





import argparse
import json
import os

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCAmelCase			   =							logging.get_logger(__name__)






def        _SCREAMING_SNAKE_CASE		(			SCREAMING_SNAKE_CASE		:Tuple    )			->    Optional[Any]:
 __lowerCAmelCase     :       List[str]						=   ConvNextVaConfig()

 if "atto" in checkpoint_url:
  __lowerCAmelCase     :       List[str]						=   [2, 2, 6, 2]
  __lowerCAmelCase     :       Optional[int]						=   [40, 80, 160, 320]
 if "femto" in checkpoint_url:
  __lowerCAmelCase     :       Optional[int]						=   [2, 2, 6, 2]
  __lowerCAmelCase     :       Optional[int]						=   [48, 96, 192, 384]
 if "pico" in checkpoint_url:
  __lowerCAmelCase     :       Optional[Any]						=   [2, 2, 6, 2]
  __lowerCAmelCase     :       int						=   [64, 128, 256, 512]
 if "nano" in checkpoint_url:
  __lowerCAmelCase     :       Dict						=   [2, 2, 8, 2]
  __lowerCAmelCase     :       Optional[int]						=   [80, 160, 320, 640]
 if "tiny" in checkpoint_url:
  __lowerCAmelCase     :       Any						=   [3, 3, 9, 3]
  __lowerCAmelCase     :       str						=   [96, 192, 384, 768]
 if "base" in checkpoint_url:
  __lowerCAmelCase     :       Dict						=   [3, 3, 27, 3]
  __lowerCAmelCase     :       Tuple						=   [128, 256, 512, 1_024]
 if "large" in checkpoint_url:
  __lowerCAmelCase     :       Tuple						=   [3, 3, 27, 3]
  __lowerCAmelCase     :       Optional[int]						=   [192, 384, 768, 1_536]
 if "huge" in checkpoint_url:
  __lowerCAmelCase     :       Optional[int]						=   [3, 3, 27, 3]
  __lowerCAmelCase     :       Dict						=   [352, 704, 1_408, 2_816]

 __lowerCAmelCase     :       List[Any]						=   1_000
 __lowerCAmelCase     :       Union[str, Any]						=   """imagenet-1k-id2label.json"""
 __lowerCAmelCase     :       List[Any]						=   (1, 1_000)

 __lowerCAmelCase     :       int						=   """huggingface/label-files"""
 __lowerCAmelCase     :       Tuple						=   num_labels
 __lowerCAmelCase     :       int						=   json.load(open(hf_hub_download(UpperCamelCase_    ,    UpperCamelCase_    ,    repo_type="""dataset"""    )    ,    """r"""    )    )
 __lowerCAmelCase     :       str						=   {int(UpperCamelCase_    ): v for k, v in idalabel.items()}

 __lowerCAmelCase     :       Tuple						=   idalabel
 __lowerCAmelCase     :       Any						=   {v: k for k, v in idalabel.items()}
 __lowerCAmelCase     :       str						=   hidden_sizes
 __lowerCAmelCase     :       Union[str, Any]						=   depths

 return config, expected_shape






def        _SCREAMING_SNAKE_CASE		(			SCREAMING_SNAKE_CASE		:str    )			->    Any:



