import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: int			)							->      Any:







  """simple docstring"""





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

  return name



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Tuple				,  __UpperCamelCase					: int			)							->      Optional[Any]:







  """simple docstring"""





  for key in orig_state_dict.copy().keys():
    SCREAMING_SNAKE_CASE__    							=   orig_state_dict.pop(__UpperCamelCase			)

    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
      SCREAMING_SNAKE_CASE__    							=   key.split("""."""			)
      SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   int(key_split[2]			), int(key_split[4]			)
      SCREAMING_SNAKE_CASE__    							=   config.vision_config.hidden_size
      if "weight" in key:
        SCREAMING_SNAKE_CASE__    							=   val[:dim, :]
        SCREAMING_SNAKE_CASE__    							=   val[dim : dim * 2, :]
        SCREAMING_SNAKE_CASE__    							=   val[-dim:, :]
      else:
        SCREAMING_SNAKE_CASE__    							=   val[:dim]
        SCREAMING_SNAKE_CASE__    							=   val[dim : dim * 2]
        SCREAMING_SNAKE_CASE__    							=   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
      SCREAMING_SNAKE_CASE__    							=   key.split("""."""			)
      SCREAMING_SNAKE_CASE__    							=   int(key_split[3]			)
      SCREAMING_SNAKE_CASE__    							=   config.text_config.hidden_size
      if "weight" in key:
        SCREAMING_SNAKE_CASE__    							=   val[:dim, :]
        SCREAMING_SNAKE_CASE__    							=   val[
            dim : dim * 2, :
        ]
        SCREAMING_SNAKE_CASE__    							=   val[-dim:, :]
      else:
        SCREAMING_SNAKE_CASE__    							=   val[:dim]
        SCREAMING_SNAKE_CASE__    							=   val[dim : dim * 2]
        SCREAMING_SNAKE_CASE__    							=   val[-dim:]
    else:
      SCREAMING_SNAKE_CASE__    							=   rename_key(__UpperCamelCase			)
      # 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
      ):
        SCREAMING_SNAKE_CASE__    							=   val.squeeze_()
      else:
        SCREAMING_SNAKE_CASE__    							=   val

  return orig_state_dict



def 			__SCREAMING_SNAKE_CASE						(      )							->      int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
  SCREAMING_SNAKE_CASE__    							=   Image.open(requests.get(__UpperCamelCase				,  stream=__UpperCamelCase			).raw			)
  return im



@torch.no_grad()
def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Optional[Any]				,  __UpperCamelCase					: List[str]				,  __UpperCamelCase					: Dict="groupvit-gcc-yfcc"				,  __UpperCamelCase					: Tuple=False			)							->      Union[str, Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   GroupViTConfig()
  SCREAMING_SNAKE_CASE__    							=   GroupViTModel(__UpperCamelCase			).eval()

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

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

  with torch.no_grad():
    SCREAMING_SNAKE_CASE__    							=   model(**__UpperCamelCase			)

  if model_name == "groupvit-gcc-yfcc":
    SCREAMING_SNAKE_CASE__    							=   torch.tensor([[13.3523, 6.3629]]			)
  elif model_name == "groupvit-gcc-redcaps":
    SCREAMING_SNAKE_CASE__    							=   torch.tensor([[16.1873, 8.6230]]			)
  else:
    raise ValueError(f"""Model name {model_name} not supported."""			)
  assert torch.allclose(outputs.logits_per_image				,  __UpperCamelCase				,  atol=1E-3			)

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

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


if __name__ == "__main__":
   __lowerCamelCase    :				Optional[int]							     =	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    :				Union[str, Any]							     =	parser.parse_args()

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

import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu







@require_cpu
class __snake_case       (		unittest.TestCase	):




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   torch.nn.Linear(10 ,			10   )
     SCREAMING_SNAKE_CASE__    							=   torch.optim.SGD(model.parameters() ,			0.1   )
     SCREAMING_SNAKE_CASE__    							=   Accelerator()
     SCREAMING_SNAKE_CASE__    							=   accelerator.prepare(_lowercase   )
     try:
       pickle.loads(pickle.dumps(_lowercase   )   )
     except Exception as e:
       self.fail(f"""Accelerated optimizer pickling failed with {e}"""   )
     AcceleratorState._reset_state()
