






import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def 		_a       ( a					:Dict					)				->     Any:
 # vision encoder
 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					:Optional[Any]      ,				a					:Any					)				->     Any:
 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    ,	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       ( )				->     Optional[int]:
 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					:Optional[int]      ,				a					:int      ,				a					:Optional[Any]="groupvit-gcc-yfcc"      ,				a					:List[Any]=False					)				->     Optional[int]:
 a						    = GroupViTConfig()
 a						    = GroupViTModel(a					).eval()

 a						    = torch.load(a      ,				map_location='''cpu'''					)['''model''']
 a						    = convert_state_dict(a      ,				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([[13.3_523, 6.3_629]]					)
 elif model_name == "groupvit-gcc-redcaps":
  a						    = 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      ,				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__":
     UpperCAmelCase__							  =						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`.",
     )
     UpperCAmelCase__							  =						parser.parse_args()

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










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase__							  =						{"processing_layoutxlm": ["LayoutXLMProcessor"]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__							  =						["LayoutXLMTokenizer"]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__							  =						["LayoutXLMTokenizerFast"]

if TYPE_CHECKING:
     from .processing_layoutxlm import LayoutXLMProcessor

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_layoutxlm import LayoutXLMTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
     import sys

     UpperCAmelCase__							  =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


