"""simple docstring"""
import numpy as np







def 		__a  (      _SCREAMING_SNAKE_CASE  )      ->Optional[int]:
  a__:	List[Any]     				=      np.exp(_SCREAMING_SNAKE_CASE  )

  # Add up the all the exponentials
  a__:	Tuple     				=      np.sum(_SCREAMING_SNAKE_CASE  )

  # Divide every exponent by the sum of all exponents
  a__:	Dict     				=      exponent_vector / sum_of_exponents

  return softmax_vector


if __name__ == "__main__":
 print(softmax((0,)))








'''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,
)


lowercase						:   Any      						=							{
    '''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 		a_(			lowerCamelCase ,					lowerCamelCase="" ,					lowerCamelCase="/"   )					->str:






       '''simple docstring'''




       lowercase	:		List[Any]    =   []

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

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




def 		a_(			lowerCamelCase   )					->Optional[Any]:






       '''simple docstring'''




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




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->Dict:






       '''simple docstring'''




       lowercase,  lowercase,  lowercase	:		Optional[Any]    =   pt_attn_layer.in_proj_weight.chunk(3 ,					dim=0   )
       lowercase,  lowercase,  lowercase	:		Optional[int]    =   pt_attn_layer.in_proj_bias.chunk(3 ,					dim=0   )

       lowercase	:		Union[str, Any]    =   pt_attn_layer.out_proj.weight
       lowercase	:		Tuple    =   pt_attn_layer.out_proj.bias

       lowercase	:		int    =   q_proj
       lowercase	:		Optional[Any]    =   q_proj_bias

       lowercase	:		str    =   k_proj
       lowercase	:		List[Any]    =   k_proj_bias

       lowercase	:		Dict    =   v_proj
       lowercase	:		List[str]    =   v_proj_bias

       lowercase	:		Optional[int]    =   out_proj_weights
       lowercase	:		Tuple    =   out_proj_bias




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->List[str]:






       '''simple docstring'''




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




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->Any:






       '''simple docstring'''




       lowercase	:		List[str]    =   pt_linear.weight
       lowercase	:		Tuple    =   pt_linear.bias




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->str:






       '''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_(			lowerCamelCase ,					lowerCamelCase   )					->List[Any]:






       '''simple docstring'''




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




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->Dict:






       '''simple docstring'''




       lowercase	:		List[str]    =   pt_model.token_embedding.weight
       lowercase	:		Optional[int]    =   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_(			lowerCamelCase ,					lowerCamelCase   )					->Tuple:






       '''simple docstring'''




       lowercase	:		List[str]    =   pt_model.text_projection.data.T

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




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->str:






       '''simple docstring'''




       lowercase	:		List[str]    =   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
       lowercase	:		Union[str, Any]    =   pt_model.visual.conva.weight.data
       lowercase	:		int    =   pt_model.visual.class_embedding
       lowercase	:		str    =   pt_model.visual.positional_embedding.data

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




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->List[Any]:






       '''simple docstring'''




       lowercase	:		Dict    =   flatten_nested_dict(flax_params['''backbone''']['''merged_class_token''']   )

       lowercase	:		Union[str, Any]    =   torch.from_numpy(flax_class_token_params['''scale''']   )
       lowercase	:		List[str]    =   torch.from_numpy(flax_class_token_params['''bias''']   )
       lowercase	:		Tuple    =   nn.Parameter(lowerCamelCase   )
       lowercase	:		Union[str, Any]    =   nn.Parameter(lowerCamelCase   )




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->List[Any]:






       '''simple docstring'''




       lowercase	:		Optional[int]    =   hf_model.state_dict()
       lowercase	:		Dict    =   {}

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

       for flax_key, v in flax_class_params.items():
              lowercase	:		List[str]    =   flax_key.replace('''/''' ,					'''.'''   )
              lowercase	:		Tuple    =   torch_key.replace('''.kernel''' ,					'''.weight'''   )
              lowercase	:		Dict    =   torch_key.replace('''Dense_0''' ,					'''dense0'''   )
              lowercase	:		Optional[int]    =   '''class_head.''' + torch_key

              if "weight" in torch_key and v.ndim == 2:
                     lowercase	:		Optional[int]    =   v.T

              lowercase	:		Optional[int]    =   nn.Parameter(torch.from_numpy(lowerCamelCase   )   )

       # Rename box prediction box flax params to pytorch HF
       lowercase	:		Optional[Any]    =   flatten_nested_dict(flax_params['''obj_box_head''']   )

       for flax_key, v in flax_box_params.items():
              lowercase	:		Dict    =   flax_key.replace('''/''' ,					'''.'''   )
              lowercase	:		Union[str, Any]    =   torch_key.replace('''.kernel''' ,					'''.weight'''   )
              lowercase	:		int    =   torch_key.replace('''_''' ,					''''''   ).lower()
              lowercase	:		Union[str, Any]    =   '''box_head.''' + torch_key

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

              lowercase	:		Tuple    =   nn.Parameter(torch.from_numpy(lowerCamelCase   )   )

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




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->Dict:






       '''simple docstring'''




