






import argparse
import json
import os

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case			:			int			   =   logging.get_logger(__name__)







def 		_UpperCamelCase      (			UpperCamelCase_				:    Tuple						)     ->      Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  ConvNextVaConfig()

						if "atto" in checkpoint_url:
												lowerCAmelCase__			  =  [2, 2, 6, 2]
												lowerCAmelCase__			  =  [40, 80, 160, 320]
						if "femto" in checkpoint_url:
												lowerCAmelCase__			  =  [2, 2, 6, 2]
												lowerCAmelCase__			  =  [48, 96, 192, 384]
						if "pico" in checkpoint_url:
												lowerCAmelCase__			  =  [2, 2, 6, 2]
												lowerCAmelCase__			  =  [64, 128, 256, 512]
						if "nano" in checkpoint_url:
												lowerCAmelCase__			  =  [2, 2, 8, 2]
												lowerCAmelCase__			  =  [80, 160, 320, 640]
						if "tiny" in checkpoint_url:
												lowerCAmelCase__			  =  [3, 3, 9, 3]
												lowerCAmelCase__			  =  [96, 192, 384, 768]
						if "base" in checkpoint_url:
												lowerCAmelCase__			  =  [3, 3, 27, 3]
												lowerCAmelCase__			  =  [128, 256, 512, 1024]
						if "large" in checkpoint_url:
												lowerCAmelCase__			  =  [3, 3, 27, 3]
												lowerCAmelCase__			  =  [192, 384, 768, 1536]
						if "huge" in checkpoint_url:
												lowerCAmelCase__			  =  [3, 3, 27, 3]
												lowerCAmelCase__			  =  [352, 704, 1408, 2816]

						lowerCAmelCase__			  =  1000
						lowerCAmelCase__			  =  'imagenet-1k-id2label.json'
						lowerCAmelCase__			  =  (1, 1000)

						lowerCAmelCase__			  =  'huggingface/label-files'
						lowerCAmelCase__			  =  num_labels
						lowerCAmelCase__			  =  json.load(open(hf_hub_download(UpperCamelCase_			,				UpperCamelCase_			,				repo_type='dataset'						)			,				'r'						)						)
						lowerCAmelCase__			  =  {int(UpperCamelCase_						): v for k, v in idalabel.items()}

						lowerCAmelCase__			  =  idalabel
						lowerCAmelCase__			  =  {v: k for k, v in idalabel.items()}
						lowerCAmelCase__			  =  hidden_sizes
						lowerCAmelCase__			  =  depths

						return config, expected_shape







def 		_UpperCamelCase      (			UpperCamelCase_				:    str						)     ->      Any:

						"""simple docstring"""



						if "downsample_layers.0.0" in name:
												lowerCAmelCase__			  =  name.replace('downsample_layers.0.0'			,				'embeddings.patch_embeddings'						)
						if "downsample_layers.0.1" in name:
												lowerCAmelCase__			  =  name.replace('downsample_layers.0.1'			,				'embeddings.norm'						)  # we rename to layernorm later on
						if "downsample_layers.1.0" in name:
												lowerCAmelCase__			  =  name.replace('downsample_layers.1.0'			,				'stages.1.downsampling_layer.0'						)
						if "downsample_layers.1.1" in name:
												lowerCAmelCase__			  =  name.replace('downsample_layers.1.1'			,				'stages.1.downsampling_layer.1'						)
						if "downsample_layers.2.0" in name:
												lowerCAmelCase__			  =  name.replace('downsample_layers.2.0'			,				'stages.2.downsampling_layer.0'						)
						if "downsample_layers.2.1" in name:
												lowerCAmelCase__			  =  name.replace('downsample_layers.2.1'			,				'stages.2.downsampling_layer.1'						)
						if "downsample_layers.3.0" in name:
												lowerCAmelCase__			  =  name.replace('downsample_layers.3.0'			,				'stages.3.downsampling_layer.0'						)
						if "downsample_layers.3.1" in name:
												lowerCAmelCase__			  =  name.replace('downsample_layers.3.1'			,				'stages.3.downsampling_layer.1'						)
						if "stages" in name and "downsampling_layer" not in name:
												# stages.0.0. for instance should be renamed to stages.0.layers.0.
												lowerCAmelCase__			  =  name[: len('stages.0'						)] + '.layers' + name[len('stages.0'						) :]
						if "gamma" in name:
												lowerCAmelCase__			  =  name.replace('gamma'			,				'weight'						)
						if "beta" in name:
												lowerCAmelCase__			  =  name.replace('beta'			,				'bias'						)
						if "stages" in name:
												lowerCAmelCase__			  =  name.replace('stages'			,				'encoder.stages'						)
						if "norm" in name:
												lowerCAmelCase__			  =  name.replace('norm'			,				'layernorm'						)
						if "head" in name:
												lowerCAmelCase__			  =  name.replace('head'			,				'classifier'						)

