



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

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

    snake_case_ :     Any				=	ConvNextConfig(
        depths=_a		,			hidden_sizes=_a		,			out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']    )
    snake_case_ :     Optional[int]				=	UperNetConfig(
        backbone_config=_a		,			auxiliary_in_channels=_a		,			num_labels=_a		,			idalabel=_a		,			labelaid=_a		,			)

    return config




def __lowercase			(							_a    ):
    snake_case_ :     Optional[int]				=	[]

    # 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		,			_a		,			_a    ):
    snake_case_ :     Optional[int]				=	dct.pop(_a    )
    snake_case_ :     Any				=	val




def __lowercase			(							_a		,			_a		,			_a    ):
    snake_case_ :     int				=	{
        '''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_ :     Optional[Any]				=	model_name_to_url[model_name]
    snake_case_ :     Optional[Any]				=	torch.hub.load_state_dict_from_url(_a		,			map_location='''cpu'''    )['''state_dict''']

    snake_case_ :     List[Any]				=	get_upernet_config(_a    )
    snake_case_ :     List[str]				=	UperNetForSemanticSegmentation(_a    )
    model.eval()

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

    # rename keys
    snake_case_ :     Dict				=	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_ :     Tuple				=	'''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
    snake_case_ :     int				=	Image.open(requests.get(_a		,			stream=_a    ).raw    ).convert('''RGB'''    )

    snake_case_ :     Union[str, Any]				=	SegformerImageProcessor()
    snake_case_ :     Any				=	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_ :     Tuple				=	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":
        snake_case_ :     Dict				=	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":
        snake_case_ :     Union[str, Any]				=	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":
        snake_case_ :     Tuple				=	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":
        snake_case_ :     Any				=	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]		,			_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__    :  Any							    =	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)










"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__    :  int							    =	logging.get_logger(__name__)

lowercase__    :  List[Any]							    =	{
    '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}




class        _UpperCAmelCase       (			lowerCAmelCase__):
 _lowerCAmelCase   :			List[Any]							=		"""gpt_neox"""

 def __init__(     self		:       List[str]  ,     lowercase_		:       str=50432  ,     lowercase_		:       List[Any]=6144  ,     lowercase_		:       List[Any]=44  ,     lowercase_		:       Union[str, Any]=64  ,     lowercase_		:       List[str]=24576  ,     lowercase_		:       List[Any]="gelu"  ,     lowercase_		:       str=0.25  ,     lowercase_		:       Optional[int]=10000  ,     lowercase_		:       Optional[int]=0.0  ,     lowercase_		:       Optional[int]=0.0  ,     lowercase_		:       int=0.1  ,     lowercase_		:       Tuple=2048  ,     lowercase_		:       Union[str, Any]=0.02  ,     lowercase_		:       List[str]=1E-5  ,     lowercase_		:       str=True  ,     lowercase_		:       str=0  ,     lowercase_		:       Union[str, Any]=2  ,     lowercase_		:       List[str]=False  ,     lowercase_		:       Optional[int]=True  ,     lowercase_		:       List[Any]=None  ,     **lowercase_		:       Optional[int]  ,     ):
     super().__init__(bos_token_id=lowercase_  ,     eos_token_id=lowercase_  ,     **lowercase_				)
     snake_case_ :     List[str]				=	vocab_size
     snake_case_ :     Optional[Any]				=	max_position_embeddings
     snake_case_ :     str				=	hidden_size
     snake_case_ :     Dict				=	num_hidden_layers
     snake_case_ :     Dict				=	num_attention_heads
     snake_case_ :     List[Any]				=	intermediate_size
     snake_case_ :     List[Any]				=	hidden_act
     snake_case_ :     str				=	rotary_pct
     snake_case_ :     Dict				=	rotary_emb_base
     snake_case_ :     Optional[int]				=	attention_dropout
     snake_case_ :     Tuple				=	hidden_dropout
     snake_case_ :     Tuple				=	classifier_dropout
     snake_case_ :     List[str]				=	initializer_range
     snake_case_ :     Union[str, Any]				=	layer_norm_eps
     snake_case_ :     Any				=	use_cache
     snake_case_ :     Optional[int]				=	tie_word_embeddings
     snake_case_ :     Any				=	use_parallel_residual
     snake_case_ :     Union[str, Any]				=	rope_scaling
     self._rope_scaling_validation()

     if self.hidden_size % self.num_attention_heads != 0:
         raise ValueError(
             '''The hidden size is not divisble by the number of attention heads! Make sure to update them!'''				)






 def 						_snake_case					(     self		:       Optional[int]				):
     if self.rope_scaling is None:
         return

     if not isinstance(self.rope_scaling  ,     lowercase_				) or len(self.rope_scaling				) != 2:
         raise ValueError(
             '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
             f"got {self.rope_scaling}"				)
     snake_case_ :     Any				=	self.rope_scaling.get('''type'''  ,     lowercase_				)
     snake_case_ :     Union[str, Any]				=	self.rope_scaling.get('''factor'''  ,     lowercase_				)
     if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
         raise ValueError(
             f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"				)
     if rope_scaling_factor is None or not isinstance(lowercase_  ,     lowercase_				) or rope_scaling_factor <= 1.0:
         raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"				)





