






from __future__ import annotations

from abc import abstractmethod
from collections.abc import Iterable
from typing import Generic, Protocol, TypeVar


class        lowercase__       (     snake_case__		):

 @abstractmethod
 def __lt__(							self						,      __UpperCAmelCase    )->   List[str]:







       '''simple docstring'''





       pass

 @abstractmethod
 def __gt__(							self						,      __UpperCAmelCase    )->   List[str]:







       '''simple docstring'''





       pass


 @abstractmethod
 def __eq__(							self						,      __UpperCAmelCase    )->   List[Any]:







       '''simple docstring'''





       pass





a_							=      TypeVar('''T''', bound=Comparable)


class        lowercase__       (     Generic[T]		):

 def __init__(							self    )->   Union[str, Any]:







       '''simple docstring'''





       lowerCAmelCase__	 =  []
       lowerCAmelCase__	 =  0

 def __repr__(							self    )->   List[Any]:







       '''simple docstring'''





       return str(self.h    )

 def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   int:







       '''simple docstring'''





       if child_idx > 0:
             return (child_idx - 1) // 2
       return None

 def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   Any:







       '''simple docstring'''





       lowerCAmelCase__	 =  2 * parent_idx + 1
       if left_child_index < self.heap_size:
             return left_child_index
       return None

 def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   Tuple:







       '''simple docstring'''





       lowerCAmelCase__	 =  2 * parent_idx + 2
       if right_child_index < self.heap_size:
             return right_child_index
       return None

 def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   int:







       '''simple docstring'''





       if index < self.heap_size:
             lowerCAmelCase__	 =  index
             lowerCAmelCase__	 =  self.left_child_idx(__UpperCAmelCase    )
             lowerCAmelCase__	 =  self.right_child_idx(__UpperCAmelCase    )
             # check which child is larger than its parent
             if left_child is not None and self.h[left_child] > self.h[violation]:
                   lowerCAmelCase__	 =  left_child
             if right_child is not None and self.h[right_child] > self.h[violation]:
                   lowerCAmelCase__	 =  right_child
             # if violation indeed exists
             if violation != index:
                   # swap to fix the violation
                   lowerCAmelCase__	 =  self.h[index], self.h[violation]
                   # fix the subsequent violation recursively if any
                   self.max_heapify(__UpperCAmelCase    )

 def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   Any:







       '''simple docstring'''





       lowerCAmelCase__	 =  list(__UpperCAmelCase    )
       lowerCAmelCase__	 =  len(self.h    )
       if self.heap_size > 1:
             # max_heapify from right to left but exclude leaves (last level)
             for i in range(self.heap_size // 2 - 1						,      -1						,      -1    ):
                   self.max_heapify(__UpperCAmelCase    )

 def 						UpperCAmelCase					(							self    )->   Tuple:







       '''simple docstring'''





       if self.heap_size >= 2:
             lowerCAmelCase__	 =  self.h[0]
             lowerCAmelCase__	 =  self.h.pop(-1    )
             self.heap_size -= 1
             self.max_heapify(0    )
             return me
       elif self.heap_size == 1:
             self.heap_size -= 1
             return self.h.pop(-1    )
       else:
             raise Exception("Empty heap"    )

 def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   Optional[Any]:







       '''simple docstring'''





       self.h.append(__UpperCAmelCase    )
       lowerCAmelCase__	 =  (self.heap_size - 1) // 2
       self.heap_size += 1
       while idx >= 0:
             self.max_heapify(__UpperCAmelCase    )
             lowerCAmelCase__	 =  (idx - 1) // 2


 def 						UpperCAmelCase					(							self    )->   Tuple:







       '''simple docstring'''





       lowerCAmelCase__	 =  self.heap_size
       for j in range(size - 1						,      0						,      -1    ):
             lowerCAmelCase__	 =  self.h[j], self.h[0]
             self.heap_size -= 1
             self.max_heapify(0    )
       lowerCAmelCase__	 =  size


if __name__ == "__main__":
 import doctest

 # run doc test
 doctest.testmod()

 # demo
 for unsorted in [
     [0],
     [2],
     [3, 5],
     [5, 3],
     [5, 5],
     [0, 0, 0, 0],
     [1, 1, 1, 1],
     [2, 2, 3, 5],
     [0, 2, 2, 3, 5],
     [2, 5, 3, 0, 2, 3, 0, 3],
     [6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
     [103, 9, 1, 7, 11, 15, 25, 201, 209, 107, 5],
     [-45, -2, -5],
 ]:
  print(F"unsorted array: {unsorted}")

  a_							=      Heap()
  heap.build_max_heap(unsorted)
  print(F"after build heap: {heap}")

  print(F"max value: {heap.extract_max()}")
  print(F"after max value removed: {heap}")

  heap.insert(100)
  print(F"after new value 100 inserted: {heap}")

  heap.heap_sort()
  print(F"heap-sorted array: {heap}\n")














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)






