
"""simple docstring"""





import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel



def       lowerCamelCase__		(			__snake_case	)							->		Optional[int]:


					"""simple docstring"""





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

					return name



def       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		Optional[Any]:


					"""simple docstring"""





					for key in orig_state_dict.copy().keys():
										_UpperCamelCase							 =  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							 =  key.split('''.'''	)
															_UpperCamelCase							 =  int(key_split[2]	), int(key_split[4]	)
															_UpperCamelCase							 =  config.vision_config.hidden_size
															if "weight" in key:
																				_UpperCamelCase							 =  val[:dim, :]
																				_UpperCamelCase							 =  val[dim : dim * 2, :]
																				_UpperCamelCase							 =  val[-dim:, :]
															else:
																				_UpperCamelCase							 =  val[:dim]
																				_UpperCamelCase							 =  val[dim : dim * 2]
																				_UpperCamelCase							 =  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							 =  key.split('''.'''	)
															_UpperCamelCase							 =  int(key_split[3]	)
															_UpperCamelCase							 =  config.text_config.hidden_size
															if "weight" in key:
																				_UpperCamelCase							 =  val[:dim, :]
																				_UpperCamelCase							 =  val[
																				    dim : dim * 2, :
																				]
																				_UpperCamelCase							 =  val[-dim:, :]
															else:
																				_UpperCamelCase							 =  val[:dim]
																				_UpperCamelCase							 =  val[dim : dim * 2]
																				_UpperCamelCase							 =  val[-dim:]
										else:
															_UpperCamelCase							 =  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							 =  val.squeeze_()
															else:
																				_UpperCamelCase							 =  val

					return orig_state_dict



def       lowerCamelCase__		(			)							->		List[Any]:


					"""simple docstring"""





					_UpperCamelCase							 =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					_UpperCamelCase							 =  Image.open(requests.get(lowerCAmelCase__,     stream=lowerCAmelCase__	).raw	)
					return im



@torch.no_grad()
def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case="groupvit-gcc-yfcc",     __snake_case=False	)							->		List[Any]:


					"""simple docstring"""





					_UpperCamelCase							 =  GroupViTConfig()
					_UpperCamelCase							 =  GroupViTModel(lowerCAmelCase__	).eval()

					_UpperCamelCase							 =  torch.load(lowerCAmelCase__,     map_location='''cpu'''	)['''model''']
					_UpperCamelCase							 =  convert_state_dict(lowerCAmelCase__,     lowerCAmelCase__	)
					_UpperCamelCase							 =  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							 =  CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32'''	)
					_UpperCamelCase							 =  prepare_img()
					_UpperCamelCase							 =  processor(text=['''a photo of a cat''', '''a photo of a dog'''],     images=lowerCAmelCase__,     padding=lowerCAmelCase__,     return_tensors='''pt'''	)

					with torch.no_grad():
										_UpperCamelCase							 =  model(**lowerCAmelCase__	)

					if model_name == "groupvit-gcc-yfcc":
										_UpperCamelCase							 =  torch.tensor([[13.3523, 6.3629]]	)
					elif model_name == "groupvit-gcc-redcaps":
										_UpperCamelCase							 =  torch.tensor([[16.1873, 8.6230]]	)
					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														=		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														=		parser.parse_args()

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








import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
lowercase__       ={
    'E': 12.70,
    'T': 9.06,
    'A': 8.17,
    'O': 7.51,
    'I': 6.97,
    'N': 6.75,
    'S': 6.33,
    'H': 6.09,
    'R': 5.99,
    'D': 4.25,
    'L': 4.03,
    'C': 2.78,
    'U': 2.76,
    'M': 2.41,
    'W': 2.36,
    'F': 2.23,
    'G': 2.02,
    'Y': 1.97,
    'P': 1.93,
    'B': 1.29,
    'V': 0.98,
    'K': 0.77,
    'J': 0.15,
    'X': 0.15,
    'Q': 0.10,
    'Z': 0.07,
}
lowercase__       ='ETAOINSHRDLCUMWFGYPBVKJXQZ'
lowercase__       ='ABCDEFGHIJKLMNOPQRSTUVWXYZ'




def    __UpperCamelCase     (				lowerCAmelCase__							:				str  ):
 __a :  List[Any]						=  {letter: 0 for letter in string.ascii_uppercase}
 for letter in message.upper():
  if letter in LETTERS:
   letter_count[letter] += 1

 return letter_count




def    __UpperCamelCase     (				lowerCAmelCase__							:				tuple  ):
 return x[0]




def    __UpperCamelCase     (				lowerCAmelCase__							:				str  ):
 __a :  Optional[Any]						=  get_letter_count(lowerCAmelCase__  )
 __a :  dict[int, list[str]]						=  {
     freq: [] for letter, freq in letter_to_freq.items()
 }
 for letter in LETTERS:
  freq_to_letter[letter_to_freq[letter]].append(lowerCAmelCase__  )

 __a :  dict[int, str]						=  {}

 for freq in freq_to_letter:
  freq_to_letter[freq].sort(key=ETAOIN.find  ,   reverse=lowerCAmelCase__  )
  __a :  int						=  ''''''.join(freq_to_letter[freq]  )

 __a :  Any						=  list(freq_to_letter_str.items()  )
 freq_pairs.sort(key=lowerCAmelCase__  ,   reverse=lowerCAmelCase__  )

 __a :  list[str]						=  [freq_pair[1] for freq_pair in freq_pairs]

 return "".join(lowerCAmelCase__  )




def    __UpperCamelCase     (				lowerCAmelCase__							:				str  ):
 __a :  int						=  get_frequency_order(lowerCAmelCase__  )
 __a :  str						=  0
 for common_letter in ETAOIN[:6]:
  if common_letter in freq_order[:6]:
   match_score += 1

 for uncommon_letter in ETAOIN[-6:]:
  if uncommon_letter in freq_order[-6:]:
   match_score += 1

 return match_score


if __name__ == "__main__":
      import doctest

      doctest.testmod()
