
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__				:      Any    		=     {
    '''vit_b32''': {
        '''embed_dim''': 512,
        '''image_resolution''': 768,
        '''context_length''': 16,
        '''vocab_size''': 49_408,
        '''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''': 49_408,
        '''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''': 49_408,
        '''vision_layers''': 24,
        '''vision_width''': 1_024,
        '''vision_patch_size''': 14,
        '''transformer_width''': 768,
        '''transformer_heads''': 12,
        '''transformer_layers''': 12,
    },
}







def 				UpperCAmelCase_(    a__ ,						a__="" ,						a__="/"   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				List[Any]				      = []

    for k, v in params.items():
        SCREAMING_SNAKE_CASE				:				int				      = 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 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



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







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = pt_attn_layer.in_proj_weight.chunk(3 ,						dim=0   )
    SCREAMING_SNAKE_CASE				:				Optional[int]				      = pt_attn_layer.in_proj_bias.chunk(3 ,						dim=0   )

    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = pt_attn_layer.out_proj.weight
    SCREAMING_SNAKE_CASE				:				Tuple				      = pt_attn_layer.out_proj.bias

    SCREAMING_SNAKE_CASE				:				int				      = q_proj
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = q_proj_bias

    SCREAMING_SNAKE_CASE				:				str				      = k_proj
    SCREAMING_SNAKE_CASE				:				List[Any]				      = k_proj_bias

    SCREAMING_SNAKE_CASE				:				Dict				      = v_proj
    SCREAMING_SNAKE_CASE				:				List[str]				      = v_proj_bias

    SCREAMING_SNAKE_CASE				:				Optional[int]				      = out_proj_weights
    SCREAMING_SNAKE_CASE				:				Tuple				      = out_proj_bias







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



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







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				List[str]				      = pt_linear.weight
    SCREAMING_SNAKE_CASE				:				Tuple				      = pt_linear.bias







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """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 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



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







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				List[str]				      = pt_model.token_embedding.weight
    SCREAMING_SNAKE_CASE				:				Optional[int]				      = 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 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				List[str]				      = pt_model.text_projection.data.T

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







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				List[str]				      = 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
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = pt_model.visual.conva.weight.data
    SCREAMING_SNAKE_CASE				:				int				      = pt_model.visual.class_embedding
    SCREAMING_SNAKE_CASE				:				str				      = pt_model.visual.positional_embedding.data

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







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Dict				      = flatten_nested_dict(flax_params['''backbone''']['''merged_class_token''']   )

    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = torch.from_numpy(flax_class_token_params['''scale''']   )
    SCREAMING_SNAKE_CASE				:				List[str]				      = torch.from_numpy(flax_class_token_params['''bias''']   )
    SCREAMING_SNAKE_CASE				:				Tuple				      = nn.Parameter(a__   )
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = nn.Parameter(a__   )







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Optional[int]				      = hf_model.state_dict()
    SCREAMING_SNAKE_CASE				:				Dict				      = {}

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

    for flax_key, v in flax_class_params.items():
        SCREAMING_SNAKE_CASE				:				List[str]				      = flax_key.replace('''/''' ,						'''.'''   )
        SCREAMING_SNAKE_CASE				:				Tuple				      = torch_key.replace('''.kernel''' ,						'''.weight'''   )
        SCREAMING_SNAKE_CASE				:				Dict				      = torch_key.replace('''Dense_0''' ,						'''dense0'''   )
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = '''class_head.''' + torch_key

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

        SCREAMING_SNAKE_CASE				:				Optional[int]				      = nn.Parameter(torch.from_numpy(a__   )   )

    # Rename box prediction box flax params to pytorch HF
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = flatten_nested_dict(flax_params['''obj_box_head''']   )

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

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

        SCREAMING_SNAKE_CASE				:				Tuple				      = 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 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



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

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

        if "bias" in torch_key and v.ndim == 2:
            SCREAMING_SNAKE_CASE				:				str				      = v.shape[0] * v.shape[1]
            SCREAMING_SNAKE_CASE				:				List[str]				      = v.reshape(a__   )

        if "weight" in torch_key and "out" in torch_key:
            SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = (v.shape[0] * v.shape[1], v.shape[2])
            SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = v.reshape(a__   ).T

