE				)
							for src, dest in rename_keys:
														rename_key(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)

							model.load_state_dict(SCREAMING_SNAKE_CASE				)

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

							_lowercase							:   Tuple 							=   SegformerImageProcessor()
							_lowercase							:   Tuple 							=   processor(SCREAMING_SNAKE_CASE     ,     return_tensors='pt'				).pixel_values

							with torch.no_grad():
														_lowercase							:   Dict 							=   model(SCREAMING_SNAKE_CASE				)

							if model_name == "upernet-convnext-tiny":
														_lowercase							:   Dict 							=   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":
														_lowercase							:   Union[str, 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":
														_lowercase							:   Dict 							=   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":
														_lowercase							:   Optional[int] 							=   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":
														_lowercase							:   str 							=   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]     ,     SCREAMING_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(SCREAMING_SNAKE_CASE				)
														print(F"""Saving processor to {pytorch_dump_folder_path}"""				)
														processor.save_pretrained(SCREAMING_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__":
							UpperCamelCase								=		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."
							)

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








import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
     import torch
     from torch import nn

     from transformers import (
         Adafactor,
         AdamW,
         get_constant_schedule,
         get_constant_schedule_with_warmup,
         get_cosine_schedule_with_warmup,
         get_cosine_with_hard_restarts_schedule_with_warmup,
         get_inverse_sqrt_schedule,
         get_linear_schedule_with_warmup,
         get_polynomial_decay_schedule_with_warmup,
     )
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						Union[str, Any]=10		):




      """simple docstring"""





      a		:Tuple									=			[]
      for _ in range(UpperCAmelCase_		):
            lrs.append(scheduler.get_lr()[0]		)
            scheduler.step()
      return lrs
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						Tuple=10		):




      """simple docstring"""





      a		:List[Any]									=			[]
      for step in range(UpperCAmelCase_		):
            lrs.append(scheduler.get_lr()[0]		)
            scheduler.step()
            if step == num_steps // 2:
                  with tempfile.TemporaryDirectory() as tmpdirname:
                        a		:Union[str, Any]									=			os.path.join(UpperCAmelCase_					,    '''schedule.bin'''		)
                        torch.save(scheduler.state_dict()					,    UpperCAmelCase_		)

                        a		:int									=			torch.load(UpperCAmelCase_		)
                        scheduler.load_state_dict(UpperCAmelCase_		)
      return lrs







@require_torch
class 						_snake_case    ( unittest.TestCase				):




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
       self.assertEqual(len(_lowerCamelCase       )   ,    len(_lowerCamelCase       )       )
       for a, b in zip(_lowerCamelCase   ,    _lowerCamelCase       ):
             self.assertAlmostEqual(_lowerCamelCase   ,    _lowerCamelCase   ,    delta=_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[str]									=			torch.tensor([0.1, -0.2, -0.1]   ,    requires_grad=_lowerCamelCase       )
       a		:int									=			torch.tensor([0.4, 0.2, -0.5]       )
       a		:int									=			nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       a		:Tuple									=			AdamW(params=[w]   ,    lr=2e-1   ,    weight_decay=0.0       )
       for _ in range(100       ):
             a		:int									=			criterion(_lowerCamelCase   ,    _lowerCamelCase       )
             loss.backward()
             optimizer.step()
             w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
             w.grad.zero_()
       self.assertListAlmostEqual(w.tolist()   ,    [0.4, 0.2, -0.5]   ,    tol=1e-2       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[Any]									=			torch.tensor([0.1, -0.2, -0.1]   ,    requires_grad=_lowerCamelCase       )
       a		:Union[str, Any]									=			torch.tensor([0.4, 0.2, -0.5]       )
       a		:List[Any]									=			nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       a		:Any									=			Adafactor(
           params=[w]   ,    lr=1e-2   ,    eps=(1e-30, 1e-3)   ,    clip_threshold=1.0   ,    decay_rate=-0.8   ,    betaa=_lowerCamelCase   ,    weight_decay=0.0   ,    relative_step=_lowerCamelCase   ,    scale_parameter=_lowerCamelCase   ,    warmup_init=_lowerCamelCase   ,    )
       for _ in range(1000       ):
             a		:str									=			criterion(_lowerCamelCase   ,    _lowerCamelCase       )
             loss.backward()
             optimizer.step()
             w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
             w.grad.zero_()
       self.assertListAlmostEqual(w.tolist()   ,    [0.4, 0.2, -0.5]   ,    tol=1e-2       )