						return name







def 		_UpperCamelCase      (			)     ->      int:

						"""simple docstring"""



						lowerCAmelCase__			  =  'http://images.cocodataset.org/val2017/000000039769.jpg'
						lowerCAmelCase__			  =  Image.open(requests.get(UpperCamelCase_			,				stream=UpperCamelCase_						).raw						)
						return im







def 		_UpperCamelCase      (			UpperCamelCase_				:    Dict						)     ->      Optional[int]:

						"""simple docstring"""



						if "224" in checkpoint_url:
												lowerCAmelCase__			  =  224
												lowerCAmelCase__			  =  224 / 256
						elif "384" in checkpoint_url:
												lowerCAmelCase__			  =  384
												lowerCAmelCase__			  =  None
						else:
												lowerCAmelCase__			  =  512
												lowerCAmelCase__			  =  None

						return ConvNextImageProcessor(
						    size=UpperCamelCase_			,				crop_pct=UpperCamelCase_			,				image_mean=[0.485, 0.456, 0.406]			,				image_std=[0.229, 0.224, 0.225]			,				resample=PILImageResampling.BICUBIC			,				)







@torch.no_grad()
def 		_UpperCamelCase      (			UpperCamelCase_				:    Optional[int]			,				UpperCamelCase_				:    Any			,				UpperCamelCase_				:    Union[str, Any]			,				UpperCamelCase_				:    int						)     ->      int:

						"""simple docstring"""



						print('Downloading original model from checkpoint...'						)
						# define ConvNeXTV2 configuration based on URL
						lowerCAmelCase__  ,				lowerCAmelCase__			  =  get_convnextva_config(UpperCamelCase_						)
						# load original state_dict from URL
						lowerCAmelCase__			  =  torch.hub.load_state_dict_from_url(UpperCamelCase_						)['model']

						print('Converting model parameters...'						)
						# rename keys
						for key in state_dict.copy().keys():
												lowerCAmelCase__			  =  state_dict.pop(UpperCamelCase_						)
												lowerCAmelCase__			  =  val
						# add prefix to all keys expect classifier head
						for key in state_dict.copy().keys():
												lowerCAmelCase__			  =  state_dict.pop(UpperCamelCase_						)
												if not key.startswith('classifier'						):
																		lowerCAmelCase__			  =  'convnextv2.' + key
												lowerCAmelCase__			  =  val

						# load HuggingFace model
						lowerCAmelCase__			  =  ConvNextVaForImageClassification(UpperCamelCase_						)
						model.load_state_dict(UpperCamelCase_						)
						model.eval()

						# Check outputs on an image, prepared by ConvNextImageProcessor
						lowerCAmelCase__			  =  convert_preprocessor(UpperCamelCase_						)
						lowerCAmelCase__			  =  preprocessor(images=prepare_img()			,				return_tensors='pt'						)
						lowerCAmelCase__			  =  model(**UpperCamelCase_						).logits

