
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    			=					{
    "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 		_lowerCamelCase       (   _a			,   _a=""			,   _a="/"  ):
  """simple docstring"""




  _lowerCamelCase      =						[]

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

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


def 		_lowerCamelCase       (   _a  ):
  """simple docstring"""




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


def 		_lowerCamelCase       (   _a			,   _a  ):
  """simple docstring"""




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

  _lowerCamelCase      =						pt_attn_layer.out_proj.weight
  _lowerCamelCase      =						pt_attn_layer.out_proj.bias

  _lowerCamelCase      =						q_proj
  _lowerCamelCase      =						q_proj_bias

  _lowerCamelCase      =						k_proj
  _lowerCamelCase      =						k_proj_bias

  _lowerCamelCase      =						v_proj
  _lowerCamelCase      =						v_proj_bias

  _lowerCamelCase      =						out_proj_weights
  _lowerCamelCase      =						out_proj_bias


def 		_lowerCamelCase       (   _a			,   _a  ):
  """simple docstring"""




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


def 		_lowerCamelCase       (   _a			,   _a  ):
  """simple docstring"""




  _lowerCamelCase      =						pt_linear.weight
  _lowerCamelCase      =						pt_linear.bias


def 		_lowerCamelCase       (   _a			,   _a  ):
  """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       (   _a			,   _a  ):
  """simple docstring"""




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


def 		_lowerCamelCase       (   _a			,   _a  ):
  """simple docstring"""




  _lowerCamelCase      =						pt_model.token_embedding.weight
  _lowerCamelCase      =						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       (   _a			,   _a  ):
  """simple docstring"""




  _lowerCamelCase      =						pt_model.text_projection.data.T

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


def 		_lowerCamelCase       (   _a			,   _a  ):
  """simple docstring"""




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

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


def 		_lowerCamelCase       (   _a			,   _a  ):
  """simple docstring"""




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

  _lowerCamelCase      =						torch.from_numpy(flax_class_token_params['''scale''']  )
  _lowerCamelCase      =						torch.from_numpy(flax_class_token_params['''bias''']  )
  _lowerCamelCase      =						nn.Parameter(_a  )
  _lowerCamelCase      =						nn.Parameter(_a  )


def 		_lowerCamelCase       (   _a			,   _a  ):
  """simple docstring"""




  _lowerCamelCase      =						hf_model.state_dict()
  _lowerCamelCase      =						{}

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

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

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

    _lowerCamelCase      =						nn.Parameter(torch.from_numpy(_a  )  )

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

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

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

    _lowerCamelCase      =						nn.Parameter(torch.from_numpy(_a  )  )

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


def 		_lowerCamelCase       (   _a			,   _a  ):
  """simple docstring"""




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

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

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

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

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

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


def 		_lowerCamelCase       (   _a  ):
  """simple docstring"""




  _lowerCamelCase      =						{}
  _lowerCamelCase      =						[]

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

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


def 		_lowerCamelCase       (   _a			,   _a  ):
  """simple docstring"""




  _lowerCamelCase      =						CLIP(**_a  )
  torch_model.eval()
  _lowerCamelCase      =						torch_model.state_dict()

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

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

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

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

    _lowerCamelCase      =						v

  _lowerCamelCase      =						_convert_attn_layers(_a  )
  new_torch_params.update(_a  )
  _lowerCamelCase      =						{}

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

  return torch_clip_params, torch_model, attn_params


@torch.no_grad()
def 		_lowerCamelCase       (   _a			,   _a			,   _a			,   _a			,   _a=None  ):
  """simple docstring"""




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

  if config_path is not None:
    _lowerCamelCase      =						OwlViTConfig.from_pretrained(_a  )
  else:
    _lowerCamelCase      =						OwlViTConfig()

  _lowerCamelCase      =						OwlViTModel(_a  ).eval()
  _lowerCamelCase      =						OwlViTForObjectDetection(_a  ).eval()

  copy_text_model_and_projection(_a			,   _a  )
  copy_vision_model_and_projection(_a			,   _a  )
  _lowerCamelCase      =						pt_backbone.logit_scale
  copy_flax_attn_params(_a			,   _a  )

  _lowerCamelCase      =						hf_backbone
  copy_class_merge_token(_a			,   _a  )
  copy_class_box_heads(_a			,   _a  )

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

  # Initialize image processor
  _lowerCamelCase      =						OwlViTImageProcessor(
      size=config.vision_config.image_size			,   crop_size=config.vision_config.image_size  )
  # Initialize tokenizer
  _lowerCamelCase      =						CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''			,   pad_token='''!'''			,   model_max_length=1_6  )

