




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_					:					Dict       						=			{
    '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   A		(__A  :       Optional[int]  ,							__A  :       str=""  ,							__A  :       Union[str, Any]="/"			)  -> Any:







     """simple docstring"""


     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   A		(__A  :       Union[str, Any]			)  -> List[str]:







     """simple docstring"""


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


def   A		(__A  :       Optional[int]  ,							__A  :       Any			)  -> Union[str, Any]:







     """simple docstring"""


     UpperCAmelCase_		=				pt_attn_layer.in_proj_weight.chunk(3  ,							dim=0			)
     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   A		(__A  :       Optional[int]  ,							__A  :       Optional[int]			)  -> List[str]:







     """simple docstring"""


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


def   A		(__A  :       Tuple  ,							__A  :       int			)  -> Tuple:







     """simple docstring"""


     UpperCAmelCase_		=				pt_linear.weight
     UpperCAmelCase_		=				pt_linear.bias


def   A		(__A  :       Any  ,							__A  :       List[Any]			)  -> int:







     """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   A		(__A  :       str  ,							__A  :       Any			)  -> str:







     """simple docstring"""


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


def   A		(__A  :       List[Any]  ,							__A  :       Any			)  -> Dict:







     """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   A		(__A  :       List[str]  ,							__A  :       str			)  -> Optional[int]:







     """simple docstring"""


     UpperCAmelCase_		=				pt_model.text_projection.data.T

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


def   A		(__A  :       Dict  ,							__A  :       Dict			)  -> 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   A		(__A  :       Tuple  ,							__A  :       Dict			)  -> 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(__A			)
     UpperCAmelCase_		=				nn.Parameter(__A			)


def   A		(__A  :       int  ,							__A  :       Tuple			)  -> Optional[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(__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   A		(__A  :       Optional[int]  ,							__A  :       Tuple			)  -> List[str]:







     """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(__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   A		(__A  :       Dict			)  -> int:







     """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(__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   A		(__A  :       List[str]  ,							__A  :       List[Any]			)  -> Any:







     """simple docstring"""


     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   A		(__A  :       Dict  ,							__A  :       Optional[int]  ,							__A  :       str  ,							__A  :       Dict  ,							__A  :       List[str]=None			)  -> List[Any]:







     """simple docstring"""


     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__":
    snake_case_					:					int       						=			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_					:					Union[str, Any]       						=			parser.parse_args()

    # Initialize PyToch clip model
    snake_case_					:					Optional[int]       						=			args.owlvit_version
    if model_name == "clip_b16":
        snake_case_					:					int       						=			CONFIGS['vit_b16']
    elif model_name == "clip_b32":
        snake_case_					:					Tuple       						=			CONFIGS['vit_b32']
    elif model_name == "clip_l14":
        snake_case_					:					List[str]       						=			CONFIGS['vit_l14']

    # Load from checkpoint and convert params to float-32
    snake_case_					:					Tuple       						=			checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)['optimizer']['target']
    snake_case_					:					Any       						=			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_					:					Tuple       						=			convert_clip_backbone(flax_params, torch_config)

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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__						:	Any	     = {
    'configuration_decision_transformer': [
        'DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'DecisionTransformerConfig',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:	Optional[Any]	     = [
       'DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'DecisionTransformerGPT2Model',
       'DecisionTransformerGPT2PreTrainedModel',
       'DecisionTransformerModel',
       'DecisionTransformerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_decision_transformer import (
       DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       DecisionTransformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_decision_transformer import (
          DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          DecisionTransformerGPTaModel,
          DecisionTransformerGPTaPreTrainedModel,
          DecisionTransformerModel,
          DecisionTransformerPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase__						:	Optional[Any]	     = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



