





import argparse
import collections

import jax
import jax.numpy as jnp
import torch
import torch.nn as nn
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository

from transformers import (
    CLIPTokenizer,
    OwlViTConfig,
    OwlViTForObjectDetection,
    OwlViTImageProcessor,
    OwlViTModel,
    OwlViTProcessor,
)


_UpperCamelCase    =	{
    '''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  lowerCAmelCase__(		lowercase    :      Any				,       lowercase    :      str=""				,       lowercase    :      Optional[int]="/"			)	->      List[Any]:
  __snake_case				:			Optional[int]            =    []

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

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







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







def  lowerCAmelCase__(		lowercase    :      Optional[int]				,       lowercase    :      List[Any]			)	->      List[Any]:
  __snake_case      ,				__snake_case      ,				__snake_case				:			Tuple            =    pt_attn_layer.in_proj_weight.chunk(3				,       dim=0			)
  __snake_case      ,				__snake_case      ,				__snake_case				:			str            =    pt_attn_layer.in_proj_bias.chunk(3				,       dim=0			)

  __snake_case				:			List[Any]            =    pt_attn_layer.out_proj.weight
  __snake_case				:			Any            =    pt_attn_layer.out_proj.bias

  __snake_case				:			str            =    q_proj
  __snake_case				:			Dict            =    q_proj_bias

  __snake_case				:			Union[str, Any]            =    k_proj
  __snake_case				:			List[Any]            =    k_proj_bias

  __snake_case				:			Tuple            =    v_proj
  __snake_case				:			Dict            =    v_proj_bias

  __snake_case				:			Optional[Any]            =    out_proj_weights
  __snake_case				:			List[str]            =    out_proj_bias







def  lowerCAmelCase__(		lowercase    :      List[Any]				,       lowercase    :      List[str]			)	->      Dict:
  copy_linear(hf_mlp.fca				,       pt_mlp.c_fc			)
  copy_linear(hf_mlp.fca				,       pt_mlp.c_proj			)







def  lowerCAmelCase__(		lowercase    :      Optional[int]				,       lowercase    :      List[str]			)	->      List[Any]:
  __snake_case				:			List[Any]            =    pt_linear.weight
  __snake_case				:			str            =    pt_linear.bias







def  lowerCAmelCase__(		lowercase    :      int				,       lowercase    :      Union[str, Any]			)	->      Union[str, Any]:
  # copy layer norms
  copy_linear(hf_layer.layer_norma				,       pt_layer.ln_a			)
  copy_linear(hf_layer.layer_norma				,       pt_layer.ln_a			)

  # copy MLP
  copy_mlp(hf_layer.mlp				,       pt_layer.mlp			)

  # copy attn
  copy_attn_layer(hf_layer.self_attn				,       pt_layer.attn			)







def  lowerCAmelCase__(		lowercase    :      List[Any]				,       lowercase    :      Any			)	->      Tuple:
  for hf_layer, pt_layer in zip(lowercase				,       lowercase			):
    copy_layer(lowercase				,       lowercase			)







def  lowerCAmelCase__(		lowercase    :      Dict				,       lowercase    :      str			)	->      Tuple:
  # copy  embeds
  __snake_case				:			List[Any]            =    pt_model.token_embedding.weight
  __snake_case				:			List[Any]            =    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  lowerCAmelCase__(		lowercase    :      Dict				,       lowercase    :      Union[str, Any]			)	->      str:
  # copy projection
  __snake_case				:			List[str]            =    pt_model.text_projection.data.T

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







def  lowerCAmelCase__(		lowercase    :      Dict				,       lowercase    :      Union[str, Any]			)	->      Optional[Any]:
  # copy projection
  __snake_case				:			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
  __snake_case				:			Optional[int]            =    pt_model.visual.conva.weight.data
  __snake_case				:			Union[str, Any]            =    pt_model.visual.class_embedding
  __snake_case				:			str            =    pt_model.visual.positional_embedding.data

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







def  lowerCAmelCase__(		lowercase    :      Union[str, Any]				,       lowercase    :      Optional[Any]			)	->      List[str]:
  __snake_case				:			Optional[Any]            =    flatten_nested_dict(flax_params["backbone"]["merged_class_token"]			)

  __snake_case				:			Optional[int]            =    torch.from_numpy(flax_class_token_params["scale"]			)
  __snake_case				:			Optional[Any]            =    torch.from_numpy(flax_class_token_params["bias"]			)
  __snake_case				:			List[str]            =    nn.Parameter(lowercase			)
  __snake_case				:			Dict            =    nn.Parameter(lowercase			)







def  lowerCAmelCase__(		lowercase    :      List[Any]				,       lowercase    :      Any			)	->      Union[str, Any]:
  __snake_case				:			Optional[Any]            =    hf_model.state_dict()
  __snake_case				:			int            =    {}

