

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": 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="/"      ):
  UpperCAmelCase          =	[]

  for k, v in params.items():
    UpperCAmelCase          =	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      ):
  return jax.tree_util.tree_map(lambda __A      : x.astype(jnp.floataa      ) if x.dtype == jnp.bfloataa else x ,							__A      )
def   _lowerCAmelCase(						__A ,							__A      ):
  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(						__A ,							__A      ):
  copy_linear(hf_mlp.fca ,							pt_mlp.c_fc      )
  copy_linear(hf_mlp.fca ,							pt_mlp.c_proj      )
def   _lowerCAmelCase(						__A ,							__A      ):
  UpperCAmelCase          =	pt_linear.weight
  UpperCAmelCase          =	pt_linear.bias
def   _lowerCAmelCase(						__A ,							__A      ):
  # copy layer norms
  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      ):
  for hf_layer, pt_layer in zip(__A ,							__A      ):
    copy_layer(__A ,							__A      )
def   _lowerCAmelCase(						__A ,							__A      ):
  # copy  embeds
  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(						__A ,							__A      ):
  # copy projection
  UpperCAmelCase          =	pt_model.text_projection.data.T

  # copy text encoder
  copy_encoder(hf_model.text_model ,							__A      )
def   _lowerCAmelCase(						__A ,							__A      ):
  # copy projection
  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(						__A ,							__A      ):
  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(__A      )
  UpperCAmelCase          =	nn.Parameter(__A      )
def   _lowerCAmelCase(						__A ,							__A      ):
  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(__A      )      )

  # 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(__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      ):
  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(__A      )

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

    # Copy flax CLIP attn params to HF PyTorch params
    UpperCAmelCase          =	torch.from_numpy(__A      )
    hf_backbone.state_dict()[torch_key].copy_(__A      )
def   _lowerCAmelCase(						__A      ):
  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(__A      )
      UpperCAmelCase          =	params[base + "out.weight"].shape[-1]
      UpperCAmelCase          =	params[base + "out.weight"].reshape(__A ,							__A      ).T
      UpperCAmelCase          =	params[base + "out.bias"]
    else:
      UpperCAmelCase          =	v
  return new_params
def   _lowerCAmelCase(						__A ,							__A      ):
  UpperCAmelCase          =	CLIP(**__A      )
  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(__A      )
  new_torch_params.update(__A      )
  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_(__A      )
    else:
      UpperCAmelCase          =	param

  return torch_clip_params, torch_model, attn_params
@torch.no_grad()
def   _lowerCAmelCase(						__A ,							__A ,							__A ,							__A ,							__A=None      ):
  UpperCAmelCase          =	Repository(__A ,							clone_from=F"google/{pytorch_dump_folder_path}"      )
  repo.git_pull()

  if config_path is not None:
    UpperCAmelCase          =	OwlViTConfig.from_pretrained(__A      )
  else:
    UpperCAmelCase          =	OwlViTConfig()

  UpperCAmelCase          =	OwlViTModel(__A      ).eval()
  UpperCAmelCase          =	OwlViTForObjectDetection(__A      ).eval()

  copy_text_model_and_projection(__A ,							__A      )
  copy_vision_model_and_projection(__A ,							__A      )
  UpperCAmelCase          =	pt_backbone.logit_scale
  copy_flax_attn_params(__A ,							__A      )

  UpperCAmelCase          =	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
  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=__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__":
      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)






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class     __magic_name__     (							_snake_case     ):
 UpperCAmelCase				     =    ["""image_processor""", """tokenizer"""]
 UpperCAmelCase				     =    """LayoutLMv2ImageProcessor"""
 UpperCAmelCase				     =    ("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")


 def __init__(    self		:			Any      ,    lowerCAmelCase__		:			Any=None      ,    lowerCAmelCase__		:			Optional[Any]=None      ,    **lowerCAmelCase__		:			int     )     ->   Union[str, Any]:
   UpperCAmelCase          =	None
   if "feature_extractor" in kwargs:
     warnings.warn(
         "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
         " instead."      ,    lowerCAmelCase__      ,    )
     UpperCAmelCase          =	kwargs.pop("feature_extractor"     )

