import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel



def   __A		(			_A					):



 """simple docstring"""


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

 return name



def   __A		(			_A							,				_A					):



 """simple docstring"""


 for key in orig_state_dict.copy().keys():
  __a       =  orig_state_dict.pop(_A					)

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

 return orig_state_dict



def   __A		(			):



 """simple docstring"""


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



@torch.no_grad()
def   __A		(			_A							,				_A							,				_A="groupvit-gcc-yfcc"							,				_A=False					):



 """simple docstring"""


 __a       =  GroupViTConfig()
 __a       =  GroupViTModel(_A					).eval()

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

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

 with torch.no_grad():
  __a       =  model(**_A					)

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

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

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


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE :	Dict							     =		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`.""",
     )
     SCREAMING_SNAKE_CASE :	Any							     =		parser.parse_args()

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



import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset



@pytest.mark.parametrize("dataset_size"							,				[None, 400 * 2**20, 600 * 2**20]					)
@pytest.mark.parametrize("input_in_memory_max_size"							,				["default", 0, 100 * 2**20, 900 * 2**20]					)
def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


 if input_in_memory_max_size != "default":
  monkeypatch.setattr(datasets.config							,				"IN_MEMORY_MAX_SIZE"							,				_A					)
 __a       =  datasets.config.IN_MEMORY_MAX_SIZE
 if input_in_memory_max_size == "default":
  assert in_memory_max_size == 0
 else:
  assert in_memory_max_size == input_in_memory_max_size
 if dataset_size and in_memory_max_size:
  __a       =  dataset_size < in_memory_max_size
 else:
  __a       =  False
 __a       =  is_small_dataset(_A					)
 assert result == expected