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

        # Copy flax CLIP attn params to HF PyTorch params
        SCREAMING_SNAKE_CASE				:				List[str]				      = torch.from_numpy(a__   )
        hf_backbone.state_dict()[torch_key].copy_(a__   )







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = {}
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = []

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

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







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Tuple				      = CLIP(**a__   )
    torch_model.eval()
    SCREAMING_SNAKE_CASE				:				Any				      = torch_model.state_dict()

    SCREAMING_SNAKE_CASE				:				List[Any]				      = flatten_nested_dict(flax_params['''backbone''']['''clip''']   )
    SCREAMING_SNAKE_CASE				:				str				      = {}

    for flax_key, v in flax_clip_params.items():
        SCREAMING_SNAKE_CASE				:				int				      = flax_key.replace('''/''' ,						'''.'''   )
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = 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'''   )
        ):
            SCREAMING_SNAKE_CASE				:				int				      = torch_key[5:]

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

        if "conv" in torch_key or "downsample.0.weight" in torch_key:
            SCREAMING_SNAKE_CASE				:				List[str]				      = 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
            SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = v.T

        SCREAMING_SNAKE_CASE				:				str				      = v

    SCREAMING_SNAKE_CASE				:				Tuple				      = _convert_attn_layers(a__   )
    new_torch_params.update(a__   )
    SCREAMING_SNAKE_CASE				:				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():
            SCREAMING_SNAKE_CASE				:				Any				      = torch.from_numpy(new_torch_params[name]   )
            torch_clip_params[name].copy_(a__   )
        else:
            SCREAMING_SNAKE_CASE				:				Optional[int]				      = param

    return torch_clip_params, torch_model, attn_params







@torch.no_grad()
def 				UpperCAmelCase_(    a__ ,						a__ ,						a__ ,						a__ ,						a__=None   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				List[str]				      = Repository(a__ ,						clone_from=F"""google/{pytorch_dump_folder_path}"""   )
    repo.git_pull()

    if config_path is not None:
        SCREAMING_SNAKE_CASE				:				Any				      = OwlViTConfig.from_pretrained(a__   )
    else:
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = OwlViTConfig()

    SCREAMING_SNAKE_CASE				:				Any				      = OwlViTModel(a__   ).eval()
    SCREAMING_SNAKE_CASE				:				str				      = OwlViTForObjectDetection(a__   ).eval()

    copy_text_model_and_projection(a__ ,						a__   )
    copy_vision_model_and_projection(a__ ,						a__   )
    SCREAMING_SNAKE_CASE				:				Any				      = pt_backbone.logit_scale
    copy_flax_attn_params(a__ ,						a__   )

    SCREAMING_SNAKE_CASE				:				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
    SCREAMING_SNAKE_CASE				:				List[Any]				      = OwlViTImageProcessor(
        size=config.vision_config.image_size ,						crop_size=config.vision_config.image_size   )
    # Initialize tokenizer
    SCREAMING_SNAKE_CASE				:				str				      = CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32''' ,						pad_token='''!''' ,						model_max_length=16   )

    # Initialize processor
    SCREAMING_SNAKE_CASE				:				Tuple				      = 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__				:      Optional[Any]    		=     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__				:      Optional[int]    		=     parser.parse_args()

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

      # Load from checkpoint and convert params to float-32
      a__				:      List[str]    		=     checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)['''optimizer''']['''target''']
      a__				:      str    		=     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__				:      Optional[int]    		=     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 os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
      from .testing_utils import (
          floats_tensor,
          load_hf_numpy,
          load_image,
          load_numpy,
          load_pt,
          nightly,
          parse_flag_from_env,
          print_tensor_test,
          require_torch_a,
          require_torch_gpu,
          skip_mps,
          slow,
          torch_all_close,
          torch_device,
      )
      from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


lowercase						:   Tuple      						=							get_logger(__name__)




def 		a_(			lowerCamelCase   )					->Any:






       '''simple docstring'''




       if version.parse(lowerCamelCase   ) < version.parse(lowerCamelCase   ):
              if "dev" in min_version:
                     lowercase	:		str    =   (
                         '''This example requires a source install from HuggingFace diffusers (see '''
                         '''`https://huggingface.co/docs/diffusers/installation#install-from-source`),'''
                     )
              else:
                     lowercase	:		List[str]    =   f"""This example requires a minimum version of {min_version},"""
              error_message += f""" but the version found is {__version__}.\n"""
              raise ImportError(lowerCamelCase   )




