





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_					     =	logging.get_logger(__name__)

lowercase_					     =	torch.device("""cpu""")




def     __UpperCamelCase						()       ->     Tuple:
  lowercase__			    =						'http://images.cocodataset.org/val2017/000000039769.jpg'
  lowercase__			    =						Image.open(requests.get(_SCREAMING_SNAKE_CASE	,		stream=_SCREAMING_SNAKE_CASE	).raw	)
  return im




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     Any:
  if swiftformer_name == "swiftformer_xs":
    return torch.tensor([-2.1_703E00, 2.1_107E00, -2.0_811E00, 8.8_685E-01, 2.4_360E-01]	)

  elif swiftformer_name == "swiftformer_s":
    return torch.tensor([3.9_636E-01, 2.3_478E-01, -1.6_963E00, -1.7_381E00, -8.6_337E-01]	)

  elif swiftformer_name == "swiftformer_l1":
    return torch.tensor([-4.2_768E-01, -4.7_429E-01, -1.0_897E00, -1.0_248E00, 3.5_523E-02]	)

  elif swiftformer_name == "swiftformer_l3":
    return torch.tensor([-2.5_330E-01, 2.4_211E-01, -6.0_185E-01, -8.2_789E-01, -6.0_446E-02]	)




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Optional[int]:
  lowercase__			    =						dct.pop(_SCREAMING_SNAKE_CASE	)
  lowercase__			    =						val




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     List[str]:
  lowercase__			    =						[]
  for k in state_dict.keys():
    lowercase__			    =						k
    if ".pwconv" in k:
      lowercase__			    =						k_new.replace('.pwconv'	,		'.point_wise_conv'	)
    if ".dwconv" in k:
      lowercase__			    =						k_new.replace('.dwconv'	,		'.depth_wise_conv'	)
    if ".Proj." in k:
      lowercase__			    =						k_new.replace('.Proj.'	,		'.proj.'	)
    if "patch_embed" in k_new:
      lowercase__			    =						k_new.replace('patch_embed'	,		'swiftformer.patch_embed.patch_embedding'	)
    if "network" in k_new:
      lowercase__			    =						k_new.split('.'	)
      if ls[2].isdigit():
        lowercase__			    =						'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]	)
      else:
        lowercase__			    =						k_new.replace('network'	,		'swiftformer.encoder.network'	)
    rename_keys.append((k, k_new)	)
  return rename_keys




@torch.no_grad()
def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Dict:
  lowercase__			    =						SwiftFormerConfig()

  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  lowercase__			    =						1000
  lowercase__			    =						'huggingface/label-files'
  lowercase__			    =						'imagenet-1k-id2label.json'
  lowercase__			    =						json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		repo_type='dataset'	)	,		'r'	)	)
  lowercase__			    =						{int(_SCREAMING_SNAKE_CASE	): v for k, v in idalabel.items()}
  lowercase__			    =						idalabel
  lowercase__			    =						{v: k for k, v in idalabel.items()}

  # size of the architecture
  if swiftformer_name == "swiftformer_xs":
    lowercase__			    =						[3, 3, 6, 4]
    lowercase__			    =						[48, 56, 112, 220]

  elif swiftformer_name == "swiftformer_s":
    lowercase__			    =						[3, 3, 9, 6]
    lowercase__			    =						[48, 64, 168, 224]

  elif swiftformer_name == "swiftformer_l1":
    lowercase__			    =						[4, 3, 10, 5]
    lowercase__			    =						[48, 96, 192, 384]

  elif swiftformer_name == "swiftformer_l3":
    lowercase__			    =						[4, 4, 12, 6]
    lowercase__			    =						[64, 128, 320, 512]

  # load state_dict of original model, remove and rename some keys
  if original_ckpt:
    if original_ckpt.startswith('https'	):
      lowercase__			    =						torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE	,		map_location='cpu'	,		check_hash=_SCREAMING_SNAKE_CASE	)
    else:
      lowercase__			    =						torch.load(_SCREAMING_SNAKE_CASE	,		map_location='cpu'	)
  lowercase__			    =						checkpoint

  lowercase__			    =						create_rename_keys(_SCREAMING_SNAKE_CASE	)
  for rename_key_src, rename_key_dest in rename_keys:
    rename_key(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)

  # load HuggingFace model
  lowercase__			    =						SwiftFormerForImageClassification(_SCREAMING_SNAKE_CASE	).eval()
  hf_model.load_state_dict(_SCREAMING_SNAKE_CASE	)

  # prepare test inputs
  lowercase__			    =						prepare_img()
  lowercase__			    =						ViTImageProcessor.from_pretrained('preprocessor_config'	)
  lowercase__			    =						processor(images=_SCREAMING_SNAKE_CASE	,		return_tensors='pt'	)

  # compare outputs from both models
  lowercase__			    =						get_expected_output(_SCREAMING_SNAKE_CASE	)
  lowercase__			    =						hf_model(inputs['pixel_values']	).logits

  assert hf_logits.shape == torch.Size([1, 1000]	)
  assert torch.allclose(hf_logits[0, 0:5]	,		_SCREAMING_SNAKE_CASE	,		atol=1E-3	)

  Path(_SCREAMING_SNAKE_CASE	).mkdir(exist_ok=_SCREAMING_SNAKE_CASE	)
  print(F"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}"""	)
  hf_model.save_pretrained(_SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
      lowercase_					     =	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--swiftformer_name""",
          default="""swiftformer_xs""",
          choices=["""swiftformer_xs""", """swiftformer_s""", """swiftformer_l1""", """swiftformer_l3"""],
          type=str,
          help="""Name of the SwiftFormer model you'd like to convert.""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""",
          default="""./converted_outputs/""",
          type=str,
          help="""Path to the output PyTorch model directory.""",
      )
      parser.add_argument("""--original_ckpt""", default=None, type=str, help="""Path to the original model checkpoint.""")

