


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__					(  ):
 __lowerCamelCase		:							List[str]		 =						'http://images.cocodataset.org/val2017/000000039769.jpg'
 __lowerCamelCase		:							str		 =						Image.open(requests.get(SCREAMING_SNAKE_CASE__      ,	stream=SCREAMING_SNAKE_CASE__						).raw						)
 return im
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 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__						):
 __lowerCamelCase		:							Optional[Any]		 =						dct.pop(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							str		 =						val
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Union[str, Any]		 =						[]
 for k in state_dict.keys():
  __lowerCamelCase		:							Optional[Any]		 =						k
  if ".pwconv" in k:
   __lowerCamelCase		:							Optional[int]		 =						k_new.replace('.pwconv'      ,	'.point_wise_conv'						)
  if ".dwconv" in k:
   __lowerCamelCase		:							int		 =						k_new.replace('.dwconv'      ,	'.depth_wise_conv'						)
  if ".Proj." in k:
   __lowerCamelCase		:							Tuple		 =						k_new.replace('.Proj.'      ,	'.proj.'						)
  if "patch_embed" in k_new:
   __lowerCamelCase		:							Tuple		 =						k_new.replace('patch_embed'      ,	'swiftformer.patch_embed.patch_embedding'						)
  if "network" in k_new:
   __lowerCamelCase		:							Optional[int]		 =						k_new.split('.'						)
   if ls[2].isdigit():
    __lowerCamelCase		:							List[str]		 =						'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]						)
   else:
    __lowerCamelCase		:							List[str]		 =						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__						):
 __lowerCamelCase		:							Tuple		 =						SwiftFormerConfig()

 # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
 __lowerCamelCase		:							List[str]		 =						1_000
 __lowerCamelCase		:							Optional[Any]		 =						'huggingface/label-files'
 __lowerCamelCase		:							str		 =						'imagenet-1k-id2label.json'
 __lowerCamelCase		:							Union[str, Any]		 =						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	repo_type='dataset'						)      ,	'r'						)						)
 __lowerCamelCase		:							Union[str, Any]		 =						{int(SCREAMING_SNAKE_CASE__						): v for k, v in idalabel.items()}
 __lowerCamelCase		:							int		 =						idalabel
 __lowerCamelCase		:							int		 =						{v: k for k, v in idalabel.items()}

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

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

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

 elif swiftformer_name == "swiftformer_l3":
  __lowerCamelCase		:							Union[str, Any]		 =						[4, 4, 12, 6]
  __lowerCamelCase		:							List[str]		 =						[64, 128, 320, 512]

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

 __lowerCamelCase		:							List[str]		 =						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
 __lowerCamelCase		:							Union[str, Any]		 =						SwiftFormerForImageClassification(SCREAMING_SNAKE_CASE__						).eval()
 hf_model.load_state_dict(SCREAMING_SNAKE_CASE__						)

 # prepare test inputs
 __lowerCamelCase		:							Optional[Any]		 =						prepare_img()
 __lowerCamelCase		:							Optional[int]		 =						ViTImageProcessor.from_pretrained('preprocessor_config'						)
 __lowerCamelCase		:							Optional[Any]		 =						processor(images=SCREAMING_SNAKE_CASE__      ,	return_tensors='pt'						)

 # compare outputs from both models
 __lowerCamelCase		:							Tuple		 =						get_expected_output(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							int		 =						hf_model(inputs['pixel_values']						).logits

 assert hf_logits.shape == torch.Size([1, 1_000]						)
 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 darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


lowercase_	   =     {
    'n_samples': 6_4,
    'horizon': 3_2,
    'num_inference_steps': 2_0,
    'n_guide_steps': 2,  # can set to 0 for faster sampling, does not use value network
    'scale_grad_by_std': True,
    'scale': 0.1,
    'eta': 0.0,
    't_grad_cutoff': 2,
    'device': 'cpu',
}


if __name__ == "__main__":
       lowercase_	   =     'hopper-medium-v2'
       lowercase_	   =     gym.make(env_name)

       lowercase_	   =     ValueGuidedRLPipeline.from_pretrained(
           'bglick13/hopper-medium-v2-value-function-hor32',
           env=env,
       )

       env.seed(0)
       lowercase_	   =     env.reset()
       lowercase_	   =     0
       lowercase_	   =     0
       lowercase_	   =     1_0_0_0
       lowercase_	   =     [obs.copy()]
       try:
              for t in tqdm.tqdm(range(T)):
                     # call the policy
                     lowercase_	   =     pipeline(obs, planning_horizon=3_2)

                     # execute action in environment
                     lowercase_   ,lowercase_   ,lowercase_   ,lowercase_	   =     env.step(denorm_actions)
                     lowercase_	   =     env.get_normalized_score(total_reward)

                     # update return
                     total_reward += reward
                     total_score += score
                     print(
                         F"""Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"""
                         F""" {total_score}"""
                     )

                     # save observations for rendering
                     rollout.append(next_observation.copy())

                     lowercase_	   =     next_observation
       except KeyboardInterrupt:
              pass

       print(F"""Total reward: {total_reward}""")

