



import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Union[str, Any]				)  ->     List[str]:
 _snake_case    :  List[str]					=					fname.split(os.path.sep				)[-1]
 return re.search(R"""^(.*)_\d+\.jpg$"""				,       SCREAMING_SNAKE_CASE__				).groups()[0]







class 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   def __init__(					self							:			Optional[int]				,		lowerCAmelCase							:			Tuple				,		lowerCAmelCase							:			int=None				,		lowerCAmelCase							:			List[Any]=None)				->				Union[str, Any]:

    """simple docstring"""







    _snake_case    :  str					=					file_names
    _snake_case    :  List[str]					=					image_transform
    _snake_case    :  str					=					label_to_id

   def __len__(					self							:			Tuple)				->				Optional[int]:

    """simple docstring"""







    return len(self.file_names)


   def __getitem__(					self							:			Optional[Any]				,		lowerCAmelCase							:			List[str])				->				str:

    """simple docstring"""







    _snake_case    :  int					=					self.file_names[idx]
    _snake_case    :  Any					=					PIL.Image.open(lowerCAmelCase)
    _snake_case    :  Tuple					=					raw_image.convert("""RGB""")
    if self.image_transform is not None:
     _snake_case    :  Tuple					=					self.image_transform(lowerCAmelCase)
    _snake_case    :  Union[str, Any]					=					extract_label(lowerCAmelCase)
    if self.label_to_id is not None:
     _snake_case    :  Optional[Any]					=					self.label_to_id[label]
    return {"image": image, "label": label}



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[Any]				,       SCREAMING_SNAKE_CASE__       :	Union[str, Any]				)  ->     List[Any]:
 # Initialize accelerator
 if args.with_tracking:
  _snake_case    :  Union[str, Any]					=					Accelerator(
      cpu=args.cpu				,       mixed_precision=args.mixed_precision				,       log_with="""all"""				,       project_dir=args.project_dir				)
 else:
  _snake_case    :  int					=					Accelerator(cpu=args.cpu				,       mixed_precision=args.mixed_precision				)

 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 _snake_case    :  Any					=					config["""lr"""]
 _snake_case    :  str					=					int(config["""num_epochs"""]				)
 _snake_case    :  Dict					=					int(config["""seed"""]				)
 _snake_case    :  int					=					int(config["""batch_size"""]				)
 _snake_case    :  Optional[Any]					=					config["""image_size"""]
 if not isinstance(SCREAMING_SNAKE_CASE__				,       (list, tuple)				):
  _snake_case    :  Optional[Any]					=					(image_size, image_size)

 # Parse out whether we are saving every epoch or after a certain number of batches
 if hasattr(args.checkpointing_steps				,       """isdigit"""				):
  if args.checkpointing_steps == "epoch":
   _snake_case    :  Tuple					=					args.checkpointing_steps
  elif args.checkpointing_steps.isdigit():
   _snake_case    :  int					=					int(args.checkpointing_steps				)
  else:
   raise ValueError(
       F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''				)
 else:
  _snake_case    :  int					=					None

 # We need to initialize the trackers we use, and also store our configuration
 if args.with_tracking:
  _snake_case    :  List[str]					=					os.path.split(SCREAMING_SNAKE_CASE__				)[-1].split("""."""				)[0]
  accelerator.init_trackers(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)

 # Grab all the image filenames
 _snake_case    :  Tuple					=					[os.path.join(args.data_dir				,       SCREAMING_SNAKE_CASE__				) for fname in os.listdir(args.data_dir				) if fname.endswith(""".jpg"""				)]

 # Build the label correspondences
 _snake_case    :  Optional[Any]					=					[extract_label(SCREAMING_SNAKE_CASE__				) for fname in file_names]
 _snake_case    :  Any					=					list(set(SCREAMING_SNAKE_CASE__				)				)
 id_to_label.sort()
 _snake_case    :  Any					=					{lbl: i for i, lbl in enumerate(SCREAMING_SNAKE_CASE__				)}

