"""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,
)


UpperCamelCase_       :       int							   =		{
    """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__      (		UpperCAmelCase__       :			Union[str, Any]		,			UpperCAmelCase__       :			int=""		,			UpperCAmelCase__       :			Any="/"			)->	Optional[int]:
						UpperCAmelCase__						=		[]

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

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


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


def   a__      (		UpperCAmelCase__       :			Tuple		,			UpperCAmelCase__       :			int			)->	str:
						UpperCAmelCase__	,      UpperCAmelCase__	,      UpperCAmelCase__						=		pt_attn_layer.in_proj_weight.chunk(3		,			dim=0			)
						UpperCAmelCase__	,      UpperCAmelCase__	,      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__      (		UpperCAmelCase__       :			Any		,			UpperCAmelCase__       :			Dict			)->	Union[str, Any]:
						copy_linear(hf_mlp.fca		,			pt_mlp.c_fc			)
						copy_linear(hf_mlp.fca		,			pt_mlp.c_proj			)


def   a__      (		UpperCAmelCase__       :			Dict		,			UpperCAmelCase__       :			Tuple			)->	str:
						UpperCAmelCase__						=		pt_linear.weight
						UpperCAmelCase__						=		pt_linear.bias


def   a__      (		UpperCAmelCase__       :			Tuple		,			UpperCAmelCase__       :			Optional[Any]			)->	List[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__      (		UpperCAmelCase__       :			Optional[Any]		,			UpperCAmelCase__       :			Optional[Any]			)->	Tuple:
						for hf_layer, pt_layer in zip(UpperCAmelCase__		,			UpperCAmelCase__			):
												copy_layer(UpperCAmelCase__		,			UpperCAmelCase__			)


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

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


def   a__      (		UpperCAmelCase__       :			Optional[Any]		,			UpperCAmelCase__       :			str			)->	Optional[int]:
						# copy projection
						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__      (		UpperCAmelCase__       :			List[str]		,			UpperCAmelCase__       :			List[str]			)->	Dict:
						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(UpperCAmelCase__			)
						UpperCAmelCase__						=		nn.Parameter(UpperCAmelCase__			)


def   a__      (		UpperCAmelCase__       :			Tuple		,			UpperCAmelCase__       :			Tuple			)->	List[str]:
						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(UpperCAmelCase__			)			)

						# 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(UpperCAmelCase__			)			)

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


def   a__      (		UpperCAmelCase__       :			List[Any]		,			UpperCAmelCase__       :			Dict			)->	Union[str, Any]:
						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(UpperCAmelCase__			)

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

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


def   a__      (		UpperCAmelCase__       :			List[str]			)->	Optional[Any]:
						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(UpperCAmelCase__			)
																		UpperCAmelCase__						=		params[base + """out.weight"""].shape[-1]
																		UpperCAmelCase__						=		params[base + """out.weight"""].reshape(UpperCAmelCase__		,			UpperCAmelCase__			).T
																		UpperCAmelCase__						=		params[base + """out.bias"""]
												else:
																		UpperCAmelCase__						=		v
						return new_params


def   a__      (		UpperCAmelCase__       :			Union[str, Any]		,			UpperCAmelCase__       :			str			)->	List[Any]:
						UpperCAmelCase__						=		CLIP(**UpperCAmelCase__			)
						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(UpperCAmelCase__			)
						new_torch_params.update(UpperCAmelCase__			)
						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_(UpperCAmelCase__			)
												else:
																		UpperCAmelCase__						=		param

						return torch_clip_params, torch_model, attn_params


@torch.no_grad()
def   a__      (		UpperCAmelCase__       :			List[str]		,			UpperCAmelCase__       :			Dict		,			UpperCAmelCase__       :			Tuple		,			UpperCAmelCase__       :			Any		,			UpperCAmelCase__       :			List[Any]=None			)->	Optional[int]:
						UpperCAmelCase__						=		Repository(UpperCAmelCase__		,			clone_from=f'google/{pytorch_dump_folder_path}'			)
						repo.git_pull()

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

						UpperCAmelCase__						=		OwlViTModel(UpperCAmelCase__			).eval()
						UpperCAmelCase__						=		OwlViTForObjectDetection(UpperCAmelCase__			).eval()

						copy_text_model_and_projection(UpperCAmelCase__		,			UpperCAmelCase__			)
						copy_vision_model_and_projection(UpperCAmelCase__		,			UpperCAmelCase__			)
						UpperCAmelCase__						=		pt_backbone.logit_scale
						copy_flax_attn_params(UpperCAmelCase__		,			UpperCAmelCase__			)

						UpperCAmelCase__						=		hf_backbone
						copy_class_merge_token(UpperCAmelCase__		,			UpperCAmelCase__			)
						copy_class_box_heads(UpperCAmelCase__		,			UpperCAmelCase__			)

						# 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=1_6			)

						# Initialize processor
						UpperCAmelCase__						=		OwlViTProcessor(image_processor=UpperCAmelCase__		,			tokenizer=UpperCAmelCase__			)
						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_       :       str							   =		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_       :       Union[str, Any]							   =		parser.parse_args()

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

						# Load from checkpoint and convert params to float-32
						UpperCamelCase_       :       Tuple							   =		checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)["""optimizer"""]["""target"""]
						UpperCamelCase_       :       Tuple							   =		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_   ,						UpperCamelCase_   ,						UpperCamelCase_       :       str							   =		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 unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
						import tensorflow as tf

						from transformers.activations_tf import get_tf_activation


@require_tf
class 			_lowercase		(   unittest.TestCase  ):

				def        A (     self       :    Any     ):


										'''simple docstring'''
										UpperCAmelCase__						=		tf.constant([-1_0_0, -1.0, -0.1, 0, 0.1, 1.0, 100.0]     )
										UpperCAmelCase__						=		get_tf_activation("""gelu"""     )
										UpperCAmelCase__						=		get_tf_activation("""gelu_10"""     )

										UpperCAmelCase__						=		gelu(_lowercase     )
										UpperCAmelCase__						=		geluaa(_lowercase     )

										UpperCAmelCase__						=		tf.where(y_gelu_aa < 10.0       ,    1.0       ,    0.0     )

										self.assertEqual(tf.math.reduce_max(_lowercase     ).numpy().item()       ,    10.0     )
										self.assertTrue(np.allclose(y_gelu * clipped_mask       ,    y_gelu_aa * clipped_mask     )     )

				def        A (     self       :    Optional[Any]     ):


										'''simple docstring'''
										get_tf_activation("""gelu"""     )
										get_tf_activation("""gelu_10"""     )
										get_tf_activation("""gelu_fast"""     )
										get_tf_activation("""gelu_new"""     )
										get_tf_activation("""glu"""     )
										get_tf_activation("""mish"""     )
										get_tf_activation("""quick_gelu"""     )
										get_tf_activation("""relu"""     )
										get_tf_activation("""sigmoid"""     )
										get_tf_activation("""silu"""     )
										get_tf_activation("""swish"""     )
										get_tf_activation("""tanh"""     )
										with self.assertRaises(_lowercase     ):
																get_tf_activation("""bogus"""     )
										with self.assertRaises(_lowercase     ):
																get_tf_activation(_lowercase     )

