




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": 512,
        "image_resolution": 768,
        "context_length": 16,
        "vocab_size": 4_9408,
        "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": 4_9408,
        "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": 4_9408,
        "vision_layers": 24,
        "vision_width": 1024,
        "vision_patch_size": 14,
        "transformer_width": 768,
        "transformer_heads": 12,
        "transformer_layers": 12,
    },
}




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_=""       ,  lowercase_="/"   )							->						Optional[int]:

    """simple docstring"""


    __UpperCamelCase			=    []

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

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




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						str:

    """simple docstring"""


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




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						Optional[int]:

    """simple docstring"""


    __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        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						Dict:

    """simple docstring"""


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




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						int:

    """simple docstring"""


    __UpperCamelCase			=    pt_linear.weight
    __UpperCamelCase			=    pt_linear.bias




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						Optional[Any]:

    """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        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						Any:

    """simple docstring"""


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




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						List[str]:

    """simple docstring"""


    __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        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						Optional[int]:

    """simple docstring"""


    __UpperCamelCase			=    pt_model.text_projection.data.T

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




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						Union[str, Any]:

    """simple docstring"""


    __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        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						List[str]:

    """simple docstring"""


    __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(lowercase_   )
    __UpperCamelCase			=    nn.Parameter(lowercase_   )




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						Any:

    """simple docstring"""


    __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(lowercase_   )   )

    # 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(lowercase_   )   )

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




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						int:

    """simple docstring"""


    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(lowercase_   )

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

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




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						Union[str, Any]:

    """simple docstring"""


    __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(lowercase_   )
            __UpperCamelCase			=    params[base + '''out.weight'''].shape[-1]
            __UpperCamelCase			=    params[base + '''out.weight'''].reshape(lowercase_       ,  lowercase_   ).T
            __UpperCamelCase			=    params[base + '''out.bias''']
        else:
            __UpperCamelCase			=    v
    return new_params




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						List[str]:

    """simple docstring"""


    __UpperCamelCase			=    CLIP(**lowercase_   )
    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(lowercase_   )
    new_torch_params.update(lowercase_   )
    __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_(lowercase_   )
        else:
            __UpperCamelCase			=    param

    return torch_clip_params, torch_model, attn_params




@torch.no_grad()
def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_=None   )							->						List[str]:

    """simple docstring"""


    __UpperCamelCase			=    Repository(lowercase_       ,  clone_from=F"google/{pytorch_dump_folder_path}"   )
    repo.git_pull()

    if config_path is not None:
        __UpperCamelCase			=    OwlViTConfig.from_pretrained(lowercase_   )
    else:
        __UpperCamelCase			=    OwlViTConfig()

    __UpperCamelCase			=    OwlViTModel(lowercase_   ).eval()
    __UpperCamelCase			=    OwlViTForObjectDetection(lowercase_   ).eval()

    copy_text_model_and_projection(lowercase_       ,  lowercase_   )
    copy_vision_model_and_projection(lowercase_       ,  lowercase_   )
    __UpperCamelCase			=    pt_backbone.logit_scale
    copy_flax_attn_params(lowercase_       ,  lowercase_   )

    __UpperCamelCase			=    hf_backbone
    copy_class_merge_token(lowercase_       ,  lowercase_   )
    copy_class_box_heads(lowercase_       ,  lowercase_   )

    # 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=lowercase_       ,  tokenizer=lowercase_   )
    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)












import argparse

import torch

from transformers import MraConfig, MraForMaskedLM




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						Any:

    """simple docstring"""


    if "model" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''model.'''       ,  ''''''   )
    if "norm1" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''norm1'''       ,  '''attention.output.LayerNorm'''   )
    if "norm2" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''norm2'''       ,  '''output.LayerNorm'''   )
    if "norm" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''norm'''       ,  '''LayerNorm'''   )
    if "transformer" in orig_key:
        __UpperCamelCase			=    orig_key.split('''.'''   )[0].split('''_'''   )[-1]
        __UpperCamelCase			=    orig_key.replace(F"transformer_{layer_num}"       ,  F"encoder.layer.{layer_num}"   )
    if "mha.attn" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mha.attn'''       ,  '''attention.self'''   )
    if "mha" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mha'''       ,  '''attention'''   )
    if "W_q" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''W_q'''       ,  '''self.query'''   )
    if "W_k" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''W_k'''       ,  '''self.key'''   )
    if "W_v" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''W_v'''       ,  '''self.value'''   )
    if "ff.0" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''ff.0'''       ,  '''intermediate.dense'''   )
    if "ff.2" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''ff.2'''       ,  '''output.dense'''   )
    if "ff" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''ff'''       ,  '''output.dense'''   )
    if "mlm_class" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mlm.mlm_class'''       ,  '''cls.predictions.decoder'''   )
    if "mlm" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mlm'''       ,  '''cls.predictions.transform'''   )
    if "backbone.backbone.encoders" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''backbone.backbone.encoders'''       ,  '''encoder.layer'''   )
    if "cls" not in orig_key:
        __UpperCamelCase			=    '''mra.''' + orig_key

    return orig_key




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						Optional[int]:

    """simple docstring"""


    for key in orig_state_dict.copy().keys():
        __UpperCamelCase			=    orig_state_dict.pop(lowercase_   )

        if ("pooler" in key) or ("sen_class" in key):
            continue
        else:
            __UpperCamelCase			=    val

    __UpperCamelCase			=    orig_state_dict['''cls.predictions.decoder.bias''']
    __UpperCamelCase			=    torch.arange(lowercase_   ).expand((1, -1)   ) + 2

    return orig_state_dict




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_   )							->						List[str]:

    """simple docstring"""


    __UpperCamelCase			=    torch.load(lowercase_       ,  map_location='''cpu'''   )['''model_state_dict''']
    __UpperCamelCase			=    MraConfig.from_json_file(lowercase_   )
    __UpperCamelCase			=    MraForMaskedLM(lowercase_   )

    __UpperCamelCase			=    convert_checkpoint_helper(config.max_position_embeddings       ,  lowercase_   )

    print(model.load_state_dict(lowercase_   )   )
    model.eval()
    model.save_pretrained(lowercase_   )

    print(F"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"   )


if __name__ == "__main__":
 a_ 		=  argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--pytorch_model_path", default=None, type=str, required=True, help="Path to Mra pytorch checkpoint."
 )
 parser.add_argument(
     "--config_file",
     default=None,
     type=str,
     required=True,
     help="The json file for Mra model config.",
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 a_ 		=  parser.parse_args()
 convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






