

"""simple docstring"""






import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel





def 	lowercase      (   __UpperCamelCase						) ->      List[Any]:





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

 return name





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      Tuple:





 for key in orig_state_dict.copy().keys():
  __magic_name__     =      orig_state_dict.pop(__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
   __magic_name__     =      key.split('''.'''						)
   __magic_name__   ,						__magic_name__     =      int(key_split[2]						), int(key_split[4]						)
   __magic_name__     =      config.vision_config.hidden_size
   if "weight" in key:
    __magic_name__     =      val[:dim, :]
    __magic_name__     =      val[dim : dim * 2, :]
    __magic_name__     =      val[-dim:, :]
   else:
    __magic_name__     =      val[:dim]
    __magic_name__     =      val[dim : dim * 2]
    __magic_name__     =      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
   __magic_name__     =      key.split('''.'''						)
   __magic_name__     =      int(key_split[3]						)
   __magic_name__     =      config.text_config.hidden_size
   if "weight" in key:
    __magic_name__     =      val[:dim, :]
    __magic_name__     =      val[
        dim : dim * 2, :
    ]
    __magic_name__     =      val[-dim:, :]
   else:
    __magic_name__     =      val[:dim]
    __magic_name__     =      val[dim : dim * 2]
    __magic_name__     =      val[-dim:]
  else:
   __magic_name__     =      rename_key(__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
   ):
    __magic_name__     =      val.squeeze_()
   else:
    __magic_name__     =      val

 return orig_state_dict





def 	lowercase      (   ) ->      int:
 __magic_name__     =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 __magic_name__     =      Image.open(requests.get(__snake_case      ,   stream=__snake_case						).raw						)
 return im





@torch.no_grad()
def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase="groupvit-gcc-yfcc"      ,   __UpperCamelCase=False						) ->      Optional[int]:
 __magic_name__     =      GroupViTConfig()
 __magic_name__     =      GroupViTModel(__snake_case						).eval()

 __magic_name__     =      torch.load(__snake_case      ,   map_location='''cpu'''						)['''model''']
 __magic_name__     =      convert_state_dict(__snake_case      ,   __snake_case						)
 __magic_name__   ,						__magic_name__     =      model.load_state_dict(__snake_case      ,   strict=__snake_case						)
 assert missing_keys == ["text_model.embeddings.position_ids"]
 assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(__snake_case						) == 0)

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

 with torch.no_grad():
  __magic_name__     =      model(**__snake_case						)

 if model_name == "groupvit-gcc-yfcc":
  __magic_name__     =      torch.tensor([[13.3_523, 6.3_629]]						)
 elif model_name == "groupvit-gcc-redcaps":
  __magic_name__     =      torch.tensor([[16.1_873, 8.6_230]]						)
 else:
  raise ValueError(f'''Model name {model_name} not supported.'''						)
 assert torch.allclose(outputs.logits_per_image      ,   __snake_case      ,   atol=1e-3						)

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

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


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

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




"""simple docstring"""






import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class        _lowercase					(     __UpperCAmelCase     ):
     _lowerCamelCase     =       (EulerDiscreteScheduler,)
     _lowerCamelCase     =       10







     def 	lowerCAmelCase__    (      self ,						**UpperCamelCase_       ):
      __magic_name__     =      {
          '''num_train_timesteps''': 1100,
          '''beta_start''': 0.0_0_0_1,
          '''beta_end''': 0.0_2,
          '''beta_schedule''': '''linear''',
      }

      config.update(**UpperCamelCase_       )
      return config







     def 	lowerCAmelCase__    (      self       ):
      for timesteps in [10, 50, 100, 1000]:
       self.check_over_configs(num_train_timesteps=UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1] ,						[0.0_0_0_2, 0.0_0_2, 0.0_2]       ):
       self.check_over_configs(beta_start=UpperCamelCase_ ,						beta_end=UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      for schedule in ["linear", "scaled_linear"]:
       self.check_over_configs(beta_schedule=UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.scheduler_classes[0]
      __magic_name__     =      self.get_scheduler_config()
      __magic_name__     =      scheduler_class(**UpperCamelCase_       )

      scheduler.set_timesteps(self.num_inference_steps       )