						# note: the logits below were obtained without center cropping
						if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([-0.3930, 0.1747, -0.5246, 0.4177, 0.4295]						)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([-0.1727, -0.5341, -0.7818, -0.4745, -0.6566]						)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([-0.0333, 0.1563, -0.9137, 0.1054, 0.0381]						)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([-0.1744, -0.1555, -0.0713, 0.0950, -0.1431]						)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([0.9996, 0.1966, -0.4386, -0.3472, 0.6661]						)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([-0.2553, -0.6708, -0.1359, 0.2518, -0.2488]						)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([-0.0673, -0.5627, -0.3753, -0.2722, 0.0178]						)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([-0.6377, -0.7458, -0.2150, 0.1184, -0.0597]						)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([1.0799, 0.2322, -0.8860, 1.0219, 0.6231]						)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([0.3766, 0.4917, -1.1426, 0.9942, 0.6024]						)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([0.4220, -0.6919, -0.4317, -0.2881, -0.6609]						)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([0.1082, -0.8286, -0.5095, 0.4681, -0.8085]						)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([-0.2419, -0.6221, 0.2176, -0.0980, -0.7527]						)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([0.0391, -0.4371, 0.3786, 0.1251, -0.2784]						)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([-0.0504, 0.5636, -0.1729, -0.6507, -0.3949]						)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([0.3560, 0.9486, 0.3149, -0.2667, -0.5138]						)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([-0.2469, -0.4550, -0.5853, -0.0810, 0.0309]						)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([-0.3090, 0.0802, -0.0682, -0.1979, -0.2826]						)
						else:
												raise ValueError(F"Unknown URL: {checkpoint_url}"						)

						assert torch.allclose(logits[0, :5]			,				UpperCamelCase_			,				atol=1e-3						)
						assert logits.shape == expected_shape
						print('Model outputs match the original results!'						)

						if save_model:
												print('Saving model to local...'						)
												# Create folder to save model
												if not os.path.isdir(UpperCamelCase_						):
																		os.mkdir(UpperCamelCase_						)

												model.save_pretrained(UpperCamelCase_						)
												preprocessor.save_pretrained(UpperCamelCase_						)

						lowerCAmelCase__			  =  'convnextv2'
						if "atto" in checkpoint_url:
												model_name += "-atto"
						if "femto" in checkpoint_url:
												model_name += "-femto"
						if "pico" in checkpoint_url:
												model_name += "-pico"
						if "nano" in checkpoint_url:
												model_name += "-nano"
						elif "tiny" in checkpoint_url:
												model_name += "-tiny"
						elif "base" in checkpoint_url:
												model_name += "-base"
						elif "large" in checkpoint_url:
												model_name += "-large"
						elif "huge" in checkpoint_url:
												model_name += "-huge"
						if "22k" in checkpoint_url and "1k" not in checkpoint_url:
												model_name += "-22k"
						elif "22k" in checkpoint_url and "1k" in checkpoint_url:
												model_name += "-22k-1k"
						elif "1k" in checkpoint_url:
												model_name += "-1k"
						if "224" in checkpoint_url:
												model_name += "-224"
						elif "384" in checkpoint_url:
												model_name += "-384"
						elif "512" in checkpoint_url:
												model_name += "-512"

						if push_to_hub:
												print(F"Pushing {model_name} to the hub..."						)
												model.push_to_hub(UpperCamelCase_						)
												preprocessor.push_to_hub(UpperCamelCase_						)


if __name__ == "__main__":
					__snake_case			:			int			   =   argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--checkpoint_url""",
					    default="""https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt""",
					    type=str,
					    help="""URL of the original ConvNeXTV2 checkpoint you'd like to convert.""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""",
					    default="""model""",
					    type=str,
					    help="""Path to the output PyTorch model directory.""",
					)
					parser.add_argument("""--save_model""", action="""store_true""", help="""Save model to local""")
					parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image preprocessor to the hub""")

					__snake_case			:			Dict			   =   parser.parse_args()
					convert_convnextva_checkpoint(
					    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
					)













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,
)


__snake_case			:			int			   =   {
    """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_				:    Dict			,				UpperCamelCase_				:    Any=""			,				UpperCamelCase_				:    List[str]="/"						)     ->      List[str]:

