

"""simple docstring"""


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": 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__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE=""      ,				__SCREAMING_SNAKE_CASE="/"				)			->			List[str]:
		__lowerCAmelCase: List[Any]					   =    []

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

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






def 					a__					(					__SCREAMING_SNAKE_CASE				)			->			Union[str, Any]:
		return jax.tree_util.tree_map(lambda __SCREAMING_SNAKE_CASE				: x.astype(jnp.floataa				) if x.dtype == jnp.bfloataa else x      ,				__SCREAMING_SNAKE_CASE				)






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			str:
		__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase: Optional[Any]					   =    pt_attn_layer.in_proj_weight.chunk(3      ,				dim=0				)
		__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase: Optional[Any]					   =    pt_attn_layer.in_proj_bias.chunk(3      ,				dim=0				)

		__lowerCAmelCase: int					   =    pt_attn_layer.out_proj.weight
		__lowerCAmelCase: Dict					   =    pt_attn_layer.out_proj.bias

		__lowerCAmelCase: Optional[int]					   =    q_proj
		__lowerCAmelCase: Union[str, Any]					   =    q_proj_bias

		__lowerCAmelCase: Tuple					   =    k_proj
		__lowerCAmelCase: int					   =    k_proj_bias

		__lowerCAmelCase: List[str]					   =    v_proj
		__lowerCAmelCase: str					   =    v_proj_bias

		__lowerCAmelCase: List[str]					   =    out_proj_weights
		__lowerCAmelCase: Optional[int]					   =    out_proj_bias






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			str:
		copy_linear(hf_mlp.fca      ,				pt_mlp.c_fc				)
		copy_linear(hf_mlp.fca      ,				pt_mlp.c_proj				)






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			Optional[Any]:
		__lowerCAmelCase: List[str]					   =    pt_linear.weight
		__lowerCAmelCase: List[Any]					   =    pt_linear.bias






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			Any:
		# 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__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			str:
		for hf_layer, pt_layer in zip(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				):
				copy_layer(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)






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

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






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			str:
		# copy projection
		__lowerCAmelCase: List[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
		__lowerCAmelCase: List[Any]					   =    pt_model.visual.conva.weight.data
		__lowerCAmelCase: str					   =    pt_model.visual.class_embedding
		__lowerCAmelCase: Union[str, Any]					   =    pt_model.visual.positional_embedding.data

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






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			int:
		__lowerCAmelCase: Optional[int]					   =    flatten_nested_dict(flax_params["backbone"]["merged_class_token"]				)

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






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			Any:
		__lowerCAmelCase: Optional[Any]					   =    hf_model.state_dict()
		__lowerCAmelCase: List[Any]					   =    {}

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

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

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

				__lowerCAmelCase: List[str]					   =    nn.Parameter(torch.from_numpy(__SCREAMING_SNAKE_CASE				)				)

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

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

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

				__lowerCAmelCase: Tuple					   =    nn.Parameter(torch.from_numpy(__SCREAMING_SNAKE_CASE				)				)

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






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			List[str]:
		for k, v in flax_attn_params.items():
				if k.startswith("transformer"				):
						__lowerCAmelCase: Tuple					   =    k.replace("transformer.resblocks"      ,				"text_model.encoder.layers"				)
				else:
						__lowerCAmelCase: str					   =    k.replace("visual.transformer.resblocks"      ,				"vision_model.encoder.layers"				)

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

				if "bias" in torch_key and v.ndim == 2:
						__lowerCAmelCase: Optional[int]					   =    v.shape[0] * v.shape[1]
						__lowerCAmelCase: List[Any]					   =    v.reshape(__SCREAMING_SNAKE_CASE				)

				if "weight" in torch_key and "out" in torch_key:
						__lowerCAmelCase: Optional[int]					   =    (v.shape[0] * v.shape[1], v.shape[2])
						__lowerCAmelCase: Optional[Any]					   =    v.reshape(__SCREAMING_SNAKE_CASE				).T

				if "weight" in torch_key and "out" not in torch_key:
						__lowerCAmelCase: str					   =    (v.shape[0], v.shape[1] * v.shape[2])
						__lowerCAmelCase: Dict					   =    v.reshape(__SCREAMING_SNAKE_CASE				).T

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






def 					a__					(					__SCREAMING_SNAKE_CASE				)			->			Tuple:
		__lowerCAmelCase: int					   =    {}
		__lowerCAmelCase: int					   =    []

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

						processed_attn_layers.append(__SCREAMING_SNAKE_CASE				)
						__lowerCAmelCase: str					   =    params[base + "out.weight"].shape[-1]
						__lowerCAmelCase: str					   =    params[base + "out.weight"].reshape(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				).T
						__lowerCAmelCase: List[Any]					   =    params[base + "out.bias"]
				else:
						__lowerCAmelCase: Any					   =    v
		return new_params






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			Optional[Any]:
		__lowerCAmelCase: Union[str, Any]					   =    CLIP(**__SCREAMING_SNAKE_CASE				)
		torch_model.eval()
		__lowerCAmelCase: Union[str, Any]					   =    torch_model.state_dict()

		__lowerCAmelCase: List[str]					   =    flatten_nested_dict(flax_params["backbone"]["clip"]				)
		__lowerCAmelCase: Tuple					   =    {}

		for flax_key, v in flax_clip_params.items():
				__lowerCAmelCase: Dict					   =    flax_key.replace("/"      ,				"."				)
				__lowerCAmelCase: List[str]					   =    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"				)
				):
						__lowerCAmelCase: str					   =    torch_key[5:]

