



'''simple docstring'''





import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel




def lowerCAmelCase    (__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 lowerCAmelCase    (__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    ,       _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 lowerCAmelCase    ():



     """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 lowerCAmelCase    (__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    ,       _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([[13.35_23, 6.36_29]])
     elif model_name == "groupvit-gcc-redcaps":
          _a           =    torch.tensor([[16.18_73, 8.62_30]])
     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__":
  lowercase_									=    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`.",
  )
  lowercase_									=    parser.parse_args()

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









import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


a__		     =     logging.get_logger(__name__)


class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	__magic_name__   :       int      	=		['input_ids', 'attention_mask']






	def __init__(					self		:						List[Any]					,     lowerCAmelCase		:						Union[str, Any]="</s>"					,     lowerCAmelCase		:						Tuple="<unk>"					,     lowerCAmelCase		:						Union[str, Any]="<pad>"					,     lowerCAmelCase		:						Tuple=125					,     lowerCAmelCase		:						str=None					,     **lowerCAmelCase		:						Optional[Any]					,     )	->       None:







						"""simple docstring"""







						if extra_ids > 0 and additional_special_tokens is None:
											__UpperCamelCase					:	str             =      [F'''<extra_id_{i}>''' for i in range(lowerCAmelCase	)]
						elif extra_ids > 0 and additional_special_tokens is not None:
											# Check that we have the right number of extra_id special tokens
											__UpperCamelCase					:	Tuple             =      len(set(filter(lambda lowerCAmelCase	: bool("""extra_id""" in str(lowerCAmelCase	)	)					,     lowerCAmelCase	)	)	)
											if extra_tokens != extra_ids:
																raise ValueError(
																    F'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
																    """ provided to ByT5Tokenizer. In this case the additional_special_tokens must include the"""
																    """ extra_ids tokens"""	)

						__UpperCamelCase					:	Tuple             =      AddedToken(lowerCAmelCase					,     lstrip=lowerCAmelCase					,     rstrip=lowerCAmelCase	) if isinstance(lowerCAmelCase					,     lowerCAmelCase	) else pad_token
						__UpperCamelCase					:	Tuple             =      AddedToken(lowerCAmelCase					,     lstrip=lowerCAmelCase					,     rstrip=lowerCAmelCase	) if isinstance(lowerCAmelCase					,     lowerCAmelCase	) else eos_token
						__UpperCamelCase					:	int             =      AddedToken(lowerCAmelCase					,     lstrip=lowerCAmelCase					,     rstrip=lowerCAmelCase	) if isinstance(lowerCAmelCase					,     lowerCAmelCase	) else unk_token

						super().__init__(
						    eos_token=lowerCAmelCase					,     unk_token=lowerCAmelCase					,     pad_token=lowerCAmelCase					,     extra_ids=lowerCAmelCase					,     additional_special_tokens=lowerCAmelCase					,     **lowerCAmelCase					,     )

						__UpperCamelCase					:	Any             =      extra_ids

						__UpperCamelCase					:	List[Any]             =      2**8  # utf is 8 bits

						# define special tokens dict
						__UpperCamelCase					:	Dict[int, str]             =      {
						    self.pad_token: 0,
						    self.eos_token: 1,
						    self.unk_token: 2,
						}
						__UpperCamelCase					:	int             =      len(self.special_tokens_encoder	)
						__UpperCamelCase					:	Tuple             =      len(lowerCAmelCase	)
						for i, token in enumerate(lowerCAmelCase	):
											__UpperCamelCase					:	Optional[int]             =      self.vocab_size + i - n
						__UpperCamelCase					:	Dict[str, int]             =      {v: k for k, v in self.special_tokens_encoder.items()}






	@property
	def 					lowerCamelCase__     (					self		:						Any	)	->       Optional[int]:







						"""simple docstring"""







						return self._utf_vocab_size + self._num_special_tokens + self._extra_ids






	def 					lowerCamelCase__     (					self		:						Optional[int]					,     lowerCAmelCase		:						List[int]					,     lowerCAmelCase		:						Optional[List[int]] = None					,     lowerCAmelCase		:						bool = False	)	->       List[int]:







						"""simple docstring"""







						if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=lowerCAmelCase					,     token_ids_a=lowerCAmelCase					,     already_has_special_tokens=lowerCAmelCase	)

