






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''': 5_1_2,
        '''image_resolution''': 7_6_8,
        '''context_length''': 1_6,
        '''vocab_size''': 4_9_4_0_8,
        '''vision_layers''': 1_2,
        '''vision_width''': 7_6_8,
        '''vision_patch_size''': 3_2,
        '''transformer_width''': 5_1_2,
        '''transformer_heads''': 8,
        '''transformer_layers''': 1_2,
    },
    '''vit_b16''': {
        '''embed_dim''': 5_1_2,
        '''image_resolution''': 7_6_8,
        '''context_length''': 1_6,
        '''vocab_size''': 4_9_4_0_8,
        '''vision_layers''': 1_2,
        '''vision_width''': 7_6_8,
        '''vision_patch_size''': 1_6,
        '''transformer_width''': 5_1_2,
        '''transformer_heads''': 8,
        '''transformer_layers''': 1_2,
    },
    '''vit_l14''': {
        '''embed_dim''': 7_6_8,
        '''image_resolution''': 8_4_0,
        '''context_length''': 1_6,
        '''vocab_size''': 4_9_4_0_8,
        '''vision_layers''': 2_4,
        '''vision_width''': 1_0_2_4,
        '''vision_patch_size''': 1_4,
        '''transformer_width''': 7_6_8,
        '''transformer_heads''': 1_2,
        '''transformer_layers''': 1_2,
    },
}







def _a				( SCREAMING_SNAKE_CASE	:    str					,      SCREAMING_SNAKE_CASE	:    Optional[Any]=""					,      SCREAMING_SNAKE_CASE	:    Union[str, Any]="/"	)					->					str:

 """simple docstring"""






 __lowerCAmelCase:     Optional[int]     			=   []

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

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







def _a				( SCREAMING_SNAKE_CASE	:    Union[str, Any]	)					->					Optional[Any]:

 """simple docstring"""






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







def _a				( SCREAMING_SNAKE_CASE	:    Any					,      SCREAMING_SNAKE_CASE	:    List[str]	)					->					int:

 """simple docstring"""






 __lowerCAmelCase			,							__lowerCAmelCase			,							__lowerCAmelCase:     List[str]     			=   pt_attn_layer.in_proj_weight.chunk(3					,      dim=0	)
 __lowerCAmelCase			,							__lowerCAmelCase			,							__lowerCAmelCase:     List[Any]     			=   pt_attn_layer.in_proj_bias.chunk(3					,      dim=0	)

 __lowerCAmelCase:     Dict     			=   pt_attn_layer.out_proj.weight
 __lowerCAmelCase:     List[str]     			=   pt_attn_layer.out_proj.bias

 __lowerCAmelCase:     str     			=   q_proj
 __lowerCAmelCase:     str     			=   q_proj_bias

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

 __lowerCAmelCase:     List[str]     			=   v_proj
 __lowerCAmelCase:     Optional[Any]     			=   v_proj_bias

 __lowerCAmelCase:     Optional[Any]     			=   out_proj_weights
 __lowerCAmelCase:     Dict     			=   out_proj_bias







def _a				( SCREAMING_SNAKE_CASE	:    Tuple					,      SCREAMING_SNAKE_CASE	:    Any	)					->					int:

 """simple docstring"""






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







def _a				( SCREAMING_SNAKE_CASE	:    List[str]					,      SCREAMING_SNAKE_CASE	:    Dict	)					->					int:

 """simple docstring"""






 __lowerCAmelCase:     List[Any]     			=   pt_linear.weight
 __lowerCAmelCase:     Optional[Any]     			=   pt_linear.bias







def _a				( SCREAMING_SNAKE_CASE	:    Tuple					,      SCREAMING_SNAKE_CASE	:    Union[str, Any]	)					->					Optional[int]:

 """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				( SCREAMING_SNAKE_CASE	:    Optional[Any]					,      SCREAMING_SNAKE_CASE	:    Optional[int]	)					->					Any:

 """simple docstring"""






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







def _a				( SCREAMING_SNAKE_CASE	:    List[str]					,      SCREAMING_SNAKE_CASE	:    int	)					->					int:

 """simple docstring"""






 __lowerCAmelCase:     int     			=   pt_model.token_embedding.weight
 __lowerCAmelCase:     str     			=   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				( SCREAMING_SNAKE_CASE	:    List[Any]					,      SCREAMING_SNAKE_CASE	:    Tuple	)					->					Optional[int]:

