


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,
)


_SCREAMING_SNAKE_CASE       :   Optional[Any]        = {
    "vit_b32": {
        "embed_dim": 5_12,
        "image_resolution": 7_68,
        "context_length": 16,
        "vocab_size": 4_94_08,
        "vision_layers": 12,
        "vision_width": 7_68,
        "vision_patch_size": 32,
        "transformer_width": 5_12,
        "transformer_heads": 8,
        "transformer_layers": 12,
    },
    "vit_b16": {
        "embed_dim": 5_12,
        "image_resolution": 7_68,
        "context_length": 16,
        "vocab_size": 4_94_08,
        "vision_layers": 12,
        "vision_width": 7_68,
        "vision_patch_size": 16,
        "transformer_width": 5_12,
        "transformer_heads": 8,
        "transformer_layers": 12,
    },
    "vit_l14": {
        "embed_dim": 7_68,
        "image_resolution": 8_40,
        "context_length": 16,
        "vocab_size": 4_94_08,
        "vision_layers": 24,
        "vision_width": 10_24,
        "vision_patch_size": 14,
        "transformer_width": 7_68,
        "transformer_heads": 12,
        "transformer_layers": 12,
    },
}



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__=""     ,   UpperCamelCase__="/"		):






				"""simple docstring"""



				__magic_name__				:					List[Any]						       =       []

				for k, v in params.items():
								__magic_name__				:					str						       =       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 						_UpperCamelCase							( UpperCamelCase__		):






				"""simple docstring"""



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



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



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

				__magic_name__				:					int						       =       pt_attn_layer.out_proj.weight
				__magic_name__				:					List[Any]						       =       pt_attn_layer.out_proj.bias

				__magic_name__				:					Any						       =       q_proj
				__magic_name__				:					Any						       =       q_proj_bias

				__magic_name__				:					Dict						       =       k_proj
				__magic_name__				:					Union[str, Any]						       =       k_proj_bias

				__magic_name__				:					Tuple						       =       v_proj
				__magic_name__				:					Optional[int]						       =       v_proj_bias

				__magic_name__				:					Dict						       =       out_proj_weights
				__magic_name__				:					Any						       =       out_proj_bias



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



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



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					List[str]						       =       pt_linear.weight
				__magic_name__				:					Union[str, Any]						       =       pt_linear.bias



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""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							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



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



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					Optional[Any]						       =       pt_model.token_embedding.weight
				__magic_name__				:					Tuple						       =       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							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					List[Any]						       =       pt_model.text_projection.data.T

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



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					Tuple						       =       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
				__magic_name__				:					Dict						       =       pt_model.visual.conva.weight.data
				__magic_name__				:					List[str]						       =       pt_model.visual.class_embedding
				__magic_name__				:					Dict						       =       pt_model.visual.positional_embedding.data

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



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					int						       =       flatten_nested_dict(flax_params["backbone"]["merged_class_token"]		)

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



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



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

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

				for flax_key, v in flax_class_params.items():
								__magic_name__				:					List[Any]						       =       flax_key.replace("/"     ,   "."		)
								__magic_name__				:					Any						       =       torch_key.replace(".kernel"     ,   ".weight"		)
								__magic_name__				:					Dict						       =       torch_key.replace("Dense_0"     ,   "dense0"		)
								__magic_name__				:					Optional[Any]						       =       "class_head." + torch_key

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

								__magic_name__				:					Optional[int]						       =       nn.Parameter(torch.from_numpy(UpperCamelCase__		)		)

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

				for flax_key, v in flax_box_params.items():
								__magic_name__				:					Tuple						       =       flax_key.replace("/"     ,   "."		)
								__magic_name__				:					Any						       =       torch_key.replace(".kernel"     ,   ".weight"		)
								__magic_name__				:					int						       =       torch_key.replace("_"     ,   ""		).lower()
								__magic_name__				:					Optional[int]						       =       "box_head." + torch_key

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

								__magic_name__				:					Tuple						       =       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 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				for k, v in flax_attn_params.items():
								if k.startswith("transformer"		):
												__magic_name__				:					List[Any]						       =       k.replace("transformer.resblocks"     ,   "text_model.encoder.layers"		)
								else:
												__magic_name__				:					str						       =       k.replace("visual.transformer.resblocks"     ,   "vision_model.encoder.layers"		)

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

								if "bias" in torch_key and v.ndim == 2:
												__magic_name__				:					str						       =       v.shape[0] * v.shape[1]
												__magic_name__				:					Dict						       =       v.reshape(UpperCamelCase__		)