 if "downsample_layers.0.0" in name:
  __lowerCAmelCase     :       Optional[Any]						=   name.replace("""downsample_layers.0.0"""    ,    """embeddings.patch_embeddings"""    )
 if "downsample_layers.0.1" in name:
  __lowerCAmelCase     :       int						=   name.replace("""downsample_layers.0.1"""    ,    """embeddings.norm"""    )  # we rename to layernorm later on
 if "downsample_layers.1.0" in name:
  __lowerCAmelCase     :       Optional[Any]						=   name.replace("""downsample_layers.1.0"""    ,    """stages.1.downsampling_layer.0"""    )
 if "downsample_layers.1.1" in name:
  __lowerCAmelCase     :       Optional[int]						=   name.replace("""downsample_layers.1.1"""    ,    """stages.1.downsampling_layer.1"""    )
 if "downsample_layers.2.0" in name:
  __lowerCAmelCase     :       Tuple						=   name.replace("""downsample_layers.2.0"""    ,    """stages.2.downsampling_layer.0"""    )
 if "downsample_layers.2.1" in name:
  __lowerCAmelCase     :       Optional[int]						=   name.replace("""downsample_layers.2.1"""    ,    """stages.2.downsampling_layer.1"""    )
 if "downsample_layers.3.0" in name:
  __lowerCAmelCase     :       Optional[int]						=   name.replace("""downsample_layers.3.0"""    ,    """stages.3.downsampling_layer.0"""    )
 if "downsample_layers.3.1" in name:
  __lowerCAmelCase     :       List[Any]						=   name.replace("""downsample_layers.3.1"""    ,    """stages.3.downsampling_layer.1"""    )
 if "stages" in name and "downsampling_layer" not in name:
  # stages.0.0. for instance should be renamed to stages.0.layers.0.
  __lowerCAmelCase     :       str						=   name[: len("""stages.0"""    )] + """.layers""" + name[len("""stages.0"""    ) :]
 if "gamma" in name:
  __lowerCAmelCase     :       List[Any]						=   name.replace("""gamma"""    ,    """weight"""    )
 if "beta" in name:
  __lowerCAmelCase     :       Optional[Any]						=   name.replace("""beta"""    ,    """bias"""    )
 if "stages" in name:
  __lowerCAmelCase     :       str						=   name.replace("""stages"""    ,    """encoder.stages"""    )
 if "norm" in name:
  __lowerCAmelCase     :       Union[str, Any]						=   name.replace("""norm"""    ,    """layernorm"""    )
 if "head" in name:
  __lowerCAmelCase     :       List[Any]						=   name.replace("""head"""    ,    """classifier"""    )

 return name






def        _SCREAMING_SNAKE_CASE		(			)			->    int:
 __lowerCAmelCase     :       Any						=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
 __lowerCAmelCase     :       List[Any]						=   Image.open(requests.get(UpperCamelCase_    ,    stream=UpperCamelCase_    ).raw    )
 return im






def        _SCREAMING_SNAKE_CASE		(			SCREAMING_SNAKE_CASE		:Dict    )			->    Optional[int]:



 if "224" in checkpoint_url:
  __lowerCAmelCase     :       Any						=   224
  __lowerCAmelCase     :       List[Any]						=   224 / 256
 elif "384" in checkpoint_url:
  __lowerCAmelCase     :       Optional[int]						=   384
  __lowerCAmelCase     :       List[Any]						=   None
 else:
  __lowerCAmelCase     :       Tuple						=   512
  __lowerCAmelCase     :       Any						=   None

 return ConvNextImageProcessor(
     size=UpperCamelCase_    ,    crop_pct=UpperCamelCase_    ,    image_mean=[0.4_85, 0.4_56, 0.4_06]    ,    image_std=[0.2_29, 0.2_24, 0.2_25]    ,    resample=PILImageResampling.BICUBIC    ,    )






@torch.no_grad()
def        _SCREAMING_SNAKE_CASE		(			SCREAMING_SNAKE_CASE		:Optional[int]    ,    SCREAMING_SNAKE_CASE		:Any    ,    SCREAMING_SNAKE_CASE		:Union[str, Any]    ,    SCREAMING_SNAKE_CASE		:int    )			->    int:
 print("""Downloading original model from checkpoint..."""    )
 # define ConvNeXTV2 configuration based on URL
 __lowerCAmelCase					,     __lowerCAmelCase     :       str						=   get_convnextva_config(UpperCamelCase_    )
 # load original state_dict from URL
 __lowerCAmelCase     :       Any						=   torch.hub.load_state_dict_from_url(UpperCamelCase_    )["""model"""]

 print("""Converting model parameters..."""    )
 # rename keys
 for key in state_dict.copy().keys():
  __lowerCAmelCase     :       Optional[Any]						=   state_dict.pop(UpperCamelCase_    )
  __lowerCAmelCase     :       List[str]						=   val
 # add prefix to all keys expect classifier head
 for key in state_dict.copy().keys():
  __lowerCAmelCase     :       Dict						=   state_dict.pop(UpperCamelCase_    )
  if not key.startswith("""classifier"""    ):
   __lowerCAmelCase     :       str						=   """convnextv2.""" + key
  __lowerCAmelCase     :       str						=   val

