code: 
  
  
  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,
  )
  
  
  A_    :					int             ={
      """vit_b32""": {
          """embed_dim""": 512,
          """image_resolution""": 768,
          """context_length""": 16,
          """vocab_size""": 49408,
          """vision_layers""": 12,
          """vision_width""": 768,
          """vision_patch_size""": 32,
          """transformer_width""": 512,
          """transformer_heads""": 8,
          """transformer_layers""": 12,
      },
      """vit_b16""": {
          """embed_dim""": 512,
          """image_resolution""": 768,
          """context_length""": 16,
          """vocab_size""": 49408,
          """vision_layers""": 12,
          """vision_width""": 768,
          """vision_patch_size""": 16,
          """transformer_width""": 512,
          """transformer_heads""": 8,
          """transformer_layers""": 12,
      },
      """vit_l14""": {
          """embed_dim""": 768,
          """image_resolution""": 840,
          """context_length""": 16,
          """vocab_size""": 49408,
          """vision_layers""": 24,
          """vision_width""": 1024,
          """vision_patch_size""": 14,
          """transformer_width""": 768,
          """transformer_heads""": 12,
          """transformer_layers""": 12,
      },
  }
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__=""       ,    UpperCAmelCase__="/"      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		[]
  
  						for k, v in params.items():
  												a_					=		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   lowerCamelCase_       (					UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						return jax.tree_util.tree_map(lambda UpperCAmelCase__      : x.astype(jnp.floataa      ) if x.dtype == jnp.bfloataa else x       ,    UpperCAmelCase__      )
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_			,   a_			,   a_					=		pt_attn_layer.in_proj_weight.chunk(3       ,    dim=0      )
  						a_			,   a_			,   a_					=		pt_attn_layer.in_proj_bias.chunk(3       ,    dim=0      )
  
  						a_					=		pt_attn_layer.out_proj.weight
  						a_					=		pt_attn_layer.out_proj.bias
  
  						a_					=		q_proj
  						a_					=		q_proj_bias
  
  						a_					=		k_proj
  						a_					=		k_proj_bias
  
  						a_					=		v_proj
  						a_					=		v_proj_bias
  
  						a_					=		out_proj_weights
  						a_					=		out_proj_bias
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						copy_linear(hf_mlp.fca       ,    pt_mlp.c_fc      )
  						copy_linear(hf_mlp.fca       ,    pt_mlp.c_proj      )
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		pt_linear.weight
  						a_					=		pt_linear.bias
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""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   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						for hf_layer, pt_layer in zip(UpperCAmelCase__       ,    UpperCAmelCase__      ):
  												copy_layer(UpperCAmelCase__       ,    UpperCAmelCase__      )
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		pt_model.token_embedding.weight
  						a_					=		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   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		pt_model.text_projection.data.T
  
  						# copy text encoder
  						copy_encoder(hf_model.text_model       ,    UpperCAmelCase__      )
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		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
  						a_					=		pt_model.visual.conva.weight.data
  						a_					=		pt_model.visual.class_embedding
  						a_					=		pt_model.visual.positional_embedding.data
  
  						# copy encoder
  						copy_layers(hf_model.vision_model.encoder.layers       ,    pt_model.visual.transformer.resblocks      )
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		flatten_nested_dict(flax_params["""backbone"""]["""merged_class_token"""]      )
  
  						a_					=		torch.from_numpy(flax_class_token_params["""scale"""]      )
  						a_					=		torch.from_numpy(flax_class_token_params["""bias"""]      )
  						a_					=		nn.Parameter(UpperCAmelCase__      )
  						a_					=		nn.Parameter(UpperCAmelCase__      )
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		hf_model.state_dict()
  						a_					=		{}
  
  						# Rename class prediction head flax params to pytorch HF
  						a_					=		flatten_nested_dict(flax_params["""class_head"""]      )
  
  						for flax_key, v in flax_class_params.items():
  												a_					=		flax_key.replace("""/"""       ,    """."""      )
  												a_					=		torch_key.replace(""".kernel"""       ,    """.weight"""      )
  												a_					=		torch_key.replace("""Dense_0"""       ,    """dense0"""      )
  												a_					=		"""class_head.""" + torch_key
  
  												if "weight" in torch_key and v.ndim == 2:
  																		a_					=		v.T
  
  												a_					=		nn.Parameter(torch.from_numpy(UpperCAmelCase__      )      )
  