 # Set the seed before splitting the data.
 np.random.seed(SCREAMING_SNAKE_CASE__				)
 torch.manual_seed(SCREAMING_SNAKE_CASE__				)
 torch.cuda.manual_seed_all(SCREAMING_SNAKE_CASE__				)

 # Split our filenames between train and validation
 _snake_case    :  Dict					=					np.random.permutation(len(SCREAMING_SNAKE_CASE__				)				)
 _snake_case    :  Tuple					=					int(0.8 * len(SCREAMING_SNAKE_CASE__				)				)
 _snake_case    :  Optional[int]					=					random_perm[:cut]
 _snake_case    :  Optional[int]					=					random_perm[cut:]

 # For training we use a simple RandomResizedCrop
 _snake_case    :  List[str]					=					Compose([RandomResizedCrop(SCREAMING_SNAKE_CASE__				,       scale=(0.5, 1.0)				), ToTensor()]				)
 _snake_case    :  str					=					PetsDataset(
     [file_names[i] for i in train_split]				,       image_transform=SCREAMING_SNAKE_CASE__				,       label_to_id=SCREAMING_SNAKE_CASE__				)

 # For evaluation, we use a deterministic Resize
 _snake_case    :  str					=					Compose([Resize(SCREAMING_SNAKE_CASE__				), ToTensor()]				)
 _snake_case    :  Dict					=					PetsDataset([file_names[i] for i in eval_split]				,       image_transform=SCREAMING_SNAKE_CASE__				,       label_to_id=SCREAMING_SNAKE_CASE__				)

 # Instantiate dataloaders.
 _snake_case    :  Tuple					=					DataLoader(SCREAMING_SNAKE_CASE__				,       shuffle=SCREAMING_SNAKE_CASE__				,       batch_size=SCREAMING_SNAKE_CASE__				,       num_workers=4				)
 _snake_case    :  int					=					DataLoader(SCREAMING_SNAKE_CASE__				,       shuffle=SCREAMING_SNAKE_CASE__				,       batch_size=SCREAMING_SNAKE_CASE__				,       num_workers=4				)

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 _snake_case    :  List[Any]					=					create_model("""resnet50d"""				,       pretrained=SCREAMING_SNAKE_CASE__				,       num_classes=len(SCREAMING_SNAKE_CASE__				)				)

 # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
 # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
 # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
 _snake_case    :  Optional[Any]					=					model.to(accelerator.device				)

 # Freezing the base model
 for param in model.parameters():
  _snake_case    :  Any					=					False
 for param in model.get_classifier().parameters():
  _snake_case    :  str					=					True

 # We normalize the batches of images to be a bit faster.
 _snake_case    :  str					=					torch.tensor(model.default_cfg["""mean"""]				)[None, :, None, None].to(accelerator.device				)
 _snake_case    :  Union[str, Any]					=					torch.tensor(model.default_cfg["""std"""]				)[None, :, None, None].to(accelerator.device				)

 # Instantiate optimizer
 _snake_case    :  Dict					=					torch.optim.Adam(params=model.parameters()				,       lr=lr / 25				)

 # Instantiate learning rate scheduler
 _snake_case    :  List[Any]					=					OneCycleLR(optimizer=SCREAMING_SNAKE_CASE__				,       max_lr=SCREAMING_SNAKE_CASE__				,       epochs=SCREAMING_SNAKE_CASE__				,       steps_per_epoch=len(SCREAMING_SNAKE_CASE__				)				)

 # Prepare everything
 # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
 # prepare method.
 _snake_case	,    _snake_case	,    _snake_case	,    _snake_case	,    _snake_case    :  Dict					=					accelerator.prepare(
     SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
 # We need to keep track of how many total steps we have iterated over
 _snake_case    :  Any					=					0
 # We also need to keep track of the starting epoch so files are named properly
 _snake_case    :  Optional[Any]					=					0

