code: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  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,
  )
  
  
  lowerCamelCase_     :     Optional[Any]    		=						{
      '''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="/"	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=[]
  
      for k, v in params.items():
          a					=parent_key + sep + k if parent_key else k
  
          if isinstance(snake_case__ ,					collections.MutableMapping	):
              items.extend(flatten_nested_dict(snake_case__ ,					snake_case__ ,					sep=snake_case__	).items()	)
          else:
              items.append((new_key, v)	)
      return dict(snake_case__	)
  def _A		(		lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      return jax.tree_util.tree_map(lambda lowercase	: x.astype(jnp.floataa	) if x.dtype == jnp.bfloataa else x ,					snake_case__	)
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """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 _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      copy_linear(hf_mlp.fca ,					pt_mlp.c_fc	)
      copy_linear(hf_mlp.fca ,					pt_mlp.c_proj	)
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=pt_linear.weight
      a					=pt_linear.bias
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      # copy layer norms
      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	):
  
  
  
  
  
  
  
      """simple docstring"""
      for hf_layer, pt_layer in zip(snake_case__ ,					snake_case__	):
          copy_layer(snake_case__ ,					snake_case__	)
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      # copy  embeds
      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 _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      # copy projection
      a					=pt_model.text_projection.data.T
  
      # copy text encoder
      copy_encoder(hf_model.text_model ,					snake_case__	)
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      # copy projection
      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 _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """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(snake_case__	)
      a					=nn.Parameter(snake_case__	)
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """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(snake_case__	)	)
  
      # 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(snake_case__	)	)
  
      # Copy flax params to PyTorch params
      for name, param in new_params.items():
          if name in pt_params.keys():
              pt_params[name].copy_(snake_case__	)
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """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(snake_case__	)
  
          if "weight" in torch_key and "out" in torch_key:
              a					=(v.shape[0] * v.shape[1], v.shape[2])
              a					=v.reshape(snake_case__	).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(snake_case__	).T
  
          # Copy flax CLIP attn params to HF PyTorch params
          a					=torch.from_numpy(snake_case__	)
          hf_backbone.state_dict()[torch_key].copy_(snake_case__	)
  def _A		(		lowercase	):
  
  
  
  
  
  
  
      """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(snake_case__	)
              a					=params[base + '''out.weight'''].shape[-1]
              a					=params[base + '''out.weight'''].reshape(snake_case__ ,					snake_case__	).T
              a					=params[base + '''out.bias''']
          else:
              a					=v
      return new_params
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=CLIP(**snake_case__	)
      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(snake_case__	)
      new_torch_params.update(snake_case__	)
      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_(snake_case__	)
          else:
              a					=param
  
      return torch_clip_params, torch_model, attn_params
  @torch.no_grad()
  def _A		(		lowercase ,					lowercase ,					lowercase ,					lowercase ,					lowercase=None	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=Repository(snake_case__ ,					clone_from=f'''google/{pytorch_dump_folder_path}'''	)
      repo.git_pull()
  
      if config_path is not None:
          a					=OwlViTConfig.from_pretrained(snake_case__	)
      else:
          a					=OwlViTConfig()
  
      a					=OwlViTModel(snake_case__	).eval()
      a					=OwlViTForObjectDetection(snake_case__	).eval()
  
      copy_text_model_and_projection(snake_case__ ,					snake_case__	)
      copy_vision_model_and_projection(snake_case__ ,					snake_case__	)
      a					=pt_backbone.logit_scale
      copy_flax_attn_params(snake_case__ ,					snake_case__	)
  
      a					=hf_backbone
      copy_class_merge_token(snake_case__ ,					snake_case__	)
      copy_class_box_heads(snake_case__ ,					snake_case__	)
  
      # 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=snake_case__ ,					tokenizer=snake_case__	)
      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__":
         lowerCamelCase_     :     int    		=						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."""
         )
         lowerCamelCase_     :     str    		=						parser.parse_args()
  
         # Initialize PyToch clip model
         lowerCamelCase_     :     Union[str, Any]    		=						args.owlvit_version
         if model_name == "clip_b16":
                lowerCamelCase_     :     Union[str, Any]    		=						CONFIGS['''vit_b16''']
         elif model_name == "clip_b32":
                lowerCamelCase_     :     Optional[Any]    		=						CONFIGS['''vit_b32''']
         elif model_name == "clip_l14":
                lowerCamelCase_     :     str    		=						CONFIGS['''vit_l14''']
  
         # Load from checkpoint and convert params to float-32
         lowerCamelCase_     :     List[str]    		=						checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)['''optimizer''']['''target''']
         lowerCamelCase_     :     Dict    		=						jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
         del variables
  
         # Convert CLIP backbone
         lowerCamelCase_     :     Any    		=						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: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from packaging import version
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  lowerCamelCase_     :     Any    		=						logging.get_logger(__name__)
  
  lowerCamelCase_     :     Tuple    		=						{
      """apple/mobilevitv2-1.0""": """https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json""",
  }
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						"mobilevitv2"
  
  
   def __init__(	self				,					__A=3				,					__A=256				,					__A=2				,					__A=2.0				,					__A="swish"				,					__A=3				,					__A=32				,					__A=0.1				,					__A=0.02				,					__A=1E-5				,					__A=512				,					__A=[6, 12, 18]				,					__A=0.1				,					__A=255				,					__A=[2, 4, 3]				,					__A=[128, 192, 256]				,					__A=1.0				,					__A=2				,					__A=0.0				,					__A=0.0				,					**__A				,					)      ->  int:
       super().__init__(**__A		)
  
       a					=num_channels
       a					=image_size
       a					=patch_size
       a					=expand_ratio
       a					=hidden_act
       a					=conv_kernel_size
       a					=output_stride
       a					=initializer_range
       a					=layer_norm_eps
       a					=n_attn_blocks
       a					=base_attn_unit_dims
       a					=width_multiplier
       a					=ffn_multiplier
       a					=ffn_dropout
       a					=attn_dropout
       a					=classifier_dropout_prob
  
       # decode head attributes for semantic segmentation
       a					=aspp_out_channels
       a					=atrous_rates
       a					=aspp_dropout_prob
       a					=semantic_loss_ignore_index
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						version.parse("1.11"    )
  
  
   @property
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Mapping[str, Mapping[int, str]]:
       return OrderedDict([('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''})]		)
  
  
   @property
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Mapping[str, Mapping[int, str]]:
       if self.task == "image-classification":
           return OrderedDict([('''logits''', {0: '''batch'''})]		)
       else:
           return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]		)
  
  
  
  
  
   @property
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  float:
       return 1E-4