  						# Rename box prediction box flax params to pytorch HF
  						a_					=		flatten_nested_dict(flax_params["""obj_box_head"""]      )
  
  						for flax_key, v in flax_box_params.items():
  												a_					=		flax_key.replace("""/"""       ,    """."""      )
  												a_					=		torch_key.replace(""".kernel"""       ,    """.weight"""      )
  												a_					=		torch_key.replace("""_"""       ,    """"""      ).lower()
  												a_					=		"""box_head.""" + torch_key
  
  												if "weight" in torch_key and v.ndim == 2:
  																		a_					=		v.T
  
  												a_					=		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   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						for k, v in flax_attn_params.items():
  												if k.startswith("""transformer"""      ):
  																		a_					=		k.replace("""transformer.resblocks"""       ,    """text_model.encoder.layers"""      )
  												else:
  																		a_					=		k.replace("""visual.transformer.resblocks"""       ,    """vision_model.encoder.layers"""      )
  
  												a_					=		torch_key.replace("""attn"""       ,    """self_attn"""      )
  												a_					=		torch_key.replace("""key"""       ,    """k_proj"""      )
  												a_					=		torch_key.replace("""value"""       ,    """v_proj"""      )
  												a_					=		torch_key.replace("""query"""       ,    """q_proj"""      )
  												a_					=		torch_key.replace("""out"""       ,    """out_proj"""      )
  
  												if "bias" in torch_key and v.ndim == 2:
  																		a_					=		v.shape[0] * v.shape[1]
  																		a_					=		v.reshape(UpperCAmelCase__      )
  
  												if "weight" in torch_key and "out" in torch_key:
  																		a_					=		(v.shape[0] * v.shape[1], v.shape[2])
  																		a_					=		v.reshape(UpperCAmelCase__      ).T
  
  												if "weight" in torch_key and "out" not in torch_key:
  																		a_					=		(v.shape[0], v.shape[1] * v.shape[2])
  																		a_					=		v.reshape(UpperCAmelCase__      ).T
  
  												# Copy flax CLIP attn params to HF PyTorch params
  												a_					=		torch.from_numpy(UpperCAmelCase__      )
  												hf_backbone.state_dict()[torch_key].copy_(UpperCAmelCase__      )
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		{}
  						a_					=		[]
  
  						for k, v in params.items():
  												if "attn." in k:
  																		a_					=		k[: k.rindex("""attn."""      ) + 5]
  																		if base in processed_attn_layers:
  																								continue
  
  																		processed_attn_layers.append(UpperCAmelCase__      )
  																		a_					=		params[base + """out.weight"""].shape[-1]
  																		a_					=		params[base + """out.weight"""].reshape(UpperCAmelCase__       ,    UpperCAmelCase__      ).T
  																		a_					=		params[base + """out.bias"""]
  												else:
  																		a_					=		v
  						return new_params
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		CLIP(**UpperCAmelCase__      )
  						torch_model.eval()
  						a_					=		torch_model.state_dict()
  
  						a_					=		flatten_nested_dict(flax_params["""backbone"""]["""clip"""]      )
  						a_					=		{}
  
  						for flax_key, v in flax_clip_params.items():
  												a_					=		flax_key.replace("""/"""       ,    """."""      )
  												a_					=		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"""      )
  												):
  																		a_					=		torch_key[5:]
  
  												a_					=		torch_key.replace("""text_projection.kernel"""       ,    """text_projection"""      )
  												a_					=		torch_key.replace("""visual.proj.kernel"""       ,    """visual.proj"""      )
  												a_					=		torch_key.replace(""".scale"""       ,    """.weight"""      )
  												a_					=		torch_key.replace(""".kernel"""       ,    """.weight"""      )
  
  												if "conv" in torch_key or "downsample.0.weight" in torch_key:
  																		a_					=		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
  																		a_					=		v.T
  
  												a_					=		v
  
  						a_					=		_convert_attn_layers(UpperCAmelCase__      )
  						new_torch_params.update(UpperCAmelCase__      )
  						a_					=		{}
  
