





'''simple docstring'''






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
A__							:  Optional[Any]    =logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')

require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt''')

A__							:  Any    =list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
A__							:  Any    =tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class 					UpperCAmelCase	:
      _lowercase:     Optional[int]			=  field(
          default='''cifar10'''				,						metadata={'''help''': '''Name of a dataset from the datasets package'''}  )
      _lowercase:     List[str]			=  field(
          default=a__				,						metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}  )
      _lowercase:     Any			=  field(
          default=a__				,						metadata={'''help''': '''The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'''}				,						)
      _lowercase:     Dict			=  field(default=a__				,						metadata={'''help''': '''A folder containing the training data.'''}  )
      _lowercase:     Union[str, Any]			=  field(default=a__				,						metadata={'''help''': '''A folder containing the validation data.'''}  )
      _lowercase:     Tuple			=  field(
          default=0.15				,						metadata={'''help''': '''Percent to split off of train for validation.'''}  )
      _lowercase:     Dict			=  field(default=32				,						metadata={'''help''': '''The size of the square patches to use for masking.'''}  )
      _lowercase:     Union[str, Any]			=  field(
          default=0.6				,						metadata={'''help''': '''Percentage of patches to mask.'''}				,						)
      _lowercase:     str			=  field(
          default=a__				,						metadata={
              '''help''': (
                  '''For debugging purposes or quicker training, truncate the number of training examples to this '''
                  '''value if set.'''
              )
          }				,						)
      _lowercase:     Optional[Any]			=  field(
          default=a__				,						metadata={
              '''help''': (
                  '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
                  '''value if set.'''
              )
          }				,						)




      def 							lowercase__       (     self     :					List[Any]    )							-> List[Any]:
            _lowerCAmelCase					=				{}
            if self.train_dir is not None:
                  _lowerCAmelCase					=				self.train_dir
            if self.validation_dir is not None:
                  _lowerCAmelCase					=				self.validation_dir
            _lowerCAmelCase					=				data_files if data_files else None




@dataclass
class 					UpperCAmelCase	:
      _lowercase:     List[Any]			=  field(
          default=a__				,						metadata={
              '''help''': (
                  '''The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a '''
                  '''checkpoint identifier on the hub. '''
                  '''Don\'t set if you want to train a model from scratch.'''
              )
          }				,						)
      _lowercase:     str			=  field(
          default=a__				,						metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(a__  )}				,						)
      _lowercase:     Optional[int]			=  field(
          default=a__				,						metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}  )
      _lowercase:     Union[str, Any]			=  field(
          default=a__				,						metadata={
              '''help''': (
                  '''Override some existing default config settings when a model is trained from scratch. Example: '''
                  '''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
              )
          }				,						)
      _lowercase:     Tuple			=  field(
          default=a__				,						metadata={'''help''': '''Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'''}				,						)
      _lowercase:     Optional[int]			=  field(
          default='''main'''				,						metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}				,						)
      _lowercase:     str			=  field(default=a__				,						metadata={'''help''': '''Name or path of preprocessor config.'''}  )
      _lowercase:     Any			=  field(
          default=a__				,						metadata={
              '''help''': (
                  '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
                  '''with private models).'''
              )
          }				,						)
      _lowercase:     Tuple			=  field(
          default=a__				,						metadata={
              '''help''': (
                  '''The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'''
              )
          }				,						)
      _lowercase:     Optional[Any]			=  field(
          default=a__				,						metadata={
              '''help''': (
                  '''The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'''
              )
          }				,						)
      _lowercase:     Tuple			=  field(
          default=a__				,						metadata={'''help''': '''Stride to use for the encoder.'''}				,						)




class 					UpperCAmelCase	:




      def __init__(     self     :					Dict     ,     __snake_case     :					int=1_92     ,     __snake_case     :					str=32     ,     __snake_case     :					int=4     ,     __snake_case     :					List[str]=0.6    )							-> List[str]:
            _lowerCAmelCase					=				input_size
            _lowerCAmelCase					=				mask_patch_size
            _lowerCAmelCase					=				model_patch_size
            _lowerCAmelCase					=				mask_ratio

            if self.input_size % self.mask_patch_size != 0:
                  raise ValueError("""Input size must be divisible by mask patch size"""    )
            if self.mask_patch_size % self.model_patch_size != 0:
                  raise ValueError("""Mask patch size must be divisible by model patch size"""    )

            _lowerCAmelCase					=				self.input_size // self.mask_patch_size
            _lowerCAmelCase					=				self.mask_patch_size // self.model_patch_size

