



"""simple docstring"""





import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def 						__lowerCAmelCase    (		__UpperCamelCase   :      List[Any]     ):


						'''simple docstring'''






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

						return name
def 						__lowerCAmelCase    (		__UpperCamelCase   :      List[Any]       ,			__UpperCamelCase   :      Optional[Any]     ):


						'''simple docstring'''






						for key in orig_state_dict.copy().keys():
												snake_case_ :			str            =      orig_state_dict.pop(__UpperCamelCase     )

												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
																		snake_case_ :			Any            =      key.split("""."""     )
																		snake_case_      ,		snake_case_ :			Union[str, Any]            =      int(key_split[2]     ), int(key_split[4]     )
																		snake_case_ :			Dict            =      config.vision_config.hidden_size
																		if "weight" in key:
																								snake_case_ :			str            =      val[:dim, :]
																								snake_case_ :			Optional[int]            =      val[dim : dim * 2, :]
																								snake_case_ :			Optional[int]            =      val[-dim:, :]
																		else:
																								snake_case_ :			List[str]            =      val[:dim]
																								snake_case_ :			Optional[Any]            =      val[dim : dim * 2]
																								snake_case_ :			Dict            =      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
																		snake_case_ :			Dict            =      key.split("""."""     )
																		snake_case_ :			str            =      int(key_split[3]     )
																		snake_case_ :			List[str]            =      config.text_config.hidden_size
																		if "weight" in key:
																								snake_case_ :			Optional[Any]            =      val[:dim, :]
																								snake_case_ :			List[str]            =      val[
																								    dim : dim * 2, :
																								]
																								snake_case_ :			Tuple            =      val[-dim:, :]
																		else:
																								snake_case_ :			List[str]            =      val[:dim]
																								snake_case_ :			Tuple            =      val[dim : dim * 2]
																								snake_case_ :			Tuple            =      val[-dim:]
												else:
																		snake_case_ :			List[str]            =      rename_key(__UpperCamelCase     )
																		# 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
																		):
																								snake_case_ :			Any            =      val.squeeze_()
																		else:
																								snake_case_ :			Any            =      val

						return orig_state_dict
def 						__lowerCAmelCase    (		):


						'''simple docstring'''






						snake_case_ :			Optional[int]            =      """http://images.cocodataset.org/val2017/000000039769.jpg"""
						snake_case_ :			Dict            =      Image.open(requests.get(__UpperCamelCase       ,			stream=__UpperCamelCase     ).raw     )
						return im
@torch.no_grad()
def 						__lowerCAmelCase    (		__UpperCamelCase   :      Any       ,			__UpperCamelCase   :      List[Any]       ,			__UpperCamelCase   :      Optional[int]="groupvit-gcc-yfcc"       ,			__UpperCamelCase   :      str=False     ):


						'''simple docstring'''






						snake_case_ :			Any            =      GroupViTConfig()
						snake_case_ :			Union[str, Any]            =      GroupViTModel(__UpperCamelCase     ).eval()

						snake_case_ :			Optional[int]            =      torch.load(__UpperCamelCase       ,			map_location="""cpu"""     )["""model"""]
						snake_case_ :			List[Any]            =      convert_state_dict(__UpperCamelCase       ,			__UpperCamelCase     )
						snake_case_      ,		snake_case_ :			int            =      model.load_state_dict(__UpperCamelCase       ,			strict=__UpperCamelCase     )
						assert missing_keys == ["text_model.embeddings.position_ids"]
						assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(__UpperCamelCase     ) == 0)

						# verify result
						snake_case_ :			List[str]            =      CLIPProcessor.from_pretrained("""openai/clip-vit-base-patch32"""     )
						snake_case_ :			List[str]            =      prepare_img()
						snake_case_ :			Optional[Any]            =      processor(text=["""a photo of a cat""", """a photo of a dog"""]       ,			images=__UpperCamelCase       ,			padding=__UpperCamelCase       ,			return_tensors="""pt"""     )

						with torch.no_grad():
												snake_case_ :			Dict            =      model(**__UpperCamelCase     )

						if model_name == "groupvit-gcc-yfcc":
												snake_case_ :			Union[str, Any]            =      torch.tensor([[13.3_523, 6.3_629]]     )
						elif model_name == "groupvit-gcc-redcaps":
												snake_case_ :			Any            =      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       ,			__UpperCamelCase       ,			atol=1E-3     )

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

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


if __name__ == "__main__":
					__lowerCAmelCase							:       Optional[Any]          =       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							:       Union[str, Any]          =       parser.parse_args()

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






from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase    	=		logging.get_logger(__name__)



class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					'''timm_backbone'''




     def __init__(					self			,    a__=None			,    a__=3			,    a__=True			,    a__=True			,    a__=None			,    **a__			,    ):
         super().__init__(**a__)
         A__					=       backbone
         A__					=       num_channels
         A__					=       features_only
         A__					=       use_pretrained_backbone
         A__					=       True
         A__					=       out_indices if out_indices is not None else (-1,)
