




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,
)


__UpperCamelCase      : int					      =   {
    "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 	__A	(			__lowerCamelCase							,    __lowerCamelCase=""							,    __lowerCamelCase="/"					)   -> Dict:
	a		       =							[]

	for k, v in params.items():
		a		       =							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:
	return jax.tree_util.tree_map(lambda __lowerCamelCase					: x.astype(jnp.floataa					) if x.dtype == jnp.bfloataa else x							,    __lowerCamelCase					)





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> Any:
	a       ,				a       ,				a		       =							pt_attn_layer.in_proj_weight.chunk(3							,    dim=0					)
	a       ,				a       ,				a		       =							pt_attn_layer.in_proj_bias.chunk(3							,    dim=0					)

	a		       =							pt_attn_layer.out_proj.weight
	a		       =							pt_attn_layer.out_proj.bias

	a		       =							q_proj
	a		       =							q_proj_bias

	a		       =							k_proj
	a		       =							k_proj_bias

	a		       =							v_proj
	a		       =							v_proj_bias

	a		       =							out_proj_weights
	a		       =							out_proj_bias





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> str:
	copy_linear(hf_mlp.fca							,    pt_mlp.c_fc					)
	copy_linear(hf_mlp.fca							,    pt_mlp.c_proj					)





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> Optional[int]:
	a		       =							pt_linear.weight
	a		       =							pt_linear.bias





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> 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							,    __lowerCamelCase					)   -> Optional[int]:
	for hf_layer, pt_layer in zip(__lowerCamelCase							,    __lowerCamelCase					):
		copy_layer(__lowerCamelCase							,    __lowerCamelCase					)





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> Optional[Any]:
	# copy  embeds
	a		       =							pt_model.token_embedding.weight
	a		       =							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							,    __lowerCamelCase					)   -> Optional[Any]:
	# copy projection
	a		       =							pt_model.text_projection.data.T

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





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> str:
	# copy projection
	a		       =							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
	a		       =							pt_model.visual.conva.weight.data
	a		       =							pt_model.visual.class_embedding
	a		       =							pt_model.visual.positional_embedding.data

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





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> List[Any]:
	a		       =							flatten_nested_dict(flax_params["""backbone"""]["""merged_class_token"""]					)

	a		       =							torch.from_numpy(flax_class_token_params["""scale"""]					)
	a		       =							torch.from_numpy(flax_class_token_params["""bias"""]					)
	a		       =							nn.Parameter(__lowerCamelCase					)
	a		       =							nn.Parameter(__lowerCamelCase					)





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> Optional[Any]:
	a		       =							hf_model.state_dict()
	a		       =							{}

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

	for flax_key, v in flax_class_params.items():
		a		       =							flax_key.replace("""/"""							,    """."""					)
		a		       =							torch_key.replace(""".kernel"""							,    """.weight"""					)
		a		       =							torch_key.replace("""Dense_0"""							,    """dense0"""					)
		a		       =							"""class_head.""" + torch_key

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

		a		       =							nn.Parameter(torch.from_numpy(__lowerCamelCase					)					)

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

	for flax_key, v in flax_box_params.items():
		a		       =							flax_key.replace("""/"""							,    """."""					)
		a		       =							torch_key.replace(""".kernel"""							,    """.weight"""					)
		a		       =							torch_key.replace("""_"""							,    """"""					).lower()
		a		       =							"""box_head.""" + torch_key

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

		a		       =							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							,    __lowerCamelCase					)   -> Dict:
	for k, v in flax_attn_params.items():
		if k.startswith("""transformer"""					):
			a		       =							k.replace("""transformer.resblocks"""							,    """text_model.encoder.layers"""					)
		else:
			a		       =							k.replace("""visual.transformer.resblocks"""							,    """vision_model.encoder.layers"""					)

		a		       =							torch_key.replace("""attn"""							,    """self_attn"""					)
		a		       =							torch_key.replace("""key"""							,    """k_proj"""					)
		a		       =							torch_key.replace("""value"""							,    """v_proj"""					)
		a		       =							torch_key.replace("""query"""							,    """q_proj"""					)
		a		       =							torch_key.replace("""out"""							,    """out_proj"""					)

		if "bias" in torch_key and v.ndim == 2:
			a		       =							v.shape[0] * v.shape[1]
			a		       =							v.reshape(__lowerCamelCase					)