				__lowerCAmelCase: Dict					   =    torch_key.replace("text_projection.kernel"      ,				"text_projection"				)
				__lowerCAmelCase: Tuple					   =    torch_key.replace("visual.proj.kernel"      ,				"visual.proj"				)
				__lowerCAmelCase: str					   =    torch_key.replace(".scale"      ,				".weight"				)
				__lowerCAmelCase: str					   =    torch_key.replace(".kernel"      ,				".weight"				)

				if "conv" in torch_key or "downsample.0.weight" in torch_key:
						__lowerCAmelCase: Tuple					   =    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
						__lowerCAmelCase: Tuple					   =    v.T

				__lowerCAmelCase: str					   =    v

		__lowerCAmelCase: Optional[int]					   =    _convert_attn_layers(__SCREAMING_SNAKE_CASE				)
		new_torch_params.update(__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase: Dict					   =    {}

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

		return torch_clip_params, torch_model, attn_params






@torch.no_grad()
def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE=None				)			->			Any:
		__lowerCAmelCase: Union[str, Any]					   =    Repository(__SCREAMING_SNAKE_CASE      ,				clone_from=F"google/{pytorch_dump_folder_path}"				)
		repo.git_pull()

		if config_path is not None:
				__lowerCAmelCase: str					   =    OwlViTConfig.from_pretrained(__SCREAMING_SNAKE_CASE				)
		else:
				__lowerCAmelCase: Dict					   =    OwlViTConfig()

		__lowerCAmelCase: Union[str, Any]					   =    OwlViTModel(__SCREAMING_SNAKE_CASE				).eval()
		__lowerCAmelCase: int					   =    OwlViTForObjectDetection(__SCREAMING_SNAKE_CASE				).eval()

		copy_text_model_and_projection(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)
		copy_vision_model_and_projection(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase: Tuple					   =    pt_backbone.logit_scale
		copy_flax_attn_params(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)

		__lowerCAmelCase: List[str]					   =    hf_backbone
		copy_class_merge_token(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)
		copy_class_box_heads(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)

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

		# Initialize image processor
		__lowerCAmelCase: int					   =    OwlViTImageProcessor(
		    size=config.vision_config.image_size      ,				crop_size=config.vision_config.image_size				)
		# Initialize tokenizer
		__lowerCAmelCase: str					   =    CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"      ,				pad_token="!"      ,				model_max_length=1_6				)

		# Initialize processor
		__lowerCAmelCase: Tuple					   =    OwlViTProcessor(image_processor=__SCREAMING_SNAKE_CASE      ,				tokenizer=__SCREAMING_SNAKE_CASE				)
		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)





"""simple docstring"""


import math






def 					a__					(					__SCREAMING_SNAKE_CASE				)			->			list:
		if n <= 0:
				raise ValueError("Only positive integers have prime factors"				)
		__lowerCAmelCase: Tuple					   =    []
		while n % 2 == 0:
				pf.append(2				)
				__lowerCAmelCase: str					   =    int(n / 2				)
		for i in range(3      ,				int(math.sqrt(__SCREAMING_SNAKE_CASE				)				) + 1      ,				2				):
				while n % i == 0:
						pf.append(__SCREAMING_SNAKE_CASE				)
						__lowerCAmelCase: List[Any]					   =    int(n / i				)
		if n > 2:
				pf.append(__SCREAMING_SNAKE_CASE				)
		return pf






def 					a__					(					__SCREAMING_SNAKE_CASE				)			->			int:
		if n <= 0:
				raise ValueError("Only positive numbers are accepted"				)
		__lowerCAmelCase: int					   =    1
		__lowerCAmelCase: str					   =    1
		while n % 2 == 0:
				temp += 1
				__lowerCAmelCase: Dict					   =    int(n / 2				)
		div *= temp
		for i in range(3      ,				int(math.sqrt(__SCREAMING_SNAKE_CASE				)				) + 1      ,				2				):
				__lowerCAmelCase: int					   =    1
				while n % i == 0:
						temp += 1
						__lowerCAmelCase: Tuple					   =    int(n / i				)
				div *= temp
		if n > 1:
				div *= 2
		return div






def 					a__					(					__SCREAMING_SNAKE_CASE				)			->			int:
		if n <= 0:
				raise ValueError("Only positive numbers are accepted"				)
		__lowerCAmelCase: Dict					   =    1
		__lowerCAmelCase: int					   =    1
		while n % 2 == 0:
				temp += 1
				__lowerCAmelCase: Tuple					   =    int(n / 2				)
		if temp > 1:
				s *= (2**temp - 1) / (2 - 1)
		for i in range(3      ,				int(math.sqrt(__SCREAMING_SNAKE_CASE				)				) + 1      ,				2				):
				__lowerCAmelCase: List[Any]					   =    1
				while n % i == 0:
						temp += 1
						__lowerCAmelCase: List[str]					   =    int(n / i				)
				if temp > 1:
						s *= (i**temp - 1) / (i - 1)
		return int(__SCREAMING_SNAKE_CASE				)






def 					a__					(					__SCREAMING_SNAKE_CASE				)			->			int:
		__lowerCAmelCase: Optional[int]					   =    n
		for x in set(prime_factors(__SCREAMING_SNAKE_CASE				)				):
				s *= (x - 1) / x
		return int(__SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
			import doctest

			doctest.testmod()


