





"""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_      =				{
    '''vit_b32''': {
        '''embed_dim''': 512,
        '''image_resolution''': 768,
        '''context_length''': 16,
        '''vocab_size''': 49_408,
        '''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''': 49_408,
        '''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''': 49_408,
        '''vision_layers''': 24,
        '''vision_width''': 1_024,
        '''vision_patch_size''': 14,
        '''transformer_width''': 768,
        '''transformer_heads''': 12,
        '''transformer_layers''': 12,
    },
}

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE="",SCREAMING_SNAKE_CASE="/"				)		->							Tuple:






       """simple docstring"""




       _UpperCAmelCase			     =  []

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

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

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE				)		->							Union[str, Any]:






       """simple docstring"""




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

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							List[str]:






       """simple docstring"""




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

       _UpperCAmelCase			     =  pt_attn_layer.out_proj.weight
       _UpperCAmelCase			     =  pt_attn_layer.out_proj.bias

       _UpperCAmelCase			     =  q_proj
       _UpperCAmelCase			     =  q_proj_bias

       _UpperCAmelCase			     =  k_proj
       _UpperCAmelCase			     =  k_proj_bias

       _UpperCAmelCase			     =  v_proj
       _UpperCAmelCase			     =  v_proj_bias

       _UpperCAmelCase			     =  out_proj_weights
       _UpperCAmelCase			     =  out_proj_bias

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							Dict:






       """simple docstring"""




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

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							Dict:






       """simple docstring"""




       _UpperCAmelCase			     =  pt_linear.weight
       _UpperCAmelCase			     =  pt_linear.bias

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							Any:






       """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     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							Optional[Any]:






       """simple docstring"""




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

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							Tuple:






       """simple docstring"""




       _UpperCAmelCase			     =  pt_model.token_embedding.weight
       _UpperCAmelCase			     =  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     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							int:






       """simple docstring"""




       _UpperCAmelCase			     =  pt_model.text_projection.data.T

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

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							Optional[int]:






       """simple docstring"""




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

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

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							Dict:






       """simple docstring"""




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

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

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							str:






       """simple docstring"""




       _UpperCAmelCase			     =  hf_model.state_dict()
       _UpperCAmelCase			     =  {}

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

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

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

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

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

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

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

              _UpperCAmelCase			     =  nn.Parameter(torch.from_numpy(SCREAMING_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_(SCREAMING_SNAKE_CASE				)

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							List[str]:






       """simple docstring"""




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

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

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

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

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

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

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE				)		->							Optional[int]:






       """simple docstring"""




       _UpperCAmelCase			     =  {}
       _UpperCAmelCase			     =  []

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

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

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							List[str]:






       """simple docstring"""




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

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

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

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

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

              _UpperCAmelCase			     =  v

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

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

       return torch_clip_params, torch_model, attn_params

@torch.no_grad()
def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE=None				)		->							Union[str, Any]:






       """simple docstring"""




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

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

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

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

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

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

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

       # Initialize processor
       _UpperCAmelCase			     =  OwlViTProcessor(image_processor=SCREAMING_SNAKE_CASE,tokenizer=SCREAMING_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_      =				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_      =				parser.parse_args()

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

    # Load from checkpoint and convert params to float-32
    lowerCAmelCase_      =				checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)['''optimizer''']['''target''']
    lowerCAmelCase_      =				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_      ,					lowerCAmelCase_      ,					lowerCAmelCase_      =				convert_clip_backbone(flax_params, torch_config)

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













"""simple docstring"""






from collections import defaultdict

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE = 5				)		->							int:






       """simple docstring"""




       _UpperCAmelCase			     =  defaultdict(SCREAMING_SNAKE_CASE				)
       _UpperCAmelCase			     =  0

       while True:
              _UpperCAmelCase			     =  get_digits(SCREAMING_SNAKE_CASE				)
              freqs[digits].append(SCREAMING_SNAKE_CASE				)

              if len(freqs[digits]				) == max_base:
                     _UpperCAmelCase			     =  freqs[digits][0] ** 3
                     return base

              num += 1

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE				)		->							str:






       """simple docstring"""




       return "".join(sorted(str(num**3				)				)				)


if __name__ == "__main__":
    print(F'''{solution() = }''')






