
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 		UpperCamelCase__  (       __lowercase    ):
   def __init__(self :					str					,				snake_case_ :					UNetaDModel					,				snake_case_ :					UNetaDModel					,				snake_case_ :					DDPMScheduler					,				snake_case_ :					Optional[int]					,				):
    super().__init__()
    __a :  int						=  value_function
    __a :  Dict						=  unet
    __a :  Dict						=  scheduler
    __a :  Dict						=  env
    __a :  List[Any]						=  env.get_dataset()
    __a :  Dict						=  {}
    for key in self.data.keys():
     try:
      __a :  Optional[int]						=  self.data[key].mean()
     except:  # noqa: E722
      pass
    __a :  List[str]						=  {}
    for key in self.data.keys():
     try:
      __a :  List[str]						=  self.data[key].std()
     except:  # noqa: E722
      pass
    __a :  Union[str, Any]						=  env.observation_space.shape[0]
    __a :  str						=  env.action_space.shape[0]
   def       lowerCAmelCase			(self :					Dict					,				snake_case_ :					Union[str, Any]					,				snake_case_ :					Tuple   ):
    return (x_in - self.means[key]) / self.stds[key]
   def       lowerCAmelCase			(self :					Optional[Any]					,				snake_case_ :					Tuple					,				snake_case_ :					Union[str, Any]   ):
    return x_in * self.stds[key] + self.means[key]
   def       lowerCAmelCase			(self :					int					,				snake_case_ :					List[str]   ):
    if type(snake_case_   ) is dict:
     return {k: self.to_torch(snake_case_   ) for k, v in x_in.items()}
    elif torch.is_tensor(snake_case_   ):
     return x_in.to(self.unet.device   )
    return torch.tensor(snake_case_					,				device=self.unet.device   )
   def       lowerCAmelCase			(self :					Any					,				snake_case_ :					Any					,				snake_case_ :					List[str]					,				snake_case_ :					Tuple   ):
    for key, val in cond.items():
     __a :  str						=  val.clone()
    return x_in
   def       lowerCAmelCase			(self :					int					,				snake_case_ :					Union[str, Any]					,				snake_case_ :					Union[str, Any]					,				snake_case_ :					Optional[int]					,				snake_case_ :					str   ):
    __a :  Union[str, Any]						=  x.shape[0]
    __a :  Optional[int]						=  None
    for i in tqdm.tqdm(self.scheduler.timesteps   ):
     # create batch of timesteps to pass into model
     __a :  List[str]						=  torch.full((batch_size,)					,				snake_case_					,				device=self.unet.device					,				dtype=torch.long   )
     for _ in range(snake_case_   ):
      with torch.enable_grad():
       x.requires_grad_()

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

       __a :  Optional[int]						=  self.scheduler._get_variance(snake_case_   )
       __a :  Dict						=  torch.exp(0.5 * posterior_variance   )
       __a :  Optional[int]						=  model_std * grad

      __a :  Optional[int]						=  0
      __a :  Union[str, Any]						=  x.detach()
      __a :  List[str]						=  x + scale * grad
      __a :  Union[str, Any]						=  self.reset_xa(snake_case_					,				snake_case_					,				self.action_dim   )

     __a :  str						=  self.unet(x.permute(0					,				2					,				1   )					,				snake_case_   ).sample.permute(0					,				2					,				1   )

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

     # apply conditions to the trajectory (set the initial state)
     __a :  List[str]						=  self.reset_xa(snake_case_					,				snake_case_					,				self.action_dim   )
     __a :  List[str]						=  self.to_torch(snake_case_   )
    return x, y







   def __call__(self :					Optional[Any]					,				snake_case_ :					Tuple					,				snake_case_ :					Any=6_4					,				snake_case_ :					List[str]=3_2					,				snake_case_ :					Optional[Any]=2					,				snake_case_ :					str=0.1   ):
    # normalize the observations and create  batch dimension
    __a :  str						=  self.normalize(snake_case_					,				'''observations'''   )
    __a :  str						=  obs[None].repeat(snake_case_					,				axis=0   )

    __a :  int						=  {0: self.to_torch(snake_case_   )}
    __a :  Optional[Any]						=  (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)
    __a :  Any						=  randn_tensor(snake_case_					,				device=self.unet.device   )
    __a :  int						=  self.reset_xa(snake_case_					,				snake_case_					,				self.action_dim   )
    __a :  int						=  self.to_torch(snake_case_   )

