






def 		A_			(		lowercase_					,       lowercase_ = 2	)							->float:







 """simple docstring"""

 return round((float(lowercase_	) * 9 / 5) + 3_2					,       lowercase_	)





def 		A_			(		lowercase_					,       lowercase_ = 2	)							->float:







 """simple docstring"""

 return round(float(lowercase_	) + 2_7_3.1_5					,       lowercase_	)





def 		A_			(		lowercase_					,       lowercase_ = 2	)							->float:







 """simple docstring"""

 return round((float(lowercase_	) * 9 / 5) + 4_9_1.6_7					,       lowercase_	)





def 		A_			(		lowercase_					,       lowercase_ = 2	)							->float:







 """simple docstring"""

 return round((float(lowercase_	) - 3_2) * 5 / 9					,       lowercase_	)





def 		A_			(		lowercase_					,       lowercase_ = 2	)							->float:







 """simple docstring"""

 return round(((float(lowercase_	) - 3_2) * 5 / 9) + 2_7_3.1_5					,       lowercase_	)





def 		A_			(		lowercase_					,       lowercase_ = 2	)							->float:







 """simple docstring"""

 return round(float(lowercase_	) + 4_5_9.6_7					,       lowercase_	)





def 		A_			(		lowercase_					,       lowercase_ = 2	)							->float:







 """simple docstring"""

 return round(float(lowercase_	) - 2_7_3.1_5					,       lowercase_	)





def 		A_			(		lowercase_					,       lowercase_ = 2	)							->float:







 """simple docstring"""

 return round(((float(lowercase_	) - 2_7_3.1_5) * 9 / 5) + 3_2					,       lowercase_	)





def 		A_			(		lowercase_					,       lowercase_ = 2	)							->float:







 """simple docstring"""

 return round((float(lowercase_	) * 9 / 5)					,       lowercase_	)





def 		A_			(		lowercase_					,       lowercase_ = 2	)							->float:







 """simple docstring"""

 return round((float(lowercase_	) - 4_9_1.6_7) * 5 / 9					,       lowercase_	)





def 		A_			(		lowercase_					,       lowercase_ = 2	)							->float:







 """simple docstring"""

 return round(float(lowercase_	) - 4_5_9.6_7					,       lowercase_	)





def 		A_			(		lowercase_					,       lowercase_ = 2	)							->float:







 """simple docstring"""

 return round((float(lowercase_	) * 5 / 9)					,       lowercase_	)





def 		A_			(		lowercase_					,       lowercase_ = 2	)							->float:







 """simple docstring"""

 return round((float(lowercase_	) * 1.2_5 + 2_7_3.1_5)					,       lowercase_	)





def 		A_			(		lowercase_					,       lowercase_ = 2	)							->float:







 """simple docstring"""

 return round((float(lowercase_	) * 2.2_5 + 3_2)					,       lowercase_	)





def 		A_			(		lowercase_					,       lowercase_ = 2	)							->float:







 """simple docstring"""

 return round((float(lowercase_	) * 1.2_5)					,       lowercase_	)





def 		A_			(		lowercase_					,       lowercase_ = 2	)							->float:







 """simple docstring"""

 return round((float(lowercase_	) * 2.2_5 + 3_2 + 4_5_9.6_7)					,       lowercase_	)


if __name__ == "__main__":
      import doctest

      doctest.testmod()












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,
)


__UpperCAmelCase      				=							{
    "vit_b32": {
        "embed_dim": 5_1_2,
        "image_resolution": 7_6_8,
        "context_length": 1_6,
        "vocab_size": 4_9_4_0_8,
        "vision_layers": 1_2,
        "vision_width": 7_6_8,
        "vision_patch_size": 3_2,
        "transformer_width": 5_1_2,
        "transformer_heads": 8,
        "transformer_layers": 1_2,
    },
    "vit_b16": {
        "embed_dim": 5_1_2,
        "image_resolution": 7_6_8,
        "context_length": 1_6,
        "vocab_size": 4_9_4_0_8,
        "vision_layers": 1_2,
        "vision_width": 7_6_8,
        "vision_patch_size": 1_6,
        "transformer_width": 5_1_2,
        "transformer_heads": 8,
        "transformer_layers": 1_2,
    },
    "vit_l14": {
        "embed_dim": 7_6_8,
        "image_resolution": 8_4_0,
        "context_length": 1_6,
        "vocab_size": 4_9_4_0_8,
        "vision_layers": 2_4,
        "vision_width": 1_0_2_4,
        "vision_patch_size": 1_4,
        "transformer_width": 7_6_8,
        "transformer_heads": 1_2,
        "transformer_layers": 1_2,
    },
}





def 		A_			(		lowercase_					,       lowercase_=""					,       lowercase_="/"	)							->Optional[Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						[]

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

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





def 		A_			(		lowercase_	)							->List[Any]:







 """simple docstring"""

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





def 		A_			(		lowercase_					,       lowercase_	)							->Any:







 """simple docstring"""

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

 SCREAMING_SNAKE_CASE							  =						pt_attn_layer.out_proj.weight
 SCREAMING_SNAKE_CASE							  =						pt_attn_layer.out_proj.bias

 SCREAMING_SNAKE_CASE							  =						q_proj
 SCREAMING_SNAKE_CASE							  =						q_proj_bias

 SCREAMING_SNAKE_CASE							  =						k_proj
 SCREAMING_SNAKE_CASE							  =						k_proj_bias

 SCREAMING_SNAKE_CASE							  =						v_proj
 SCREAMING_SNAKE_CASE							  =						v_proj_bias