 # Potentially load in the weights and states from a previous save
 if args.resume_from_checkpoint:
  if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
   accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}'''				)
   accelerator.load_state(args.resume_from_checkpoint				)
   _snake_case    :  str					=					os.path.basename(args.resume_from_checkpoint				)
  else:
   # Get the most recent checkpoint
   _snake_case    :  int					=					[f.name for f in os.scandir(os.getcwd()				) if f.is_dir()]
   dirs.sort(key=os.path.getctime				)
   _snake_case    :  Optional[Any]					=					dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  # Extract `epoch_{i}` or `step_{i}`
  _snake_case    :  Tuple					=					os.path.splitext(SCREAMING_SNAKE_CASE__				)[0]

  if "epoch" in training_difference:
   _snake_case    :  Dict					=					int(training_difference.replace("""epoch_"""				,       """"""				)				) + 1
   _snake_case    :  int					=					None
  else:
   _snake_case    :  Any					=					int(training_difference.replace("""step_"""				,       """"""				)				)
   _snake_case    :  Dict					=					resume_step // len(SCREAMING_SNAKE_CASE__				)
   resume_step -= starting_epoch * len(SCREAMING_SNAKE_CASE__				)

    # Now we train the model
 for epoch in range(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				):
  model.train()
  if args.with_tracking:
   _snake_case    :  Dict					=					0
  if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
   # We need to skip steps until we reach the resumed step
   _snake_case    :  Union[str, Any]					=					accelerator.skip_first_batches(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
   overall_step += resume_step
  else:
   # After the first iteration though, we need to go back to the original dataloader
   _snake_case    :  Union[str, Any]					=					train_dataloader
  for batch in active_dataloader:
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   _snake_case    :  List[str]					=					{k: v.to(accelerator.device				) for k, v in batch.items()}
   _snake_case    :  Optional[int]					=					(batch["""image"""] - mean) / std
   _snake_case    :  int					=					model(SCREAMING_SNAKE_CASE__				)
   _snake_case    :  Dict					=					torch.nn.functional.cross_entropy(SCREAMING_SNAKE_CASE__				,       batch["""label"""]				)
   # We keep track of the loss at each epoch
   if args.with_tracking:
    total_loss += loss.detach().float()
   accelerator.backward(SCREAMING_SNAKE_CASE__				)
   optimizer.step()
   lr_scheduler.step()
   optimizer.zero_grad()
   overall_step += 1
   if isinstance(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				):
    _snake_case    :  Tuple					=					F'''step_{overall_step}'''
    if overall_step % checkpointing_steps == 0:
     if args.output_dir is not None:
      _snake_case    :  Any					=					os.path.join(args.output_dir				,       SCREAMING_SNAKE_CASE__				)
     accelerator.save_state(SCREAMING_SNAKE_CASE__				)
  model.eval()
  _snake_case    :  Dict					=					0
  _snake_case    :  str					=					0
  for step, batch in enumerate(SCREAMING_SNAKE_CASE__				):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   _snake_case    :  str					=					{k: v.to(accelerator.device				) for k, v in batch.items()}
   _snake_case    :  Dict					=					(batch["""image"""] - mean) / std
   with torch.no_grad():
    _snake_case    :  int					=					model(SCREAMING_SNAKE_CASE__				)
   _snake_case    :  List[Any]					=					outputs.argmax(dim=-1				)
   _snake_case	,    _snake_case    :  int					=					accelerator.gather_for_metrics((predictions, batch["""label"""])				)
   _snake_case    :  List[Any]					=					predictions == references
   num_elems += accurate_preds.shape[0]
   accurate += accurate_preds.long().sum()

  _snake_case    :  Dict					=					accurate.item() / num_elems
  # Use accelerator.print to print only on the main process.
  accelerator.print(F'''epoch {epoch}: {100 * eval_metric:.2f}'''				)
  if args.with_tracking:
   accelerator.log(
       {
           """accuracy""": 100 * eval_metric,
           """train_loss""": total_loss.item() / len(SCREAMING_SNAKE_CASE__				),
           """epoch""": epoch,
       }				,       step=SCREAMING_SNAKE_CASE__				,       )
  if checkpointing_steps == "epoch":
   _snake_case    :  List[str]					=					F'''epoch_{epoch}'''
   if args.output_dir is not None:
    _snake_case    :  Dict					=					os.path.join(args.output_dir				,       SCREAMING_SNAKE_CASE__				)
   accelerator.save_state(SCREAMING_SNAKE_CASE__				)

