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    :  Union[str, Any]										= {
    '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   a_					(_lowerCAmelCase  :   Dict			,	_lowerCAmelCase  :   str=""			,	_lowerCAmelCase  :   int="/"       )->				Tuple:
    snake_case:						str         =   []

    for k, v in params.items():
        snake_case:						List[Any]         =   parent_key + sep + k if parent_key else k

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

def   a_					(_lowerCAmelCase  :   str       )->				Tuple:
    return jax.tree_util.tree_map(lambda _lowerCAmelCase       : x.astype(jnp.floataa       ) if x.dtype == jnp.bfloataa else x			,	_lowerCAmelCase       )

def   a_					(_lowerCAmelCase  :   int			,	_lowerCAmelCase  :   List[str]       )->				List[Any]:
    snake_case    , snake_case    , snake_case:						Optional[Any]         =   pt_attn_layer.in_proj_weight.chunk(3			,	dim=0       )
    snake_case    , snake_case    , snake_case:						List[Any]         =   pt_attn_layer.in_proj_bias.chunk(3			,	dim=0       )

    snake_case:						List[Any]         =   pt_attn_layer.out_proj.weight
    snake_case:						Any         =   pt_attn_layer.out_proj.bias

    snake_case:						Union[str, Any]         =   q_proj
    snake_case:						List[str]         =   q_proj_bias

    snake_case:						Any         =   k_proj
    snake_case:						str         =   k_proj_bias

    snake_case:						Optional[int]         =   v_proj
    snake_case:						Dict         =   v_proj_bias

    snake_case:						Optional[Any]         =   out_proj_weights
    snake_case:						Tuple         =   out_proj_bias

def   a_					(_lowerCAmelCase  :   Tuple			,	_lowerCAmelCase  :   int       )->				Optional[int]:
    copy_linear(hf_mlp.fca			,	pt_mlp.c_fc       )
    copy_linear(hf_mlp.fca			,	pt_mlp.c_proj       )

def   a_					(_lowerCAmelCase  :   Tuple			,	_lowerCAmelCase  :   Tuple       )->				Optional[Any]:
    snake_case:						List[Any]         =   pt_linear.weight
    snake_case:						Any         =   pt_linear.bias

def   a_					(_lowerCAmelCase  :   Union[str, Any]			,	_lowerCAmelCase  :   List[Any]       )->				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_					(_lowerCAmelCase  :   Optional[int]			,	_lowerCAmelCase  :   List[str]       )->				Tuple:
    for hf_layer, pt_layer in zip(_lowerCAmelCase			,	_lowerCAmelCase       ):
        copy_layer(_lowerCAmelCase			,	_lowerCAmelCase       )

def   a_					(_lowerCAmelCase  :   Optional[Any]			,	_lowerCAmelCase  :   int       )->				str:
    # copy  embeds
    snake_case:						Union[str, Any]         =   pt_model.token_embedding.weight
    snake_case:						Dict         =   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_					(_lowerCAmelCase  :   Union[str, Any]			,	_lowerCAmelCase  :   Union[str, Any]       )->				str:
    # copy projection
    snake_case:						Any         =   pt_model.text_projection.data.T

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

def   a_					(_lowerCAmelCase  :   Union[str, Any]			,	_lowerCAmelCase  :   int       )->				Tuple:
    # copy projection
    snake_case:						Optional[Any]         =   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
    snake_case:						List[Any]         =   pt_model.visual.conva.weight.data
    snake_case:						List[str]         =   pt_model.visual.class_embedding
    snake_case:						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_					(_lowerCAmelCase  :   Any			,	_lowerCAmelCase  :   Optional[Any]       )->				Any:
    snake_case:						Union[str, Any]         =   flatten_nested_dict(flax_params["""backbone"""]["""merged_class_token"""]       )

    snake_case:						Tuple         =   torch.from_numpy(flax_class_token_params["""scale"""]       )
    snake_case:						Union[str, Any]         =   torch.from_numpy(flax_class_token_params["""bias"""]       )
    snake_case:						Optional[int]         =   nn.Parameter(_lowerCAmelCase       )
    snake_case:						int         =   nn.Parameter(_lowerCAmelCase       )

def   a_					(_lowerCAmelCase  :   Optional[int]			,	_lowerCAmelCase  :   List[str]       )->				List[Any]:
    snake_case:						List[str]         =   hf_model.state_dict()
    snake_case:						Tuple         =   {}

