

import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Union[str, Any]      ):



     '''simple docstring'''





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

     return name

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Dict     ,					SCREAMING_SNAKE_CASE_		:   Optional[Any]      ):



     '''simple docstring'''





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

     return orig_state_dict

def      snake_case__      (   ):



     '''simple docstring'''





     lowercase__   :       int				=	'http://images.cocodataset.org/val2017/000000039769.jpg'
     lowercase__   :       Dict				=	Image.open(requests.get(SCREAMING_SNAKE_CASE_     ,					stream=SCREAMING_SNAKE_CASE_      ).raw      )
     return im

@torch.no_grad()
def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Any     ,					SCREAMING_SNAKE_CASE_		:   int     ,					SCREAMING_SNAKE_CASE_		:   Optional[int]="groupvit-gcc-yfcc"     ,					SCREAMING_SNAKE_CASE_		:   Optional[Any]=False      ):



     '''simple docstring'''





     lowercase__   :       Optional[int]				=	GroupViTConfig()
     lowercase__   :       Union[str, Any]				=	GroupViTModel(SCREAMING_SNAKE_CASE_      ).eval()

     lowercase__   :       Dict				=	torch.load(SCREAMING_SNAKE_CASE_     ,					map_location='cpu'      )['model']
     lowercase__   :       str				=	convert_state_dict(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
     lowercase__				,			lowercase__   :       Dict				=	model.load_state_dict(SCREAMING_SNAKE_CASE_     ,					strict=SCREAMING_SNAKE_CASE_      )
     assert missing_keys == ["text_model.embeddings.position_ids"]
     assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(SCREAMING_SNAKE_CASE_      ) == 0)

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

     with torch.no_grad():
          lowercase__   :       Tuple				=	model(**SCREAMING_SNAKE_CASE_      )

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

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

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


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

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




import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   BertModel     ,					SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   str      ):



     '''simple docstring'''





     lowercase__   :       Tuple				=	('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

     lowercase__   :       Dict				=	(
         ('layer.', 'layer_'),
         ('word_embeddings.weight', 'word_embeddings'),
         ('position_embeddings.weight', 'position_embeddings'),
         ('token_type_embeddings.weight', 'token_type_embeddings'),
         ('.', '/'),
         ('LayerNorm/weight', 'LayerNorm/gamma'),
         ('LayerNorm/bias', 'LayerNorm/beta'),
         ('weight', 'kernel'),
     )

     if not os.path.isdir(SCREAMING_SNAKE_CASE_      ):
          os.makedirs(SCREAMING_SNAKE_CASE_      )

     lowercase__   :       str				=	model.state_dict()

     def to_tf_var_name(SCREAMING_SNAKE_CASE_		:   str      ):
          for patt, repl in iter(SCREAMING_SNAKE_CASE_      ):
               lowercase__   :       Optional[int]				=	name.replace(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
          return f"""bert/{name}"""

     def create_tf_var(SCREAMING_SNAKE_CASE_		:   np.ndarray     ,					SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   tf.Session      ):
          lowercase__   :       Optional[Any]				=	tf.dtypes.as_dtype(tensor.dtype      )
          lowercase__   :       List[str]				=	tf.get_variable(dtype=SCREAMING_SNAKE_CASE_     ,					shape=tensor.shape     ,					name=SCREAMING_SNAKE_CASE_     ,					initializer=tf.zeros_initializer()      )
          session.run(tf.variables_initializer([tf_var]      )      )
          session.run(SCREAMING_SNAKE_CASE_      )
          return tf_var

     tf.reset_default_graph()
     with tf.Session() as session:
          for var_name in state_dict:
               lowercase__   :       Tuple				=	to_tf_var_name(SCREAMING_SNAKE_CASE_      )
               lowercase__   :       str				=	state_dict[var_name].numpy()
               if any(x in var_name for x in tensors_to_transpose      ):
                    lowercase__   :       str				=	torch_tensor.T
               lowercase__   :       Any				=	create_tf_var(tensor=SCREAMING_SNAKE_CASE_     ,					name=SCREAMING_SNAKE_CASE_     ,					session=SCREAMING_SNAKE_CASE_      )
               tf.keras.backend.set_value(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
               lowercase__   :       Optional[int]				=	session.run(SCREAMING_SNAKE_CASE_      )
               print(f"""Successfully created {tf_name}: {np.allclose(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )}"""      )

          lowercase__   :       Tuple				=	tf.train.Saver(tf.trainable_variables()      )
          saver.save(SCREAMING_SNAKE_CASE_     ,					os.path.join(SCREAMING_SNAKE_CASE_     ,					model_name.replace('-'     ,					'_'      ) + '.ckpt'      )      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Optional[Any]=None      ):



     '''simple docstring'''





     lowercase__   :       List[str]				=	argparse.ArgumentParser()
     parser.add_argument('--model_name'     ,					type=SCREAMING_SNAKE_CASE_     ,					required=SCREAMING_SNAKE_CASE_     ,					help='model name e.g. bert-base-uncased'      )
     parser.add_argument(
         '--cache_dir'     ,					type=SCREAMING_SNAKE_CASE_     ,					default=SCREAMING_SNAKE_CASE_     ,					required=SCREAMING_SNAKE_CASE_     ,					help='Directory containing pytorch model'      )
     parser.add_argument('--pytorch_model_path'     ,					type=SCREAMING_SNAKE_CASE_     ,					required=SCREAMING_SNAKE_CASE_     ,					help='/path/to/<pytorch-model-name>.bin'      )
     parser.add_argument('--tf_cache_dir'     ,					type=SCREAMING_SNAKE_CASE_     ,					required=SCREAMING_SNAKE_CASE_     ,					help='Directory in which to save tensorflow model'      )
     lowercase__   :       List[str]				=	parser.parse_args(SCREAMING_SNAKE_CASE_      )

     lowercase__   :       int				=	BertModel.from_pretrained(
         pretrained_model_name_or_path=args.model_name     ,					state_dict=torch.load(args.pytorch_model_path      )     ,					cache_dir=args.cache_dir     ,					)

     convert_pytorch_checkpoint_to_tf(model=SCREAMING_SNAKE_CASE_     ,					ckpt_dir=args.tf_cache_dir     ,					model_name=args.model_name      )


if __name__ == "__main__":
       main()