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

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

								# Copy flax CLIP attn params to HF PyTorch params
								__magic_name__				:					Optional[int]						       =       torch.from_numpy(UpperCamelCase__		)
								hf_backbone.state_dict()[torch_key].copy_(UpperCamelCase__		)



def 						_UpperCamelCase							( UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					Dict						       =       {}
				__magic_name__				:					Optional[int]						       =       []

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

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



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					int						       =       CLIP(**UpperCamelCase__		)
				torch_model.eval()
				__magic_name__				:					Dict						       =       torch_model.state_dict()

				__magic_name__				:					Optional[int]						       =       flatten_nested_dict(flax_params["backbone"]["clip"]		)
				__magic_name__				:					Dict						       =       {}

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

								__magic_name__				:					str						       =       torch_key.replace("text_projection.kernel"     ,   "text_projection"		)
								__magic_name__				:					Optional[int]						       =       torch_key.replace("visual.proj.kernel"     ,   "visual.proj"		)
								__magic_name__				:					List[str]						       =       torch_key.replace(".scale"     ,   ".weight"		)
								__magic_name__				:					Optional[int]						       =       torch_key.replace(".kernel"     ,   ".weight"		)

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

								__magic_name__				:					Optional[Any]						       =       v

				__magic_name__				:					List[str]						       =       _convert_attn_layers(UpperCamelCase__		)
				new_torch_params.update(UpperCamelCase__		)
				__magic_name__				:					Optional[Any]						       =       {}

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

				return torch_clip_params, torch_model, attn_params



@torch.no_grad()
def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__=None		):






				"""simple docstring"""



				__magic_name__				:					Any						       =       Repository(UpperCamelCase__     ,   clone_from=F"""google/{pytorch_dump_folder_path}"""		)
				repo.git_pull()

				if config_path is not None:
								__magic_name__				:					Any						       =       OwlViTConfig.from_pretrained(UpperCamelCase__		)
				else:
								__magic_name__				:					Any						       =       OwlViTConfig()

				__magic_name__				:					List[str]						       =       OwlViTModel(UpperCamelCase__		).eval()
				__magic_name__				:					Any						       =       OwlViTForObjectDetection(UpperCamelCase__		).eval()

				copy_text_model_and_projection(UpperCamelCase__     ,   UpperCamelCase__		)
				copy_vision_model_and_projection(UpperCamelCase__     ,   UpperCamelCase__		)
				__magic_name__				:					List[Any]						       =       pt_backbone.logit_scale
				copy_flax_attn_params(UpperCamelCase__     ,   UpperCamelCase__		)

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

				# Initialize processor
				__magic_name__				:					Optional[int]						       =       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__":
		_SCREAMING_SNAKE_CASE       :   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."
		)
		_SCREAMING_SNAKE_CASE       :   Dict        = parser.parse_args()

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

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

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property






class 		_snake_case    (				unittest.TestCase		):






						'''simple docstring'''







						@cached_property
						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	List[Any]:
										return BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"     )







						@cached_property
						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	Optional[int]:
										return BlenderbotTokenizerFast.from_pretrained("facebook/blenderbot-3B"     )







						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	Optional[int]:
										__magic_name__				:					Tuple						       =       self.tokenizer_ab
										__magic_name__				:					Optional[int]						       =       " I am a small frog."
										__magic_name__				:					Optional[int]						       =       tok([src_text]      ,							padding=__UpperCamelCase      ,							truncation=__UpperCamelCase     )["input_ids"]
										__magic_name__				:					int						       =       tok.batch_decode(__UpperCamelCase      ,							skip_special_tokens=__UpperCamelCase      ,							clean_up_tokenization_spaces=__UpperCamelCase     )[0]
										assert src_text == decoded







						def lowerCAmelCase__ (  self:	Optional[int]     )							->	Any:
										__magic_name__				:					Tuple						       =       self.rust_tokenizer_ab
										__magic_name__				:					str						       =       " I am a small frog."
										__magic_name__				:					Union[str, Any]						       =       tok([src_text]      ,							padding=__UpperCamelCase      ,							truncation=__UpperCamelCase     )["input_ids"]
										__magic_name__				:					Any						       =       tok.batch_decode(__UpperCamelCase      ,							skip_special_tokens=__UpperCamelCase      ,							clean_up_tokenization_spaces=__UpperCamelCase     )[0]
										assert src_text == decoded







						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	str:
										assert self.tokenizer_ab.add_prefix_space
										assert self.tokenizer_ab([" Sam", "Sam"]     ).input_ids == [[5502, 2], [5502, 2]]







						def lowerCAmelCase__ (  self:	List[str]     )							->	List[Any]:
										assert self.rust_tokenizer_ab.add_prefix_space
										assert self.rust_tokenizer_ab([" Sam", "Sam"]     ).input_ids == [[5502, 2], [5502, 2]]