						# normal case: some special tokens
						if token_ids_a is None:
											return ([0] * len(lowerCAmelCase	)) + [1]
						return ([0] * len(lowerCAmelCase	)) + [1] + ([0] * len(lowerCAmelCase	)) + [1]






	def 					lowerCamelCase__     (					self		:						Tuple					,     lowerCAmelCase		:						List[int]	)	->       List[int]:







						"""simple docstring"""







						if len(lowerCAmelCase	) > 0 and token_ids[-1] == self.eos_token_id:
											warnings.warn(
											    F'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
											    """ eos tokens being added."""	)
											return token_ids
						else:
											return token_ids + [self.eos_token_id]






	def 					lowerCamelCase__     (					self		:						List[Any]					,     lowerCAmelCase		:						List[int]					,     lowerCAmelCase		:						Optional[List[int]] = None	)	->       List[int]:







						"""simple docstring"""







						__UpperCamelCase					:	Any             =      [self.eos_token_id]

						if token_ids_a is None:
											return len(token_ids_a + eos	) * [0]
						return len(token_ids_a + eos + token_ids_a + eos	) * [0]






	def 					lowerCamelCase__     (					self		:						List[str]					,     lowerCAmelCase		:						List[int]					,     lowerCAmelCase		:						Optional[List[int]] = None	)	->       List[int]:







						"""simple docstring"""







						__UpperCamelCase					:	List[str]             =      self._add_eos_if_not_present(lowerCAmelCase	)
						if token_ids_a is None:
											return token_ids_a
						else:
											__UpperCamelCase					:	List[Any]             =      self._add_eos_if_not_present(lowerCAmelCase	)
											return token_ids_a + token_ids_a






	def 					lowerCamelCase__     (					self		:						str					,     lowerCAmelCase		:						str	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[int]             =      [chr(lowerCAmelCase	) for i in text.encode("""utf-8"""	)]
						return tokens






	def 					lowerCamelCase__     (					self		:						Union[str, Any]					,     lowerCAmelCase		:						Optional[int]	)	->       int:







						"""simple docstring"""







						if token in self.special_tokens_encoder:
											__UpperCamelCase					:	Dict             =      self.special_tokens_encoder[token]
						elif token in self.added_tokens_encoder:
											__UpperCamelCase					:	Tuple             =      self.added_tokens_encoder[token]
						elif len(lowerCAmelCase	) != 1:
											__UpperCamelCase					:	Dict             =      self.unk_token_id
						else:
											__UpperCamelCase					:	Union[str, Any]             =      ord(lowerCAmelCase	) + self._num_special_tokens
						return token_id






	def 					lowerCamelCase__     (					self		:						Union[str, Any]					,     lowerCAmelCase		:						Dict	)	->       Optional[Any]:







						"""simple docstring"""







						if index in self.special_tokens_decoder:
											__UpperCamelCase					:	str             =      self.special_tokens_decoder[index]
						else:
											__UpperCamelCase					:	Any             =      chr(index - self._num_special_tokens	)
						return token






	def 					lowerCamelCase__     (					self		:						str					,     lowerCAmelCase		:						List[Any]	)	->       List[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	Union[str, Any]             =      b""""""
						for token in tokens:
											if token in self.special_tokens_decoder:
																__UpperCamelCase					:	Optional[Any]             =      self.special_tokens_decoder[token].encode("""utf-8"""	)
											elif token in self.added_tokens_decoder:
																__UpperCamelCase					:	Optional[int]             =      self.special_tokens_decoder[token].encode("""utf-8"""	)
											elif token in self.special_tokens_encoder:
																__UpperCamelCase					:	Tuple             =      token.encode("""utf-8"""	)
											elif token in self.added_tokens_encoder:
																__UpperCamelCase					:	Tuple             =      token.encode("""utf-8"""	)
											else:
																__UpperCamelCase					:	Union[str, Any]             =      bytes([ord(lowerCAmelCase	)]	)
											bstring += tok_string
						__UpperCamelCase					:	Optional[int]             =      bstring.decode("""utf-8"""					,     errors="""ignore"""	)
						return string






	def 					lowerCamelCase__     (					self		:						Optional[int]					,     lowerCAmelCase		:						str					,     lowerCAmelCase		:						Optional[str] = None	)	->       Tuple[str]:







						"""simple docstring"""







						return ()






