



"""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 		__snake_case		( SCREAMING_SNAKE_CASE__      :		List[Any]							)    ->  int:





     '''simple docstring'''

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

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

     _UpperCAmelCase     :      Any														=       ConvNextConfig(
         depths=SCREAMING_SNAKE_CASE__		,    hidden_sizes=SCREAMING_SNAKE_CASE__		,    out_features=["stage1", "stage2", "stage3", "stage4"]							)
     _UpperCAmelCase     :      Dict														=       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 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[Any]							)    ->  Dict:





     '''simple docstring'''

     _UpperCAmelCase     :      Tuple														=       []

     # 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 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int		,    SCREAMING_SNAKE_CASE__      :		int		,    SCREAMING_SNAKE_CASE__      :		Any							)    ->  Tuple:





     '''simple docstring'''

     _UpperCAmelCase     :      Dict														=       dct.pop(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      Any														=       val

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[Any]		,    SCREAMING_SNAKE_CASE__      :		int		,    SCREAMING_SNAKE_CASE__      :		Union[str, Any]							)    ->  Dict:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[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",
     }
     _UpperCAmelCase     :      List[Any]														=       model_name_to_url[model_name]
     _UpperCAmelCase     :      Optional[Any]														=       torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__		,    map_location="cpu"							)["state_dict"]

     _UpperCAmelCase     :      str														=       get_upernet_config(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      Tuple														=       UperNetForSemanticSegmentation(SCREAMING_SNAKE_CASE__							)
     model.eval()

     # replace "bn" => "batch_norm"
     for key in state_dict.copy().keys():
          _UpperCAmelCase     :      Optional[int]														=       state_dict.pop(SCREAMING_SNAKE_CASE__							)
          if "bn" in key:
               _UpperCAmelCase     :      Optional[Any]														=       key.replace("bn"		,    "batch_norm"							)
          _UpperCAmelCase     :      str														=       val

     # rename keys
     _UpperCAmelCase     :      str														=       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     :      Optional[int]														=       "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     :      List[str]														=       SegformerImageProcessor()
     _UpperCAmelCase     :      List[Any]														=       processor(SCREAMING_SNAKE_CASE__		,    return_tensors="pt"							).pixel_values

     with torch.no_grad():
          _UpperCAmelCase     :      List[Any]														=       model(SCREAMING_SNAKE_CASE__							)

     if model_name == "upernet-convnext-tiny":
          _UpperCAmelCase     :      Tuple														=       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":
          _UpperCAmelCase     :      Dict														=       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":
          _UpperCAmelCase     :      int														=       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":
          _UpperCAmelCase     :      List[str]														=       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":
          _UpperCAmelCase     :      Optional[Any]														=       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]		,    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__":
 _lowerCAmelCase							:   List[str]				=				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."
 )

 _lowerCAmelCase							:   Optional[int]				=				parser.parse_args()
 convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""




import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Any		,    SCREAMING_SNAKE_CASE__      :		Union[str, Any]		,    SCREAMING_SNAKE_CASE__      :		int		,    SCREAMING_SNAKE_CASE__      :		Optional[int]		,    SCREAMING_SNAKE_CASE__      :		Any							)    ->  Any:





     '''simple docstring'''

     _UpperCAmelCase     :      Any														=       TapasConfig.from_json_file(SCREAMING_SNAKE_CASE__							)
     # set absolute/relative position embeddings parameter
     _UpperCAmelCase     :      Optional[Any]														=       reset_position_index_per_cell

     # set remaining parameters of TapasConfig as well as the model based on the task
     if task == "SQA":
          _UpperCAmelCase     :      Optional[int]														=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE__							)
     elif task == "WTQ":
          # run_task_main.py hparams
          _UpperCAmelCase     :      List[str]														=       4
          _UpperCAmelCase     :      int														=       True
          # hparam_utils.py hparams
          _UpperCAmelCase     :      Optional[int]														=       0.664_694
          _UpperCAmelCase     :      Any														=       0.207_951
          _UpperCAmelCase     :      Any														=       0.121_194
          _UpperCAmelCase     :      List[Any]														=       True
          _UpperCAmelCase     :      Dict														=       True
          _UpperCAmelCase     :      Tuple														=       False
          _UpperCAmelCase     :      List[Any]														=       0.0_352_513

          _UpperCAmelCase     :      Union[str, Any]														=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE__							)
     elif task == "WIKISQL_SUPERVISED":
          # run_task_main.py hparams
          _UpperCAmelCase     :      List[str]														=       4
          _UpperCAmelCase     :      Optional[int]														=       False
          # hparam_utils.py hparams
          _UpperCAmelCase     :      List[Any]														=       36.4_519
          _UpperCAmelCase     :      Union[str, Any]														=       0.903_421
          _UpperCAmelCase     :      List[Any]														=       222.088
          _UpperCAmelCase     :      Optional[Any]														=       True
          _UpperCAmelCase     :      str														=       True
          _UpperCAmelCase     :      Union[str, Any]														=       True
          _UpperCAmelCase     :      str														=       0.763_141

          _UpperCAmelCase     :      Optional[Any]														=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE__							)
     elif task == "TABFACT":
          _UpperCAmelCase     :      int														=       TapasForSequenceClassification(config=SCREAMING_SNAKE_CASE__							)
     elif task == "MLM":
          _UpperCAmelCase     :      List[str]														=       TapasForMaskedLM(config=SCREAMING_SNAKE_CASE__							)
     elif task == "INTERMEDIATE_PRETRAINING":
          _UpperCAmelCase     :      Optional[Any]														=       TapasModel(config=SCREAMING_SNAKE_CASE__							)
     else:
          raise ValueError(f'Task {task} not supported.'							)

     print(f'Building PyTorch model from configuration: {config}'							)
     # Load weights from tf checkpoint
     load_tf_weights_in_tapas(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)

     # Save pytorch-model (weights and configuration)
     print(f'Save PyTorch model to {pytorch_dump_path}'							)
     model.save_pretrained(SCREAMING_SNAKE_CASE__							)

     # Save tokenizer files
     print(f'Save tokenizer files to {pytorch_dump_path}'							)
     _UpperCAmelCase     :      Optional[int]														=       TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"		,    model_max_length=512							)
     tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__							)

     print("Used relative position embeddings:"		,    model.config.reset_position_index_per_cell							)


if __name__ == "__main__":
 _lowerCAmelCase							:   Union[str, Any]				=				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--task", default="SQA", type=str, help="Model task for which to convert a checkpoint. Defaults to SQA."
 )
 parser.add_argument(
     "--reset_position_index_per_cell",
     default=False,
     action="store_true",
     help="Whether to use relative position embeddings or not. Defaults to True.",
 )
 parser.add_argument(
     "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
 )
 parser.add_argument(
     "--tapas_config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained TAPAS model. \n"
         "This specifies the model architecture."
     ),
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 _lowerCAmelCase							:   Tuple				=				parser.parse_args()
 convert_tf_checkpoint_to_pytorch(
     args.task,
     args.reset_position_index_per_cell,
     args.tf_checkpoint_path,
     args.tapas_config_file,
     args.pytorch_dump_path,
 )