            _lowerCAmelCase					=				self.rand_size**2
            _lowerCAmelCase					=				int(np.ceil(self.token_count * self.mask_ratio    )    )




      def __call__(     self     :					Dict    )							-> Dict:
            _lowerCAmelCase					=				np.random.permutation(self.token_count    )[: self.mask_count]
            _lowerCAmelCase					=				np.zeros(self.token_count     ,     dtype=__a    )
            _lowerCAmelCase					=				1

            _lowerCAmelCase					=				mask.reshape((self.rand_size, self.rand_size)    )
            _lowerCAmelCase					=				mask.repeat(self.scale     ,     axis=0    ).repeat(self.scale     ,     axis=1    )

            return torch.tensor(mask.flatten()    )




def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				torch.stack([example["""pixel_values"""] for example in examples]    )
      _lowerCAmelCase					=				torch.stack([example["""mask"""] for example in examples]    )
      return {"pixel_values": pixel_values, "bool_masked_pos": mask}
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
      if len(sys.argv    ) == 2 and sys.argv[1].endswith(""".json"""    ):
            # If we pass only one argument to the script and it's the path to a json file,
            # let's parse it to get our arguments.
            _lowerCAmelCase					=				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
      else:
            _lowerCAmelCase					=				parser.parse_args_into_dataclasses()

      # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
      # information sent is the one passed as arguments along with your Python/PyTorch versions.
      send_example_telemetry("""run_mim"""				,      A__				,      A__    )

      # Setup logging
      logging.basicConfig(
          format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""				,      datefmt="""%m/%d/%Y %H:%M:%S"""				,      handlers=[logging.StreamHandler(sys.stdout    )]				,      )

      if training_args.should_log:
            # The default of training_args.log_level is passive, so we set log level at info here to have that default.
            transformers.utils.logging.set_verbosity_info()

      _lowerCAmelCase					=				training_args.get_process_log_level()
      logger.setLevel(A__    )
      transformers.utils.logging.set_verbosity(A__    )
      transformers.utils.logging.enable_default_handler()
      transformers.utils.logging.enable_explicit_format()

      # Log on each process the small summary:
      logger.warning(
          f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
          + f"distributed training: {bool(training_args.local_rank != -1    )}, 16-bits training: {training_args.fpaa}"    )
      logger.info(f"Training/evaluation parameters {training_args}"    )

      # Detecting last checkpoint.
      _lowerCAmelCase					=				None
      if os.path.isdir(training_args.output_dir    ) and training_args.do_train and not training_args.overwrite_output_dir:
            _lowerCAmelCase					=				get_last_checkpoint(training_args.output_dir    )
            if last_checkpoint is None and len(os.listdir(training_args.output_dir    )    ) > 0:
                  raise ValueError(
                      f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                      """Use --overwrite_output_dir to overcome."""    )
            elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
                  logger.info(
                      f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                      """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""    )

    # Initialize our dataset.
      _lowerCAmelCase					=				load_dataset(
          data_args.dataset_name				,      data_args.dataset_config_name				,      data_files=data_args.data_files				,      cache_dir=model_args.cache_dir				,      use_auth_token=True if model_args.use_auth_token else None				,      )

      # If we don't have a validation split, split off a percentage of train as validation.
      _lowerCAmelCase					=				None if """validation""" in ds.keys() else data_args.train_val_split
      if isinstance(data_args.train_val_split				,      A__    ) and data_args.train_val_split > 0.0:
            _lowerCAmelCase					=				ds["""train"""].train_test_split(data_args.train_val_split    )
            _lowerCAmelCase					=				split["""train"""]
            _lowerCAmelCase					=				split["""test"""]

      # Create config
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.
      _lowerCAmelCase					=				{
          """cache_dir""": model_args.cache_dir,
          """revision""": model_args.model_revision,
          """use_auth_token""": True if model_args.use_auth_token else None,
      }
      if model_args.config_name_or_path:
            _lowerCAmelCase					=				AutoConfig.from_pretrained(model_args.config_name_or_path				,      **A__    )
      elif model_args.model_name_or_path:
            _lowerCAmelCase					=				AutoConfig.from_pretrained(model_args.model_name_or_path				,      **A__    )
      else:
            _lowerCAmelCase					=				CONFIG_MAPPING[model_args.model_type]()
            logger.warning("""You are instantiating a new config instance from scratch."""    )
            if model_args.config_overrides is not None:
                  logger.info(f"Overriding config: {model_args.config_overrides}"    )
                  config.update_from_string(model_args.config_overrides    )
                  logger.info(f"New config: {config}"    )

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
      if hasattr(A__				,      """decoder_type"""    ):
            _lowerCAmelCase					=				"""simmim"""