      __magic_name__     =      torch.manual_seed(0       )

      __magic_name__     =      self.dummy_model()
      __magic_name__     =      self.dummy_sample_deter * scheduler.init_noise_sigma
      __magic_name__     =      sample.to(UpperCamelCase_       )

      for i, t in enumerate(scheduler.timesteps       ):
       __magic_name__     =      scheduler.scale_model_input(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      model(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      scheduler.step(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						generator=UpperCamelCase_       )
       __magic_name__     =      output.prev_sample

      __magic_name__     =      torch.sum(torch.abs(UpperCamelCase_       )       )
      __magic_name__     =      torch.mean(torch.abs(UpperCamelCase_       )       )

      assert abs(result_sum.item() - 1_0.0_8_0_7       ) < 1E-2
      assert abs(result_mean.item() - 0.0_1_3_1       ) < 1E-3







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.scheduler_classes[0]
      __magic_name__     =      self.get_scheduler_config(prediction_type='''v_prediction'''       )
      __magic_name__     =      scheduler_class(**UpperCamelCase_       )

      scheduler.set_timesteps(self.num_inference_steps       )

      __magic_name__     =      torch.manual_seed(0       )

      __magic_name__     =      self.dummy_model()
      __magic_name__     =      self.dummy_sample_deter * scheduler.init_noise_sigma
      __magic_name__     =      sample.to(UpperCamelCase_       )

      for i, t in enumerate(scheduler.timesteps       ):
       __magic_name__     =      scheduler.scale_model_input(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      model(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      scheduler.step(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						generator=UpperCamelCase_       )
       __magic_name__     =      output.prev_sample

      __magic_name__     =      torch.sum(torch.abs(UpperCamelCase_       )       )
      __magic_name__     =      torch.mean(torch.abs(UpperCamelCase_       )       )

      assert abs(result_sum.item() - 0.0_0_0_2       ) < 1E-2
      assert abs(result_mean.item() - 2.2_6_7_6E-0_6       ) < 1E-3







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.scheduler_classes[0]
      __magic_name__     =      self.get_scheduler_config()
      __magic_name__     =      scheduler_class(**UpperCamelCase_       )

      scheduler.set_timesteps(self.num_inference_steps ,						device=UpperCamelCase_       )

      __magic_name__     =      torch.manual_seed(0       )

      __magic_name__     =      self.dummy_model()
      __magic_name__     =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      __magic_name__     =      sample.to(UpperCamelCase_       )

      for t in scheduler.timesteps:
       __magic_name__     =      scheduler.scale_model_input(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      model(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      scheduler.step(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						generator=UpperCamelCase_       )
       __magic_name__     =      output.prev_sample

      __magic_name__     =      torch.sum(torch.abs(UpperCamelCase_       )       )
      __magic_name__     =      torch.mean(torch.abs(UpperCamelCase_       )       )

      assert abs(result_sum.item() - 1_0.0_8_0_7       ) < 1E-2
      assert abs(result_mean.item() - 0.0_1_3_1       ) < 1E-3







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.scheduler_classes[0]
      __magic_name__     =      self.get_scheduler_config()
      __magic_name__     =      scheduler_class(**UpperCamelCase_ ,						use_karras_sigmas=UpperCamelCase_       )

      scheduler.set_timesteps(self.num_inference_steps ,						device=UpperCamelCase_       )

      __magic_name__     =      torch.manual_seed(0       )

      __magic_name__     =      self.dummy_model()
      __magic_name__     =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      __magic_name__     =      sample.to(UpperCamelCase_       )

      for t in scheduler.timesteps:
       __magic_name__     =      scheduler.scale_model_input(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      model(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      scheduler.step(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						generator=UpperCamelCase_       )
       __magic_name__     =      output.prev_sample

      __magic_name__     =      torch.sum(torch.abs(UpperCamelCase_       )       )
      __magic_name__     =      torch.mean(torch.abs(UpperCamelCase_       )       )

      assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9       ) < 1E-2
      assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3       ) < 1E-3