 if args.with_tracking:
  accelerator.end_training()



def       lowercase       (   )  ->     int:
 _snake_case    :  List[str]					=					argparse.ArgumentParser(description="""Simple example of training script."""				)
 parser.add_argument("""--data_dir"""				,       required=SCREAMING_SNAKE_CASE__				,       help="""The data folder on disk."""				)
 parser.add_argument("""--fp16"""				,       action="""store_true"""				,       help="""If passed, will use FP16 training."""				)
 parser.add_argument(
     """--mixed_precision"""				,       type=SCREAMING_SNAKE_CASE__				,       default=SCREAMING_SNAKE_CASE__				,       choices=["""no""", """fp16""", """bf16""", """fp8"""]				,       help="""Whether to use mixed precision. Choose"""
     """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
     """and an Nvidia Ampere GPU."""				,       )
 parser.add_argument("""--cpu"""				,       action="""store_true"""				,       help="""If passed, will train on the CPU."""				)
 parser.add_argument(
     """--checkpointing_steps"""				,       type=SCREAMING_SNAKE_CASE__				,       default=SCREAMING_SNAKE_CASE__				,       help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""				,       )
 parser.add_argument(
     """--output_dir"""				,       type=SCREAMING_SNAKE_CASE__				,       default="""."""				,       help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""				,       )
 parser.add_argument(
     """--resume_from_checkpoint"""				,       type=SCREAMING_SNAKE_CASE__				,       default=SCREAMING_SNAKE_CASE__				,       help="""If the training should continue from a checkpoint folder."""				,       )
 parser.add_argument(
     """--with_tracking"""				,       action="""store_true"""				,       help="""Whether to load in all available experiment trackers from the environment and use them for logging."""				,       )
 parser.add_argument(
     """--project_dir"""				,       type=SCREAMING_SNAKE_CASE__				,       default="""logs"""				,       help="""Location on where to store experiment tracking logs` and relevent project information"""				,       )
 _snake_case    :  List[str]					=					parser.parse_args()
 _snake_case    :  List[str]					=					{"""lr""": 3e-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 224}
 training_function(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)


if __name__ == "__main__":
  main()





