
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,
)


_snake_case		  =   {
    "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 lowerCamelCase_       (			A			:      Dict		,					A			:      Union[str, Any]=""		,					A			:      Dict="/"						):




      """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			:      Any						):




      """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			:      int		,					A			:      Dict						):




      """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			:      Any		,					A			:      Optional[int]						):




      """simple docstring"""
      copy_linear(hf_mlp.fca		,					pt_mlp.c_fc						)
      copy_linear(hf_mlp.fca		,					pt_mlp.c_proj						)
def lowerCamelCase_       (			A			:      int		,					A			:      List[str]						):




      """simple docstring"""
      lowerCAmelCase_						=      pt_linear.weight
      lowerCAmelCase_						=      pt_linear.bias
def lowerCamelCase_       (			A			:      Optional[Any]		,					A			:      int						):




      """simple docstring"""
      # 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			:      Optional[Any]		,					A			:      str						):




      """simple docstring"""
      for hf_layer, pt_layer in zip(A		,					A						):
            copy_layer(A		,					A						)
def lowerCamelCase_       (			A			:      Tuple		,					A			:      Optional[Any]						):




      """simple docstring"""
      # copy  embeds
      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			:      Union[str, Any]		,					A			:      Optional[Any]						):




      """simple docstring"""
      # copy projection
      lowerCAmelCase_						=      pt_model.text_projection.data.T

      # copy text encoder
      copy_encoder(hf_model.text_model		,					A						)
def lowerCamelCase_       (			A			:      Dict		,					A			:      Optional[Any]						):




      """simple docstring"""
      # copy projection
      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			:      Optional[Any]		,					A			:      Any						):




      """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			:      Dict		,					A			:      Union[str, Any]						):




      """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			:      Tuple		,					A			:      Dict						):




      """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			:      Optional[int]						):




      """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			:      int		,					A			:      Dict						):




      """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			:      List[Any]		,					A			:      Union[str, Any]		,					A			:      Optional[int]		,					A			:      Dict		,					A			:      Dict=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=16						)

      # 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__":
 _snake_case		  =   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."
 )
 _snake_case		  =   parser.parse_args()

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

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

 # Convert CLIP backbone
 _snake_case  , _snake_case  , _snake_case		  =   convert_clip_backbone(flax_params, torch_config)

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





_snake_case		  =   {
    "+": lambda x, y: x + y,
    "-": lambda x, y: x - y,
    "*": lambda x, y: x * y,
    "/": lambda x, y: x / y,
}
def lowerCamelCase_       (			A			:      List[Any]						):




      """simple docstring"""
      return c.isdigit()
def lowerCamelCase_       (			A			:      List[Any]						):




      """simple docstring"""
      lowerCAmelCase_						=      []

      # iterate over the string in reverse order
      for c in expression.split()[::-1]:
            # push operand to stack
            if is_operand(A						):
                  stack.append(int(A						)						)

            else:
                  # pop values from stack can calculate the result
                  # push the result onto the stack again
                  lowerCAmelCase_						=      stack.pop()
                  lowerCAmelCase_						=      stack.pop()
                  stack.append(calc[c](A		,					A						)						)

      return stack.pop()


# Driver code
if __name__ == "__main__":
 _snake_case		  =   "+ 9 * 2 6"
 print(evaluate(test_expression))

 _snake_case		  =   "/ * 10 2 + 4 1 "
 print(evaluate(test_expression))



