
'''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,
)


A							     = {
    "vit_b32": {
        "embed_dim": 5_1_2,
        "image_resolution": 7_6_8,
        "context_length": 1_6,
        "vocab_size": 4_9_4_0_8,
        "vision_layers": 1_2,
        "vision_width": 7_6_8,
        "vision_patch_size": 3_2,
        "transformer_width": 5_1_2,
        "transformer_heads": 8,
        "transformer_layers": 1_2,
    },
    "vit_b16": {
        "embed_dim": 5_1_2,
        "image_resolution": 7_6_8,
        "context_length": 1_6,
        "vocab_size": 4_9_4_0_8,
        "vision_layers": 1_2,
        "vision_width": 7_6_8,
        "vision_patch_size": 1_6,
        "transformer_width": 5_1_2,
        "transformer_heads": 8,
        "transformer_layers": 1_2,
    },
    "vit_l14": {
        "embed_dim": 7_6_8,
        "image_resolution": 8_4_0,
        "context_length": 1_6,
        "vocab_size": 4_9_4_0_8,
        "vision_layers": 2_4,
        "vision_width": 1_0_2_4,
        "vision_patch_size": 1_4,
        "transformer_width": 7_6_8,
        "transformer_heads": 1_2,
        "transformer_layers": 1_2,
    },
}
def 	snake_case_							(				_a  :				Any			,       _a  :				str=""			,       _a  :				Dict="/"      ):
      snake_case_    							=				[]

      for k, v in params.items():
            snake_case_    							=				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 	snake_case_							(				_a  :				str      ):
      return jax.tree_util.tree_map(lambda _a      : x.astype(jnp.floataa      ) if x.dtype == jnp.bfloataa else x			,       _a      )
def 	snake_case_							(				_a  :				Optional[int]			,       _a  :				Optional[Any]      ):
      snake_case_							,						snake_case_							,						snake_case_    							=				pt_attn_layer.in_proj_weight.chunk(3			,       dim=0      )
      snake_case_							,						snake_case_							,						snake_case_    							=				pt_attn_layer.in_proj_bias.chunk(3			,       dim=0      )

      snake_case_    							=				pt_attn_layer.out_proj.weight
      snake_case_    							=				pt_attn_layer.out_proj.bias

      snake_case_    							=				q_proj
      snake_case_    							=				q_proj_bias

      snake_case_    							=				k_proj
      snake_case_    							=				k_proj_bias

      snake_case_    							=				v_proj
      snake_case_    							=				v_proj_bias

      snake_case_    							=				out_proj_weights
      snake_case_    							=				out_proj_bias
def 	snake_case_							(				_a  :				Optional[Any]			,       _a  :				Tuple      ):
      copy_linear(hf_mlp.fca			,       pt_mlp.c_fc      )
      copy_linear(hf_mlp.fca			,       pt_mlp.c_proj      )
def 	snake_case_							(				_a  :				Any			,       _a  :				List[str]      ):
      snake_case_    							=				pt_linear.weight
      snake_case_    							=				pt_linear.bias
def 	snake_case_							(				_a  :				List[str]			,       _a  :				int      ):
      # 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 	snake_case_							(				_a  :				List[str]			,       _a  :				str      ):
      for hf_layer, pt_layer in zip(_a			,       _a      ):
            copy_layer(_a			,       _a      )
def 	snake_case_							(				_a  :				List[str]			,       _a  :				Dict      ):
      # copy  embeds
      snake_case_    							=				pt_model.token_embedding.weight
      snake_case_    							=				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 	snake_case_							(				_a  :				Tuple			,       _a  :				Optional[int]      ):
      # copy projection
      snake_case_    							=				pt_model.text_projection.data.T

      # copy text encoder
      copy_encoder(hf_model.text_model			,       _a      )
def 	snake_case_							(				_a  :				Union[str, Any]			,       _a  :				int      ):
      # copy projection
      snake_case_    							=				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
      snake_case_    							=				pt_model.visual.conva.weight.data
      snake_case_    							=				pt_model.visual.class_embedding
      snake_case_    							=				pt_model.visual.positional_embedding.data

      # copy encoder
      copy_layers(hf_model.vision_model.encoder.layers			,       pt_model.visual.transformer.resblocks      )
def 	snake_case_							(				_a  :				Any			,       _a  :				Dict      ):
      snake_case_    							=				flatten_nested_dict(flax_params["backbone"]["merged_class_token"]      )

      snake_case_    							=				torch.from_numpy(flax_class_token_params["scale"]      )
      snake_case_    							=				torch.from_numpy(flax_class_token_params["bias"]      )
      snake_case_    							=				nn.Parameter(_a      )
      snake_case_    							=				nn.Parameter(_a      )
def 	snake_case_							(				_a  :				Tuple			,       _a  :				Tuple      ):
      snake_case_    							=				hf_model.state_dict()
      snake_case_    							=				{}

