


import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


SCREAMING_SNAKE_CASE       :		List[str]         =				logging.get_logger(__name__)
logging.set_verbosity_info()




def     UpperCamelCase    (      _a       ,		_a		)					->							Optional[int]:
     '''simple docstring'''





     if "xprophetnet" in prophetnet_checkpoint_path:
          lowercase_       :List[str]     =       XLMProphetNetForConditionalGenerationOld.from_pretrained(_a		)
          lowercase_    ,      lowercase_       :int     =       XLMProphetNetForConditionalGeneration.from_pretrained(
              _a       ,		output_loading_info=_a		)
     else:
          lowercase_       :str     =       ProphetNetForConditionalGenerationOld.from_pretrained(_a		)
          lowercase_    ,      lowercase_       :Dict     =       ProphetNetForConditionalGeneration.from_pretrained(
              _a       ,		output_loading_info=_a		)

     lowercase_       :int     =       ['''key_proj''', '''value_proj''', '''query_proj''']

     lowercase_       :str     =       {
         '''self_attn''': '''ngram_self_attn''',
         '''cross_attn''': '''encoder_attn''',
         '''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
         '''feed_forward_layer_norm''': '''final_layer_norm''',
         '''feed_forward''': '''''',
         '''intermediate''': '''fc1''',
         '''output''': '''fc2''',
         '''key_proj''': '''k_proj''',
         '''query_proj''': '''q_proj''',
         '''value_proj''': '''v_proj''',
         '''word_embeddings''': '''embed_tokens''',
         '''embeddings_layer_norm''': '''emb_layer_norm''',
         '''relative_pos_embeddings''': '''relative_linear''',
         '''ngram_embeddings''': '''ngram_input_embed''',
         '''position_embeddings''': '''embed_positions''',
     }

     for key in loading_info["missing_keys"]:
          lowercase_       :Dict     =       key.split('''.'''		)

          if attributes[0] == "lm_head":
               lowercase_       :List[Any]     =       prophet
               lowercase_       :str     =       prophet_old
          else:
               lowercase_       :Any     =       prophet.prophetnet
               lowercase_       :Optional[Any]     =       prophet_old.model

          lowercase_       :int     =       False
          for attribute in attributes:
               if attribute in mapping:
                    lowercase_       :List[str]     =       mapping[attribute]
                    if not hasattr(_a       ,		_a		) and len(_a		) > 0:
                         lowercase_       :Any     =       attribute
               elif hasattr(_a       ,		_a		):
                    lowercase_       :Dict     =       attribute

               if attribute == "weight":
                    assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
                    lowercase_       :Dict     =       old_model.weight
                    logger.info(f"{attribute} is initialized."		)
                    lowercase_       :Any     =       True
                    break
               elif attribute == "bias":
                    assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
                    lowercase_       :Tuple     =       old_model.bias
                    logger.info(f"{attribute} is initialized"		)
                    lowercase_       :int     =       True
                    break
               elif attribute in special_keys and hasattr(_a       ,		'''in_proj_weight'''		):
                    lowercase_       :Optional[Any]     =       old_model.in_proj_weight.shape[0] // 3
                    lowercase_       :Any     =       getattr(_a       ,		_a		)
                    param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
                    param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
                    if attribute == "query_proj":
                         lowercase_       :List[str]     =       nn.Parameter(old_model.in_proj_weight[:embed_dim, :]		)
                         lowercase_       :str     =       nn.Parameter(old_model.in_proj_bias[:embed_dim]		)

                    elif attribute == "key_proj":
                         lowercase_       :Optional[Any]     =       nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]		)
                         lowercase_       :Union[str, Any]     =       nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]		)
                    elif attribute == "value_proj":
                         lowercase_       :Dict     =       nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]		)
                         lowercase_       :Dict     =       nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]		)
                    lowercase_       :int     =       True
                    break
               elif attribute == "position_embeddings":
                    assert (
                        model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
                    ), "Hidden size has to match"
                    assert model.position_embeddings.weight.shape[0] == 5_1_2, "We want 512 position_embeddings."
                    lowercase_       :Optional[Any]     =       nn.Parameter(old_model.embed_positions.weight[:5_1_2, :]		)
                    lowercase_       :str     =       True
                    break

               if attribute.isdigit():
                    lowercase_       :Any     =       model[int(_a		)]
                    lowercase_       :int     =       old_model[int(_a		)]
               else:
                    lowercase_       :List[str]     =       getattr(_a       ,		_a		)

                    if old_attribute == "":
                         lowercase_       :int     =       old_model
                    else:
                         if not hasattr(_a       ,		_a		):
                              raise ValueError(f"{old_model} does not have {old_attribute}"		)
                         lowercase_       :Any     =       getattr(_a       ,		_a		)

          if not is_key_init:
               raise ValueError(f"{key} was not correctly initialized!"		)

     print(f"Saving model to {pytorch_dump_folder_path}"		)
     prophet.save_pretrained(_a		)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE       :		int         =				argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--prophetnet_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   SCREAMING_SNAKE_CASE       :		Union[str, Any]         =				parser.parse_args()
   convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)





import math

SCREAMING_SNAKE_CASE       :		List[str]         =				10
SCREAMING_SNAKE_CASE       :		Dict         =				7
SCREAMING_SNAKE_CASE       :		int         =				BALLS_PER_COLOUR * NUM_COLOURS




def     UpperCamelCase    (      _a = 2_0		)					->							str:
     '''simple docstring'''





     lowercase_       :List[str]     =       math.comb(_a       ,		_a		)
     lowercase_       :Tuple     =       math.comb(NUM_BALLS - BALLS_PER_COLOUR       ,		_a		)

     lowercase_       :Dict     =       NUM_COLOURS * (1 - missing_colour / total)

     return f"{result:.9f}"


if __name__ == "__main__":
   print(solution(20))