 # load HuggingFace model
 __lowerCAmelCase     :       Union[str, Any]						=   ConvNextVaForImageClassification(UpperCamelCase_    )
 model.load_state_dict(UpperCamelCase_    )
 model.eval()

 # Check outputs on an image, prepared by ConvNextImageProcessor
 __lowerCAmelCase     :       Dict						=   convert_preprocessor(UpperCamelCase_    )
 __lowerCAmelCase     :       Optional[Any]						=   preprocessor(images=prepare_img()    ,    return_tensors="""pt"""    )
 __lowerCAmelCase     :       List[Any]						=   model(**UpperCamelCase_    ).logits

 # note: the logits below were obtained without center cropping
 if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
  __lowerCAmelCase     :       str						=   torch.tensor([-0.39_30, 0.17_47, -0.52_46, 0.41_77, 0.42_95]    )
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
  __lowerCAmelCase     :       List[str]						=   torch.tensor([-0.17_27, -0.53_41, -0.78_18, -0.47_45, -0.65_66]    )
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
  __lowerCAmelCase     :       Dict						=   torch.tensor([-0.03_33, 0.15_63, -0.91_37, 0.10_54, 0.03_81]    )
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
  __lowerCAmelCase     :       List[Any]						=   torch.tensor([-0.17_44, -0.15_55, -0.07_13, 0.09_50, -0.14_31]    )
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
  __lowerCAmelCase     :       Union[str, Any]						=   torch.tensor([0.99_96, 0.19_66, -0.43_86, -0.34_72, 0.66_61]    )
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
  __lowerCAmelCase     :       Tuple						=   torch.tensor([-0.25_53, -0.67_08, -0.13_59, 0.25_18, -0.24_88]    )
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
  __lowerCAmelCase     :       List[Any]						=   torch.tensor([-0.06_73, -0.56_27, -0.37_53, -0.27_22, 0.01_78]    )
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
  __lowerCAmelCase     :       Any						=   torch.tensor([-0.63_77, -0.74_58, -0.21_50, 0.11_84, -0.05_97]    )
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
  __lowerCAmelCase     :       Any						=   torch.tensor([1.07_99, 0.23_22, -0.88_60, 1.02_19, 0.62_31]    )
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
  __lowerCAmelCase     :       Any						=   torch.tensor([0.37_66, 0.49_17, -1.14_26, 0.99_42, 0.60_24]    )
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
  __lowerCAmelCase     :       Union[str, Any]						=   torch.tensor([0.42_20, -0.69_19, -0.43_17, -0.28_81, -0.66_09]    )
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
  __lowerCAmelCase     :       List[str]						=   torch.tensor([0.10_82, -0.82_86, -0.50_95, 0.46_81, -0.80_85]    )
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
  __lowerCAmelCase     :       List[Any]						=   torch.tensor([-0.24_19, -0.62_21, 0.21_76, -0.09_80, -0.75_27]    )
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
  __lowerCAmelCase     :       Optional[Any]						=   torch.tensor([0.03_91, -0.43_71, 0.37_86, 0.12_51, -0.27_84]    )
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
  __lowerCAmelCase     :       Tuple						=   torch.tensor([-0.05_04, 0.56_36, -0.17_29, -0.65_07, -0.39_49]    )
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
  __lowerCAmelCase     :       List[str]						=   torch.tensor([0.35_60, 0.94_86, 0.31_49, -0.26_67, -0.51_38]    )
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
  __lowerCAmelCase     :       int						=   torch.tensor([-0.24_69, -0.45_50, -0.58_53, -0.08_10, 0.03_09]    )
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
  __lowerCAmelCase     :       Optional[Any]						=   torch.tensor([-0.30_90, 0.08_02, -0.06_82, -0.19_79, -0.28_26]    )
 else:
  raise ValueError(F'''Unknown URL: {checkpoint_url}'''    )

 assert torch.allclose(logits[0, :5]    ,    UpperCamelCase_    ,    atol=1e-3    )
 assert logits.shape == expected_shape
 print("""Model outputs match the original results!"""    )

 if save_model:
  print("""Saving model to local..."""    )
  # Create folder to save model
  if not os.path.isdir(UpperCamelCase_    ):
   os.mkdir(UpperCamelCase_    )

  model.save_pretrained(UpperCamelCase_    )
  preprocessor.save_pretrained(UpperCamelCase_    )