						"""simple docstring"""



						lowerCAmelCase__			  =  []

						for k, v in params.items():
												lowerCAmelCase__			  =  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_				:    Tuple						)     ->      Optional[int]:

						"""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_				:    Optional[int]			,				UpperCamelCase_				:    Union[str, Any]						)     ->      List[Any]:

						"""simple docstring"""



						lowerCAmelCase__  ,				lowerCAmelCase__  ,				lowerCAmelCase__			  =  pt_attn_layer.in_proj_weight.chunk(3			,				dim=0						)
						lowerCAmelCase__  ,				lowerCAmelCase__  ,				lowerCAmelCase__			  =  pt_attn_layer.in_proj_bias.chunk(3			,				dim=0						)

						lowerCAmelCase__			  =  pt_attn_layer.out_proj.weight
						lowerCAmelCase__			  =  pt_attn_layer.out_proj.bias

						lowerCAmelCase__			  =  q_proj
						lowerCAmelCase__			  =  q_proj_bias

						lowerCAmelCase__			  =  k_proj
						lowerCAmelCase__			  =  k_proj_bias

						lowerCAmelCase__			  =  v_proj
						lowerCAmelCase__			  =  v_proj_bias

						lowerCAmelCase__			  =  out_proj_weights
						lowerCAmelCase__			  =  out_proj_bias







def 		_UpperCamelCase      (			UpperCamelCase_				:    Tuple			,				UpperCamelCase_				:    Union[str, Any]						)     ->      Union[str, Any]:

						"""simple docstring"""



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







def 		_UpperCamelCase      (			UpperCamelCase_				:    Union[str, Any]			,				UpperCamelCase_				:    Tuple						)     ->      Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  pt_linear.weight
						lowerCAmelCase__			  =  pt_linear.bias







def 		_UpperCamelCase      (			UpperCamelCase_				:    Any			,				UpperCamelCase_				:    Optional[Any]						)     ->      Tuple:

						"""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_				:    Optional[int]			,				UpperCamelCase_				:    List[str]						)     ->      Optional[int]:

						"""simple docstring"""



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







def 		_UpperCamelCase      (			UpperCamelCase_				:    Dict			,				UpperCamelCase_				:    Union[str, Any]						)     ->      Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  pt_model.token_embedding.weight
						lowerCAmelCase__			  =  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_				:    Optional[int]			,				UpperCamelCase_				:    Dict						)     ->      Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  pt_model.text_projection.data.T

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







def 		_UpperCamelCase      (			UpperCamelCase_				:    Any			,				UpperCamelCase_				:    str						)     ->      Tuple:

						"""simple docstring"""



						lowerCAmelCase__			  =  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__			  =  pt_model.visual.conva.weight.data
						lowerCAmelCase__			  =  pt_model.visual.class_embedding
						lowerCAmelCase__			  =  pt_model.visual.positional_embedding.data

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







def 		_UpperCamelCase      (			UpperCamelCase_				:    List[Any]			,				UpperCamelCase_				:    Any						)     ->      List[str]:

						"""simple docstring"""



						lowerCAmelCase__			  =  flatten_nested_dict(flax_params['backbone']['merged_class_token']						)

						lowerCAmelCase__			  =  torch.from_numpy(flax_class_token_params['scale']						)
						lowerCAmelCase__			  =  torch.from_numpy(flax_class_token_params['bias']						)
						lowerCAmelCase__			  =  nn.Parameter(UpperCamelCase_						)
						lowerCAmelCase__			  =  nn.Parameter(UpperCamelCase_						)







def 		_UpperCamelCase      (			UpperCamelCase_				:    str			,				UpperCamelCase_				:    Union[str, Any]						)     ->      Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  hf_model.state_dict()
						lowerCAmelCase__			  =  {}

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

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

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

												lowerCAmelCase__			  =  nn.Parameter(torch.from_numpy(UpperCamelCase_						)						)

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

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

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

												lowerCAmelCase__			  =  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_				:    Tuple			,				UpperCamelCase_				:    List[str]						)     ->      str:

