



'''simple docstring'''






import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel




def     snake_case_					(     lowerCAmelCase_		)->		List[Any]:





       '''simple docstring'''


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

       return name




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		Dict:





       '''simple docstring'''


       for key in orig_state_dict.copy().keys():
              _UpperCAmelCase  :						Union[str, 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
                     _UpperCAmelCase  :						Any			     = key.split("""."""		)
                     _UpperCAmelCase     ,_UpperCAmelCase  :						Dict			     = int(key_split[2]		), int(key_split[4]		)
                     _UpperCAmelCase  :						int			     = config.vision_config.hidden_size
                     if "weight" in key:
                            _UpperCAmelCase  :						Any			     = val[:dim, :]
                            _UpperCAmelCase  :						List[Any]			     = val[dim : dim * 2, :]
                            _UpperCAmelCase  :						Optional[int]			     = val[-dim:, :]
                     else:
                            _UpperCAmelCase  :						Optional[int]			     = val[:dim]
                            _UpperCAmelCase  :						Union[str, Any]			     = val[dim : dim * 2]
                            _UpperCAmelCase  :						Dict			     = 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
                     _UpperCAmelCase  :						List[str]			     = key.split("""."""		)
                     _UpperCAmelCase  :						List[Any]			     = int(key_split[3]		)
                     _UpperCAmelCase  :						Tuple			     = config.text_config.hidden_size
                     if "weight" in key:
                            _UpperCAmelCase  :						int			     = val[:dim, :]
                            _UpperCAmelCase  :						str			     = val[
                                dim : dim * 2, :
                            ]
                            _UpperCAmelCase  :						str			     = val[-dim:, :]
                     else:
                            _UpperCAmelCase  :						Optional[int]			     = val[:dim]
                            _UpperCAmelCase  :						Union[str, Any]			     = val[dim : dim * 2]
                            _UpperCAmelCase  :						Optional[int]			     = val[-dim:]
              else:
                     _UpperCAmelCase  :						Dict			     = 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
                     ):
                            _UpperCAmelCase  :						Any			     = val.squeeze_()
                     else:
                            _UpperCAmelCase  :						List[Any]			     = val

       return orig_state_dict




def     snake_case_					(     )->		Optional[Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						Optional[int]			     = """http://images.cocodataset.org/val2017/000000039769.jpg"""
       _UpperCAmelCase  :						Union[str, Any]			     = Image.open(requests.get(lowerCAmelCase_       ,   stream=lowerCAmelCase_		).raw		)
       return im




@torch.no_grad()
def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_="groupvit-gcc-yfcc"       ,   lowerCAmelCase_=False		)->		int:





       '''simple docstring'''


       _UpperCAmelCase  :						List[Any]			     = GroupViTConfig()
       _UpperCAmelCase  :						Any			     = GroupViTModel(lowerCAmelCase_		).eval()

       _UpperCAmelCase  :						Dict			     = torch.load(lowerCAmelCase_       ,   map_location="""cpu"""		)["""model"""]
       _UpperCAmelCase  :						Optional[int]			     = convert_state_dict(lowerCAmelCase_       ,   lowerCAmelCase_		)
       _UpperCAmelCase     ,_UpperCAmelCase  :						str			     = 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
       _UpperCAmelCase  :						int			     = CLIPProcessor.from_pretrained("""openai/clip-vit-base-patch32"""		)
       _UpperCAmelCase  :						Union[str, Any]			     = prepare_img()
       _UpperCAmelCase  :						List[Any]			     = processor(text=["""a photo of a cat""", """a photo of a dog"""]       ,   images=lowerCAmelCase_       ,   padding=lowerCAmelCase_       ,   return_tensors="""pt"""		)

       with torch.no_grad():
              _UpperCAmelCase  :						List[str]			     = model(**lowerCAmelCase_		)

       if model_name == "groupvit-gcc-yfcc":
              _UpperCAmelCase  :						List[str]			     = torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]]		)
       elif model_name == "groupvit-gcc-redcaps":
              _UpperCAmelCase  :						Optional[int]			     = torch.tensor([[1_6.1_8_7_3, 8.6_2_3_0]]		)
       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__":
   A_							: Any						 =	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`.""",
   )
   A_							: Union[str, Any]						 =	parser.parse_args()

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








'''simple docstring'''






import argparse
from collections import OrderedDict
from pathlib import Path

import torch

from transformers import (
    VisualBertConfig,
    VisualBertForMultipleChoice,
    VisualBertForPreTraining,
    VisualBertForQuestionAnswering,
    VisualBertForVisualReasoning,
)
from transformers.utils import logging


logging.set_verbosity_info()
A_							: Union[str, Any]						 =	logging.get_logger(__name__)

A_							: Union[str, Any]						 =	[
    ("""bert.bert""", """visual_bert"""),
    ("""bert.cls""", """cls"""),
    ("""bert.classifier""", """cls"""),
    ("""token_type_embeddings_visual""", """visual_token_type_embeddings"""),
    ("""position_embeddings_visual""", """visual_position_embeddings"""),
    ("""projection""", """visual_projection"""),
]