 __lowerCAmelCase     :       Tuple						=   """convnextv2"""
 if "atto" in checkpoint_url:
  model_name += "-atto"
 if "femto" in checkpoint_url:
  model_name += "-femto"
 if "pico" in checkpoint_url:
  model_name += "-pico"
 if "nano" in checkpoint_url:
  model_name += "-nano"
 elif "tiny" in checkpoint_url:
  model_name += "-tiny"
 elif "base" in checkpoint_url:
  model_name += "-base"
 elif "large" in checkpoint_url:
  model_name += "-large"
 elif "huge" in checkpoint_url:
  model_name += "-huge"
 if "22k" in checkpoint_url and "1k" not in checkpoint_url:
  model_name += "-22k"
 elif "22k" in checkpoint_url and "1k" in checkpoint_url:
  model_name += "-22k-1k"
 elif "1k" in checkpoint_url:
  model_name += "-1k"
 if "224" in checkpoint_url:
  model_name += "-224"
 elif "384" in checkpoint_url:
  model_name += "-384"
 elif "512" in checkpoint_url:
  model_name += "-512"

 if push_to_hub:
  print(F'''Pushing {model_name} to the hub...'''    )
  model.push_to_hub(UpperCamelCase_    )
  preprocessor.push_to_hub(UpperCamelCase_    )


if __name__ == "__main__":
      _UpperCAmelCase			   =							argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--checkpoint_url',
          default='https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt',
          type=str,
          help='URL of the original ConvNeXTV2 checkpoint you\'d like to convert.',
      )
      parser.add_argument(
          '--pytorch_dump_folder_path',
          default='model',
          type=str,
          help='Path to the output PyTorch model directory.',
      )
      parser.add_argument('--save_model', action='store_true', help='Save model to local')
      parser.add_argument('--push_to_hub', action='store_true', help='Push model and image preprocessor to the hub')

      _UpperCAmelCase			   =							parser.parse_args()
      convert_convnextva_checkpoint(
          args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
      )







import argparse
import collections

import jax
import jax.numpy as jnp
import torch
import torch.nn as nn
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository

from transformers import (
    CLIPTokenizer,
    OwlViTConfig,
    OwlViTForObjectDetection,
    OwlViTImageProcessor,
    OwlViTModel,
    OwlViTProcessor,
)


__snake_case			:			int			   =   {
    """vit_b32""": {
        """embed_dim""": 5_12,
        """image_resolution""": 7_68,
        """context_length""": 16,
        """vocab_size""": 4_94_08,
        """vision_layers""": 12,
        """vision_width""": 7_68,
        """vision_patch_size""": 32,
        """transformer_width""": 5_12,
        """transformer_heads""": 8,
        """transformer_layers""": 12,
    },
    """vit_b16""": {
        """embed_dim""": 5_12,
        """image_resolution""": 7_68,
        """context_length""": 16,
        """vocab_size""": 4_94_08,
        """vision_layers""": 12,
        """vision_width""": 7_68,
        """vision_patch_size""": 16,
        """transformer_width""": 5_12,
        """transformer_heads""": 8,
        """transformer_layers""": 12,
    },
    """vit_l14""": {
        """embed_dim""": 7_68,
        """image_resolution""": 8_40,
        """context_length""": 16,
        """vocab_size""": 4_94_08,
        """vision_layers""": 24,
        """vision_width""": 10_24,
        """vision_patch_size""": 14,
        """transformer_width""": 7_68,
        """transformer_heads""": 12,
        """transformer_layers""": 12,
    },
}







def 		_UpperCamelCase      (			UpperCamelCase_				:    Dict			,				UpperCamelCase_				:    Any=""			,				UpperCamelCase_				:    List[str]="/"						)     ->      List[str]:

						"""simple docstring"""



						lowerCAmelCase__			  =  []

						for k, v in params.items():
												lowerCAmelCase__			  =  parent_key + sep + k if parent_key else k

												if isinstance(UpperCamelCase_			,				collections.MutableMapping						):
																		items.extend(flatten_nested_dict(UpperCamelCase_			,				UpperCamelCase_			,				sep=UpperCamelCase_						).items()						)
												else:
																		items.append((new_key, v)						)
						return dict(UpperCamelCase_						)







def 		_UpperCamelCase      (			UpperCamelCase_				:    Tuple						)     ->      Optional[int]:

						"""simple docstring"""



						return jax.tree_util.tree_map(lambda UpperCamelCase_						: x.astype(jnp.floataa						) if x.dtype == jnp.bfloataa else x			,				UpperCamelCase_						)







def 		_UpperCamelCase      (			UpperCamelCase_				:    Optional[int]			,				UpperCamelCase_				:    Union[str, Any]						)     ->      List[Any]:

						"""simple docstring"""



						lowerCAmelCase__  ,				lowerCAmelCase__  ,				lowerCAmelCase__			  =  pt_attn_layer.in_proj_weight.chunk(3			,				dim=0						)
						lowerCAmelCase__  ,				lowerCAmelCase__  ,				lowerCAmelCase__			  =  pt_attn_layer.in_proj_bias.chunk(3			,				dim=0						)

						lowerCAmelCase__			  =  pt_attn_layer.out_proj.weight
						lowerCAmelCase__			  =  pt_attn_layer.out_proj.bias

						lowerCAmelCase__			  =  q_proj
						lowerCAmelCase__			  =  q_proj_bias

						lowerCAmelCase__			  =  k_proj
						lowerCAmelCase__			  =  k_proj_bias

						lowerCAmelCase__			  =  v_proj
						lowerCAmelCase__			  =  v_proj_bias

						lowerCAmelCase__			  =  out_proj_weights
						lowerCAmelCase__			  =  out_proj_bias







def 		_UpperCamelCase      (			UpperCamelCase_				:    Tuple			,				UpperCamelCase_				:    Union[str, Any]						)     ->      Union[str, Any]:

						"""simple docstring"""



						copy_linear(hf_mlp.fca			,				pt_mlp.c_fc						)
						copy_linear(hf_mlp.fca			,				pt_mlp.c_proj						)







def 		_UpperCamelCase      (			UpperCamelCase_				:    Union[str, Any]			,				UpperCamelCase_				:    Tuple						)     ->      Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  pt_linear.weight
						lowerCAmelCase__			  =  pt_linear.bias







def 		_UpperCamelCase      (			UpperCamelCase_				:    Any			,				UpperCamelCase_				:    Optional[Any]						)     ->      Tuple:

						"""simple docstring"""



						copy_linear(hf_layer.layer_norma			,				pt_layer.ln_a						)
						copy_linear(hf_layer.layer_norma			,				pt_layer.ln_a						)

						# copy MLP
						copy_mlp(hf_layer.mlp			,				pt_layer.mlp						)

						# copy attn
						copy_attn_layer(hf_layer.self_attn			,				pt_layer.attn						)







def 		_UpperCamelCase      (			UpperCamelCase_				:    Optional[int]			,				UpperCamelCase_				:    List[str]						)     ->      Optional[int]:

						"""simple docstring"""



						for hf_layer, pt_layer in zip(UpperCamelCase_			,				UpperCamelCase_						):
												copy_layer(UpperCamelCase_			,				UpperCamelCase_						)







def 		_UpperCamelCase      (			UpperCamelCase_				:    Dict			,				UpperCamelCase_				:    Union[str, Any]						)     ->      Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  pt_model.token_embedding.weight
						lowerCAmelCase__			  =  pt_model.positional_embedding

						# copy layer norm
						copy_linear(hf_encoder.final_layer_norm			,				pt_model.ln_final						)

						# copy hidden layers
						copy_layers(hf_encoder.encoder.layers			,				pt_model.transformer.resblocks						)







def 		_UpperCamelCase      (			UpperCamelCase_				:    Optional[int]			,				UpperCamelCase_				:    Dict						)     ->      Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  pt_model.text_projection.data.T