 """simple docstring"""






 __lowerCAmelCase:     Union[str, Any]     			=   pt_model.text_projection.data.T

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







def _a				( SCREAMING_SNAKE_CASE	:    str					,      SCREAMING_SNAKE_CASE	:    Union[str, Any]	)					->					Any:

 """simple docstring"""






 __lowerCAmelCase:     int     			=   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:     Dict     			=   pt_model.visual.conva.weight.data
 __lowerCAmelCase:     List[Any]     			=   pt_model.visual.class_embedding
 __lowerCAmelCase:     Optional[Any]     			=   pt_model.visual.positional_embedding.data

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







def _a				( SCREAMING_SNAKE_CASE	:    List[str]					,      SCREAMING_SNAKE_CASE	:    Optional[Any]	)					->					Any:

 """simple docstring"""






 __lowerCAmelCase:     Union[str, Any]     			=   flatten_nested_dict(flax_params['backbone']['merged_class_token']	)

 __lowerCAmelCase:     Any     			=   torch.from_numpy(flax_class_token_params['scale']	)
 __lowerCAmelCase:     Optional[int]     			=   torch.from_numpy(flax_class_token_params['bias']	)
 __lowerCAmelCase:     str     			=   nn.Parameter(snake_case__	)
 __lowerCAmelCase:     Tuple     			=   nn.Parameter(snake_case__	)







def _a				( SCREAMING_SNAKE_CASE	:    List[Any]					,      SCREAMING_SNAKE_CASE	:    List[Any]	)					->					Dict:

 """simple docstring"""






 __lowerCAmelCase:     Any     			=   hf_model.state_dict()
 __lowerCAmelCase:     List[Any]     			=   {}

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

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

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

  __lowerCAmelCase:     Tuple     			=   nn.Parameter(torch.from_numpy(snake_case__	)	)

 # Rename box prediction box flax params to pytorch HF
 __lowerCAmelCase:     Optional[int]     			=   flatten_nested_dict(flax_params['obj_box_head']	)

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

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

  __lowerCAmelCase:     List[Any]     			=   nn.Parameter(torch.from_numpy(snake_case__	)	)

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







def _a				( SCREAMING_SNAKE_CASE	:    str					,      SCREAMING_SNAKE_CASE	:    Optional[Any]	)					->					Dict:

 """simple docstring"""






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

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

  if "bias" in torch_key and v.ndim == 2:
   __lowerCAmelCase:     Tuple     			=   v.shape[0] * v.shape[1]
   __lowerCAmelCase:     List[Any]     			=   v.reshape(snake_case__	)

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

  if "weight" in torch_key and "out" not in torch_key:
   __lowerCAmelCase:     Optional[int]     			=   (v.shape[0], v.shape[1] * v.shape[2])
   __lowerCAmelCase:     List[Any]     			=   v.reshape(snake_case__	).T

  # Copy flax CLIP attn params to HF PyTorch params
  __lowerCAmelCase:     Dict     			=   torch.from_numpy(snake_case__	)
  hf_backbone.state_dict()[torch_key].copy_(snake_case__	)







def _a				( SCREAMING_SNAKE_CASE	:    Any	)					->					List[str]:

 """simple docstring"""






 __lowerCAmelCase:     Any     			=   {}
 __lowerCAmelCase:     Tuple     			=   []

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

   processed_attn_layers.append(snake_case__	)
   __lowerCAmelCase:     str     			=   params[base + 'out.weight'].shape[-1]
   __lowerCAmelCase:     Union[str, Any]     			=   params[base + 'out.weight'].reshape(snake_case__					,      snake_case__	).T
   __lowerCAmelCase:     List[str]     			=   params[base + 'out.bias']
  else:
   __lowerCAmelCase:     Union[str, Any]     			=   v
 return new_params







def _a				( SCREAMING_SNAKE_CASE	:    int					,      SCREAMING_SNAKE_CASE	:    Optional[Any]	)					->					str:

 """simple docstring"""






 __lowerCAmelCase:     int     			=   CLIP(**snake_case__	)
 torch_model.eval()
 __lowerCAmelCase:     Optional[int]     			=   torch_model.state_dict()

 __lowerCAmelCase:     List[Any]     			=   flatten_nested_dict(flax_params['backbone']['clip']	)
 __lowerCAmelCase:     List[Any]     			=   {}

 for flax_key, v in flax_clip_params.items():
  __lowerCAmelCase:     Any     			=   flax_key.replace('/'					,      '.'	)
  __lowerCAmelCase:     Optional[Any]     			=   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:     Dict     			=   torch_key[5:]