A_							: int						 =	[
    """nlvr2_coco_pre_trained.th""",
    """nlvr2_fine_tuned.th""",
    """nlvr2_pre_trained.th""",
    """vcr_coco_pre_train.th""",
    """vcr_fine_tune.th""",
    """vcr_pre_train.th""",
    """vqa_coco_pre_trained.th""",
    """vqa_fine_tuned.th""",
    """vqa_pre_trained.th""",
]




def     snake_case_					(     lowerCAmelCase_		)->		Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase  :						Dict			     = torch.load(lowerCAmelCase_       ,   map_location="""cpu"""		)
       return sd




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_=rename_keys_prefix		)->		Optional[Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						Any			     = OrderedDict()
       _UpperCAmelCase  :						Any			     = torch.arange(config.max_position_embeddings		).expand((1, -1)		)
       # detector_d = OrderedDict()
       for key in d:
              if "detector" in key:
                     # detector_d[key.replace('detector.','')] = d[key]
                     continue
              _UpperCAmelCase  :						Dict			     = key
              for name_pair in rename_keys_prefix:
                     _UpperCAmelCase  :						str			     = new_key.replace(name_pair[0]       ,   name_pair[1]		)
              _UpperCAmelCase  :						str			     = d[key]
              if key == "bert.cls.predictions.decoder.weight":
                     # Old bert code didn't have `decoder.bias`, but was added separately
                     _UpperCAmelCase  :						int			     = new_d["""cls.predictions.bias"""]
       return new_d




@torch.no_grad()
def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		Any:





       '''simple docstring'''


       assert (
           checkpoint_path.split("""/"""		)[-1] in ACCEPTABLE_CHECKPOINTS
       ), F'''The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'''

       # Get Config
       if "pre" in checkpoint_path:
              _UpperCAmelCase  :						Optional[int]			     = """pretraining"""
              if "vcr" in checkpoint_path:
                     _UpperCAmelCase  :						Optional[int]			     = {"""visual_embedding_dim""": 512}
              elif "vqa_advanced" in checkpoint_path:
                     _UpperCAmelCase  :						List[Any]			     = {"""visual_embedding_dim""": 2048}
              elif "vqa" in checkpoint_path:
                     _UpperCAmelCase  :						Any			     = {"""visual_embedding_dim""": 2048}
              elif "nlvr" in checkpoint_path:
                     _UpperCAmelCase  :						Any			     = {"""visual_embedding_dim""": 1024}
              else:
                     raise NotImplementedError(F'''No implementation found for `{checkpoint_path}`.'''		)
       else:
              if "vcr" in checkpoint_path:
                     _UpperCAmelCase  :						str			     = {"""visual_embedding_dim""": 512}
                     _UpperCAmelCase  :						int			     = """multichoice"""
              elif "vqa_advanced" in checkpoint_path:
                     _UpperCAmelCase  :						str			     = {"""visual_embedding_dim""": 2048}
                     _UpperCAmelCase  :						int			     = """vqa_advanced"""
              elif "vqa" in checkpoint_path:
                     _UpperCAmelCase  :						List[str]			     = {"""visual_embedding_dim""": 2048, """num_labels""": 3129}
                     _UpperCAmelCase  :						int			     = """vqa"""
              elif "nlvr" in checkpoint_path:
                     _UpperCAmelCase  :						int			     = {
                         """visual_embedding_dim""": 1024,
                         """num_labels""": 2,
                     }
                     _UpperCAmelCase  :						Optional[Any]			     = """nlvr"""

       _UpperCAmelCase  :						int			     = VisualBertConfig(**lowerCAmelCase_		)

       # Load State Dict
       _UpperCAmelCase  :						Any			     = load_state_dict(lowerCAmelCase_		)

       _UpperCAmelCase  :						Optional[int]			     = get_new_dict(lowerCAmelCase_       ,   lowerCAmelCase_		)

       if model_type == "pretraining":
              _UpperCAmelCase  :						List[str]			     = VisualBertForPreTraining(lowerCAmelCase_		)
       elif model_type == "vqa":
              _UpperCAmelCase  :						Optional[int]			     = VisualBertForQuestionAnswering(lowerCAmelCase_		)
       elif model_type == "nlvr":
              _UpperCAmelCase  :						str			     = VisualBertForVisualReasoning(lowerCAmelCase_		)
       elif model_type == "multichoice":
              _UpperCAmelCase  :						Dict			     = VisualBertForMultipleChoice(lowerCAmelCase_		)

       model.load_state_dict(lowerCAmelCase_		)
       # Save Checkpoints
       Path(lowerCAmelCase_		).mkdir(exist_ok=lowerCAmelCase_		)
       model.save_pretrained(lowerCAmelCase_		)


if __name__ == "__main__":
   A_							: Optional[Any]						 =	argparse.ArgumentParser()
   # Required parameters
   parser.add_argument("""orig_checkpoint_path""", type=str, help="""A path to .th on local filesystem.""")
   parser.add_argument("""pytorch_dump_folder_path""", type=str, help="""Path to the output PyTorch model.""")
   A_							: int						 =	parser.parse_args()
   convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)