import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Union[str, Any]				)  ->     Optional[Any]:
 # vision encoder
 if "img_encoder.pos_embed" in name:
  _snake_case    :  Any					=					name.replace("""img_encoder.pos_embed"""				,       """vision_model.embeddings.position_embeddings"""				)
 if "img_encoder.patch_embed.proj" in name:
  _snake_case    :  Union[str, Any]					=					name.replace("""img_encoder.patch_embed.proj"""				,       """vision_model.embeddings.patch_embeddings.projection"""				)
 if "img_encoder.patch_embed.norm" in name:
  _snake_case    :  Tuple					=					name.replace("""img_encoder.patch_embed.norm"""				,       """vision_model.embeddings.layernorm"""				)
 if "img_encoder.layers" in name:
  _snake_case    :  Optional[Any]					=					name.replace("""img_encoder.layers"""				,       """vision_model.encoder.stages"""				)
 if "blocks" in name and "res" not in name:
  _snake_case    :  Union[str, Any]					=					name.replace("""blocks"""				,       """layers"""				)
 if "attn" in name and "pre_assign" not in name:
  _snake_case    :  Any					=					name.replace("""attn"""				,       """self_attn"""				)
 if "proj" in name and "self_attn" in name and "text" not in name:
  _snake_case    :  int					=					name.replace("""proj"""				,       """out_proj"""				)
 if "pre_assign_attn.attn.proj" in name:
  _snake_case    :  Union[str, Any]					=					name.replace("""pre_assign_attn.attn.proj"""				,       """pre_assign_attn.attn.out_proj"""				)
 if "norm1" in name:
  _snake_case    :  Any					=					name.replace("""norm1"""				,       """layer_norm1"""				)
 if "norm2" in name and "pre_assign" not in name:
  _snake_case    :  Dict					=					name.replace("""norm2"""				,       """layer_norm2"""				)
 if "img_encoder.norm" in name:
  _snake_case    :  Union[str, Any]					=					name.replace("""img_encoder.norm"""				,       """vision_model.layernorm"""				)
 # text encoder
 if "text_encoder.token_embedding" in name:
  _snake_case    :  Dict					=					name.replace("""text_encoder.token_embedding"""				,       """text_model.embeddings.token_embedding"""				)
 if "text_encoder.positional_embedding" in name:
  _snake_case    :  str					=					name.replace("""text_encoder.positional_embedding"""				,       """text_model.embeddings.position_embedding.weight"""				)
 if "text_encoder.transformer.resblocks." in name:
  _snake_case    :  int					=					name.replace("""text_encoder.transformer.resblocks."""				,       """text_model.encoder.layers."""				)
 if "ln_1" in name:
  _snake_case    :  Union[str, Any]					=					name.replace("""ln_1"""				,       """layer_norm1"""				)
 if "ln_2" in name:
  _snake_case    :  Optional[Any]					=					name.replace("""ln_2"""				,       """layer_norm2"""				)
 if "c_fc" in name:
  _snake_case    :  Union[str, Any]					=					name.replace("""c_fc"""				,       """fc1"""				)
 if "c_proj" in name:
  _snake_case    :  Tuple					=					name.replace("""c_proj"""				,       """fc2"""				)
 if "text_encoder" in name:
  _snake_case    :  Any					=					name.replace("""text_encoder"""				,       """text_model"""				)
 if "ln_final" in name:
  _snake_case    :  str					=					name.replace("""ln_final"""				,       """final_layer_norm"""				)
 # projection layers
 if "img_projector.linear_hidden." in name:
  _snake_case    :  List[Any]					=					name.replace("""img_projector.linear_hidden."""				,       """visual_projection."""				)
 if "img_projector.linear_out." in name:
  _snake_case    :  Optional[int]					=					name.replace("""img_projector.linear_out."""				,       """visual_projection.3."""				)
 if "text_projector.linear_hidden" in name:
  _snake_case    :  List[Any]					=					name.replace("""text_projector.linear_hidden"""				,       """text_projection"""				)
 if "text_projector.linear_out" in name:
  _snake_case    :  Tuple					=					name.replace("""text_projector.linear_out"""				,       """text_projection.3"""				)

 return name



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	List[str]				,       SCREAMING_SNAKE_CASE__       :	int				)  ->     List[str]:
 for key in orig_state_dict.copy().keys():
  _snake_case    :  Tuple					=					orig_state_dict.pop(SCREAMING_SNAKE_CASE__				)

  if "qkv" in key:
   # weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
   # we need to split them up into separate matrices/vectors
   _snake_case    :  Any					=					key.split("""."""				)
   _snake_case	,    _snake_case    :  List[Any]					=					int(key_split[2]				), int(key_split[4]				)
   _snake_case    :  List[Any]					=					config.vision_config.hidden_size
   if "weight" in key:
    _snake_case    :  List[Any]					=					val[:dim, :]
    _snake_case    :  Union[str, Any]					=					val[dim : dim * 2, :]
    _snake_case    :  int					=					val[-dim:, :]
   else:
    _snake_case    :  Union[str, Any]					=					val[:dim]
    _snake_case    :  str					=					val[dim : dim * 2]
    _snake_case    :  Dict					=					val[-dim:]
  elif "in_proj" in key:
   # weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
   # we need to split them up into separate matrices/vectors
   _snake_case    :  int					=					key.split("""."""				)
   _snake_case    :  Optional[int]					=					int(key_split[3]				)
   _snake_case    :  Union[str, Any]					=					config.text_config.hidden_size
   if "weight" in key:
    _snake_case    :  int					=					val[:dim, :]
    _snake_case    :  Tuple					=					val[
        dim : dim * 2, :
    ]
    _snake_case    :  int					=					val[-dim:, :]
   else:
    _snake_case    :  Tuple					=					val[:dim]
    _snake_case    :  Tuple					=					val[dim : dim * 2]
    _snake_case    :  str					=					val[-dim:]
  else:
   _snake_case    :  Tuple					=					rename_key(SCREAMING_SNAKE_CASE__				)
   # squeeze if necessary
   if (
       "text_projection.0" in new_name
       or "text_projection.3" in new_name
       or "visual_projection.0" in new_name
       or "visual_projection.3" in new_name
   ):
    _snake_case    :  Any					=					val.squeeze_()
   else:
    _snake_case    :  Optional[int]					=					val

