




def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		Dict:

    """simple docstring"""


    if number != int(lowercase_					):
        raise ValueError('''factorial() only accepts integral values'''					)
    if number < 0:
        raise ValueError('''factorial() not defined for negative values'''					)
    __UpperCamelCase     						=  1
    for i in range(1    ,   number + 1					):
        value *= i
    return value




def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		Union[str, Any]:

    """simple docstring"""


    if not isinstance(lowercase_    ,   lowercase_					):
        raise ValueError('''factorial() only accepts integral values'''					)
    if n < 0:
        raise ValueError('''factorial() not defined for negative values'''					)
    return 1 if n == 0 or n == 1 else n * factorial(n - 1					)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 a_      						=   int(input("Enter a positive integer: ").strip() or 0)
 print(f"""factorial{n} is {factorial(n)}""")













"""simple docstring"""





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,
)


_lowerCamelCase       =	{
    '''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 		lowerCAmelCase_	(  lowercase_   :							Tuple ,      lowercase_   :							List[Any]="" ,      lowercase_   :							List[Any]="/"		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[int]												=       []

 for k, v in params.items():
  __SCREAMING_SNAKE_CASE :			List[str]												=       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		):

 '''simple docstring'''






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




def 		lowerCAmelCase_	(  lowercase_   :							List[Any] ,      lowercase_   :							Union[str, Any]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Optional[Any]												=       pt_attn_layer.in_proj_weight.chunk(3 ,      dim=0		)
 __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Dict												=       pt_attn_layer.in_proj_bias.chunk(3 ,      dim=0		)

 __SCREAMING_SNAKE_CASE :			Any												=       pt_attn_layer.out_proj.weight
 __SCREAMING_SNAKE_CASE :			str												=       pt_attn_layer.out_proj.bias

 __SCREAMING_SNAKE_CASE :			Any												=       q_proj
 __SCREAMING_SNAKE_CASE :			List[str]												=       q_proj_bias

 __SCREAMING_SNAKE_CASE :			Any												=       k_proj
 __SCREAMING_SNAKE_CASE :			Optional[int]												=       k_proj_bias

 __SCREAMING_SNAKE_CASE :			List[str]												=       v_proj
 __SCREAMING_SNAKE_CASE :			List[str]												=       v_proj_bias

 __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       out_proj_weights
 __SCREAMING_SNAKE_CASE :			int												=       out_proj_bias




def 		lowerCAmelCase_	(  lowercase_   :							int ,      lowercase_   :							Dict		):

 '''simple docstring'''






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




def 		lowerCAmelCase_	(  lowercase_   :							Optional[int] ,      lowercase_   :							Tuple		):

 '''simple docstring'''






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




def 		lowerCAmelCase_	(  lowercase_   :							Any ,      lowercase_   :							Any		):

 '''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 		lowerCAmelCase_	(  lowercase_   :							List[Any] ,      lowercase_   :							Any		):

 '''simple docstring'''






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




def 		lowerCAmelCase_	(  lowercase_   :							List[str] ,      lowercase_   :							Tuple		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[int]												=       pt_model.token_embedding.weight
 __SCREAMING_SNAKE_CASE :			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 		lowerCAmelCase_	(  lowercase_   :							Optional[int] ,      lowercase_   :							int		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[int]												=       pt_model.text_projection.data.T

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




def 		lowerCAmelCase_	(  lowercase_   :							Any ,      lowercase_   :							Union[str, Any]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			str												=       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
 __SCREAMING_SNAKE_CASE :			Dict												=       pt_model.visual.conva.weight.data
 __SCREAMING_SNAKE_CASE :			Tuple												=       pt_model.visual.class_embedding
 __SCREAMING_SNAKE_CASE :			Dict												=       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[int]		):

 '''simple docstring'''






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

 __SCREAMING_SNAKE_CASE :			Dict												=       torch.from_numpy(flax_class_token_params['''scale''']		)
 __SCREAMING_SNAKE_CASE :			List[str]												=       torch.from_numpy(flax_class_token_params['''bias''']		)
 __SCREAMING_SNAKE_CASE :			Dict												=       nn.Parameter(lowercase_		)
 __SCREAMING_SNAKE_CASE :			List[Any]												=       nn.Parameter(lowercase_		)




def 		lowerCAmelCase_	(  lowercase_   :							Optional[Any] ,      lowercase_   :							Union[str, Any]		):

 '''simple docstring'''






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

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

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

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

  __SCREAMING_SNAKE_CASE :			List[str]												=       nn.Parameter(torch.from_numpy(lowercase_		)		)

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

 for flax_key, v in flax_box_params.items():
  __SCREAMING_SNAKE_CASE :			Tuple												=       flax_key.replace('''/''' ,      '''.'''		)
  __SCREAMING_SNAKE_CASE :			List[str]												=       torch_key.replace('''.kernel''' ,      '''.weight'''		)
  __SCREAMING_SNAKE_CASE :			Tuple												=       torch_key.replace('''_''' ,      ''''''		).lower()
  __SCREAMING_SNAKE_CASE :			List[str]												=       '''box_head.''' + torch_key