  						# Copy flax CLIP backbone params to PyTorch params
  						for name, param in new_torch_params.items():
  												if name in torch_clip_params.keys():
  																		a_					=		torch.from_numpy(new_torch_params[name]      )
  																		torch_clip_params[name].copy_(UpperCAmelCase__      )
  												else:
  																		a_					=		param
  
  						return torch_clip_params, torch_model, attn_params
  
  
  
  
  @torch.no_grad()
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__=None      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		Repository(UpperCAmelCase__       ,    clone_from=F"google/{pytorch_dump_folder_path}"      )
  						repo.git_pull()
  
  						if config_path is not None:
  												a_					=		OwlViTConfig.from_pretrained(UpperCAmelCase__      )
  						else:
  												a_					=		OwlViTConfig()
  
  						a_					=		OwlViTModel(UpperCAmelCase__      ).eval()
  						a_					=		OwlViTForObjectDetection(UpperCAmelCase__      ).eval()
  
  						copy_text_model_and_projection(UpperCAmelCase__       ,    UpperCAmelCase__      )
  						copy_vision_model_and_projection(UpperCAmelCase__       ,    UpperCAmelCase__      )
  						a_					=		pt_backbone.logit_scale
  						copy_flax_attn_params(UpperCAmelCase__       ,    UpperCAmelCase__      )
  
  						a_					=		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
  						a_					=		OwlViTImageProcessor(
  						    size=config.vision_config.image_size       ,    crop_size=config.vision_config.image_size      )
  						# Initialize tokenizer
  						a_					=		CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""       ,    pad_token="""!"""       ,    model_max_length=16      )
  
  						# Initialize processor
  						a_					=		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__":
  	A_    :					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."""
  	)
  	A_    :					Dict             =parser.parse_args()
  
  	# Initialize PyToch clip model
  	A_    :					Any             =args.owlvit_version
  	if model_name == "clip_b16":
  		A_    :					Any             =CONFIGS["""vit_b16"""]
  	elif model_name == "clip_b32":
  		A_    :					List[str]             =CONFIGS["""vit_b32"""]
  	elif model_name == "clip_l14":
  		A_    :					str             =CONFIGS["""vit_l14"""]
  
  	# Load from checkpoint and convert params to float-32
  	A_    :					Dict             =checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)["""optimizer"""]["""target"""]
  	A_    :					int             =jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
  	del variables
  
  	# Convert CLIP backbone
  	A_       ,	A_       ,	A_    :					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)
style_context: 
  
  
  import importlib
  import sys
  from argparse import REMAINDER, ArgumentParser
  from pathlib import Path
  
  import torch_xla.distributed.xla_multiprocessing as xmp
  
  
  
  
  def   lowerCamelCase_       (					):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		ArgumentParser(
  						    description=(
  						        """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
  						    )      )
  
  						# Optional arguments for the launch helper
  						parser.add_argument("""--num_cores"""       ,    type=UpperCAmelCase__       ,    default=1       ,    help="""Number of TPU cores to use (1 or 8)."""      )
  
  						# positional
  						parser.add_argument(
  						    """training_script"""       ,    type=UpperCAmelCase__       ,    help=(
  						        """The full path to the single TPU training """
  						        """program/script to be launched in parallel, """
  						        """followed by all the arguments for the """
  						        """training script"""
  						    )       ,    )
  
  						# rest from the training program
  						parser.add_argument("""training_script_args"""       ,    nargs=UpperCAmelCase__      )
  
  						return parser.parse_args()
  
  
  
  
  def   lowerCamelCase_       (					):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		parse_args()
  
  						# Import training_script as a module.
  						a_					=		Path(args.training_script      )
  						sys.path.append(str(script_fpath.parent.resolve()      )      )
  						a_					=		script_fpath.stem
  						a_					=		importlib.import_module(UpperCAmelCase__      )
  
  						# Patch sys.argv
  						a_					=		[args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores      )]
  
  						xmp.spawn(mod._mp_fn       ,    args=()       ,    nprocs=args.num_cores      )
  
  
  if __name__ == "__main__":
  	main()