      lowercase_					     =	parser.parse_args()
      convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)









import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):


   def __init__(					self    :       Optional[Any]    ,  a    :       UNetaDModel    ,  a    :       UNetaDModel    ,  a    :       DDPMScheduler    ,  a    :       Any    ,  )->     Dict:





     """simple docstring"""







     super().__init__()
     lowercase__			    =						value_function
     lowercase__			    =						unet
     lowercase__			    =						scheduler
     lowercase__			    =						env
     lowercase__			    =						env.get_dataset()
     lowercase__			    =						{}
     for key in self.data.keys():
       try:
         lowercase__			    =						self.data[key].mean()
       except:  # noqa: E722
         pass
     lowercase__			    =						{}
     for key in self.data.keys():
       try:
         lowercase__			    =						self.data[key].std()
       except:  # noqa: E722
         pass
     lowercase__			    =						env.observation_space.shape[0]
     lowercase__			    =						env.action_space.shape[0]


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any]    ,  a    :       Any    ,  a    :       int )->     Dict:





     """simple docstring"""







     return (x_in - self.means[key]) / self.stds[key]


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[int]    ,  a    :       str    ,  a    :       List[str] )->     str:





     """simple docstring"""







     return x_in * self.stds[key] + self.means[key]


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str]    ,  a    :       Tuple )->     Tuple:





     """simple docstring"""







     if type(a ) is dict:
       return {k: self.to_torch(a ) for k, v in x_in.items()}
     elif torch.is_tensor(a ):
       return x_in.to(self.unet.device )
     return torch.tensor(a    ,  device=self.unet.device )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any]    ,  a    :       Optional[int]    ,  a    :       Dict    ,  a    :       Optional[Any] )->     List[Any]:





     """simple docstring"""







     for key, val in cond.items():
       lowercase__			    =						val.clone()
     return x_in


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple    ,  a    :       Optional[Any]    ,  a    :       Any    ,  a    :       Optional[Any]    ,  a    :       Optional[int] )->     List[Any]:





     """simple docstring"""







     lowercase__			    =						x.shape[0]
     lowercase__			    =						None
     for i in tqdm.tqdm(self.scheduler.timesteps ):
       # create batch of timesteps to pass into model
       lowercase__			    =						torch.full((batch_size,)    ,  a    ,  device=self.unet.device    ,  dtype=torch.long )
       for _ in range(a ):
         with torch.enable_grad():
           x.requires_grad_()

           # permute to match dimension for pre-trained models
           lowercase__			    =						self.value_function(x.permute(0    ,  2    ,  1 )    ,  a ).sample
           lowercase__			    =						torch.autograd.grad([y.sum()]    ,  [x] )[0]

           lowercase__			    =						self.scheduler._get_variance(a )
           lowercase__			    =						torch.exp(0.5 * posterior_variance )
           lowercase__			    =						model_std * grad

         lowercase__			    =						0
         lowercase__			    =						x.detach()
         lowercase__			    =						x + scale * grad
         lowercase__			    =						self.reset_xa(a    ,  a    ,  self.action_dim )

       lowercase__			    =						self.unet(x.permute(0    ,  2    ,  1 )    ,  a ).sample.permute(0    ,  2    ,  1 )

       # TODO: verify deprecation of this kwarg
       lowercase__			    =						self.scheduler.step(a    ,  a    ,  a    ,  predict_epsilon=a )['prev_sample']

       # apply conditions to the trajectory (set the initial state)
       lowercase__			    =						self.reset_xa(a    ,  a    ,  self.action_dim )
       lowercase__			    =						self.to_torch(a )
     return x, y


   def __call__(					self    :       Any    ,  a    :       Tuple    ,  a    :       int=64    ,  a    :       Tuple=32    ,  a    :       List[Any]=2    ,  a    :       List[str]=0.1 )->     List[Any]:





     """simple docstring"""







     lowercase__			    =						self.normalize(a    ,  'observations' )
     lowercase__			    =						obs[None].repeat(a    ,  axis=0 )

     lowercase__			    =						{0: self.to_torch(a )}
     lowercase__			    =						(batch_size, planning_horizon, self.state_dim + self.action_dim)

     # generate initial noise and apply our conditions (to make the trajectories start at current state)
     lowercase__			    =						randn_tensor(a    ,  device=self.unet.device )
     lowercase__			    =						self.reset_xa(a    ,  a    ,  self.action_dim )
     lowercase__			    =						self.to_torch(a )

     # run the diffusion process
     lowercase__    ,					lowercase__			    =						self.run_diffusion(a    ,  a    ,  a    ,  a )

     # sort output trajectories by value
     lowercase__			    =						y.argsort(0    ,  descending=a ).squeeze()
     lowercase__			    =						x[sorted_idx]
     lowercase__			    =						sorted_values[:, :, : self.action_dim]
     lowercase__			    =						actions.detach().cpu().numpy()
     lowercase__			    =						self.de_normalize(a    ,  key='actions' )

     # select the action with the highest value
     if y is not None:
       lowercase__			    =						0
     else:
       # if we didn't run value guiding, select a random action
       lowercase__			    =						np.random.randint(0    ,  a )

     lowercase__			    =						denorm_actions[selected_index, 0]
     return denorm_actions