      # adapt config
      _lowerCAmelCase					=				model_args.image_size if model_args.image_size is not None else config.image_size
      _lowerCAmelCase					=				model_args.patch_size if model_args.patch_size is not None else config.patch_size
      _lowerCAmelCase					=				(
          model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
      )

      config.update(
          {
              """image_size""": model_args.image_size,
              """patch_size""": model_args.patch_size,
              """encoder_stride""": model_args.encoder_stride,
          }    )

      # create image processor
      if model_args.image_processor_name:
            _lowerCAmelCase					=				AutoImageProcessor.from_pretrained(model_args.image_processor_name				,      **A__    )
      elif model_args.model_name_or_path:
            _lowerCAmelCase					=				AutoImageProcessor.from_pretrained(model_args.model_name_or_path				,      **A__    )
      else:
            _lowerCAmelCase					=				{
                conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
            }
            _lowerCAmelCase					=				IMAGE_PROCESSOR_TYPES[model_args.model_type]()

      # create model
      if model_args.model_name_or_path:
            _lowerCAmelCase					=				AutoModelForMaskedImageModeling.from_pretrained(
                model_args.model_name_or_path				,      from_tf=bool(""".ckpt""" in model_args.model_name_or_path    )				,      config=A__				,      cache_dir=model_args.cache_dir				,      revision=model_args.model_revision				,      use_auth_token=True if model_args.use_auth_token else None				,      )
      else:
            logger.info("""Training new model from scratch"""    )
            _lowerCAmelCase					=				AutoModelForMaskedImageModeling.from_config(A__    )

      if training_args.do_train:
            _lowerCAmelCase					=				ds["""train"""].column_names
      else:
            _lowerCAmelCase					=				ds["""validation"""].column_names

      if data_args.image_column_name is not None:
            _lowerCAmelCase					=				data_args.image_column_name
      elif "image" in column_names:
            _lowerCAmelCase					=				"""image"""
      elif "img" in column_names:
            _lowerCAmelCase					=				"""img"""
      else:
            _lowerCAmelCase					=				column_names[0]

      # transformations as done in original SimMIM paper
      # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
      _lowerCAmelCase					=				Compose(
          [
              Lambda(lambda lowerCAmelCase    : img.convert("""RGB"""    ) if img.mode != "RGB" else img    ),
              RandomResizedCrop(model_args.image_size				,      scale=(0.67, 1.0)				,      ratio=(3.0 / 4.0, 4.0 / 3.0)    ),
              RandomHorizontalFlip(),
              ToTensor(),
              Normalize(mean=image_processor.image_mean				,      std=image_processor.image_std    ),
          ]    )

      # create mask generator
      _lowerCAmelCase					=				MaskGenerator(
          input_size=model_args.image_size				,      mask_patch_size=data_args.mask_patch_size				,      model_patch_size=model_args.patch_size				,      mask_ratio=data_args.mask_ratio				,      )

      def preprocess_images(lowerCAmelCase    ):
            _lowerCAmelCase					=				[transforms(A__    ) for image in examples[image_column_name]]
            _lowerCAmelCase					=				[mask_generator() for i in range(len(examples[image_column_name]    )    )]

            return examples

      if training_args.do_train:
            if "train" not in ds:
                  raise ValueError("""--do_train requires a train dataset"""    )
            if data_args.max_train_samples is not None:
                  _lowerCAmelCase					=				ds["""train"""].shuffle(seed=training_args.seed    ).select(range(data_args.max_train_samples    )    )
            # Set the training transforms
            ds["train"].set_transform(A__    )

      if training_args.do_eval:
            if "validation" not in ds:
                  raise ValueError("""--do_eval requires a validation dataset"""    )
            if data_args.max_eval_samples is not None:
                  _lowerCAmelCase					=				(
                      ds["""validation"""].shuffle(seed=training_args.seed    ).select(range(data_args.max_eval_samples    )    )
                  )
            # Set the validation transforms
            ds["validation"].set_transform(A__    )

      # Initialize our trainer
      _lowerCAmelCase					=				Trainer(
          model=A__				,      args=A__				,      train_dataset=ds["""train"""] if training_args.do_train else None				,      eval_dataset=ds["""validation"""] if training_args.do_eval else None				,      tokenizer=A__				,      data_collator=A__				,      )

      # Training
      if training_args.do_train:
            _lowerCAmelCase					=				None
            if training_args.resume_from_checkpoint is not None:
                  _lowerCAmelCase					=				training_args.resume_from_checkpoint
            elif last_checkpoint is not None:
                  _lowerCAmelCase					=				last_checkpoint
            _lowerCAmelCase					=				trainer.train(resume_from_checkpoint=A__    )
            trainer.save_model()
            trainer.log_metrics("""train"""				,      train_result.metrics    )
            trainer.save_metrics("""train"""				,      train_result.metrics    )
            trainer.save_state()