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

              lowercase	:		Optional[Any]    =   torch_key.replace('''attn''' ,					'''self_attn'''   )
              lowercase	:		Optional[int]    =   torch_key.replace('''key''' ,					'''k_proj'''   )
              lowercase	:		Dict    =   torch_key.replace('''value''' ,					'''v_proj'''   )
              lowercase	:		List[Any]    =   torch_key.replace('''query''' ,					'''q_proj'''   )
              lowercase	:		Any    =   torch_key.replace('''out''' ,					'''out_proj'''   )

              if "bias" in torch_key and v.ndim == 2:
                     lowercase	:		str    =   v.shape[0] * v.shape[1]
                     lowercase	:		List[str]    =   v.reshape(lowerCamelCase   )

              if "weight" in torch_key and "out" in torch_key:
                     lowercase	:		Union[str, Any]    =   (v.shape[0] * v.shape[1], v.shape[2])
                     lowercase	:		Union[str, Any]    =   v.reshape(lowerCamelCase   ).T

              if "weight" in torch_key and "out" not in torch_key:
                     lowercase	:		Optional[int]    =   (v.shape[0], v.shape[1] * v.shape[2])
                     lowercase	:		List[Any]    =   v.reshape(lowerCamelCase   ).T

              # Copy flax CLIP attn params to HF PyTorch params
              lowercase	:		List[str]    =   torch.from_numpy(lowerCamelCase   )
              hf_backbone.state_dict()[torch_key].copy_(lowerCamelCase   )




def 		a_(			lowerCamelCase   )					->Any:






       '''simple docstring'''




       lowercase	:		Union[str, Any]    =   {}
       lowercase	:		Union[str, Any]    =   []

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

                     processed_attn_layers.append(lowerCamelCase   )
                     lowercase	:		Dict    =   params[base + '''out.weight'''].shape[-1]
                     lowercase	:		Union[str, Any]    =   params[base + '''out.weight'''].reshape(lowerCamelCase ,					lowerCamelCase   ).T
                     lowercase	:		List[str]    =   params[base + '''out.bias''']
              else:
                     lowercase	:		Dict    =   v
       return new_params




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->Dict:






       '''simple docstring'''




       lowercase	:		Tuple    =   CLIP(**lowerCamelCase   )
       torch_model.eval()
       lowercase	:		Any    =   torch_model.state_dict()

       lowercase	:		List[Any]    =   flatten_nested_dict(flax_params['''backbone''']['''clip''']   )
       lowercase	:		str    =   {}

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

              lowercase	:		List[Any]    =   torch_key.replace('''text_projection.kernel''' ,					'''text_projection'''   )
              lowercase	:		Any    =   torch_key.replace('''visual.proj.kernel''' ,					'''visual.proj'''   )
              lowercase	:		Union[str, Any]    =   torch_key.replace('''.scale''' ,					'''.weight'''   )
              lowercase	:		Optional[Any]    =   torch_key.replace('''.kernel''' ,					'''.weight'''   )

              if "conv" in torch_key or "downsample.0.weight" in torch_key:
                     lowercase	:		List[str]    =   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
                     lowercase	:		Union[str, Any]    =   v.T

              lowercase	:		str    =   v

       lowercase	:		Tuple    =   _convert_attn_layers(lowerCamelCase   )
       new_torch_params.update(lowerCamelCase   )
       lowercase	:		Union[str, Any]    =   {}

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

       return torch_clip_params, torch_model, attn_params




@torch.no_grad()
def 		a_(			lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase=None   )					->Tuple:






       '''simple docstring'''




       lowercase	:		List[str]    =   Repository(lowerCamelCase ,					clone_from=f"""google/{pytorch_dump_folder_path}"""   )
       repo.git_pull()

       if config_path is not None:
              lowercase	:		Any    =   OwlViTConfig.from_pretrained(lowerCamelCase   )
       else:
              lowercase	:		Optional[int]    =   OwlViTConfig()

       lowercase	:		Any    =   OwlViTModel(lowerCamelCase   ).eval()
       lowercase	:		str    =   OwlViTForObjectDetection(lowerCamelCase   ).eval()

       copy_text_model_and_projection(lowerCamelCase ,					lowerCamelCase   )
       copy_vision_model_and_projection(lowerCamelCase ,					lowerCamelCase   )
       lowercase	:		Any    =   pt_backbone.logit_scale
       copy_flax_attn_params(lowerCamelCase ,					lowerCamelCase   )

       lowercase	:		Optional[Any]    =   hf_backbone
       copy_class_merge_token(lowerCamelCase ,					lowerCamelCase   )
       copy_class_box_heads(lowerCamelCase ,					lowerCamelCase   )

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

       # Initialize image processor
       lowercase	:		List[Any]    =   OwlViTImageProcessor(
           size=config.vision_config.image_size ,					crop_size=config.vision_config.image_size   )
       # Initialize tokenizer
       lowercase	:		str    =   CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32''' ,					pad_token='''!''' ,					model_max_length=16   )

       # Initialize processor
       lowercase	:		Tuple    =   OwlViTProcessor(image_processor=lowerCamelCase ,					tokenizer=lowerCamelCase   )
       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__":
      lowercase						:   Optional[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.'''
      )
      lowercase						:   Optional[int]      						=							parser.parse_args()

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

      # Load from checkpoint and convert params to float-32
      lowercase						:   List[str]      						=							checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)['''optimizer''']['''target''']
      lowercase						:   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
      lowercase						,						lowercase						,						lowercase						:   Optional[int]      						=							convert_clip_backbone(flax_params, torch_config)

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