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

  __SCREAMING_SNAKE_CASE :			Optional[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_   :							Optional[Any] ,      lowercase_   :							Optional[Any]		):

 '''simple docstring'''






 for k, v in flax_attn_params.items():
  if k.startswith('''transformer'''		):
   __SCREAMING_SNAKE_CASE :			Optional[int]												=       k.replace('''transformer.resblocks''' ,      '''text_model.encoder.layers'''		)
  else:
   __SCREAMING_SNAKE_CASE :			List[Any]												=       k.replace('''visual.transformer.resblocks''' ,      '''vision_model.encoder.layers'''		)

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

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

  if "weight" in torch_key and "out" in torch_key:
   __SCREAMING_SNAKE_CASE :			List[Any]												=       (v.shape[0] * v.shape[1], v.shape[2])
   __SCREAMING_SNAKE_CASE :			List[str]												=       v.reshape(lowercase_		).T

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

  # Copy flax CLIP attn params to HF PyTorch params
  __SCREAMING_SNAKE_CASE :			Optional[int]												=       torch.from_numpy(lowercase_		)
  hf_backbone.state_dict()[torch_key].copy_(lowercase_		)




def 		lowerCAmelCase_	(  lowercase_   :							Optional[int]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			int												=       {}
 __SCREAMING_SNAKE_CASE :			int												=       []

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

   processed_attn_layers.append(lowercase_		)
   __SCREAMING_SNAKE_CASE :			Optional[Any]												=       params[base + '''out.weight'''].shape[-1]
   __SCREAMING_SNAKE_CASE :			Optional[Any]												=       params[base + '''out.weight'''].reshape(lowercase_ ,      lowercase_		).T
   __SCREAMING_SNAKE_CASE :			Dict												=       params[base + '''out.bias''']
  else:
   __SCREAMING_SNAKE_CASE :			Any												=       v
 return new_params




def 		lowerCAmelCase_	(  lowercase_   :							str ,      lowercase_   :							Dict		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			int												=       CLIP(**lowercase_		)
 torch_model.eval()
 __SCREAMING_SNAKE_CASE :			List[str]												=       torch_model.state_dict()

 __SCREAMING_SNAKE_CASE :			str												=       flatten_nested_dict(flax_params['''backbone''']['''clip''']		)
 __SCREAMING_SNAKE_CASE :			str												=       {}

 for flax_key, v in flax_clip_params.items():
  __SCREAMING_SNAKE_CASE :			int												=       flax_key.replace('''/''' ,      '''.'''		)
  __SCREAMING_SNAKE_CASE :			Tuple												=       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'''		)
  ):
   __SCREAMING_SNAKE_CASE :			List[str]												=       torch_key[5:]

  __SCREAMING_SNAKE_CASE :			Optional[int]												=       torch_key.replace('''text_projection.kernel''' ,      '''text_projection'''		)
  __SCREAMING_SNAKE_CASE :			str												=       torch_key.replace('''visual.proj.kernel''' ,      '''visual.proj'''		)
  __SCREAMING_SNAKE_CASE :			Tuple												=       torch_key.replace('''.scale''' ,      '''.weight'''		)
  __SCREAMING_SNAKE_CASE :			Dict												=       torch_key.replace('''.kernel''' ,      '''.weight'''		)

  if "conv" in torch_key or "downsample.0.weight" in torch_key:
   __SCREAMING_SNAKE_CASE :			Optional[int]												=       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
   __SCREAMING_SNAKE_CASE :			str												=       v.T

  __SCREAMING_SNAKE_CASE :			Any												=       v

 __SCREAMING_SNAKE_CASE :			Optional[int]												=       _convert_attn_layers(lowercase_		)
 new_torch_params.update(lowercase_		)
 __SCREAMING_SNAKE_CASE :			List[Any]												=       {}

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

 return torch_clip_params, torch_model, attn_params




@torch.no_grad()
def 		lowerCAmelCase_	(  lowercase_   :							Any ,      lowercase_   :							Union[str, Any] ,      lowercase_   :							str ,      lowercase_   :							Optional[Any] ,      lowercase_   :							List[Any]=None		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			int												=       Repository(lowercase_ ,      clone_from=F'''google/{pytorch_dump_folder_path}'''		)
 repo.git_pull()

 if config_path is not None:
  __SCREAMING_SNAKE_CASE :			Optional[Any]												=       OwlViTConfig.from_pretrained(lowercase_		)
 else:
  __SCREAMING_SNAKE_CASE :			int												=       OwlViTConfig()

 __SCREAMING_SNAKE_CASE :			List[str]												=       OwlViTModel(lowercase_		).eval()
 __SCREAMING_SNAKE_CASE :			Tuple												=       OwlViTForObjectDetection(lowercase_		).eval()

 copy_text_model_and_projection(lowercase_ ,      lowercase_		)
 copy_vision_model_and_projection(lowercase_ ,      lowercase_		)
 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       pt_backbone.logit_scale
 copy_flax_attn_params(lowercase_ ,      lowercase_		)

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

 # Initialize processor
 __SCREAMING_SNAKE_CASE :			Optional[int]												=       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__":
    _lowerCamelCase       =	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.'''
    )
    _lowerCamelCase       =	parser.parse_args()

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

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

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

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

