






from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like


def     _a					(       UpperCamelCase_   :			Any      ,				UpperCamelCase_   :			Optional[Any]=3				)	->				int:
      """simple docstring"""







      lowerCAmelCase__	 =  int(mask / 2				)
      # copy image size
      lowerCAmelCase__	 =  zeros_like(snake_case__				)
      for i in range(snake_case__      ,				gray_img.shape[0] - bd				):
            for j in range(snake_case__      ,				gray_img.shape[1] - bd				):
                  # get mask according with mask
                  lowerCAmelCase__	 =  ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]				)
                  # calculate mask median
                  lowerCAmelCase__	 =  sort(snake_case__				)[inta(divide((multiply(snake_case__      ,				snake_case__				))      ,				2				) + 1				)]
                  lowerCAmelCase__	 =  median
      return median_img


if __name__ == "__main__":
 # read original image
 a_							=      imread('''../image_data/lena.jpg''')
 # turn image in gray scale value
 a_							=      cvtColor(img, COLOR_BGR2GRAY)

 # get values with two different mask size
 a_							=      median_filter(gray, 3)
 a_							=      median_filter(gray, 5)

 # show result images
 imshow('''median filter with 3x3 mask''', medianaxa)
 imshow('''median filter with 5x5 mask''', medianaxa)
 waitKey(0)














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''': 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_   :			Dict      ,				UpperCamelCase_   :			Union[str, Any]=""      ,				UpperCamelCase_   :			Optional[Any]="/"				)	->				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     _a					(       UpperCamelCase_   :			Optional[Any]				)	->				Union[str, Any]:
						"""simple docstring"""







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


def     _a					(       UpperCamelCase_   :			Union[str, Any]      ,				UpperCamelCase_   :			Dict				)	->				str:
						"""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     _a					(       UpperCamelCase_   :			str      ,				UpperCamelCase_   :			Optional[int]				)	->				Tuple:
						"""simple docstring"""







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


def     _a					(       UpperCamelCase_   :			int      ,				UpperCamelCase_   :			int				)	->				int:
						"""simple docstring"""







						lowerCAmelCase__	 =  pt_linear.weight
						lowerCAmelCase__	 =  pt_linear.bias


def     _a					(       UpperCamelCase_   :			Optional[Any]      ,				UpperCamelCase_   :			Optional[int]				)	->				str:
						"""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     _a					(       UpperCamelCase_   :			List[Any]      ,				UpperCamelCase_   :			Tuple				)	->				Tuple:
						"""simple docstring"""







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


def     _a					(       UpperCamelCase_   :			Optional[int]      ,				UpperCamelCase_   :			str				)	->				Optional[int]:
						"""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     _a					(       UpperCamelCase_   :			Tuple      ,				UpperCamelCase_   :			Optional[Any]				)	->				Optional[int]:
						"""simple docstring"""







						lowerCAmelCase__	 =  pt_model.text_projection.data.T

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


def     _a					(       UpperCamelCase_   :			List[str]      ,				UpperCamelCase_   :			Dict				)	->				Optional[Any]:
						"""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     _a					(       UpperCamelCase_   :			List[str]      ,				UpperCamelCase_   :			Optional[Any]				)	->				Union[str, Any]:
						"""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     _a					(       UpperCamelCase_   :			List[str]      ,				UpperCamelCase_   :			Optional[int]				)	->				Optional[int]:
						"""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     _a					(       UpperCamelCase_   :			Union[str, Any]      ,				UpperCamelCase_   :			List[str]				)	->				List[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     _a					(       UpperCamelCase_   :			Union[str, Any]				)	->				Optional[int]:
						"""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     _a					(       UpperCamelCase_   :			List[Any]      ,				UpperCamelCase_   :			List[str]				)	->				List[str]:
						"""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     _a					(       UpperCamelCase_   :			Dict      ,				UpperCamelCase_   :			Union[str, Any]      ,				UpperCamelCase_   :			Union[str, Any]      ,				UpperCamelCase_   :			List[Any]      ,				UpperCamelCase_   :			str=None				)	->				str:
						"""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__":
	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)