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

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

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

    __snake_case				:			str            =    nn.Parameter(torch.from_numpy(lowercase			)			)

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

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

    if "weight" in torch_key and v.ndim == 2:
      __snake_case				:			Optional[Any]            =    v.T

    __snake_case				:			Union[str, Any]            =    nn.Parameter(torch.from_numpy(lowercase			)			)

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







def  lowerCAmelCase__(		lowercase    :      int				,       lowercase    :      Tuple			)	->      Tuple:
  for k, v in flax_attn_params.items():
    if k.startswith("transformer"			):
      __snake_case				:			int            =    k.replace("transformer.resblocks"				,       "text_model.encoder.layers"			)
    else:
      __snake_case				:			Optional[int]            =    k.replace("visual.transformer.resblocks"				,       "vision_model.encoder.layers"			)

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

    if "bias" in torch_key and v.ndim == 2:
      __snake_case				:			Tuple            =    v.shape[0] * v.shape[1]
      __snake_case				:			Optional[Any]            =    v.reshape(lowercase			)

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

    if "weight" in torch_key and "out" not in torch_key:
      __snake_case				:			List[str]            =    (v.shape[0], v.shape[1] * v.shape[2])
      __snake_case				:			Union[str, Any]            =    v.reshape(lowercase			).T

    # Copy flax CLIP attn params to HF PyTorch params
    __snake_case				:			Union[str, Any]            =    torch.from_numpy(lowercase			)
    hf_backbone.state_dict()[torch_key].copy_(lowercase			)







def  lowerCAmelCase__(		lowercase    :      Any			)	->      Optional[int]:
  __snake_case				:			Optional[int]            =    {}
  __snake_case				:			Any            =    []

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

      processed_attn_layers.append(lowercase			)
      __snake_case				:			List[str]            =    params[base + "out.weight"].shape[-1]
      __snake_case				:			str            =    params[base + "out.weight"].reshape(lowercase				,       lowercase			).T
      __snake_case				:			Dict            =    params[base + "out.bias"]
    else:
      __snake_case				:			Union[str, Any]            =    v
  return new_params







def  lowerCAmelCase__(		lowercase    :      Union[str, Any]				,       lowercase    :      Union[str, Any]			)	->      int:
  __snake_case				:			Any            =    CLIP(**lowercase			)
  torch_model.eval()
  __snake_case				:			int            =    torch_model.state_dict()

  __snake_case				:			Any            =    flatten_nested_dict(flax_params["backbone"]["clip"]			)
  __snake_case				:			str            =    {}

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

    __snake_case				:			Dict            =    torch_key.replace("text_projection.kernel"				,       "text_projection"			)
    __snake_case				:			List[Any]            =    torch_key.replace("visual.proj.kernel"				,       "visual.proj"			)
    __snake_case				:			str            =    torch_key.replace(".scale"				,       ".weight"			)
    __snake_case				:			List[str]            =    torch_key.replace(".kernel"				,       ".weight"			)

    if "conv" in torch_key or "downsample.0.weight" in torch_key:
      __snake_case				:			Union[str, Any]            =    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
      __snake_case				:			int            =    v.T

    __snake_case				:			Dict            =    v

  __snake_case				:			List[str]            =    _convert_attn_layers(lowercase			)
  new_torch_params.update(lowercase			)
  __snake_case				:			Dict            =    {}

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

  return torch_clip_params, torch_model, attn_params







@torch.no_grad()
def  lowerCAmelCase__(		lowercase    :      Any				,       lowercase    :      str				,       lowercase    :      Union[str, Any]				,       lowercase    :      int				,       lowercase    :      Union[str, Any]=None			)	->      Any:
  __snake_case				:			Optional[Any]            =    Repository(lowercase				,       clone_from=f"""google/{pytorch_dump_folder_path}"""			)
  repo.git_pull()

  if config_path is not None:
    __snake_case				:			Union[str, Any]            =    OwlViTConfig.from_pretrained(lowercase			)
  else:
    __snake_case				:			Tuple            =    OwlViTConfig()

  __snake_case				:			Optional[int]            =    OwlViTModel(lowercase			).eval()
  __snake_case				:			Optional[Any]            =    OwlViTForObjectDetection(lowercase			).eval()

  copy_text_model_and_projection(lowercase				,       lowercase			)
  copy_vision_model_and_projection(lowercase				,       lowercase			)
  __snake_case				:			List[str]            =    pt_backbone.logit_scale
  copy_flax_attn_params(lowercase				,       lowercase			)

  __snake_case				:			Optional[int]            =    hf_backbone
  copy_class_merge_token(lowercase				,       lowercase			)
  copy_class_box_heads(lowercase				,       lowercase			)