      # Evaluation
      if training_args.do_eval:
            _lowerCAmelCase					=				trainer.evaluate()
            trainer.log_metrics("""eval"""				,      A__    )
            trainer.save_metrics("""eval"""				,      A__    )

      # Write model card and (optionally) push to hub
      _lowerCAmelCase					=				{
          """finetuned_from""": model_args.model_name_or_path,
          """tasks""": """masked-image-modeling""",
          """dataset""": data_args.dataset_name,
          """tags""": ["""masked-image-modeling"""],
      }
      if training_args.push_to_hub:
            trainer.push_to_hub(**A__    )
      else:
            trainer.create_model_card(**A__    )


if __name__ == "__main__":
      main()










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    __lowerCamelCase   (      A__						: int					)     ->    List[str]:
   lowerCamelCase_						:   int		= 384
   if "tiny" in model_name:
      lowerCamelCase_						:   str		= [3, 3, 9, 3]
      lowerCamelCase_						:   List[str]		= [96, 192, 384, 768]
   if "small" in model_name:
      lowerCamelCase_						:   Any		= [3, 3, 27, 3]
      lowerCamelCase_						:   List[Any]		= [96, 192, 384, 768]
   if "base" in model_name:
      lowerCamelCase_						:   Optional[Any]		= [3, 3, 27, 3]
      lowerCamelCase_						:   int		= [128, 256, 512, 1024]
      lowerCamelCase_						:   int		= 512
   if "large" in model_name:
      lowerCamelCase_						:   Union[str, Any]		= [3, 3, 27, 3]
      lowerCamelCase_						:   Dict		= [192, 384, 768, 1536]
      lowerCamelCase_						:   Union[str, Any]		= 768
   if "xlarge" in model_name:
      lowerCamelCase_						:   Union[str, Any]		= [3, 3, 27, 3]
      lowerCamelCase_						:   Optional[int]		= [256, 512, 1024, 2048]
      lowerCamelCase_						:   List[str]		= 1024

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

   lowerCamelCase_						:   Dict		= ConvNextConfig(
       depths=A__						,						hidden_sizes=A__						,						out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]					)
   lowerCamelCase_						:   str		= UperNetConfig(
       backbone_config=A__						,						auxiliary_in_channels=A__						,						num_labels=A__						,						idalabel=A__						,						labelaid=A__						,						)

   return config


def    __lowerCamelCase   (      A__						: Optional[Any]					)     ->    Optional[Any]:
   lowerCamelCase_						:   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    __lowerCamelCase   (      A__						: Optional[Any]						,						A__						: List[Any]						,						A__						: int					)     ->    Union[str, Any]:
   lowerCamelCase_						:   Dict		= dct.pop(A__					)
   lowerCamelCase_						:   int		= val


def    __lowerCamelCase   (      A__						: Tuple						,						A__						: str						,						A__						: Any					)     ->    Optional[int]:
   lowerCamelCase_						:   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""",
   }
   lowerCamelCase_						:   List[str]		= model_name_to_url[model_name]
   lowerCamelCase_						:   Dict		= torch.hub.load_state_dict_from_url(A__						,						map_location="""cpu"""					)["""state_dict"""]

   lowerCamelCase_						:   List[Any]		= get_upernet_config(A__					)
   lowerCamelCase_						:   Any		= UperNetForSemanticSegmentation(A__					)
   model.eval()

   # replace "bn" => "batch_norm"
   for key in state_dict.copy().keys():
      lowerCamelCase_						:   Tuple		= state_dict.pop(A__					)
      if "bn" in key:
         lowerCamelCase_						:   Dict		= key.replace("""bn"""						,						"""batch_norm"""					)
      lowerCamelCase_						:   Tuple		= val

   # rename keys
   lowerCamelCase_						:   List[Any]		= create_rename_keys(A__					)
   for src, dest in rename_keys:
      rename_key(A__						,						A__						,						A__					)

   model.load_state_dict(A__					)

   # verify on image
   lowerCamelCase_						:   Any		= """https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
   lowerCamelCase_						:   Optional[int]		= Image.open(requests.get(A__						,						stream=A__					).raw					).convert("""RGB"""					)

   lowerCamelCase_						:   List[str]		= SegformerImageProcessor()
   lowerCamelCase_						:   Union[str, Any]		= processor(A__						,						return_tensors="""pt"""					).pixel_values

   with torch.no_grad():
      lowerCamelCase_						:   List[str]		= model(A__					)

   if model_name == "upernet-convnext-tiny":
      lowerCamelCase_						:   Dict		= 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":
      lowerCamelCase_						:   Optional[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":
      lowerCamelCase_						:   str		= 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":
      lowerCamelCase_						:   List[str]		= 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":
      lowerCamelCase_						:   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__":
      snake_case__				:	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.'
      )

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





