





"""simple docstring"""


import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel

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

						return name

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						for key in orig_state_dict.copy().keys():
												UpperCamelCase				:		Tuple				   =			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
																		UpperCamelCase				:		List[Any]				   =			key.split("""."""     )
																		UpperCamelCase   ,						UpperCamelCase				:		List[str]				   =			int(key_split[2]     ), int(key_split[4]     )
																		UpperCamelCase				:		Tuple				   =			config.vision_config.hidden_size
																		if "weight" in key:
																								UpperCamelCase				:		str				   =			val[:dim, :]
																								UpperCamelCase				:		int				   =			val[dim : dim * 2, :]
																								UpperCamelCase				:		Optional[Any]				   =			val[-dim:, :]
																		else:
																								UpperCamelCase				:		Any				   =			val[:dim]
																								UpperCamelCase				:		Optional[Any]				   =			val[dim : dim * 2]
																								UpperCamelCase				:		Union[str, Any]				   =			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				:		Optional[int]				   =			key.split("""."""     )
																		UpperCamelCase				:		List[Any]				   =			int(key_split[3]     )
																		UpperCamelCase				:		Union[str, Any]				   =			config.text_config.hidden_size
																		if "weight" in key:
																								UpperCamelCase				:		Dict				   =			val[:dim, :]
																								UpperCamelCase				:		Dict				   =			val[
																								    dim : dim * 2, :
																								]
																								UpperCamelCase				:		int				   =			val[-dim:, :]
																		else:
																								UpperCamelCase				:		str				   =			val[:dim]
																								UpperCamelCase				:		Union[str, Any]				   =			val[dim : dim * 2]
																								UpperCamelCase				:		List[Any]				   =			val[-dim:]
												else:
																		UpperCamelCase				:		Optional[Any]				   =			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
																		):
																								UpperCamelCase				:		List[Any]				   =			val.squeeze_()
																		else:
																								UpperCamelCase				:		List[str]				   =			val

						return orig_state_dict

def      UpperCamelCase							():
						UpperCamelCase				:		str				   =			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						UpperCamelCase				:		Optional[int]				   =			Image.open(requests.get(SCREAMING_SNAKE_CASE							,     stream=SCREAMING_SNAKE_CASE     ).raw     )
						return im

@torch.no_grad()
def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE="groupvit-gcc-yfcc"							,     SCREAMING_SNAKE_CASE=False     ):
						UpperCamelCase				:		str				   =			GroupViTConfig()
						UpperCamelCase				:		Union[str, Any]				   =			GroupViTModel(SCREAMING_SNAKE_CASE     ).eval()

						UpperCamelCase				:		Tuple				   =			torch.load(SCREAMING_SNAKE_CASE							,     map_location="""cpu"""     )["""model"""]
						UpperCamelCase				:		Union[str, Any]				   =			convert_state_dict(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						UpperCamelCase   ,						UpperCamelCase				:		int				   =			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
						UpperCamelCase				:		List[Any]				   =			CLIPProcessor.from_pretrained("""openai/clip-vit-base-patch32"""     )
						UpperCamelCase				:		Optional[int]				   =			prepare_img()
						UpperCamelCase				:		List[str]				   =			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():
												UpperCamelCase				:		Optional[int]				   =			model(**SCREAMING_SNAKE_CASE     )

						if model_name == "groupvit-gcc-yfcc":
												UpperCamelCase				:		Optional[int]				   =			torch.tensor([[13.35_23, 6.36_29]]     )
						elif model_name == "groupvit-gcc-redcaps":
												UpperCamelCase				:		List[str]				   =			torch.tensor([[16.18_73, 8.62_30]]     )
						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__":
		__magic_name__       :					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`.""",
		)
		__magic_name__       :					str						=			parser.parse_args()

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







import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


lowerCAmelCase__    :int       =				logging.get_logger(__name__)

def 			lowerCAmelCase__					(     a__:						Dict			)    ->						List[str]:


							'''simple docstring'''





							_UpperCAmelCase											=     R'\w+[.]\d+'
							_UpperCAmelCase											=     re.findall(a__     ,    a__			)
							for pat in pats:
														_UpperCAmelCase											=     key.replace(a__     ,    '_'.join(pat.split('.'			)			)			)
							return key

def 			lowerCAmelCase__					(     a__:						Optional[int]     ,    a__:						Dict     ,    a__:						List[Any]			)    ->						str:


							'''simple docstring'''





							_UpperCAmelCase											=     pt_tuple_key[:-1] + ('scale',)
							if (
							    any('norm' in str_ for str_ in pt_tuple_key			)
							    and (pt_tuple_key[-1] == "bias")
							    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
							    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
							):
														_UpperCAmelCase											=     pt_tuple_key[:-1] + ('scale',)
														return renamed_pt_tuple_key, pt_tensor
							elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
														_UpperCAmelCase											=     pt_tuple_key[:-1] + ('scale',)
														return renamed_pt_tuple_key, pt_tensor

							# embedding
							if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
														_UpperCAmelCase											=     pt_tuple_key[:-1] + ('embedding',)
														return renamed_pt_tuple_key, pt_tensor

							# conv layer
							_UpperCAmelCase											=     pt_tuple_key[:-1] + ('kernel',)
							if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
														_UpperCAmelCase											=     pt_tensor.transpose(2     ,    3     ,    1     ,    0			)
														return renamed_pt_tuple_key, pt_tensor

							# linear layer
							_UpperCAmelCase											=     pt_tuple_key[:-1] + ('kernel',)
							if pt_tuple_key[-1] == "weight":
														_UpperCAmelCase											=     pt_tensor.T
														return renamed_pt_tuple_key, pt_tensor

							# old PyTorch layer norm weight
							_UpperCAmelCase											=     pt_tuple_key[:-1] + ('weight',)
							if pt_tuple_key[-1] == "gamma":
														return renamed_pt_tuple_key, pt_tensor

							# old PyTorch layer norm bias
							_UpperCAmelCase											=     pt_tuple_key[:-1] + ('bias',)
							if pt_tuple_key[-1] == "beta":
														return renamed_pt_tuple_key, pt_tensor

							return pt_tuple_key, pt_tensor







def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						Optional[Any]     ,    a__:						int=4_2			)    ->						List[str]:


							'''simple docstring'''





							_UpperCAmelCase											=     {k: v.numpy() for k, v in pt_state_dict.items()}

							# Step 2: Since the model is stateless, get random Flax params
							_UpperCAmelCase											=     flax_model.init_weights(PRNGKey(a__			)			)

							_UpperCAmelCase											=     flatten_dict(a__			)
							_UpperCAmelCase											=     {}

							# Need to change some parameters name to match Flax names
							for pt_key, pt_tensor in pt_state_dict.items():
														_UpperCAmelCase											=     rename_key(a__			)
														_UpperCAmelCase											=     tuple(renamed_pt_key.split('.'			)			)

														# Correctly rename weight parameters
														_UpperCAmelCase						,			_UpperCAmelCase											=     rename_key_and_reshape_tensor(a__     ,    a__     ,    a__			)

														if flax_key in random_flax_state_dict:
																					if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																												raise ValueError(
																												    F'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
																												    F'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''			)

        # also add unexpected weight so that warning is thrown
														_UpperCAmelCase											=     jnp.asarray(a__			)

							return unflatten_dict(a__			)
