





from __future__ import annotations

import random



class 				__snake_case  :


    '''simple docstring'''







    lowerCamelCase__						:		Tuple							=  6



    def __init__(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= list(range(1      ,							Dice.NUM_SIDES + 1    )    )




    def    lowercase_				(					self    ):





        '''simple docstring'''

        return random.choice(self.sides    )





def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_ = 2  )							->	list[float]:
    SCREAMING_SNAKE_CASE__       							= [Dice() for i in range(lowerCAmelCase_  )]
    SCREAMING_SNAKE_CASE__       							= [0] * (len(lowerCAmelCase_  ) * Dice.NUM_SIDES + 1)
    for _ in range(lowerCAmelCase_  ):
        count_of_sum[sum(dice.roll() for dice in dices  )] += 1
    SCREAMING_SNAKE_CASE__       							= [round((count * 1_0_0) / num_throws   ,       2  ) for count in count_of_sum]
    return probability[num_dice:]  # remove probability of sums that never appear


if __name__ == "__main__":
  import doctest

  doctest.testmod()







import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	str:
    SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= pt_weights[f'''{prefix}.in_proj_weight'''].chunk(3   ,       dim=0  )
    SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= pt_weights[f'''{prefix}.in_proj_bias'''].chunk(3   ,       dim=0  )

    SCREAMING_SNAKE_CASE__       							= pt_weights[f'''{prefix}.out_proj.weight''']
    SCREAMING_SNAKE_CASE__       							= pt_weights[f'''{prefix}.out_proj.bias''']

    SCREAMING_SNAKE_CASE__       							= q_proj
    SCREAMING_SNAKE_CASE__       							= q_proj_bias

    SCREAMING_SNAKE_CASE__       							= k_proj
    SCREAMING_SNAKE_CASE__       							= k_proj_bias

    SCREAMING_SNAKE_CASE__       							= v_proj
    SCREAMING_SNAKE_CASE__       							= v_proj_bias

    SCREAMING_SNAKE_CASE__       							= out_proj_weights
    SCREAMING_SNAKE_CASE__       							= out_proj_bias


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Dict:
    copy_linear(hf_mlp.fca   ,       lowerCAmelCase_   ,       f'''{prefix}.c_fc'''  )
    copy_linear(hf_mlp.fca   ,       lowerCAmelCase_   ,       f'''{prefix}.c_proj'''  )


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	str:
    SCREAMING_SNAKE_CASE__       							= pt_weights[f'''{prefix}.weight'''].data
    SCREAMING_SNAKE_CASE__       							= pt_weights[f'''{prefix}.bias'''].data


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Dict:
    # copy layer norms
    copy_linear(hf_layer.layer_norma   ,       lowerCAmelCase_   ,       f'''{prefix}.ln_1'''  )
    copy_linear(hf_layer.layer_norma   ,       lowerCAmelCase_   ,       f'''{prefix}.ln_2'''  )

    # copy MLP
    copy_mlp(hf_layer.mlp   ,       lowerCAmelCase_   ,       f'''{prefix}.mlp'''  )

    # copy attn
    copy_attn_layer(hf_layer.self_attn   ,       lowerCAmelCase_   ,       f'''{prefix}.attn'''  )


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Optional[int]:
    for layer_id, hf_layer in enumerate(lowerCAmelCase_  ):
        copy_layer(lowerCAmelCase_   ,       lowerCAmelCase_   ,       f'''{prefix}.{layer_id}'''  )


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Any:
    # copy projection
    SCREAMING_SNAKE_CASE__       							= pt_weights['''text_projection'''].data.T

    # copy text encoder
    for name, param in hf_model.text_model.named_parameters():
        SCREAMING_SNAKE_CASE__       							= pt_weights[f'''bert.{name}'''].data


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	List[str]:
    # copy projection
    SCREAMING_SNAKE_CASE__       							= pt_weights['''visual.proj'''].data.T

    # copy layer norms
    copy_linear(hf_model.vision_model.pre_layrnorm   ,       lowerCAmelCase_   ,       '''visual.ln_pre'''  )
    copy_linear(hf_model.vision_model.post_layernorm   ,       lowerCAmelCase_   ,       '''visual.ln_post'''  )

    # copy embeddings
    SCREAMING_SNAKE_CASE__       							= pt_weights['''visual.conv1.weight'''].data
    SCREAMING_SNAKE_CASE__       							= pt_weights['''visual.class_embedding'''].data
    SCREAMING_SNAKE_CASE__       							= pt_weights['''visual.positional_embedding'''].data

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


@torch.no_grad()
def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_=None  )							->	str:
    assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
    SCREAMING_SNAKE_CASE__       							= ChineseCLIPConfig.from_pretrained(lowerCAmelCase_  )

    SCREAMING_SNAKE_CASE__       							= ChineseCLIPModel(lowerCAmelCase_  ).eval()

    SCREAMING_SNAKE_CASE__       							= torch.load(lowerCAmelCase_   ,       map_location='''cpu'''  )['''state_dict''']
    SCREAMING_SNAKE_CASE__       							= {(name[7:] if name.startswith('''module.'''  ) else name): value for name, value in pt_weights.items()}

    copy_text_model_and_projection(lowerCAmelCase_   ,       lowerCAmelCase_  )
    copy_vision_model_and_projection(lowerCAmelCase_   ,       lowerCAmelCase_  )
    SCREAMING_SNAKE_CASE__       							= pt_weights['''logit_scale'''].data

    hf_model.save_pretrained(lowerCAmelCase_  )


if __name__ == "__main__":
  _A				:							Union[str, Any]							= argparse.ArgumentParser()
  parser.add_argument(
      """--pytorch_dump_folder_path""",
      default=None,
      type=str,
      help="""Path to the output folder storing converted hf PyTorch model.""",
  )
  parser.add_argument(
      """--checkpoint_path""", default=None, type=str, help="""Path to original github format ChineseCLIP checkpoint."""
  )
  parser.add_argument(
      """--config_path""", default=None, required=True, type=str, help="""Path to hf config.json of model to convert."""
  )
  _A				:							Union[str, Any]							= parser.parse_args()

  convert_chinese_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
  print("""The conversion is finished!""")
