

'''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 	_A				(lowerCAmelCase__  :Dict		)   ->  str:




     '''simple docstring'''



     _a			      =							3_84
     if "tiny" in model_name:
          _a			      =							[3, 3, 9, 3]
          _a			      =							[96, 1_92, 3_84, 7_68]
     if "small" in model_name:
          _a			      =							[3, 3, 27, 3]
          _a			      =							[96, 1_92, 3_84, 7_68]
     if "base" in model_name:
          _a			      =							[3, 3, 27, 3]
          _a			      =							[1_28, 2_56, 5_12, 10_24]
          _a			      =							5_12
     if "large" in model_name:
          _a			      =							[3, 3, 27, 3]
          _a			      =							[1_92, 3_84, 7_68, 15_36]
          _a			      =							7_68
     if "xlarge" in model_name:
          _a			      =							[3, 3, 27, 3]
          _a			      =							[2_56, 5_12, 10_24, 20_48]
          _a			      =							10_24

     # set label information
     _a			      =							1_50
     _a			      =							'huggingface/label-files'
     _a			      =							'ade20k-id2label.json'
     _a			      =							json.load(open(hf_hub_download(lowerCAmelCase__				,						lowerCAmelCase__				,						repo_type='dataset'		)				,						'r'		)		)
     _a			      =							{int(lowerCAmelCase__		): v for k, v in idalabel.items()}
     _a			      =							{v: k for k, v in idalabel.items()}

     _a			      =							ConvNextConfig(
         depths=lowerCAmelCase__				,						hidden_sizes=lowerCAmelCase__				,						out_features=['stage1', 'stage2', 'stage3', 'stage4']		)
     _a			      =							UperNetConfig(
         backbone_config=lowerCAmelCase__				,						auxiliary_in_channels=lowerCAmelCase__				,						num_labels=lowerCAmelCase__				,						idalabel=lowerCAmelCase__				,						labelaid=lowerCAmelCase__				,						)

     return config






def 	_A				(lowerCAmelCase__  :str		)   ->  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 	_A				(lowerCAmelCase__  :int				,						lowerCAmelCase__  :Any				,						lowerCAmelCase__  :Union[str, Any]		)   ->  List[str]:




     '''simple docstring'''



     _a			      =							dct.pop(lowerCAmelCase__		)
     _a			      =							val






def 	_A				(lowerCAmelCase__  :int				,						lowerCAmelCase__  :Optional[int]				,						lowerCAmelCase__  :Optional[int]		)   ->  Any:




     '''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(lowerCAmelCase__				,						map_location='cpu'		)['state_dict']

     _a			      =							get_upernet_config(lowerCAmelCase__		)
     _a			      =							UperNetForSemanticSegmentation(lowerCAmelCase__		)
     model.eval()

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

     # rename keys
     _a			      =							create_rename_keys(lowerCAmelCase__		)
     for src, dest in rename_keys:
          rename_key(lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__		)

     model.load_state_dict(lowerCAmelCase__		)

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

     _a			      =							SegformerImageProcessor()
     _a			      =							processor(lowerCAmelCase__				,						return_tensors='pt'		).pixel_values

     with torch.no_grad():
          _a			      =							model(lowerCAmelCase__		)

     if model_name == "upernet-convnext-tiny":
          _a			      =							torch.tensor(
              [[-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.7_7_4_6, -8.7_7_4_6, -8.6_1_3_0]]		)
     elif model_name == "upernet-convnext-small":
          _a			      =							torch.tensor(
              [[-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.7_6_3_8, -8.7_6_3_8, -8.6_2_4_0]]		)
     elif model_name == "upernet-convnext-base":
          _a			      =							torch.tensor(
              [[-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.7_6_6_9, -8.7_6_6_9, -8.6_0_2_1]]		)
     elif model_name == "upernet-convnext-large":
          _a			      =							torch.tensor(
              [[-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_3_1_0, -8.6_3_1_0, -8.5_9_6_4]]		)
     elif model_name == "upernet-convnext-xlarge":
          _a			      =							torch.tensor(
              [[-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_3_7_9, -8.4_3_7_9, -8.3_4_1_2]]		)
     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__":
 a_					:    Union[str, 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."
 )

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





'''simple docstring'''






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
 from PIL import Image




class  a     (     _SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	"""dandelin/vilt-b32-finetuned-vqa"""
 _lowerCAmelCase 					=	(
     """This is a tool that answers a question about an image. It takes an input named `image` which should be the """
     """image containing the information, as well as a `question` which should be the question in English. It """
     """returns a text that is the answer to the question."""
 )
 _lowerCAmelCase 					=	"""image_qa"""
 _lowerCAmelCase 					=	AutoProcessor
 _lowerCAmelCase 					=	AutoModelForVisualQuestionAnswering

 _lowerCAmelCase 					=	["""image""", """text"""]
 _lowerCAmelCase 					=	["""text"""]
 def __init__( self      ,				*__magic_name__      ,				**__magic_name__						)					->							Tuple:
      requires_backends(self      ,				['vision']						)
      super().__init__(*__magic_name__      ,				**__magic_name__						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							Tuple:
      return self.pre_processor(__magic_name__      ,				__magic_name__      ,				return_tensors='pt'						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Any:
      with torch.no_grad():
           return self.model(**__magic_name__						).logits




 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Optional[int]:
      _a			      =							outputs.argmax(-1						).item()
      return self.model.config.idalabel[idx]