    # Rename class prediction head flax params to pytorch HF
    snake_case:						int         =   flatten_nested_dict(flax_params["""class_head"""]       )

    for flax_key, v in flax_class_params.items():
        snake_case:						List[str]         =   flax_key.replace("""/"""			,	"""."""       )
        snake_case:						int         =   torch_key.replace(""".kernel"""			,	""".weight"""       )
        snake_case:						Union[str, Any]         =   torch_key.replace("""Dense_0"""			,	"""dense0"""       )
        snake_case:						Union[str, Any]         =   """class_head.""" + torch_key

        if "weight" in torch_key and v.ndim == 2:
            snake_case:						Union[str, Any]         =   v.T

        snake_case:						List[str]         =   nn.Parameter(torch.from_numpy(_lowerCAmelCase       )       )

    # Rename box prediction box flax params to pytorch HF
    snake_case:						Any         =   flatten_nested_dict(flax_params["""obj_box_head"""]       )

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

        if "weight" in torch_key and v.ndim == 2:
            snake_case:						int         =   v.T

        snake_case:						List[str]         =   nn.Parameter(torch.from_numpy(_lowerCAmelCase       )       )

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

def   a_					(_lowerCAmelCase  :   int			,	_lowerCAmelCase  :   Optional[int]       )->				Optional[int]:
    for k, v in flax_attn_params.items():
        if k.startswith("""transformer"""       ):
            snake_case:						List[str]         =   k.replace("""transformer.resblocks"""			,	"""text_model.encoder.layers"""       )
        else:
            snake_case:						List[Any]         =   k.replace("""visual.transformer.resblocks"""			,	"""vision_model.encoder.layers"""       )

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

        if "bias" in torch_key and v.ndim == 2:
            snake_case:						Tuple         =   v.shape[0] * v.shape[1]
            snake_case:						Optional[int]         =   v.reshape(_lowerCAmelCase       )

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

        if "weight" in torch_key and "out" not in torch_key:
            snake_case:						List[str]         =   (v.shape[0], v.shape[1] * v.shape[2])
            snake_case:						Any         =   v.reshape(_lowerCAmelCase       ).T

        # Copy flax CLIP attn params to HF PyTorch params
        snake_case:						List[Any]         =   torch.from_numpy(_lowerCAmelCase       )
        hf_backbone.state_dict()[torch_key].copy_(_lowerCAmelCase       )

def   a_					(_lowerCAmelCase  :   int       )->				Any:
    snake_case:						str         =   {}
    snake_case:						Tuple         =   []

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

            processed_attn_layers.append(_lowerCAmelCase       )
            snake_case:						Optional[int]         =   params[base + """out.weight"""].shape[-1]
            snake_case:						Dict         =   params[base + """out.weight"""].reshape(_lowerCAmelCase			,	_lowerCAmelCase       ).T
            snake_case:						List[str]         =   params[base + """out.bias"""]
        else:
            snake_case:						Union[str, Any]         =   v
    return new_params

def   a_					(_lowerCAmelCase  :   Union[str, Any]			,	_lowerCAmelCase  :   Optional[Any]       )->				int:
    snake_case:						Optional[int]         =   CLIP(**_lowerCAmelCase       )
    torch_model.eval()
    snake_case:						Tuple         =   torch_model.state_dict()

    snake_case:						str         =   flatten_nested_dict(flax_params["""backbone"""]["""clip"""]       )
    snake_case:						Optional[int]         =   {}

    for flax_key, v in flax_clip_params.items():
        snake_case:						int         =   flax_key.replace("""/"""			,	"""."""       )
        snake_case:						Union[str, 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"""       )
        ):
            snake_case:						Any         =   torch_key[5:]

        snake_case:						Any         =   torch_key.replace("""text_projection.kernel"""			,	"""text_projection"""       )
        snake_case:						int         =   torch_key.replace("""visual.proj.kernel"""			,	"""visual.proj"""       )
        snake_case:						Dict         =   torch_key.replace(""".scale"""			,	""".weight"""       )
        snake_case:						Any         =   torch_key.replace(""".kernel"""			,	""".weight"""       )