						# copy text encoder
						copy_encoder(hf_model.text_model			,				UpperCamelCase_						)







def 		_UpperCamelCase      (			UpperCamelCase_				:    Any			,				UpperCamelCase_				:    str						)     ->      Tuple:

						"""simple docstring"""



						lowerCAmelCase__			  =  pt_model.visual.proj.data.T

						# copy layer norms
						copy_linear(hf_model.vision_model.pre_layernorm			,				pt_model.visual.ln_pre						)
						copy_linear(hf_model.vision_model.post_layernorm			,				pt_model.visual.ln_post						)

						# copy embeds
						lowerCAmelCase__			  =  pt_model.visual.conva.weight.data
						lowerCAmelCase__			  =  pt_model.visual.class_embedding
						lowerCAmelCase__			  =  pt_model.visual.positional_embedding.data

						# copy encoder
						copy_layers(hf_model.vision_model.encoder.layers			,				pt_model.visual.transformer.resblocks						)







def 		_UpperCamelCase      (			UpperCamelCase_				:    List[Any]			,				UpperCamelCase_				:    Any						)     ->      List[str]:

						"""simple docstring"""



						lowerCAmelCase__			  =  flatten_nested_dict(flax_params['backbone']['merged_class_token']						)

						lowerCAmelCase__			  =  torch.from_numpy(flax_class_token_params['scale']						)
						lowerCAmelCase__			  =  torch.from_numpy(flax_class_token_params['bias']						)
						lowerCAmelCase__			  =  nn.Parameter(UpperCamelCase_						)
						lowerCAmelCase__			  =  nn.Parameter(UpperCamelCase_						)







def 		_UpperCamelCase      (			UpperCamelCase_				:    str			,				UpperCamelCase_				:    Union[str, Any]						)     ->      Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  hf_model.state_dict()
						lowerCAmelCase__			  =  {}

						# Rename class prediction head flax params to pytorch HF
						lowerCAmelCase__			  =  flatten_nested_dict(flax_params['class_head']						)

						for flax_key, v in flax_class_params.items():
												lowerCAmelCase__			  =  flax_key.replace('/'			,				'.'						)
												lowerCAmelCase__			  =  torch_key.replace('.kernel'			,				'.weight'						)
												lowerCAmelCase__			  =  torch_key.replace('Dense_0'			,				'dense0'						)
												lowerCAmelCase__			  =  'class_head.' + torch_key

												if "weight" in torch_key and v.ndim == 2:
																		lowerCAmelCase__			  =  v.T

												lowerCAmelCase__			  =  nn.Parameter(torch.from_numpy(UpperCamelCase_						)						)

						# Rename box prediction box flax params to pytorch HF
						lowerCAmelCase__			  =  flatten_nested_dict(flax_params['obj_box_head']						)

						for flax_key, v in flax_box_params.items():
												lowerCAmelCase__			  =  flax_key.replace('/'			,				'.'						)
												lowerCAmelCase__			  =  torch_key.replace('.kernel'			,				'.weight'						)
												lowerCAmelCase__			  =  torch_key.replace('_'			,				''						).lower()
												lowerCAmelCase__			  =  'box_head.' + torch_key

												if "weight" in torch_key and v.ndim == 2:
																		lowerCAmelCase__			  =  v.T

												lowerCAmelCase__			  =  nn.Parameter(torch.from_numpy(UpperCamelCase_						)						)

						# Copy flax params to PyTorch params
						for name, param in new_params.items():
												if name in pt_params.keys():
																		pt_params[name].copy_(UpperCamelCase_						)







def 		_UpperCamelCase      (			UpperCamelCase_				:    Tuple			,				UpperCamelCase_				:    List[str]						)     ->      str:

