



import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel



def 			__a	(       lowerCAmelCase_  :   Union[str, Any]							) ->					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 			__a	(       lowerCAmelCase_  :   List[str]     ,lowerCAmelCase_  :   Optional[int]							) ->					str:






						'''simple docstring'''



						for key in orig_state_dict.copy().keys():
												UpperCAmelCase_=  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
																		UpperCAmelCase_=  key.split("""."""							)
																		UpperCAmelCase_,  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(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
																		):
																								UpperCAmelCase_=  val.squeeze_()
																		else:
																								UpperCAmelCase_=  val

						return orig_state_dict



def 			__a	(       ) ->					str:






						'''simple docstring'''



						UpperCAmelCase_=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
						UpperCAmelCase_=  Image.open(requests.get(a_     ,stream=a_							).raw							)
						return im



@torch.no_grad()
def 			__a	(       lowerCAmelCase_  :   Optional[Any]     ,lowerCAmelCase_  :   Tuple     ,lowerCAmelCase_  :   Optional[int]="groupvit-gcc-yfcc"     ,lowerCAmelCase_  :   Optional[Any]=False							) ->					List[Any]:






						'''simple docstring'''



						UpperCAmelCase_=  GroupViTConfig()
						UpperCAmelCase_=  GroupViTModel(a_							).eval()

						UpperCAmelCase_=  torch.load(a_     ,map_location="""cpu"""							)["""model"""]
						UpperCAmelCase_=  convert_state_dict(a_     ,a_							)
						UpperCAmelCase_,  UpperCAmelCase_=  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
						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=a_     ,padding=a_     ,return_tensors="""pt"""							)

						with torch.no_grad():
												UpperCAmelCase_=  model(**a_							)

						if model_name == "groupvit-gcc-yfcc":
												UpperCAmelCase_=  torch.tensor([[13.3_523, 6.3_629]]							)
						elif model_name == "groupvit-gcc-redcaps":
												UpperCAmelCase_=  torch.tensor([[16.1_873, 8.6_230]]							)
						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__":
	__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)












from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate




@dataclass(frozen=snake_case_							)
class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					field(default='''language-modeling'''			,   metadata={'''include_in_asdict_even_if_is_default''': True}							)

	lowerCAmelCase						=					Features({'''text''': Value('''string'''							)}							)
	lowerCAmelCase						=					Features({}							)
	lowerCAmelCase						=					"text"




	@property
	def 				_UpperCamelCase     (			self   )			->       Dict[str, str]:
								return {self.text_column: "text"}