        if "conv" in torch_key or "downsample.0.weight" in torch_key:
            snake_case:						Optional[int]         =   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
            snake_case:						Optional[Any]         =   v.T

        snake_case:						Union[str, Any]         =   v

    snake_case:						Optional[int]         =   _convert_attn_layers(_lowerCAmelCase       )
    new_torch_params.update(_lowerCAmelCase       )
    snake_case:						Tuple         =   {}

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

    return torch_clip_params, torch_model, attn_params

@torch.no_grad()
def   a_					(_lowerCAmelCase  :   Tuple			,	_lowerCAmelCase  :   Any			,	_lowerCAmelCase  :   int			,	_lowerCAmelCase  :   Optional[int]			,	_lowerCAmelCase  :   Any=None       )->				List[Any]:
    snake_case:						List[Any]         =   Repository(_lowerCAmelCase			,	clone_from=F"google/{pytorch_dump_folder_path}"       )
    repo.git_pull()

    if config_path is not None:
        snake_case:						Optional[Any]         =   OwlViTConfig.from_pretrained(_lowerCAmelCase       )
    else:
        snake_case:						Union[str, Any]         =   OwlViTConfig()

    snake_case:						List[Any]         =   OwlViTModel(_lowerCAmelCase       ).eval()
    snake_case:						str         =   OwlViTForObjectDetection(_lowerCAmelCase       ).eval()

    copy_text_model_and_projection(_lowerCAmelCase			,	_lowerCAmelCase       )
    copy_vision_model_and_projection(_lowerCAmelCase			,	_lowerCAmelCase       )
    snake_case:						Dict         =   pt_backbone.logit_scale
    copy_flax_attn_params(_lowerCAmelCase			,	_lowerCAmelCase       )

    snake_case:						List[Any]         =   hf_backbone
    copy_class_merge_token(_lowerCAmelCase			,	_lowerCAmelCase       )
    copy_class_box_heads(_lowerCAmelCase			,	_lowerCAmelCase       )

    # Save HF model
    hf_model.save_pretrained(repo.local_dir       )

    # Initialize image processor
    snake_case:						List[str]         =   OwlViTImageProcessor(
        size=config.vision_config.image_size			,	crop_size=config.vision_config.image_size       )
    # Initialize tokenizer
    snake_case:						Optional[int]         =   CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""			,	pad_token="""!"""			,	model_max_length=16       )

    # Initialize processor
    snake_case:						Any         =   OwlViTProcessor(image_processor=_lowerCAmelCase			,	tokenizer=_lowerCAmelCase       )
    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    :  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.'
    )
    __lowerCAmelCase    :  int										= parser.parse_args()

    # Initialize PyToch clip model
    __lowerCAmelCase    :  List[str]										= args.owlvit_version
    if model_name == "clip_b16":
        __lowerCAmelCase    :  Union[str, Any]										= CONFIGS['vit_b16']
    elif model_name == "clip_b32":
        __lowerCAmelCase    :  List[str]										= CONFIGS['vit_b32']
    elif model_name == "clip_l14":
        __lowerCAmelCase    :  List[Any]										= CONFIGS['vit_l14']

    # Load from checkpoint and convert params to float-32
    __lowerCAmelCase    :  int										= checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)['optimizer']['target']
    __lowerCAmelCase    :  Dict										= 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    :  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)



import warnings

from ...utils import logging
from .image_processing_conditional_detr import ConditionalDetrImageProcessor


__lowerCAmelCase    :  Tuple										= logging.get_logger(__name__)
class 				lowerCamelCase			(							__snake_case					):






      def __init__(      self  ,		*__lowerCamelCase  ,		**__lowerCamelCase						)     ->				None:


          '''simple docstring'''







          warnings.warn(
              """The class ConditionalDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
              """ Please use ConditionalDetrImageProcessor instead."""  ,		__lowerCamelCase  ,		)
          super().__init__(*__lowerCamelCase  ,		**__lowerCamelCase						)