						"""simple docstring"""



						for k, v in flax_attn_params.items():
												if k.startswith('transformer'						):
																		lowerCAmelCase__			  =  k.replace('transformer.resblocks'			,				'text_model.encoder.layers'						)
												else:
																		lowerCAmelCase__			  =  k.replace('visual.transformer.resblocks'			,				'vision_model.encoder.layers'						)

												lowerCAmelCase__			  =  torch_key.replace('attn'			,				'self_attn'						)
												lowerCAmelCase__			  =  torch_key.replace('key'			,				'k_proj'						)
												lowerCAmelCase__			  =  torch_key.replace('value'			,				'v_proj'						)
												lowerCAmelCase__			  =  torch_key.replace('query'			,				'q_proj'						)
												lowerCAmelCase__			  =  torch_key.replace('out'			,				'out_proj'						)

												if "bias" in torch_key and v.ndim == 2:
																		lowerCAmelCase__			  =  v.shape[0] * v.shape[1]
																		lowerCAmelCase__			  =  v.reshape(UpperCamelCase_						)

												if "weight" in torch_key and "out" in torch_key:
																		lowerCAmelCase__			  =  (v.shape[0] * v.shape[1], v.shape[2])
																		lowerCAmelCase__			  =  v.reshape(UpperCamelCase_						).T

												if "weight" in torch_key and "out" not in torch_key:
																		lowerCAmelCase__			  =  (v.shape[0], v.shape[1] * v.shape[2])
																		lowerCAmelCase__			  =  v.reshape(UpperCamelCase_						).T

												# Copy flax CLIP attn params to HF PyTorch params
												lowerCAmelCase__			  =  torch.from_numpy(UpperCamelCase_						)
												hf_backbone.state_dict()[torch_key].copy_(UpperCamelCase_						)







def 		_UpperCamelCase      (			UpperCamelCase_				:    Any						)     ->      Dict:

						"""simple docstring"""



						lowerCAmelCase__			  =  {}
						lowerCAmelCase__			  =  []

						for k, v in params.items():
												if "attn." in k:
																		lowerCAmelCase__			  =  k[: k.rindex('attn.'						) + 5]
																		if base in processed_attn_layers:
																								continue

																		processed_attn_layers.append(UpperCamelCase_						)
																		lowerCAmelCase__			  =  params[base + 'out.weight'].shape[-1]
																		lowerCAmelCase__			  =  params[base + 'out.weight'].reshape(UpperCamelCase_			,				UpperCamelCase_						).T
																		lowerCAmelCase__			  =  params[base + 'out.bias']
												else:
																		lowerCAmelCase__			  =  v
						return new_params







def 		_UpperCamelCase      (			UpperCamelCase_				:    List[Any]			,				UpperCamelCase_				:    int						)     ->      Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  CLIP(**UpperCamelCase_						)
						torch_model.eval()
						lowerCAmelCase__			  =  torch_model.state_dict()

						lowerCAmelCase__			  =  flatten_nested_dict(flax_params['backbone']['clip']						)
						lowerCAmelCase__			  =  {}

						for flax_key, v in flax_clip_params.items():
												lowerCAmelCase__			  =  flax_key.replace('/'			,				'.'						)
												lowerCAmelCase__			  =  torch_key.replace('text.token_embedding.embedding'			,				'token_embedding.kernel'						)

												if (
												    torch_key.startswith('text.transformer'						)
												    or torch_key.startswith('text.text_projection'						)
												    or torch_key.startswith('text.ln_final'						)
												    or torch_key.startswith('text.positional_embedding'						)
												):
																		lowerCAmelCase__			  =  torch_key[5:]

												lowerCAmelCase__			  =  torch_key.replace('text_projection.kernel'			,				'text_projection'						)
												lowerCAmelCase__			  =  torch_key.replace('visual.proj.kernel'			,				'visual.proj'						)
												lowerCAmelCase__			  =  torch_key.replace('.scale'			,				'.weight'						)
												lowerCAmelCase__			  =  torch_key.replace('.kernel'			,				'.weight'						)

												if "conv" in torch_key or "downsample.0.weight" in torch_key:
																		lowerCAmelCase__			  =  v.transpose(3			,				2			,				0			,				1						)

												elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
																		# Fully connected layers are transposed, embeddings are not
																		lowerCAmelCase__			  =  v.T

												lowerCAmelCase__			  =  v

						lowerCAmelCase__			  =  _convert_attn_layers(UpperCamelCase_						)
						new_torch_params.update(UpperCamelCase_						)
						lowerCAmelCase__			  =  {}