 SCREAMING_SNAKE_CASE							  =						out_proj_weights
 SCREAMING_SNAKE_CASE							  =						out_proj_bias





def 		A_			(		lowercase_					,       lowercase_	)							->List[str]:







 """simple docstring"""

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





def 		A_			(		lowercase_					,       lowercase_	)							->str:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						pt_linear.weight
 SCREAMING_SNAKE_CASE							  =						pt_linear.bias





def 		A_			(		lowercase_					,       lowercase_	)							->Dict:







 """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 		A_			(		lowercase_					,       lowercase_	)							->str:







 """simple docstring"""

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





def 		A_			(		lowercase_					,       lowercase_	)							->Tuple:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						pt_model.token_embedding.weight
 SCREAMING_SNAKE_CASE							  =						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 		A_			(		lowercase_					,       lowercase_	)							->Union[str, Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						pt_model.text_projection.data.T

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





def 		A_			(		lowercase_					,       lowercase_	)							->Optional[int]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						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
 SCREAMING_SNAKE_CASE							  =						pt_model.visual.conva.weight.data
 SCREAMING_SNAKE_CASE							  =						pt_model.visual.class_embedding
 SCREAMING_SNAKE_CASE							  =						pt_model.visual.positional_embedding.data

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





def 		A_			(		lowercase_					,       lowercase_	)							->Dict:







 """simple docstring"""

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

 SCREAMING_SNAKE_CASE							  =						torch.from_numpy(flax_class_token_params['scale']	)
 SCREAMING_SNAKE_CASE							  =						torch.from_numpy(flax_class_token_params['bias']	)
 SCREAMING_SNAKE_CASE							  =						nn.Parameter(lowercase_	)
 SCREAMING_SNAKE_CASE							  =						nn.Parameter(lowercase_	)





def 		A_			(		lowercase_					,       lowercase_	)							->int:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						hf_model.state_dict()
 SCREAMING_SNAKE_CASE							  =						{}

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

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

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

  SCREAMING_SNAKE_CASE							  =						nn.Parameter(torch.from_numpy(lowercase_	)	)

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

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

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

  SCREAMING_SNAKE_CASE							  =						nn.Parameter(torch.from_numpy(lowercase_	)	)

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





def 		A_			(		lowercase_					,       lowercase_	)							->int:







 """simple docstring"""

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

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

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

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

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

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





def 		A_			(		lowercase_	)							->Any:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						{}
 SCREAMING_SNAKE_CASE							  =						[]

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

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





def 		A_			(		lowercase_					,       lowercase_	)							->List[str]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						CLIP(**lowercase_	)
 torch_model.eval()
 SCREAMING_SNAKE_CASE							  =						torch_model.state_dict()

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

 for flax_key, v in flax_clip_params.items():
  SCREAMING_SNAKE_CASE							  =						flax_key.replace('/'					,       '.'	)
  SCREAMING_SNAKE_CASE							  =						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'	)
  ):
   SCREAMING_SNAKE_CASE							  =						torch_key[5:]

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

  if "conv" in torch_key or "downsample.0.weight" in torch_key:
   SCREAMING_SNAKE_CASE							  =						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
   SCREAMING_SNAKE_CASE							  =						v.T

  SCREAMING_SNAKE_CASE							  =						v

 SCREAMING_SNAKE_CASE							  =						_convert_attn_layers(lowercase_	)
 new_torch_params.update(lowercase_	)
 SCREAMING_SNAKE_CASE							  =						{}

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

 return torch_clip_params, torch_model, attn_params





@torch.no_grad()
def 		A_			(		lowercase_					,       lowercase_					,       lowercase_					,       lowercase_					,       lowercase_=None	)							->List[str]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						Repository(lowercase_					,       clone_from=f'''google/{pytorch_dump_folder_path}'''	)
 repo.git_pull()

 if config_path is not None:
  SCREAMING_SNAKE_CASE							  =						OwlViTConfig.from_pretrained(lowercase_	)
 else:
  SCREAMING_SNAKE_CASE							  =						OwlViTConfig()

 SCREAMING_SNAKE_CASE							  =						OwlViTModel(lowercase_	).eval()
 SCREAMING_SNAKE_CASE							  =						OwlViTForObjectDetection(lowercase_	).eval()

 copy_text_model_and_projection(lowercase_					,       lowercase_	)
 copy_vision_model_and_projection(lowercase_					,       lowercase_	)
 SCREAMING_SNAKE_CASE							  =						pt_backbone.logit_scale
 copy_flax_attn_params(lowercase_					,       lowercase_	)

 SCREAMING_SNAKE_CASE							  =						hf_backbone
 copy_class_merge_token(lowercase_					,       lowercase_	)
 copy_class_box_heads(lowercase_					,       lowercase_	)

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

 # Initialize image processor
 SCREAMING_SNAKE_CASE							  =						OwlViTImageProcessor(
     size=config.vision_config.image_size					,       crop_size=config.vision_config.image_size	)
 # Initialize tokenizer
 SCREAMING_SNAKE_CASE							  =						CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'					,       pad_token='!'					,       model_max_length=1_6	)

 # Initialize processor
 SCREAMING_SNAKE_CASE							  =						OwlViTProcessor(image_processor=lowercase_					,       tokenizer=lowercase_	)
 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__":
      __UpperCAmelCase      				=							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."
      )
      __UpperCAmelCase      				=							parser.parse_args()

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

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

      # Convert CLIP backbone
      __UpperCAmelCase				,	__UpperCAmelCase				,	__UpperCAmelCase      				=							convert_clip_backbone(flax_params, torch_config)

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




