
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 		UpperCAmelCase     (      a_		)						->      int:







				"""simple docstring"""



				__A       =					3_8_4
				if "tiny" in model_name:
								__A       =					[3, 3, 9, 3]
								__A       =					[9_6, 1_9_2, 3_8_4, 7_6_8]
				if "small" in model_name:
								__A       =					[3, 3, 2_7, 3]
								__A       =					[9_6, 1_9_2, 3_8_4, 7_6_8]
				if "base" in model_name:
								__A       =					[3, 3, 2_7, 3]
								__A       =					[1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
								__A       =					5_1_2
				if "large" in model_name:
								__A       =					[3, 3, 2_7, 3]
								__A       =					[1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
								__A       =					7_6_8
				if "xlarge" in model_name:
								__A       =					[3, 3, 2_7, 3]
								__A       =					[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]
								__A       =					1_0_2_4

				# set label information
				__A       =					1_5_0
				__A       =					"huggingface/label-files"
				__A       =					"ade20k-id2label.json"
				__A       =					json.load(open(hf_hub_download(a_						,  a_						,  repo_type="dataset"		)						,  "r"		)		)
				__A       =					{int(a_		): v for k, v in idalabel.items()}
				__A       =					{v: k for k, v in idalabel.items()}

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

				return config


def 		UpperCAmelCase     (      a_		)						->      List[str]:







				"""simple docstring"""



				__A       =					[]

				# 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 		UpperCAmelCase     (      a_						,  a_						,  a_		)						->      Optional[int]:







				"""simple docstring"""



				__A       =					dct.pop(a_		)
				__A       =					val


def 		UpperCAmelCase     (      a_						,  a_						,  a_		)						->      List[str]:







				"""simple docstring"""



				__A       =					{
				    "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",
				}
				__A       =					model_name_to_url[model_name]
				__A       =					torch.hub.load_state_dict_from_url(a_						,  map_location="cpu"		)["state_dict"]

				__A       =					get_upernet_config(a_		)
				__A       =					UperNetForSemanticSegmentation(a_		)
				model.eval()

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

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

				model.load_state_dict(a_		)

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

				__A       =					SegformerImageProcessor()
				__A       =					processor(a_						,  return_tensors="pt"		).pixel_values

				with torch.no_grad():
								__A       =					model(a_		)

				if model_name == "upernet-convnext-tiny":
								__A       =					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":
								__A       =					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":
								__A       =					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":
								__A       =					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":
								__A       =					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__":
					SCREAMING_SNAKE_CASE				: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.'
					)

					SCREAMING_SNAKE_CASE				:List[str]									=							parser.parse_args()
					convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''





from typing import TYPE_CHECKING

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


UpperCamelCase_		:     Optional[Any]          =		{
    '''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
    '''tokenization_roc_bert''': ['''RoCBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   pass

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_		:     Union[str, Any]          =		[
       '''ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''RoCBertForCausalLM''',
       '''RoCBertForMaskedLM''',
       '''RoCBertForMultipleChoice''',
       '''RoCBertForPreTraining''',
       '''RoCBertForQuestionAnswering''',
       '''RoCBertForSequenceClassification''',
       '''RoCBertForTokenClassification''',
       '''RoCBertLayer''',
       '''RoCBertModel''',
       '''RoCBertPreTrainedModel''',
       '''load_tf_weights_in_roc_bert''',
   ]

if TYPE_CHECKING:
   from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
   from .tokenization_roc_bert import RoCBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      raise OptionalDependencyNotAvailable()

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roc_bert import (
          ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          RoCBertForCausalLM,
          RoCBertForMaskedLM,
          RoCBertForMultipleChoice,
          RoCBertForPreTraining,
          RoCBertForQuestionAnswering,
          RoCBertForSequenceClassification,
          RoCBertForTokenClassification,
          RoCBertLayer,
          RoCBertModel,
          RoCBertPreTrainedModel,
          load_tf_weights_in_roc_bert,
      )


else:
   import sys

   UpperCamelCase_		:     int          =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