    # run the diffusion process
    __a				,       __a :  str						=  self.run_diffusion(snake_case_					,				snake_case_					,				snake_case_					,				snake_case_   )

    # sort output trajectories by value
    __a :  Optional[int]						=  y.argsort(0					,				descending=snake_case_   ).squeeze()
    __a :  Union[str, Any]						=  x[sorted_idx]
    __a :  str						=  sorted_values[:, :, : self.action_dim]
    __a :  Tuple						=  actions.detach().cpu().numpy()
    __a :  Dict						=  self.de_normalize(snake_case_					,				key='''actions'''   )

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

    __a :  Optional[int]						=  denorm_actions[selected_index, 0]
    return denorm_actions


import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel




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

 return name




def    __UpperCamelCase     (				lowerCAmelCase__							:				Union[str, Any]  ,   lowerCAmelCase__							:				Tuple  ):
 for key in orig_state_dict.copy().keys():
  __a :  List[Any]						=  orig_state_dict.pop(lowerCAmelCase__  )

  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
   __a :  Tuple						=  key.split('''.'''  )
   __a				,       __a :  List[Any]						=  int(key_split[2]  ), int(key_split[4]  )
   __a :  List[Any]						=  config.vision_config.hidden_size
   if "weight" in key:
    __a :  int						=  val[:dim, :]
    __a :  List[str]						=  val[dim : dim * 2, :]
    __a :  List[Any]						=  val[-dim:, :]
   else:
    __a :  List[str]						=  val[:dim]
    __a :  int						=  val[dim : dim * 2]
    __a :  Any						=  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
   __a :  int						=  key.split('''.'''  )
   __a :  str						=  int(key_split[3]  )
   __a :  List[Any]						=  config.text_config.hidden_size
   if "weight" in key:
    __a :  List[str]						=  val[:dim, :]
    __a :  Any						=  val[
        dim : dim * 2, :
    ]
    __a :  Dict						=  val[-dim:, :]
   else:
    __a :  List[str]						=  val[:dim]
    __a :  Any						=  val[dim : dim * 2]
    __a :  Any						=  val[-dim:]
  else:
   __a :  Union[str, Any]						=  rename_key(lowerCAmelCase__  )
   # 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
   ):
    __a :  List[Any]						=  val.squeeze_()
   else:
    __a :  Dict						=  val

 return orig_state_dict




def    __UpperCamelCase     (				):
 __a :  Optional[int]						=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 __a :  str						=  Image.open(requests.get(lowerCAmelCase__  ,   stream=lowerCAmelCase__  ).raw  )
 return im




@torch.no_grad()
def    __UpperCamelCase     (				lowerCAmelCase__							:				List[Any]  ,   lowerCAmelCase__							:				Optional[int]  ,   lowerCAmelCase__							:				Optional[Any]="groupvit-gcc-yfcc"  ,   lowerCAmelCase__							:				int=False  ):
 __a :  Union[str, Any]						=  GroupViTConfig()
 __a :  int						=  GroupViTModel(lowerCAmelCase__  ).eval()

 __a :  Any						=  torch.load(lowerCAmelCase__  ,   map_location='''cpu'''  )['''model''']
 __a :  Optional[Any]						=  convert_state_dict(lowerCAmelCase__  ,   lowerCAmelCase__  )
 __a				,       __a :  Dict						=  model.load_state_dict(lowerCAmelCase__  ,   strict=lowerCAmelCase__  )
 assert missing_keys == ["text_model.embeddings.position_ids"]
 assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(lowerCAmelCase__  ) == 0)

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

 with torch.no_grad():
  __a :  Tuple						=  model(**lowerCAmelCase__  )

 if model_name == "groupvit-gcc-yfcc":
  __a :  List[str]						=  torch.tensor([[13.35_23, 6.36_29]]  )
 elif model_name == "groupvit-gcc-redcaps":
  __a :  List[str]						=  torch.tensor([[16.18_73, 8.62_30]]  )
 else:
  raise ValueError(f"Model name {model_name} not supported."  )
 assert torch.allclose(outputs.logits_per_image  ,   lowerCAmelCase__  ,   atol=1e-3  )

 processor.save_pretrained(lowerCAmelCase__  )
 model.save_pretrained(lowerCAmelCase__  )
 print('''Successfully saved processor and model to'''  ,   lowerCAmelCase__  )

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


if __name__ == "__main__":
      lowercase__       =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`.',
      )
      lowercase__       =parser.parse_args()

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