@require_torch
class 						_snake_case    ( unittest.TestCase				):
 SCREAMING_SNAKE_CASE__ =			nn.Linear(50		,       50				) if is_torch_available() else None
 SCREAMING_SNAKE_CASE__ =			AdamW(m.parameters()		,       lr=10.0				) if is_torch_available() else None
 SCREAMING_SNAKE_CASE__ =			10




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase=None       ):
       self.assertEqual(len(_lowerCamelCase       )   ,    len(_lowerCamelCase       )       )
       for a, b in zip(_lowerCamelCase   ,    _lowerCamelCase       ):
             self.assertAlmostEqual(_lowerCamelCase   ,    _lowerCamelCase   ,    delta=_lowerCamelCase   ,    msg=_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Union[str, Any]									=			{'''num_warmup_steps''': 2, '''num_training_steps''': 10}
       # schedulers doct format
       # function: (sched_args_dict, expected_learning_rates)
       a		:Any									=			{
           get_constant_schedule: ({}, [10.0] * self.num_steps),
           get_constant_schedule_with_warmup: (
               {'''num_warmup_steps''': 4},
               [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
           ),
           get_linear_schedule_with_warmup: (
               {**common_kwargs},
               [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
           ),
           get_cosine_schedule_with_warmup: (
               {**common_kwargs},
               [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
           ),
           get_cosine_with_hard_restarts_schedule_with_warmup: (
               {**common_kwargs, '''num_cycles''': 2},
               [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
           ),
           get_polynomial_decay_schedule_with_warmup: (
               {**common_kwargs, '''power''': 2.0, '''lr_end''': 1e-7},
               [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
           ),
           get_inverse_sqrt_schedule: (
               {'''num_warmup_steps''': 2},
               [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
           ),
       }

       for scheduler_func, data in scheds.items():
             a				,							a		:List[str]									=			data

             a		:str									=			scheduler_func(self.optimizer   ,    **_lowerCamelCase       )
             self.assertEqual(len([scheduler.get_lr()[0]]       )   ,    1       )
             a		:Optional[Any]									=			unwrap_schedule(_lowerCamelCase   ,    self.num_steps       )
             self.assertListAlmostEqual(
                 _lowerCamelCase   ,    _lowerCamelCase   ,    tol=1e-2   ,    msg=F'''failed for {scheduler_func} in normal scheduler'''   ,    )

             a		:List[Any]									=			scheduler_func(self.optimizer   ,    **_lowerCamelCase       )
             if scheduler_func.__name__ != "get_constant_schedule":
                   LambdaScheduleWrapper.wrap_scheduler(_lowerCamelCase       )  # wrap to test picklability of the schedule
             a		:Dict									=			unwrap_and_save_reload_schedule(_lowerCamelCase   ,    self.num_steps       )
             self.assertListEqual(_lowerCamelCase   ,    _lowerCamelCase   ,    msg=F'''failed for {scheduler_func} in save and reload'''       )







class 						_snake_case    :




 def __init__(						self   ,    _lowerCamelCase       ):
       a		:Dict									=			fn




 def __call__(						self   ,    *_lowerCamelCase   ,    **_lowerCamelCase       ):
       return self.fn(*_lowerCamelCase   ,    **_lowerCamelCase       )




 @classmethod
 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase       ):
       a		:str									=			list(map(self   ,    scheduler.lr_lambdas       )       )