 return orig_state_dict



def       lowercase       (   )  ->     List[str]:
 _snake_case    :  List[str]					=					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 _snake_case    :  Dict					=					Image.open(requests.get(SCREAMING_SNAKE_CASE__				,       stream=SCREAMING_SNAKE_CASE__				).raw				)
 return im



@torch.no_grad()
def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Any				,       SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	int="groupvit-gcc-yfcc"				,       SCREAMING_SNAKE_CASE__       :	Optional[int]=False				)  ->     Optional[Any]:
 _snake_case    :  Any					=					GroupViTConfig()
 _snake_case    :  List[Any]					=					GroupViTModel(SCREAMING_SNAKE_CASE__				).eval()

 _snake_case    :  Optional[int]					=					torch.load(SCREAMING_SNAKE_CASE__				,       map_location="""cpu"""				)["""model"""]
 _snake_case    :  Tuple					=					convert_state_dict(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
 _snake_case	,    _snake_case    :  int					=					model.load_state_dict(SCREAMING_SNAKE_CASE__				,       strict=SCREAMING_SNAKE_CASE__				)
 assert missing_keys == ["text_model.embeddings.position_ids"]
 assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(SCREAMING_SNAKE_CASE__				) == 0)

 # verify result
 _snake_case    :  List[Any]					=					CLIPProcessor.from_pretrained("""openai/clip-vit-base-patch32"""				)
 _snake_case    :  List[Any]					=					prepare_img()
 _snake_case    :  int					=					processor(text=["""a photo of a cat""", """a photo of a dog"""]				,       images=SCREAMING_SNAKE_CASE__				,       padding=SCREAMING_SNAKE_CASE__				,       return_tensors="""pt"""				)

 with torch.no_grad():
  _snake_case    :  Optional[int]					=					model(**SCREAMING_SNAKE_CASE__				)

 if model_name == "groupvit-gcc-yfcc":
  _snake_case    :  Union[str, Any]					=					torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]]				)
 elif model_name == "groupvit-gcc-redcaps":
  _snake_case    :  Union[str, Any]					=					torch.tensor([[1_6.1_8_7_3, 8.6_2_3_0]]				)
 else:
  raise ValueError(F'''Model name {model_name} not supported.'''				)
 assert torch.allclose(outputs.logits_per_image				,       SCREAMING_SNAKE_CASE__				,       atol=1e-3				)

 processor.save_pretrained(SCREAMING_SNAKE_CASE__				)
 model.save_pretrained(SCREAMING_SNAKE_CASE__				)
 print("""Successfully saved processor and model to"""				,       SCREAMING_SNAKE_CASE__				)

 if push_to_hub:
  print("""Pushing to the hub..."""				)
  processor.push_to_hub(SCREAMING_SNAKE_CASE__				,       organization="""nielsr"""				)
  model.push_to_hub(SCREAMING_SNAKE_CASE__				,       organization="""nielsr"""				)


if __name__ == "__main__":
  a__    =	argparse.ArgumentParser()
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to dump the processor and PyTorch model."""
  )
  parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to GroupViT checkpoint""")
  parser.add_argument(
      """--model_name""",
      default="""groupvit-gccy-fcc""",
      type=str,
      help="""Name of the model. Expecting either 'groupvit-gcc-yfcc' or 'groupvit-gcc-redcaps'""",
  )
  parser.add_argument(
      """--push_to_hub""",
      action="""store_true""",
      help="""Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.""",
  )
  a__    =	parser.parse_args()

  convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