						"""simple docstring"""



						for k, v in flax_attn_params.items():
												if k.startswith('transformer'						):
																		lowerCAmelCase__			  =  k.replace('transformer.resblocks'			,				'text_model.encoder.layers'						)
												else:
																		lowerCAmelCase__			  =  k.replace('visual.transformer.resblocks'			,				'vision_model.encoder.layers'						)

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

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

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

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

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







def 		_UpperCamelCase      (			UpperCamelCase_				:    Any						)     ->      Dict:

						"""simple docstring"""



						lowerCAmelCase__			  =  {}
						lowerCAmelCase__			  =  []

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

																		processed_attn_layers.append(UpperCamelCase_						)
																		lowerCAmelCase__			  =  params[base + 'out.weight'].shape[-1]
																		lowerCAmelCase__			  =  params[base + 'out.weight'].reshape(UpperCamelCase_			,				UpperCamelCase_						).T
																		lowerCAmelCase__			  =  params[base + 'out.bias']
												else:
																		lowerCAmelCase__			  =  v
						return new_params







def 		_UpperCamelCase      (			UpperCamelCase_				:    List[Any]			,				UpperCamelCase_				:    int						)     ->      Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  CLIP(**UpperCamelCase_						)
						torch_model.eval()
						lowerCAmelCase__			  =  torch_model.state_dict()

						lowerCAmelCase__			  =  flatten_nested_dict(flax_params['backbone']['clip']						)
						lowerCAmelCase__			  =  {}

						for flax_key, v in flax_clip_params.items():
												lowerCAmelCase__			  =  flax_key.replace('/'			,				'.'						)
												lowerCAmelCase__			  =  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__			  =  torch_key[5:]

												lowerCAmelCase__			  =  torch_key.replace('text_projection.kernel'			,				'text_projection'						)
												lowerCAmelCase__			  =  torch_key.replace('visual.proj.kernel'			,				'visual.proj'						)
												lowerCAmelCase__			  =  torch_key.replace('.scale'			,				'.weight'						)
												lowerCAmelCase__			  =  torch_key.replace('.kernel'			,				'.weight'						)

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

												lowerCAmelCase__			  =  v

						lowerCAmelCase__			  =  _convert_attn_layers(UpperCamelCase_						)
						new_torch_params.update(UpperCamelCase_						)
						lowerCAmelCase__			  =  {}

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

						return torch_clip_params, torch_model, attn_params







@torch.no_grad()
def 		_UpperCamelCase      (			UpperCamelCase_				:    Any			,				UpperCamelCase_				:    Union[str, Any]			,				UpperCamelCase_				:    Dict			,				UpperCamelCase_				:    List[Any]			,				UpperCamelCase_				:    List[Any]=None						)     ->      Any:

						"""simple docstring"""



						lowerCAmelCase__			  =  Repository(UpperCamelCase_			,				clone_from=F"google/{pytorch_dump_folder_path}"						)
						repo.git_pull()

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

						lowerCAmelCase__			  =  OwlViTModel(UpperCamelCase_						).eval()
						lowerCAmelCase__			  =  OwlViTForObjectDetection(UpperCamelCase_						).eval()

						copy_text_model_and_projection(UpperCamelCase_			,				UpperCamelCase_						)
						copy_vision_model_and_projection(UpperCamelCase_			,				UpperCamelCase_						)
						lowerCAmelCase__			  =  pt_backbone.logit_scale
						copy_flax_attn_params(UpperCamelCase_			,				UpperCamelCase_						)

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

						# Initialize processor
						lowerCAmelCase__			  =  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__":
					__snake_case			:			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."""
					)
					__snake_case			:			Any			   =   parser.parse_args()

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

					# Load from checkpoint and convert params to float-32
					__snake_case			:			List[str]			   =   checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)["""optimizer"""]["""target"""]
					__snake_case			:			List[str]			   =   jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
					del variables

					# Convert CLIP backbone
					__snake_case   ,       __snake_case   ,       __snake_case			:			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)