						# Copy flax CLIP backbone params to PyTorch params
						for name, param in new_torch_params.items():
												if name in torch_clip_params.keys():
																		lowerCAmelCase__			  =  torch.from_numpy(new_torch_params[name]						)
																		torch_clip_params[name].copy_(UpperCamelCase_						)
												else:
																		lowerCAmelCase__			  =  param

						return torch_clip_params, torch_model, attn_params







@torch.no_grad()
def 		_UpperCamelCase      (			UpperCamelCase_				:    Any			,				UpperCamelCase_				:    Union[str, Any]			,				UpperCamelCase_				:    Dict			,				UpperCamelCase_				:    List[Any]			,				UpperCamelCase_				:    List[Any]=None						)     ->      Any:

						"""simple docstring"""



						lowerCAmelCase__			  =  Repository(UpperCamelCase_			,				clone_from=F"google/{pytorch_dump_folder_path}"						)
						repo.git_pull()

						if config_path is not None:
												lowerCAmelCase__			  =  OwlViTConfig.from_pretrained(UpperCamelCase_						)
						else:
												lowerCAmelCase__			  =  OwlViTConfig()

						lowerCAmelCase__			  =  OwlViTModel(UpperCamelCase_						).eval()
						lowerCAmelCase__			  =  OwlViTForObjectDetection(UpperCamelCase_						).eval()

						copy_text_model_and_projection(UpperCamelCase_			,				UpperCamelCase_						)
						copy_vision_model_and_projection(UpperCamelCase_			,				UpperCamelCase_						)
						lowerCAmelCase__			  =  pt_backbone.logit_scale
						copy_flax_attn_params(UpperCamelCase_			,				UpperCamelCase_						)

						lowerCAmelCase__			  =  hf_backbone
						copy_class_merge_token(UpperCamelCase_			,				UpperCamelCase_						)
						copy_class_box_heads(UpperCamelCase_			,				UpperCamelCase_						)

						# Save HF model
						hf_model.save_pretrained(repo.local_dir						)

						# Initialize image processor
						lowerCAmelCase__			  =  OwlViTImageProcessor(
						    size=config.vision_config.image_size			,				crop_size=config.vision_config.image_size						)
						# Initialize tokenizer
						lowerCAmelCase__			  =  CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'			,				pad_token='!'			,				model_max_length=16						)

						# Initialize processor
						lowerCAmelCase__			  =  OwlViTProcessor(image_processor=UpperCamelCase_			,				tokenizer=UpperCamelCase_						)
						image_processor.save_pretrained(repo.local_dir						)
						processor.save_pretrained(repo.local_dir						)

						repo.git_add()
						repo.git_commit('Upload model and processor'						)
						repo.git_push()


if __name__ == "__main__":
					__snake_case			:			Union[str, Any]			   =   argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--owlvit_version""",
					    default=None,
					    type=str,
					    required=True,
					    help="""OWL-ViT model name [clip_b16, clip_b32, clip_l14].""",
					)
					parser.add_argument(
					    """--owlvit_checkpoint""", default=None, type=str, required=True, help="""Path to flax model checkpoint."""
					)
					parser.add_argument("""--hf_config""", default=None, type=str, required=True, help="""Path to HF model config.""")
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default="""hf_model""", type=str, help="""Path to the output PyTorch model."""
					)
					__snake_case			:			Any			   =   parser.parse_args()

					# Initialize PyToch clip model
					__snake_case			:			int			   =   args.owlvit_version
					if model_name == "clip_b16":
										__snake_case			:			Optional[Any]			   =   CONFIGS["""vit_b16"""]
					elif model_name == "clip_b32":
										__snake_case			:			Optional[int]			   =   CONFIGS["""vit_b32"""]
					elif model_name == "clip_l14":
										__snake_case			:			int			   =   CONFIGS["""vit_l14"""]

					# Load from checkpoint and convert params to float-32
					__snake_case			:			List[str]			   =   checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)["""optimizer"""]["""target"""]
					__snake_case			:			List[str]			   =   jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
					del variables

					# Convert CLIP backbone
					__snake_case   ,       __snake_case   ,       __snake_case			:			Tuple			   =   convert_clip_backbone(flax_params, torch_config)

					convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)





