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': 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       a__      (					a  ,   a=""  ,   a="/"							)		->	List[Any]:
       A_							:       Optional[int]	      =				[]

       for k, v in params.items():
              A_							:       List[str]	      =				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       a__      (					a							)		->	List[Any]:
       return jax.tree_util.tree_map(lambda a							: x.astype(jnp.floataa							) if x.dtype == jnp.bfloataa else x  ,   _a							)


def       a__      (					a  ,   a							)		->	List[Any]:
       A_      ,							A_      ,							A_							:       int	      =				pt_attn_layer.in_proj_weight.chunk(3  ,   dim=0							)
       A_      ,							A_      ,							A_							:       Optional[int]	      =				pt_attn_layer.in_proj_bias.chunk(3  ,   dim=0							)

       A_							:       Union[str, Any]	      =				pt_attn_layer.out_proj.weight
       A_							:       List[Any]	      =				pt_attn_layer.out_proj.bias

       A_							:       List[Any]	      =				q_proj
       A_							:       Any	      =				q_proj_bias

       A_							:       Optional[Any]	      =				k_proj
       A_							:       Union[str, Any]	      =				k_proj_bias

       A_							:       Union[str, Any]	      =				v_proj
       A_							:       Optional[Any]	      =				v_proj_bias

       A_							:       List[str]	      =				out_proj_weights
       A_							:       List[str]	      =				out_proj_bias


def       a__      (					a  ,   a							)		->	Any:
       copy_linear(hf_mlp.fca  ,   pt_mlp.c_fc							)
       copy_linear(hf_mlp.fca  ,   pt_mlp.c_proj							)


def       a__      (					a  ,   a							)		->	Tuple:
       A_							:       int	      =				pt_linear.weight
       A_							:       Optional[Any]	      =				pt_linear.bias


def       a__      (					a  ,   a							)		->	List[str]:
       # 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       a__      (					a  ,   a							)		->	Optional[Any]:
       for hf_layer, pt_layer in zip(_a  ,   _a							):
              copy_layer(_a  ,   _a							)


def       a__      (					a  ,   a							)		->	Dict:
       # copy  embeds
       A_							:       Union[str, Any]	      =				pt_model.token_embedding.weight
       A_							:       Optional[Any]	      =				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__      (					a  ,   a							)		->	Any:
       # copy projection
       A_							:       Optional[int]	      =				pt_model.text_projection.data.T

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


def       a__      (					a  ,   a							)		->	Optional[Any]:
       # copy projection
       A_							:       Tuple	      =				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
       A_							:       List[Any]	      =				pt_model.visual.conva.weight.data
       A_							:       int	      =				pt_model.visual.class_embedding
       A_							:       List[Any]	      =				pt_model.visual.positional_embedding.data

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


def       a__      (					a  ,   a							)		->	Union[str, Any]:
       A_							:       List[Any]	      =				flatten_nested_dict(flax_params['''backbone''']['''merged_class_token''']							)

       A_							:       Dict	      =				torch.from_numpy(flax_class_token_params['''scale''']							)
       A_							:       int	      =				torch.from_numpy(flax_class_token_params['''bias''']							)
       A_							:       Optional[Any]	      =				nn.Parameter(_a							)
       A_							:       Any	      =				nn.Parameter(_a							)


def       a__      (					a  ,   a							)		->	Optional[int]:
       A_							:       int	      =				hf_model.state_dict()
       A_							:       List[Any]	      =				{}

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

       for flax_key, v in flax_class_params.items():
              A_							:       Dict	      =				flax_key.replace('''/'''  ,   '''.'''							)
              A_							:       Union[str, Any]	      =				torch_key.replace('''.kernel'''  ,   '''.weight'''							)
              A_							:       Any	      =				torch_key.replace('''Dense_0'''  ,   '''dense0'''							)
              A_							:       Any	      =				'''class_head.''' + torch_key

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

              A_							:       List[str]	      =				nn.Parameter(torch.from_numpy(_a							)							)

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

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

              if "weight" in torch_key and v.ndim == 2:
                     A_							:       Optional[Any]	      =				v.T

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

              A_							:       Tuple	      =				torch_key.replace('''attn'''  ,   '''self_attn'''							)
              A_							:       Union[str, Any]	      =				torch_key.replace('''key'''  ,   '''k_proj'''							)
              A_							:       Optional[int]	      =				torch_key.replace('''value'''  ,   '''v_proj'''							)
              A_							:       Dict	      =				torch_key.replace('''query'''  ,   '''q_proj'''							)
              A_							:       List[Any]	      =				torch_key.replace('''out'''  ,   '''out_proj'''							)

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

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

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

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


def       a__      (					a							)		->	Union[str, Any]:
       A_							:       Optional[Any]	      =				{}
       A_							:       Any	      =				[]

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

                     processed_attn_layers.append(_a							)
                     A_							:       str	      =				params[base + '''out.weight'''].shape[-1]
                     A_							:       Optional[int]	      =				params[base + '''out.weight'''].reshape(_a  ,   _a							).T
                     A_							:       List[Any]	      =				params[base + '''out.bias''']
              else:
                     A_							:       Union[str, Any]	      =				v
       return new_params


def       a__      (					a  ,   a							)		->	Optional[Any]:
       A_							:       str	      =				CLIP(**_a							)
       torch_model.eval()
       A_							:       Tuple	      =				torch_model.state_dict()

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

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

              A_							:       Any	      =				torch_key.replace('''text_projection.kernel'''  ,   '''text_projection'''							)
              A_							:       List[Any]	      =				torch_key.replace('''visual.proj.kernel'''  ,   '''visual.proj'''							)
              A_							:       str	      =				torch_key.replace('''.scale'''  ,   '''.weight'''							)
              A_							:       List[str]	      =				torch_key.replace('''.kernel'''  ,   '''.weight'''							)

              if "conv" in torch_key or "downsample.0.weight" in torch_key:
                     A_							:       Union[str, 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
                     A_							:       Tuple	      =				v.T

              A_							:       List[Any]	      =				v

       A_							:       Tuple	      =				_convert_attn_layers(_a							)
       new_torch_params.update(_a							)
       A_							:       Union[str, Any]	      =				{}

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

       return torch_clip_params, torch_model, attn_params


@torch.no_grad()
def       a__      (					a  ,   a  ,   a  ,   a  ,   a=None							)		->	Tuple:
       A_							:       int	      =				Repository(_a  ,   clone_from=f"""google/{pytorch_dump_folder_path}"""							)
       repo.git_pull()

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

       A_							:       Dict	      =				OwlViTModel(_a							).eval()
       A_							:       Optional[int]	      =				OwlViTForObjectDetection(_a							).eval()

       copy_text_model_and_projection(_a  ,   _a							)
       copy_vision_model_and_projection(_a  ,   _a							)
       A_							:       List[Any]	      =				pt_backbone.logit_scale
       copy_flax_attn_params(_a  ,   _a							)

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

       # Initialize processor
       A_							:       Optional[int]	      =				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)




'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A							     = {
    "configuration_marian": ["MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarianConfig", "MarianOnnxConfig"],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A							     = ["MarianTokenizer"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A							     = [
         "MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST",
         "MarianForCausalLM",
         "MarianModel",
         "MarianMTModel",
         "MarianPreTrainedModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A							     = ["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A							     = ["FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel"]

if TYPE_CHECKING:
     from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_marian import MarianTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_marian import (
              MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
              MarianForCausalLM,
              MarianModel,
              MarianMTModel,
              MarianPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
     import sys

     A							     = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