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

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

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





def 	__A	(			__lowerCamelCase					)   -> Any:
	a		       =							{}
	a		       =							[]

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

			processed_attn_layers.append(__lowerCamelCase					)
			a		       =							params[base + """out.weight"""].shape[-1]
			a		       =							params[base + """out.weight"""].reshape(__lowerCamelCase							,    __lowerCamelCase					).T
			a		       =							params[base + """out.bias"""]
		else:
			a		       =							v
	return new_params





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> List[Any]:
	a		       =							CLIP(**__lowerCamelCase					)
	torch_model.eval()
	a		       =							torch_model.state_dict()

	a		       =							flatten_nested_dict(flax_params["""backbone"""]["""clip"""]					)
	a		       =							{}

	for flax_key, v in flax_clip_params.items():
		a		       =							flax_key.replace("""/"""							,    """."""					)
		a		       =							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"""					)
		):
			a		       =							torch_key[5:]

		a		       =							torch_key.replace("""text_projection.kernel"""							,    """text_projection"""					)
		a		       =							torch_key.replace("""visual.proj.kernel"""							,    """visual.proj"""					)
		a		       =							torch_key.replace(""".scale"""							,    """.weight"""					)
		a		       =							torch_key.replace(""".kernel"""							,    """.weight"""					)

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

		a		       =							v

	a		       =							_convert_attn_layers(__lowerCamelCase					)
	new_torch_params.update(__lowerCamelCase					)
	a		       =							{}

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

	return torch_clip_params, torch_model, attn_params





@torch.no_grad()
def 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase=None					)   -> Optional[int]:
	a		       =							Repository(__lowerCamelCase							,    clone_from=f'google/{pytorch_dump_folder_path}'					)
	repo.git_pull()

	if config_path is not None:
		a		       =							OwlViTConfig.from_pretrained(__lowerCamelCase					)
	else:
		a		       =							OwlViTConfig()

	a		       =							OwlViTModel(__lowerCamelCase					).eval()
	a		       =							OwlViTForObjectDetection(__lowerCamelCase					).eval()

	copy_text_model_and_projection(__lowerCamelCase							,    __lowerCamelCase					)
	copy_vision_model_and_projection(__lowerCamelCase							,    __lowerCamelCase					)
	a		       =							pt_backbone.logit_scale
	copy_flax_attn_params(__lowerCamelCase							,    __lowerCamelCase					)

	a		       =							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
	a		       =							OwlViTImageProcessor(
	    size=config.vision_config.image_size							,    crop_size=config.vision_config.image_size					)
	# Initialize tokenizer
	a		       =							CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""							,    pad_token="""!"""							,    model_max_length=16					)

	# Initialize processor
	a		       =							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__":
		__UpperCamelCase      : Union[str, 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."
		)
		__UpperCamelCase      : List[Any]					      =   parser.parse_args()

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

		# Load from checkpoint and convert params to float-32
		__UpperCamelCase      : List[Any]					      =   checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)["optimizer"]["target"]
		__UpperCamelCase      : 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
		__UpperCamelCase      : 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 math





def 	__A	(			__lowerCamelCase					)   -> bool:

	if 1 < number < 4:
		# 2 and 3 are primes
		return True
	elif number < 2 or number % 2 == 0 or number % 3 == 0:
		# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
		return False

	# All primes number are in format of 6k +/- 1
	for i in range(5							,    int(math.sqrt(__lowerCamelCase					) + 1					)							,    6					):
		if number % i == 0 or number % (i + 2) == 0:
			return False
	return True





def 	__A	(			__lowerCamelCase = 6008_5147_5143					)   -> int:

	try:
		a		       =							int(__lowerCamelCase					)
	except (TypeError, ValueError):
		raise TypeError("""Parameter n must be int or castable to int."""					)
	if n <= 0:
		raise ValueError("""Parameter n must be greater than or equal to one."""					)
	a		       =							0
	if is_prime(__lowerCamelCase					):
		return n
	while n % 2 == 0:
		n //= 2
	if is_prime(__lowerCamelCase					):
		return n
	for i in range(3							,    int(math.sqrt(__lowerCamelCase					)					) + 1							,    2					):
		if n % i == 0:
			if is_prime(n // i					):
				a		       =							n // i
				break
			elif is_prime(__lowerCamelCase					):
				a		       =							i
	return max_number


if __name__ == "__main__":
		print(F'{solution() = }')