  # Save HF model
  hf_model.save_pretrained(repo.local_dir			)

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

  # Initialize processor
  __snake_case				:			Dict            =    OwlViTProcessor(image_processor=lowercase				,       tokenizer=lowercase			)
  image_processor.save_pretrained(repo.local_dir			)
  processor.save_pretrained(repo.local_dir			)

  repo.git_add()
  repo.git_commit("Upload model and processor"			)
  repo.git_push()


if __name__ == "__main__":
     _UpperCamelCase    =	argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--owlvit_version''',
         default=None,
         type=str,
         required=True,
         help='''OWL-ViT model name [clip_b16, clip_b32, clip_l14].''',
     )
     parser.add_argument(
         '''--owlvit_checkpoint''', default=None, type=str, required=True, help='''Path to flax model checkpoint.'''
     )
     parser.add_argument('''--hf_config''', default=None, type=str, required=True, help='''Path to HF model config.''')
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default='''hf_model''', type=str, help='''Path to the output PyTorch model.'''
     )
     _UpperCamelCase    =	parser.parse_args()

     # Initialize PyToch clip model
     _UpperCamelCase    =	args.owlvit_version
     if model_name == "clip_b16":
          _UpperCamelCase    =	CONFIGS['''vit_b16''']
     elif model_name == "clip_b32":
          _UpperCamelCase    =	CONFIGS['''vit_b32''']
     elif model_name == "clip_l14":
          _UpperCamelCase    =	CONFIGS['''vit_l14''']

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

     # Convert CLIP backbone
     _UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase    =	convert_clip_backbone(flax_params, torch_config)

     convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)








from __future__ import annotations

import sys




class 			_lowerCamelCase	:



      """simple docstring"""






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






        '''simple docstring'''






        __snake_case				:			str            =    letter
        __snake_case				:			int            =    freq
        __snake_case				:			dict[str, str]            =    {}







      def __repr__(	self				)    ->				str:






        '''simple docstring'''






        return F"""{self.letter}:{self.freq}"""




class 			_lowerCamelCase	:



      """simple docstring"""






      def __init__(	self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				Any:






        '''simple docstring'''






        __snake_case				:			int            =    freq
        __snake_case				:			Letter | TreeNode            =    left
        __snake_case				:			Letter | TreeNode            =    right







def  lowerCAmelCase__(		lowercase    :      str			)	->      list[Letter]:
  __snake_case				:			dict[str, int]            =    {}
  with open(lowercase			) as f:
    while True:
      __snake_case				:			List[Any]            =    f.read(1			)
      if not c:
        break
      __snake_case				:			Optional[Any]            =    chars[c] + 1 if c in chars else 1
  return sorted((Letter(lowercase				,       lowercase			) for c, f in chars.items())				,       key=lambda lowercase			: x.freq			)







def  lowerCAmelCase__(		lowercase    :      list[Letter]			)	->      Letter | TreeNode:
  __snake_case				:			list[Letter | TreeNode]            =    letters  # type: ignore
  while len(lowercase			) > 1:
    __snake_case				:			Any            =    response.pop(0			)
    __snake_case				:			Optional[int]            =    response.pop(0			)
    __snake_case				:			str            =    left.freq + right.freq
    __snake_case				:			Dict            =    TreeNode(lowercase				,       lowercase				,       lowercase			)
    response.append(lowercase			)
    response.sort(key=lambda lowercase			: x.freq			)
  return response[0]







def  lowerCAmelCase__(		lowercase    :      Letter | TreeNode				,       lowercase    :      str			)	->      list[Letter]:
  if isinstance(lowercase				,       lowercase			):
    __snake_case				:			List[str]            =    bitstring
    return [root]
  __snake_case				:			TreeNode            =    root  # type: ignore
  __snake_case				:			Union[str, Any]            =    []
  letters += traverse_tree(treenode.left				,       bitstring + "0"			)
  letters += traverse_tree(treenode.right				,       bitstring + "1"			)
  return letters







def  lowerCAmelCase__(		lowercase    :      str			)	->      None:
  __snake_case				:			List[Any]            =    parse_file(lowercase			)
  __snake_case				:			List[str]            =    build_tree(lowercase			)
  __snake_case				:			Optional[int]            =    {
      k: v for letter in traverse_tree(lowercase				,       ""			) for k, v in letter.bitstring.items()
  }
  print(f"""Huffman Coding  of {file_path}: """			)
  with open(lowercase			) as f:
    while True:
      __snake_case				:			int            =    f.read(1			)
      if not c:
        break
      print(letters[c]				,       end=" "			)
  print()


if __name__ == "__main__":
     # pass the file path to the huffman function
     huffman(sys.argv[1])