  __lowerCAmelCase:     int     			=   torch_key.replace('text_projection.kernel'					,      'text_projection'	)
  __lowerCAmelCase:     Any     			=   torch_key.replace('visual.proj.kernel'					,      'visual.proj'	)
  __lowerCAmelCase:     str     			=   torch_key.replace('.scale'					,      '.weight'	)
  __lowerCAmelCase:     Any     			=   torch_key.replace('.kernel'					,      '.weight'	)

  if "conv" in torch_key or "downsample.0.weight" in torch_key:
   __lowerCAmelCase:     List[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
   __lowerCAmelCase:     Tuple     			=   v.T

  __lowerCAmelCase:     List[Any]     			=   v

 __lowerCAmelCase:     List[Any]     			=   _convert_attn_layers(snake_case__	)
 new_torch_params.update(snake_case__	)
 __lowerCAmelCase:     Union[str, Any]     			=   {}

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

 return torch_clip_params, torch_model, attn_params







@torch.no_grad()
def _a				( SCREAMING_SNAKE_CASE	:    List[str]					,      SCREAMING_SNAKE_CASE	:    int					,      SCREAMING_SNAKE_CASE	:    int					,      SCREAMING_SNAKE_CASE	:    Tuple					,      SCREAMING_SNAKE_CASE	:    Any=None	)					->					Optional[Any]:

 """simple docstring"""






 __lowerCAmelCase:     Tuple     			=   Repository(snake_case__					,      clone_from=f'''google/{pytorch_dump_folder_path}'''	)
 repo.git_pull()

 if config_path is not None:
  __lowerCAmelCase:     Tuple     			=   OwlViTConfig.from_pretrained(snake_case__	)
 else:
  __lowerCAmelCase:     str     			=   OwlViTConfig()

 __lowerCAmelCase:     List[Any]     			=   OwlViTModel(snake_case__	).eval()
 __lowerCAmelCase:     Union[str, Any]     			=   OwlViTForObjectDetection(snake_case__	).eval()

 copy_text_model_and_projection(snake_case__					,      snake_case__	)
 copy_vision_model_and_projection(snake_case__					,      snake_case__	)
 __lowerCAmelCase:     Union[str, Any]     			=   pt_backbone.logit_scale
 copy_flax_attn_params(snake_case__					,      snake_case__	)

 __lowerCAmelCase:     Optional[int]     			=   hf_backbone
 copy_class_merge_token(snake_case__					,      snake_case__	)
 copy_class_box_heads(snake_case__					,      snake_case__	)

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

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

 # Initialize processor
 __lowerCAmelCase:     List[Any]     			=   OwlViTProcessor(image_processor=snake_case__					,      tokenizer=snake_case__	)
 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	 =     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 math import floor, sqrt







def _a				( SCREAMING_SNAKE_CASE	:    int	)					->					int:

 """simple docstring"""






 __lowerCAmelCase:     Optional[int]     			=   0.0
 __lowerCAmelCase:     Any     			=   1.0
 __lowerCAmelCase:     List[str]     			=   int(sqrt(SCREAMING_SNAKE_CASE	)	)
 __lowerCAmelCase:     Union[str, Any]     			=   root
 __lowerCAmelCase:     List[str]     			=   0
 while integer_part != 2 * root:
  __lowerCAmelCase:     List[str]     			=   denominator * integer_part - numerator
  __lowerCAmelCase:     Optional[int]     			=   (n - numerator**2) / denominator
  __lowerCAmelCase:     Union[str, Any]     			=   int((root + numerator) / denominator	)
  period += 1
 return period







def _a				( SCREAMING_SNAKE_CASE	:    int = 1_00_00	)					->					int:

 """simple docstring"""






 __lowerCAmelCase:     Optional[int]     			=   0
 for i in range(2					,      n + 1	):
  __lowerCAmelCase:     List[str]     			=   sqrt(SCREAMING_SNAKE_CASE	)
  if sr - floor(SCREAMING_SNAKE_CASE	) != 0 and continuous_fraction_period(SCREAMING_SNAKE_CASE	) % 2 == 1:
   count_odd_periods += 1
 return count_odd_periods


if __name__ == "__main__":
  print(f"{solution(int(input().strip()))}")





