


import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel

def 			_a  (				SCREAMING_SNAKE_CASE	:				List[str]     ):







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

 return name

def 			_a  (				SCREAMING_SNAKE_CASE	:				Union[str, Any]    , SCREAMING_SNAKE_CASE	:				Tuple     ):







 """simple docstring"""



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

 return orig_state_dict

def 			_a  (				):







 """simple docstring"""



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

@torch.no_grad()
def 			_a  (				SCREAMING_SNAKE_CASE	:				Dict    , SCREAMING_SNAKE_CASE	:				Tuple    , SCREAMING_SNAKE_CASE	:				Any="groupvit-gcc-yfcc"    , SCREAMING_SNAKE_CASE	:				List[str]=False     ):







 """simple docstring"""



 UpperCamelCase__					:    List[Any]          =   GroupViTConfig()
 UpperCamelCase__					:    Optional[int]          =   GroupViTModel(SCREAMING_SNAKE_CASE     ).eval()

 UpperCamelCase__					:    Union[str, Any]          =   torch.load(SCREAMING_SNAKE_CASE    , map_location='''cpu'''     )['''model''']
 UpperCamelCase__					:    List[Any]          =   convert_state_dict(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Any          =   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
 UpperCamelCase__					:    Optional[int]          =   CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32'''     )
 UpperCamelCase__					:    Optional[int]          =   prepare_img()
 UpperCamelCase__					:    Dict          =   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():
  UpperCamelCase__					:    Union[str, Any]          =   model(**SCREAMING_SNAKE_CASE     )

 if model_name == "groupvit-gcc-yfcc":
  UpperCamelCase__					:    Union[str, Any]          =   torch.tensor([[13.3523, 6.3629]]     )
 elif model_name == "groupvit-gcc-redcaps":
  UpperCamelCase__					:    Any          =   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__":
       __UpperCamelCase       :       int						       =				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       :       List[Any]						       =				parser.parse_args()

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






import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class       __magic_name__	(     __lowerCAmelCase					,				unittest.TestCase):
     A:		Optional[int]			=      BarthezTokenizer
     A:		Optional[int]			=      BarthezTokenizerFast
     A:		str			=      True
     A:		Optional[int]			=      True


     def      UpperCAmelCase__	(      self							:     List[Any]			)  ->      str:





      '''simple docstring'''






      super().setUp()

      UpperCamelCase__					:    Optional[Any]          =   BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''			)
      tokenizer.save_pretrained(self.tmpdirname			)
      tokenizer.save_pretrained(self.tmpdirname							,	legacy_format=lowerCamelCase__			)
      UpperCamelCase__					:    int          =   tokenizer


     def      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      str:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   '''<pad>'''
      UpperCamelCase__					:    List[str]          =   1

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase__			)							,	lowerCamelCase__			)
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase__			)							,	lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     int			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    int          =   list(self.get_tokenizer().get_vocab().keys()			)

      self.assertEqual(vocab_keys[0]							,	'''<s>'''			)
      self.assertEqual(vocab_keys[1]							,	'''<pad>'''			)
      self.assertEqual(vocab_keys[-1]							,	'''<mask>'''			)
      self.assertEqual(len(lowerCamelCase__			)							,	101122			)


     def      UpperCAmelCase__	(      self							:     Optional[Any]			)  ->      str:





      '''simple docstring'''






      self.assertEqual(self.get_tokenizer().vocab_size							,	101122			)


     @require_torch
     def      UpperCAmelCase__	(      self							:     Dict			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
      UpperCamelCase__					:    Any          =   [0, 57, 3018, 70307, 91, 2]

      UpperCamelCase__					:    Optional[int]          =   self.tokenizer(
          lowerCamelCase__							,	max_length=len(lowerCamelCase__			)							,	padding=lowerCamelCase__							,	truncation=lowerCamelCase__							,	return_tensors='''pt'''			)
      self.assertIsInstance(lowerCamelCase__							,	lowerCamelCase__			)

      self.assertEqual((2, 6)							,	batch.input_ids.shape			)
      self.assertEqual((2, 6)							,	batch.attention_mask.shape			)
      UpperCamelCase__					:    List[Any]          =   batch.input_ids.tolist()[0]
      self.assertListEqual(lowerCamelCase__							,	lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     str			)  ->      List[Any]:





      '''simple docstring'''






      if not self.test_rust_tokenizer:
       return

      UpperCamelCase__					:    List[Any]          =   self.get_tokenizer()
      UpperCamelCase__					:    Optional[int]          =   self.get_rust_tokenizer()

      UpperCamelCase__					:    Any          =   '''I was born in 92000, and this is falsé.'''

      UpperCamelCase__					:    List[str]          =   tokenizer.tokenize(lowerCamelCase__			)
      UpperCamelCase__					:    Tuple          =   rust_tokenizer.tokenize(lowerCamelCase__			)
      self.assertListEqual(lowerCamelCase__							,	lowerCamelCase__			)

      UpperCamelCase__					:    Tuple          =   tokenizer.encode(lowerCamelCase__							,	add_special_tokens=lowerCamelCase__			)
      UpperCamelCase__					:    str          =   rust_tokenizer.encode(lowerCamelCase__							,	add_special_tokens=lowerCamelCase__			)
      self.assertListEqual(lowerCamelCase__							,	lowerCamelCase__			)

      UpperCamelCase__					:    Tuple          =   self.get_rust_tokenizer()
      UpperCamelCase__					:    Optional[int]          =   tokenizer.encode(lowerCamelCase__			)
      UpperCamelCase__					:    str          =   rust_tokenizer.encode(lowerCamelCase__			)
      self.assertListEqual(lowerCamelCase__							,	lowerCamelCase__			)


     @slow
     def      UpperCAmelCase__	(      self							:     List[str]			)  ->      Dict:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   {'''input_ids''': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
      # fmt: on

      # moussaKam/mbarthez is a french model. So we also use french texts.
      UpperCamelCase__					:    int          =   [
          '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
          '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
          '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
          '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
          '''telles que la traduction et la synthèse de texte.''',
      ]

      self.tokenizer_integration_test_util(
          expected_encoding=lowerCamelCase__							,	model_name='''moussaKam/mbarthez'''							,	revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''							,	sequences=lowerCamelCase__							,	)


