

import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int      )			->      Optional[int]:




				'''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__							(	SCREAMING_SNAKE_CASE_:							Optional[int]	,       SCREAMING_SNAKE_CASE_:							Optional[int]      )			->      Tuple:




				'''simple docstring'''





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

				return orig_state_dict



def 					lowerCAmelCase__							(	)			->      Dict:




				'''simple docstring'''





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



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




				'''simple docstring'''





				A__        =			GroupViTConfig()
				A__        =			GroupViTModel(SCREAMING_SNAKE_CASE__      ).eval()

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

				with torch.no_grad():
								A__        =			model(**SCREAMING_SNAKE_CASE__      )

				if model_name == "groupvit-gcc-yfcc":
								A__        =			torch.tensor([[13.3523, 6.3629]]      )
				elif model_name == "groupvit-gcc-redcaps":
								A__        =			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__":
	lowerCAmelCase__	   = 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`.""",
	)
	lowerCAmelCase__	   = parser.parse_args()

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




def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int      )			->      int:




				'''simple docstring'''





				if divisor % 5 == 0 or divisor % 2 == 0:
								return 0
				A__        =			1
				A__        =			1
				while repunit:
								A__        =			(1_0 * repunit + 1) % divisor
								repunit_index += 1
				return repunit_index



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int = 1_0_0_0_0_0_0      )			->      int:




				'''simple docstring'''





				A__        =			limit - 1
				if divisor % 2 == 0:
								divisor += 1
				while least_divisible_repunit(SCREAMING_SNAKE_CASE_      ) <= limit:
								divisor += 2
				return divisor


if __name__ == "__main__":
	print(f"""{solution() = }""")

