


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 			_a  (				SCREAMING_SNAKE_CASE	:				Union[str, Any]     ):







	"""simple docstring"""



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

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

	UpperCamelCase__					:    List[str]          =   ConvNextConfig(
	    depths=SCREAMING_SNAKE_CASE    , hidden_sizes=SCREAMING_SNAKE_CASE    , out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']     )
	UpperCamelCase__					:    str          =   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 			_a  (				SCREAMING_SNAKE_CASE	:				List[str]     ):







	"""simple docstring"""



	UpperCamelCase__					:    List[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 			_a  (				SCREAMING_SNAKE_CASE	:				List[Any]    , SCREAMING_SNAKE_CASE	:				Dict    , SCREAMING_SNAKE_CASE	:				int     ):







	"""simple docstring"""



	UpperCamelCase__					:    Any          =   dct.pop(SCREAMING_SNAKE_CASE     )
	UpperCamelCase__					:    List[Any]          =   val

def 			_a  (				SCREAMING_SNAKE_CASE	:				Union[str, Any]    , SCREAMING_SNAKE_CASE	:				Any    , SCREAMING_SNAKE_CASE	:				Tuple     ):







	"""simple docstring"""



	UpperCamelCase__					:    List[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__					:    List[Any]          =   torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE    , map_location='''cpu'''     )['''state_dict''']

	UpperCamelCase__					:    int          =   get_upernet_config(SCREAMING_SNAKE_CASE     )
	UpperCamelCase__					:    Dict          =   UperNetForSemanticSegmentation(SCREAMING_SNAKE_CASE     )
	model.eval()

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

	# rename keys
	UpperCamelCase__					:    Tuple          =   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
	UpperCamelCase__					:    List[str]          =   '''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
	UpperCamelCase__					:    Any          =   Image.open(requests.get(SCREAMING_SNAKE_CASE    , stream=SCREAMING_SNAKE_CASE     ).raw     ).convert('''RGB'''     )

	UpperCamelCase__					:    Dict          =   SegformerImageProcessor()
	UpperCamelCase__					:    Optional[Any]          =   processor(SCREAMING_SNAKE_CASE    , return_tensors='''pt'''     ).pixel_values

	with torch.no_grad():
		UpperCamelCase__					:    Tuple          =   model(SCREAMING_SNAKE_CASE     )

	if model_name == "upernet-convnext-tiny":
		UpperCamelCase__					:    Optional[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__					:    Optional[int]          =   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__					:    List[Any]          =   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__					:    List[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       :       Dict						       =				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       :       int						       =				parser.parse_args()
							convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__UpperCamelCase       :       Optional[int]						       =				{
    "configuration_rag": ["RagConfig"],
    "retrieval_rag": ["RagRetriever"],
    "tokenization_rag": ["RagTokenizer"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase       :       Optional[int]						       =				[
           "RagModel",
           "RagPreTrainedModel",
           "RagSequenceForGeneration",
           "RagTokenForGeneration",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase       :       List[Any]						       =				[
           "TFRagModel",
           "TFRagPreTrainedModel",
           "TFRagSequenceForGeneration",
           "TFRagTokenForGeneration",
       ]


if TYPE_CHECKING:
       from .configuration_rag import RagConfig
       from .retrieval_rag import RagRetriever
       from .tokenization_rag import RagTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_rag import (
                  TFRagModel,
                  TFRagPreTrainedModel,
                  TFRagSequenceForGeneration,
                  TFRagTokenForGeneration,
              )

else:
       import sys

       __UpperCamelCase       :       List[Any]						       =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


