


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,
)


__UpperCAmelCase			:			List[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						(				SCREAMING_SNAKE_CASE_   :		Tuple				,      SCREAMING_SNAKE_CASE_   :		Any=""				,      SCREAMING_SNAKE_CASE_   :		Tuple="/"							):






       """simple docstring"""





       UpperCamelCase     :						List[Any] 						=		[]

       for k, v in params.items():
              UpperCamelCase     :						str 						=		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   a						(				SCREAMING_SNAKE_CASE_   :		List[str]							):






       """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   a						(				SCREAMING_SNAKE_CASE_   :		List[str]				,      SCREAMING_SNAKE_CASE_   :		Optional[int]							):






       """simple docstring"""





       UpperCamelCase						,			UpperCamelCase						,			UpperCamelCase     :						Any 						=		pt_attn_layer.in_proj_weight.chunk(3				,      dim=0							)
       UpperCamelCase						,			UpperCamelCase						,			UpperCamelCase     :						str 						=		pt_attn_layer.in_proj_bias.chunk(3				,      dim=0							)

       UpperCamelCase     :						Optional[int] 						=		pt_attn_layer.out_proj.weight
       UpperCamelCase     :						List[str] 						=		pt_attn_layer.out_proj.bias

       UpperCamelCase     :						Tuple 						=		q_proj
       UpperCamelCase     :						Optional[Any] 						=		q_proj_bias

       UpperCamelCase     :						Dict 						=		k_proj
       UpperCamelCase     :						Tuple 						=		k_proj_bias

       UpperCamelCase     :						int 						=		v_proj
       UpperCamelCase     :						Optional[int] 						=		v_proj_bias

       UpperCamelCase     :						int 						=		out_proj_weights
       UpperCamelCase     :						List[Any] 						=		out_proj_bias


def   a						(				SCREAMING_SNAKE_CASE_   :		Optional[Any]				,      SCREAMING_SNAKE_CASE_   :		Tuple							):






       """simple docstring"""





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


def   a						(				SCREAMING_SNAKE_CASE_   :		Optional[Any]				,      SCREAMING_SNAKE_CASE_   :		List[Any]							):






       """simple docstring"""





       UpperCamelCase     :						Tuple 						=		pt_linear.weight
       UpperCamelCase     :						Dict 						=		pt_linear.bias


def   a						(				SCREAMING_SNAKE_CASE_   :		int				,      SCREAMING_SNAKE_CASE_   :		List[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						(				SCREAMING_SNAKE_CASE_   :		Any				,      SCREAMING_SNAKE_CASE_   :		Union[str, 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   a						(				SCREAMING_SNAKE_CASE_   :		Dict				,      SCREAMING_SNAKE_CASE_   :		Tuple							):






       """simple docstring"""





       UpperCamelCase     :						int 						=		pt_model.token_embedding.weight
       UpperCamelCase     :						str 						=		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						(				SCREAMING_SNAKE_CASE_   :		Optional[int]				,      SCREAMING_SNAKE_CASE_   :		Optional[int]							):






       """simple docstring"""





       UpperCamelCase     :						Any 						=		pt_model.text_projection.data.T

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


def   a						(				SCREAMING_SNAKE_CASE_   :		str				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]							):






       """simple docstring"""





       UpperCamelCase     :						Union[str, Any] 						=		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     :						Optional[Any] 						=		pt_model.visual.conva.weight.data
       UpperCamelCase     :						Optional[Any] 						=		pt_model.visual.class_embedding
       UpperCamelCase     :						Any 						=		pt_model.visual.positional_embedding.data

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


def   a						(				SCREAMING_SNAKE_CASE_   :		Optional[Any]				,      SCREAMING_SNAKE_CASE_   :		Any							):






       """simple docstring"""





       UpperCamelCase     :						Tuple 						=		flatten_nested_dict(flax_params['''backbone''']['''merged_class_token''']							)

       UpperCamelCase     :						Union[str, Any] 						=		torch.from_numpy(flax_class_token_params['''scale''']							)
       UpperCamelCase     :						Tuple 						=		torch.from_numpy(flax_class_token_params['''bias''']							)
       UpperCamelCase     :						Optional[int] 						=		nn.Parameter(SCREAMING_SNAKE_CASE_							)
       UpperCamelCase     :						Optional[int] 						=		nn.Parameter(SCREAMING_SNAKE_CASE_							)


def   a						(				SCREAMING_SNAKE_CASE_   :		List[Any]				,      SCREAMING_SNAKE_CASE_   :		int							):






       """simple docstring"""





       UpperCamelCase     :						Union[str, Any] 						=		hf_model.state_dict()
       UpperCamelCase     :						Optional[int] 						=		{}

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

       for flax_key, v in flax_class_params.items():
              UpperCamelCase     :						Any 						=		flax_key.replace('''/'''				,      '''.'''							)
              UpperCamelCase     :						int 						=		torch_key.replace('''.kernel'''				,      '''.weight'''							)
              UpperCamelCase     :						Tuple 						=		torch_key.replace('''Dense_0'''				,      '''dense0'''							)
              UpperCamelCase     :						str 						=		'''class_head.''' + torch_key

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

              UpperCamelCase     :						Optional[int] 						=		nn.Parameter(torch.from_numpy(SCREAMING_SNAKE_CASE_							)							)

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

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

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

              UpperCamelCase     :						str 						=		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   a						(				SCREAMING_SNAKE_CASE_   :		Dict				,      SCREAMING_SNAKE_CASE_   :		int							):