   UpperCAmelCase          =	image_processor if image_processor is not None else feature_extractor
   if image_processor is None:
     raise ValueError("You need to specify an `image_processor`."     )
   if tokenizer is None:
     raise ValueError("You need to specify a `tokenizer`."     )

   super().__init__(lowerCAmelCase__      ,    lowerCAmelCase__     )


 def __call__(    self		:			int      ,    lowerCAmelCase__		:			Tuple      ,    lowerCAmelCase__		:			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,    lowerCAmelCase__		:			Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None      ,    lowerCAmelCase__		:			Union[List[List[int]], List[List[List[int]]]] = None      ,    lowerCAmelCase__		:			Optional[Union[List[int], List[List[int]]]] = None      ,    lowerCAmelCase__		:			bool = True      ,    lowerCAmelCase__		:			Union[bool, str, PaddingStrategy] = False      ,    lowerCAmelCase__		:			Union[bool, str, TruncationStrategy] = False      ,    lowerCAmelCase__		:			Optional[int] = None      ,    lowerCAmelCase__		:			int = 0      ,    lowerCAmelCase__		:			Optional[int] = None      ,    lowerCAmelCase__		:			Optional[bool] = None      ,    lowerCAmelCase__		:			Optional[bool] = None      ,    lowerCAmelCase__		:			bool = False      ,    lowerCAmelCase__		:			bool = False      ,    lowerCAmelCase__		:			bool = False      ,    lowerCAmelCase__		:			bool = False      ,    lowerCAmelCase__		:			bool = True      ,    lowerCAmelCase__		:			Optional[Union[str, TensorType]] = None      ,    **lowerCAmelCase__		:			Any      ,    )     ->   BatchEncoding:
   # verify input
   if self.image_processor.apply_ocr and (boxes is not None):
     raise ValueError(
         "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."     )

   if self.image_processor.apply_ocr and (word_labels is not None):
     raise ValueError(
         "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."     )

   if return_overflowing_tokens is True and return_offsets_mapping is False:
     raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."     )

   # first, apply the image processor
   UpperCAmelCase          =	self.image_processor(images=lowerCAmelCase__      ,    return_tensors=lowerCAmelCase__     )

   # second, apply the tokenizer
   if text is not None and self.image_processor.apply_ocr and text_pair is None:
     if isinstance(lowerCAmelCase__      ,    lowerCAmelCase__     ):
       UpperCAmelCase          =	[text]  # add batch dimension (as the image processor always adds a batch dimension)
     UpperCAmelCase          =	features["words"]

   UpperCAmelCase          =	self.tokenizer(
       text=text if text is not None else features["words"]      ,    text_pair=text_pair if text_pair is not None else None      ,    boxes=boxes if boxes is not None else features["boxes"]      ,    word_labels=lowerCAmelCase__      ,    add_special_tokens=lowerCAmelCase__      ,    padding=lowerCAmelCase__      ,    truncation=lowerCAmelCase__      ,    max_length=lowerCAmelCase__      ,    stride=lowerCAmelCase__      ,    pad_to_multiple_of=lowerCAmelCase__      ,    return_token_type_ids=lowerCAmelCase__      ,    return_attention_mask=lowerCAmelCase__      ,    return_overflowing_tokens=lowerCAmelCase__      ,    return_special_tokens_mask=lowerCAmelCase__      ,    return_offsets_mapping=lowerCAmelCase__      ,    return_length=lowerCAmelCase__      ,    verbose=lowerCAmelCase__      ,    return_tensors=lowerCAmelCase__      ,    **lowerCAmelCase__      ,    )

   # add pixel values
   UpperCAmelCase          =	features.pop("pixel_values"     )
   if return_overflowing_tokens is True:
     UpperCAmelCase          =	self.get_overflowing_images(lowerCAmelCase__      ,    encoded_inputs["overflow_to_sample_mapping"]     )
   UpperCAmelCase          =	images

   return encoded_inputs


 def _UpperCamelCase						(    self		:			str      ,    lowerCAmelCase__		:			Union[str, Any]      ,    lowerCAmelCase__		:			Union[str, Any]     )     ->   int:
   # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
   UpperCAmelCase          =	[]
   for sample_idx in overflow_to_sample_mapping:
     images_with_overflow.append(images[sample_idx]     )

   if len(lowerCAmelCase__     ) != len(lowerCAmelCase__     ):
     raise ValueError(
         "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
         f" {len(lowerCAmelCase__     )} and {len(lowerCAmelCase__     )}"     )

   return images_with_overflow


 def _UpperCamelCase						(    self		:			Optional[Any]      ,    *lowerCAmelCase__		:			int      ,    **lowerCAmelCase__		:			Union[str, Any]     )     ->   List[str]:
   return self.tokenizer.batch_decode(*lowerCAmelCase__      ,    **lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			Union[str, Any]      ,    *lowerCAmelCase__		:			Tuple      ,    **lowerCAmelCase__		:			Union[str, Any]     )     ->   List[Any]:
   return self.tokenizer.decode(*lowerCAmelCase__      ,    **lowerCAmelCase__     )


 @property
 def _UpperCamelCase						(    self		:			int     )     ->   List[str]:
   return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]


 @property
 def _UpperCamelCase						(    self		:			Dict     )     ->   List[str]:
   warnings.warn(
       "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."      ,    lowerCAmelCase__      ,    )
   return self.image_processor_class


 @property
 def _UpperCamelCase						(    self		:			List[Any]     )     ->   int:
   warnings.warn(
       "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."      ,    lowerCAmelCase__      ,    )
   return self.image_processor



