



import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel







def snake_case__	(      __SCREAMING_SNAKE_CASE			)		->				Union[str, Any]:
		# vision encoder
		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 snake_case__	(      __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			)		->				Dict:
		for key in orig_state_dict.copy().keys():
				UpperCAmelCase_						       =					orig_state_dict.pop(__lowercase			)

				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(__lowercase			)
						# 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 snake_case__	(      )		->				Optional[Any]:
		UpperCAmelCase_						       =					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
		UpperCAmelCase_						       =					Image.open(requests.get(__lowercase      ,  stream=__lowercase			).raw			)
		return im







@torch.no_grad()
def snake_case__	(      __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE="groupvit-gcc-yfcc"      ,  __SCREAMING_SNAKE_CASE=False			)		->				Any:
		UpperCAmelCase_						       =					GroupViTConfig()
		UpperCAmelCase_						       =					GroupViTModel(__lowercase			).eval()

		UpperCAmelCase_						       =					torch.load(__lowercase      ,  map_location="cpu"			)['''model''']
		UpperCAmelCase_						       =					convert_state_dict(__lowercase      ,  __lowercase			)
		UpperCAmelCase_						       =					model.load_state_dict(__lowercase      ,  strict=__lowercase			)
		assert missing_keys == ["text_model.embeddings.position_ids"]
		assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(__lowercase			) == 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=__lowercase      ,  padding=__lowercase      ,  return_tensors="pt"			)

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

		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      ,  __lowercase      ,  atol=1E-3			)

		processor.save_pretrained(__lowercase			)
		model.save_pretrained(__lowercase			)
		print("Successfully saved processor and model to"      ,  __lowercase			)

		if push_to_hub:
				print("Pushing to the hub..."			)
				processor.push_to_hub(__lowercase      ,  organization="nielsr"			)
				model.push_to_hub(__lowercase      ,  organization="nielsr"			)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE  					=    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`.",
			)
			SCREAMING_SNAKE_CASE  					=    parser.parse_args()

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









from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Optional[Any]     =   ["image_processor", "tokenizer"]
		__snake_case      :       Tuple     =   "BridgeTowerImageProcessor"
		__snake_case      :       List[str]     =   ("RobertaTokenizer", "RobertaTokenizerFast")







		def __init__(					self       ,A__       ,A__		):
						super().__init__(A__       ,A__		)







		def __call__(					self       ,A__       ,A__ = None       ,A__ = True       ,A__ = False       ,A__ = None       ,A__ = None       ,A__ = 0       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = False       ,A__ = False       ,A__ = False       ,A__ = False       ,A__ = True       ,A__ = None       ,**A__       ,):
						_A	:				List[Any]   =      self.tokenizer(
						    text=A__       ,add_special_tokens=A__       ,padding=A__       ,truncation=A__       ,max_length=A__       ,stride=A__       ,pad_to_multiple_of=A__       ,return_token_type_ids=A__       ,return_attention_mask=A__       ,return_overflowing_tokens=A__       ,return_special_tokens_mask=A__       ,return_offsets_mapping=A__       ,return_length=A__       ,verbose=A__       ,return_tensors=A__       ,**A__       ,)
						# add pixel_values + pixel_mask
						_A	:				Optional[Any]   =      self.image_processor(
						    A__       ,return_tensors=A__       ,do_normalize=A__       ,do_center_crop=A__       ,**A__		)
						encoding.update(A__		)

						return encoding







		def  A__      (					self       ,*A__       ,**A__		):
						return self.tokenizer.batch_decode(*A__       ,**A__		)







		def  A__      (					self       ,*A__       ,**A__		):
						return self.tokenizer.decode(*A__       ,**A__		)







		@property
		def  A__      (					self		):
						_A	:				Union[str, Any]   =      self.tokenizer.model_input_names
						_A	:				str   =      self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)