  # Initialize processor
  _lowerCamelCase      =						OwlViTProcessor(image_processor=_a			,   tokenizer=_a  )
  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    			=					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    			=					parser.parse_args()

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

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

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






from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any

class 		__magic_name__				:


  """simple docstring"""





  _UpperCamelCase						 = ("_front", "_back", "_len")

  @dataclass
  class 		__magic_name__				:


    """simple docstring"""





    _UpperCamelCase						 = None
    _UpperCamelCase						 = None
    _UpperCamelCase						 = None







  class 		__magic_name__				:


    """simple docstring"""





    _UpperCamelCase						 = ("_cur",)


    def __init__(					self	,		a__		):
      _lowerCamelCase      =						cur


    def __iter__(					self		):
      return self


    def     _UpperCAmelCase  (					self		):
      if self._cur is None:
        # finished iterating
        raise StopIteration
      _lowerCamelCase      =						self._cur.val
      _lowerCamelCase      =						self._cur.next_node

      return val







  def __init__(					self	,		a__ = None		):
    _lowerCamelCase      =						None
    _lowerCamelCase      =						None
    _lowerCamelCase      =						0

    if iterable is not None:
      # append every value to the deque
      for val in iterable:
        self.append(a__		)


  def     _UpperCAmelCase  (					self	,		a__		):
    _lowerCamelCase      =						self._Node(a__	,		a__	,		a__		)
    if self.is_empty():
      # front = back
      _lowerCamelCase      =						_lowerCamelCase      =						node
      _lowerCamelCase      =						1
    else:
      # connect nodes
      _lowerCamelCase      =						node
      _lowerCamelCase      =						self._back
      _lowerCamelCase      =						node  # assign new back to the new node

      self._len += 1

      # make sure there were no errors
      assert not self.is_empty(), "Error on appending value."


  def     _UpperCAmelCase  (					self	,		a__		):
    _lowerCamelCase      =						self._Node(a__	,		a__	,		a__		)
    if self.is_empty():
      # front = back
      _lowerCamelCase      =						_lowerCamelCase      =						node
      _lowerCamelCase      =						1
    else:
      # connect nodes
      _lowerCamelCase      =						self._front
      _lowerCamelCase      =						node
      _lowerCamelCase      =						node  # assign new front to the new node

      self._len += 1

      # make sure there were no errors
      assert not self.is_empty(), "Error on appending value."


  def     _UpperCAmelCase  (					self	,		a__		):
    for val in iterable:
      self.append(a__		)


  def     _UpperCAmelCase  (					self	,		a__		):
    for val in iterable:
      self.appendleft(a__		)


  def     _UpperCAmelCase  (					self		):
    assert not self.is_empty(), "Deque is empty."

    _lowerCamelCase      =						self._back
    _lowerCamelCase      =						self._back.prev_node  # set new back
    # drop the last node - python will deallocate memory automatically
    _lowerCamelCase      =						None

    self._len -= 1

    return topop.val


  def     _UpperCAmelCase  (					self		):
    assert not self.is_empty(), "Deque is empty."

    _lowerCamelCase      =						self._front
    _lowerCamelCase      =						self._front.next_node  # set new front and drop the first node
    _lowerCamelCase      =						None

    self._len -= 1

    return topop.val


  def     _UpperCAmelCase  (					self		):
    return self._front is None


  def __len__(					self		):
    return self._len


  def __eq__(					self	,		a__		):

    if not isinstance(a__	,		a__		):
      return NotImplemented

    _lowerCamelCase      =						self._front
    _lowerCamelCase      =						other._front

    # if the length of the dequeues are not the same, they are not equal
    if len(self		) != len(a__		):
      return False

    while me is not None and oth is not None:
      # compare every value
      if me.val != oth.val:
        return False
      _lowerCamelCase      =						me.next_node
      _lowerCamelCase      =						oth.next_node

    return True


  def __iter__(					self		):
    return Deque._Iterator(self._front		)


  def __repr__(					self		):
    _lowerCamelCase      =						[]
    _lowerCamelCase      =						self._front
    while aux is not None:
      # append the values in a list to display
      values_list.append(aux.val		)
      _lowerCamelCase      =						aux.next_node

    return f'''[{', '.join(repr(a__		) for val in values_list		)}]'''


if __name__ == "__main__":
  import doctest

  doctest.testmod()