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

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

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

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

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

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

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

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

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

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

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

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

            # Copy flax CLIP attn params to HF PyTorch params
            snake_case_    							=				torch.from_numpy(_a      )
            hf_backbone.state_dict()[torch_key].copy_(_a      )
def 	snake_case_							(				_a  :				str      ):
      snake_case_    							=				{}
      snake_case_    							=				[]

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

                  processed_attn_layers.append(_a      )
                  snake_case_    							=				params[base + "out.weight"].shape[-1]
                  snake_case_    							=				params[base + "out.weight"].reshape(_a			,       _a      ).T
                  snake_case_    							=				params[base + "out.bias"]
            else:
                  snake_case_    							=				v
      return new_params
def 	snake_case_							(				_a  :				Any			,       _a  :				Any      ):
      snake_case_    							=				CLIP(**_a      )
      torch_model.eval()
      snake_case_    							=				torch_model.state_dict()

      snake_case_    							=				flatten_nested_dict(flax_params["backbone"]["clip"]      )
      snake_case_    							=				{}

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

            snake_case_    							=				torch_key.replace("text_projection.kernel"			,       "text_projection"      )
            snake_case_    							=				torch_key.replace("visual.proj.kernel"			,       "visual.proj"      )
            snake_case_    							=				torch_key.replace(".scale"			,       ".weight"      )
            snake_case_    							=				torch_key.replace(".kernel"			,       ".weight"      )

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

            snake_case_    							=				v

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

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

      return torch_clip_params, torch_model, attn_params
@torch.no_grad()
def 	snake_case_							(				_a  :				Optional[Any]			,       _a  :				int			,       _a  :				int			,       _a  :				Dict			,       _a  :				str=None      ):
      snake_case_    							=				Repository(_a			,       clone_from=F'''google/{pytorch_dump_folder_path}'''      )
      repo.git_pull()

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

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

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

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

      # Initialize processor
      snake_case_    							=				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__":
     A							     = 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."
     )
     A							     = parser.parse_args()

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

     # Load from checkpoint and convert params to float-32
     A							     = checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)["optimizer"]["target"]
     A							     = jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
     del variables

     # Convert CLIP backbone
     A     ,  A     ,  A							     = 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 sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize



class 			lowerCAmelCase__ :



  '''simple docstring'''






  def __init__(self	:			Optional[Any]						,			lowerCAmelCase_	:			List[str]						,			lowerCAmelCase_	:			Optional[int]=sys.maxsize     ) ->							Tuple:
        snake_case_    							=				"bilinear"
        snake_case_    							=				max_size
        snake_case_    							=				short_edge_length


  def __call__(self	:			int						,			lowerCAmelCase_	:			Tuple     ) ->							int:
        snake_case_    							=				[]
        for img in imgs:
              snake_case_							,						snake_case_    							=				img.shape[:2]
              # later: provide list and randomly choose index for resize
              snake_case_    							=				np.random.randint(self.short_edge_length[0]						,			self.short_edge_length[1] + 1     )
              if size == 0:
                    return img
              snake_case_    							=				size * 1.0 / min(lowerCAmelCase_						,			lowerCAmelCase_     )
              if h < w:
                    snake_case_							,						snake_case_    							=				size, scale * w
              else:
                    snake_case_							,						snake_case_    							=				scale * h, size
              if max(lowerCAmelCase_						,			lowerCAmelCase_     ) > self.max_size:
                    snake_case_    							=				self.max_size * 1.0 / max(lowerCAmelCase_						,			lowerCAmelCase_     )
                    snake_case_    							=				newh * scale
                    snake_case_    							=				neww * scale
              snake_case_    							=				int(neww + 0.5     )
              snake_case_    							=				int(newh + 0.5     )

              if img.dtype == np.uinta:
                    snake_case_    							=				Image.fromarray(lowerCAmelCase_     )
                    snake_case_    							=				pil_image.resize((neww, newh)						,			PILImageResampling.BILINEAR     )
                    snake_case_    							=				np.asarray(lowerCAmelCase_     )
              else:
                    snake_case_    							=				img.permute(2						,			0						,			1     ).unsqueeze(0     )  # 3, 0, 1)  # hw(c) -> nchw
                    snake_case_    							=				nn.functional.interpolate(
                        lowerCAmelCase_						,			(newh, neww)						,			mode=self.interp_method						,			align_corners=lowerCAmelCase_     ).squeeze(0     )
              img_augs.append(lowerCAmelCase_     )

        return img_augs



class 			lowerCAmelCase__ :



