






'''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": 5_12,
        "image_resolution": 7_68,
        "context_length": 16,
        "vocab_size": 4_94_08,
        "vision_layers": 12,
        "vision_width": 7_68,
        "vision_patch_size": 32,
        "transformer_width": 5_12,
        "transformer_heads": 8,
        "transformer_layers": 12,
    },
    "vit_b16": {
        "embed_dim": 5_12,
        "image_resolution": 7_68,
        "context_length": 16,
        "vocab_size": 4_94_08,
        "vision_layers": 12,
        "vision_width": 7_68,
        "vision_patch_size": 16,
        "transformer_width": 5_12,
        "transformer_heads": 8,
        "transformer_layers": 12,
    },
    "vit_l14": {
        "embed_dim": 7_68,
        "image_resolution": 8_40,
        "context_length": 16,
        "vocab_size": 4_94_08,
        "vision_layers": 24,
        "vision_width": 10_24,
        "vision_patch_size": 14,
        "transformer_width": 7_68,
        "transformer_heads": 12,
        "transformer_layers": 12,
    },
}


def 			__UpperCamelCase				(			lowercase_					:     str      ,					lowercase_					:     Union[str, Any]=""      ,					lowercase_					:     List[Any]="/"      ):
       """simple docstring"""




       a_	    = []

       for k, v in params.items():
              a_	    = 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 			__UpperCamelCase				(			lowercase_					:     int      ):
       """simple docstring"""




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


def 			__UpperCamelCase				(			lowercase_					:     int      ,					lowercase_					:     List[Any]      ):
       """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 			__UpperCamelCase				(			lowercase_					:     List[Any]      ,					lowercase_					:     Any      ):
       """simple docstring"""




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


def 			__UpperCamelCase				(			lowercase_					:     str      ,					lowercase_					:     Optional[int]      ):
       """simple docstring"""




       a_	    = pt_linear.weight
       a_	    = pt_linear.bias


def 			__UpperCamelCase				(			lowercase_					:     Dict      ,					lowercase_					:     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 			__UpperCamelCase				(			lowercase_					:     Optional[int]      ,					lowercase_					:     Optional[Any]      ):
       """simple docstring"""




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


def 			__UpperCamelCase				(			lowercase_					:     Union[str, Any]      ,					lowercase_					:     Dict      ):
       """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 			__UpperCamelCase				(			lowercase_					:     str      ,					lowercase_					:     Tuple      ):
       """simple docstring"""




       a_	    = pt_model.text_projection.data.T

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


def 			__UpperCamelCase				(			lowercase_					:     Union[str, Any]      ,					lowercase_					:     Union[str, Any]      ):
       """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 			__UpperCamelCase				(			lowercase_					:     Union[str, Any]      ,					lowercase_					:     Union[str, Any]      ):
       """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(lowercase_      )
       a_	    = nn.Parameter(lowercase_      )


def 			__UpperCamelCase				(			lowercase_					:     Optional[int]      ,					lowercase_					:     Optional[int]      ):
       """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(lowercase_      )      )

       # 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(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 			__UpperCamelCase				(			lowercase_					:     Any      ,					lowercase_					:     int      ):
       """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(lowercase_      )

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

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


def 			__UpperCamelCase				(			lowercase_					:     Optional[Any]      ):
       """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(lowercase_      )
                     a_	    = params[base + 'out.weight'].shape[-1]
                     a_	    = params[base + 'out.weight'].reshape(lowercase_      ,					lowercase_      ).T
                     a_	    = params[base + 'out.bias']
              else:
                     a_	    = v
       return new_params


def 			__UpperCamelCase				(			lowercase_					:     Tuple      ,					lowercase_					:     Tuple      ):
       """simple docstring"""




       a_	    = CLIP(**lowercase_      )
       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(lowercase_      )
       new_torch_params.update(lowercase_      )
       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_(lowercase_      )
              else:
                     a_	    = param

       return torch_clip_params, torch_model, attn_params


@torch.no_grad()
def 			__UpperCamelCase				(			lowercase_					:     List[Any]      ,					lowercase_					:     Any      ,					lowercase_					:     List[str]      ,					lowercase_					:     str      ,					lowercase_					:     List[Any]=None      ):
       """simple docstring"""




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

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

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

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

       a_	    = 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
       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=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__":
  __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'''







import re


def 			__UpperCamelCase				(			lowercase_					:     int      ):
       """simple docstring"""




       def docstring_decorator(lowercase_					:     int      ):
              a_	    = fn.__doc__
              a_	    = func_doc.split('\n'      )
              a_	    = 0
              while i < len(lowercase_      ) and re.search(R'^\s*Examples?:\s*$'      ,					lines[i]      ) is None:
                     i += 1
              if i < len(lowercase_      ):
                     a_	    = example_docstring
                     a_	    = '\n'.join(lowercase_      )
              else:
                     raise ValueError(
                         F'The function {fn} should have an empty \'Examples:\' in its docstring as placeholder, '
                         F'current docstring is:\n{func_doc}'      )
              a_	    = func_doc
              return fn

       return docstring_decorator