       """simple docstring"""





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

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

              if "bias" in torch_key and v.ndim == 2:
                     UpperCamelCase     :						Union[str, Any] 						=		v.shape[0] * v.shape[1]
                     UpperCamelCase     :						Union[str, Any] 						=		v.reshape(SCREAMING_SNAKE_CASE_							)

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

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

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


def   a						(				SCREAMING_SNAKE_CASE_   :		List[Any]							):






       """simple docstring"""





       UpperCamelCase     :						str 						=		{}
       UpperCamelCase     :						Optional[Any] 						=		[]

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

                     processed_attn_layers.append(SCREAMING_SNAKE_CASE_							)
                     UpperCamelCase     :						Optional[Any] 						=		params[base + '''out.weight'''].shape[-1]
                     UpperCamelCase     :						Any 						=		params[base + '''out.weight'''].reshape(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							).T
                     UpperCamelCase     :						Optional[int] 						=		params[base + '''out.bias''']
              else:
                     UpperCamelCase     :						Dict 						=		v
       return new_params


def   a						(				SCREAMING_SNAKE_CASE_   :		Any				,      SCREAMING_SNAKE_CASE_   :		Union[str, Any]							):






       """simple docstring"""





       UpperCamelCase     :						Optional[int] 						=		CLIP(**SCREAMING_SNAKE_CASE_							)
       torch_model.eval()
       UpperCamelCase     :						Union[str, Any] 						=		torch_model.state_dict()

       UpperCamelCase     :						Dict 						=		flatten_nested_dict(flax_params['''backbone''']['''clip''']							)
       UpperCamelCase     :						Tuple 						=		{}

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

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

              if "conv" in torch_key or "downsample.0.weight" in torch_key:
                     UpperCamelCase     :						List[Any] 						=		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     :						Tuple 						=		v.T

              UpperCamelCase     :						int 						=		v

       UpperCamelCase     :						Tuple 						=		_convert_attn_layers(SCREAMING_SNAKE_CASE_							)
       new_torch_params.update(SCREAMING_SNAKE_CASE_							)
       UpperCamelCase     :						Optional[Any] 						=		{}

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

       return torch_clip_params, torch_model, attn_params


@torch.no_grad()
def   a						(				SCREAMING_SNAKE_CASE_   :		str				,      SCREAMING_SNAKE_CASE_   :		Union[str, Any]				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]				,      SCREAMING_SNAKE_CASE_   :		List[str]				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]=None							):






       """simple docstring"""





       UpperCamelCase     :						List[Any] 						=		Repository(SCREAMING_SNAKE_CASE_				,      clone_from=F"""google/{pytorch_dump_folder_path}"""							)
       repo.git_pull()

       if config_path is not None:
              UpperCamelCase     :						List[str] 						=		OwlViTConfig.from_pretrained(SCREAMING_SNAKE_CASE_							)
       else:
              UpperCamelCase     :						int 						=		OwlViTConfig()

       UpperCamelCase     :						Any 						=		OwlViTModel(SCREAMING_SNAKE_CASE_							).eval()
       UpperCamelCase     :						Dict 						=		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     :						List[str] 						=		pt_backbone.logit_scale
       copy_flax_attn_params(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							)

       UpperCamelCase     :						Optional[int] 						=		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     :						int 						=		OwlViTImageProcessor(
           size=config.vision_config.image_size				,      crop_size=config.vision_config.image_size							)
       # Initialize tokenizer
       UpperCamelCase     :						Dict 						=		CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''				,      pad_token='''!'''				,      model_max_length=1_6							)

       # Initialize processor
       UpperCamelCase     :						List[str] 						=		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__":
  __UpperCAmelCase			:			List[str]			      =					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."
  )
  __UpperCAmelCase			:			Union[str, Any]			      =					parser.parse_args()

  # Initialize PyToch clip model
  __UpperCAmelCase			:			List[str]			      =					args.owlvit_version
  if model_name == "clip_b16":
    __UpperCAmelCase			:			Union[str, Any]			      =					CONFIGS["vit_b16"]
  elif model_name == "clip_b32":
    __UpperCAmelCase			:			str			      =					CONFIGS["vit_b32"]
  elif model_name == "clip_l14":
    __UpperCAmelCase			:			Any			      =					CONFIGS["vit_l14"]

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

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





def   a						(				SCREAMING_SNAKE_CASE_   :		Any							):






       """simple docstring"""





       UpperCamelCase     :						int 						=		len(SCREAMING_SNAKE_CASE_							)
       for i in range(length - 1							):
              UpperCamelCase     :						Optional[int] 						=		False
              for j in range(length - 1 - i							):
                     if collection[j] > collection[j + 1]:
                            UpperCamelCase     :						Optional[int] 						=		True
                            UpperCamelCase						,			UpperCamelCase     :						Union[str, Any] 						=		collection[j + 1], collection[j]
              if not swapped:
                     break  # Stop iteration if the collection is sorted.
       return collection


if __name__ == "__main__":
  import doctest
  import time

  doctest.testmod()

  __UpperCAmelCase			:			Any			      =					input("Enter numbers separated by a comma:").strip()
  __UpperCAmelCase			:			List[str]			      =					[int(item) for item in user_input.split(",")]
  __UpperCAmelCase			:			List[Any]			      =					time.process_time()
  print(*bubble_sort(unsorted), sep=",")
  print(f'''Processing time: {(time.process_time() - start)%1E9 + 7}''')