  '''simple docstring'''






  def __init__(self	:			Tuple						,			lowerCAmelCase_	:			Optional[Any]     ) ->							int:
        snake_case_    							=				ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]						,			cfg.INPUT.MAX_SIZE_TEST     )
        snake_case_    							=				cfg.INPUT.FORMAT
        snake_case_    							=				cfg.SIZE_DIVISIBILITY
        snake_case_    							=				cfg.PAD_VALUE
        snake_case_    							=				cfg.INPUT.MAX_SIZE_TEST
        snake_case_    							=				cfg.MODEL.DEVICE
        snake_case_    							=				torch.tensor(cfg.MODEL.PIXEL_STD     ).to(self.device     ).view(len(cfg.MODEL.PIXEL_STD     )						,			1						,			1     )
        snake_case_    							=				torch.tensor(cfg.MODEL.PIXEL_MEAN     ).to(self.device     ).view(len(cfg.MODEL.PIXEL_STD     )						,			1						,			1     )
        snake_case_    							=				lambda lowerCAmelCase_     : (x - self.pixel_mean) / self.pixel_std

  def 		_lowerCamelCase						(self	:			Union[str, Any]						,			lowerCAmelCase_	:			Tuple     ) ->							Any:
        snake_case_    							=				tuple(max(lowerCAmelCase_     ) for s in zip(*[img.shape for img in images]     )     )
        snake_case_    							=				[im.shape[-2:] for im in images]
        snake_case_    							=				[
            nn.functional.pad(
                lowerCAmelCase_						,			[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]						,			value=self.pad_value						,			)
            for size, im in zip(lowerCAmelCase_						,			lowerCAmelCase_     )
        ]

        return torch.stack(lowerCAmelCase_     ), torch.tensor(lowerCAmelCase_     )


  def __call__(self	:			Tuple						,			lowerCAmelCase_	:			Optional[int]						,			lowerCAmelCase_	:			Optional[Any]=False     ) ->							int:
        with torch.no_grad():
              if not isinstance(lowerCAmelCase_						,			lowerCAmelCase_     ):
                    snake_case_    							=				[images]
              if single_image:
                    assert len(lowerCAmelCase_     ) == 1
              for i in range(len(lowerCAmelCase_     )     ):
                    if isinstance(images[i]						,			torch.Tensor     ):
                          images.insert(lowerCAmelCase_						,			images.pop(lowerCAmelCase_     ).to(self.device     ).float()     )
                    elif not isinstance(images[i]						,			torch.Tensor     ):
                          images.insert(
                              lowerCAmelCase_						,			torch.as_tensor(img_tensorize(images.pop(lowerCAmelCase_     )						,			input_format=self.input_format     )     )
                              .to(self.device     )
                              .float()						,			)
            # resize smallest edge
              snake_case_    							=				torch.tensor([im.shape[:2] for im in images]     )
              snake_case_    							=				self.aug(lowerCAmelCase_     )
              # transpose images and convert to torch tensors
              # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
              # now normalize before pad to avoid useless arithmetic
              snake_case_    							=				[self.normalizer(lowerCAmelCase_     ) for x in images]
              # now pad them to do the following operations
              snake_case_							,						snake_case_    							=				self.pad(lowerCAmelCase_     )
              # Normalize

              if self.size_divisibility > 0:
                    raise NotImplementedError()
              # pad
              snake_case_    							=				torch.true_divide(lowerCAmelCase_						,			lowerCAmelCase_     )
              if single_image:
                    return images[0], sizes[0], scales_yx[0]
              else:
                    return images, sizes, scales_yx

def 	snake_case_							(				_a  :				Tuple			,       _a  :				List[Any]      ):
      boxes[:, 0::2] *= scale_yx[:, 1]
      boxes[:, 1::2] *= scale_yx[:, 0]
      return boxes




def 	snake_case_							(				_a  :				Optional[int]			,       _a  :				Tuple[int, int]      ):
      assert torch.isfinite(_a      ).all(), "Box tensor contains infinite or NaN!"
      snake_case_							,						snake_case_    							=				box_size
      tensor[:, 0].clamp_(min=0			,       max=_a      )
      tensor[:, 1].clamp_(min=0			,       max=_a      )
      tensor[:, 2].clamp_(min=0			,       max=_a      )
      tensor[:, 3].clamp_(min=0			,       max=_a      )

