
"""simple docstring"""






import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken






@torch.no_grad()
def 						_snake_case       (	lowercase__		:  int      ,     lowercase__		:  List[Any]      ,     lowercase__		:  Optional[Any]      ,     lowercase__		:  Tuple      ,     lowercase__		:  Dict		)  ->				List[str]:


			'''simple docstring'''





			with open(lowercase__		) as metadata_file:
						lowerCAmelCase_    :Any			     =  json.load(lowercase__		)
			lowerCAmelCase_    :List[str]			     =  LukeConfig(use_entity_aware_attention=lowercase__      ,     **metadata["""model_config"""]		)

			# Load in the weights from the checkpoint_path
			lowerCAmelCase_    :List[str]			     =  torch.load(lowercase__      ,     map_location="""cpu"""		)

			# Load the entity vocab file
			lowerCAmelCase_    :Tuple			     =  load_entity_vocab(lowercase__		)

			lowerCAmelCase_    :str			     =  RobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]		)

			# Add special tokens to the token vocabulary for downstream tasks
			lowerCAmelCase_    :Optional[int]			     =  AddedToken("""<ent>"""      ,     lstrip=lowercase__      ,     rstrip=lowercase__		)
			lowerCAmelCase_    :Optional[Any]			     =  AddedToken("""<ent2>"""      ,     lstrip=lowercase__      ,     rstrip=lowercase__		)
			tokenizer.add_special_tokens({"""additional_special_tokens""": [entity_token_a, entity_token_a]}		)
			config.vocab_size += 2

			print(f"""Saving tokenizer to {pytorch_dump_folder_path}"""		)
			tokenizer.save_pretrained(lowercase__		)
			with open(os.path.join(lowercase__      ,     LukeTokenizer.vocab_files_names["""entity_vocab_file"""]		)      ,     """w"""		) as f:
						json.dump(lowercase__      ,     lowercase__		)

			lowerCAmelCase_    :Any			     =  LukeTokenizer.from_pretrained(lowercase__		)

			# Initialize the embeddings of the special tokens
			lowerCAmelCase_    :Dict			     =  state_dict["""embeddings.word_embeddings.weight"""]
			lowerCAmelCase_    :Tuple			     =  word_emb[tokenizer.convert_tokens_to_ids(["""@"""]		)[0]].unsqueeze(0		)
			lowerCAmelCase_    :Optional[Any]			     =  word_emb[tokenizer.convert_tokens_to_ids(["""#"""]		)[0]].unsqueeze(0		)
			lowerCAmelCase_    :Optional[int]			     =  torch.cat([word_emb, ent_emb, enta_emb]		)

			# Initialize the query layers of the entity-aware self-attention mechanism
			for layer_index in range(config.num_hidden_layers		):
						for matrix_name in ["query.weight", "query.bias"]:
									lowerCAmelCase_    :Tuple			     =  f"""encoder.layer.{layer_index}.attention.self."""
									lowerCAmelCase_    :Tuple			     =  state_dict[prefix + matrix_name]
									lowerCAmelCase_    :Dict			     =  state_dict[prefix + matrix_name]
									lowerCAmelCase_    :Tuple			     =  state_dict[prefix + matrix_name]

									 # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
			lowerCAmelCase_    :Union[str, Any]			     =  state_dict["""entity_embeddings.entity_embeddings.weight"""]
			lowerCAmelCase_    :Optional[Any]			     =  entity_emb[entity_vocab["""[MASK]"""]]

			lowerCAmelCase_    :Union[str, Any]			     =  LukeModel(config=lowercase__		).eval()

			lowerCAmelCase_     ,       lowerCAmelCase_    :int			     =  model.load_state_dict(lowercase__      ,     strict=lowercase__		)
			if not (len(lowercase__		) == 1 and missing_keys[0] == "embeddings.position_ids"):
						raise ValueError(f"""Missing keys {", ".join(lowercase__		)}. Expected only missing embeddings.position_ids"""		)
			if not (all(key.startswith("""entity_predictions"""		) or key.startswith("""lm_head"""		) for key in unexpected_keys		)):
						raise ValueError(
						    """Unexpected keys"""
						    f""" {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions"		) or key.startswith("lm_head"		))]		)}"""		)

			# Check outputs
			lowerCAmelCase_    :int			     =  LukeTokenizer.from_pretrained(lowercase__      ,     task="""entity_classification"""		)

			lowerCAmelCase_    :Optional[int]			     =  (
			    """Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"""
			    """ new world number one avoid a humiliating second- round exit at Wimbledon ."""
			)
			lowerCAmelCase_    :Tuple			     =  (3_9, 4_2)
			lowerCAmelCase_    :Optional[Any]			     =  tokenizer(lowercase__      ,     entity_spans=[span]      ,     add_prefix_space=lowercase__      ,     return_tensors="""pt"""		)

			lowerCAmelCase_    :Optional[int]			     =  model(**lowercase__		)

			# Verify word hidden states
			if model_size == "large":
						lowerCAmelCase_    :Dict			     =  torch.Size((1, 4_2, 1_0_2_4)		)
						lowerCAmelCase_    :Optional[int]			     =  torch.tensor(
						    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]		)
			else:  # base
						lowerCAmelCase_    :List[Any]			     =  torch.Size((1, 4_2, 7_6_8)		)
						lowerCAmelCase_    :Optional[Any]			     =  torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]		)

			if not (outputs.last_hidden_state.shape == expected_shape):
						raise ValueError(
						    f"""Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}"""		)
			if not torch.allclose(outputs.last_hidden_state[0, :3, :3]      ,     lowercase__      ,     atol=1E-4		):
						raise ValueError

			# Verify entity hidden states
			if model_size == "large":
						lowerCAmelCase_    :List[Any]			     =  torch.Size((1, 1, 1_0_2_4)		)
						lowerCAmelCase_    :List[str]			     =  torch.tensor([[0.0466, -0.0106, -0.0179]]		)
			else:  # base
						lowerCAmelCase_    :str			     =  torch.Size((1, 1, 7_6_8)		)
						lowerCAmelCase_    :Optional[int]			     =  torch.tensor([[0.1457, 0.1044, 0.0174]]		)

			if not (outputs.entity_last_hidden_state.shape != expected_shape):
						raise ValueError(
						    f"""Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"""
						    f""" {expected_shape}"""		)
			if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]      ,     lowercase__      ,     atol=1E-4		):
						raise ValueError

			# Finally, save our PyTorch model and tokenizer
			print("""Saving PyTorch model to {}""".format(lowercase__		)		)
			model.save_pretrained(lowercase__		)






def 						_snake_case       (	lowercase__		:  str		)  ->				List[Any]:


			'''simple docstring'''





			lowerCAmelCase_    :List[str]			     =  {}
			with open(lowercase__      ,     """r"""      ,     encoding="""utf-8"""		) as f:
						for index, line in enumerate(lowercase__		):
									lowerCAmelCase_     ,       lowerCAmelCase_    :Any			     =  line.rstrip().split("""\t"""		)
									lowerCAmelCase_    :Optional[int]			     =  index

			return entity_vocab


if __name__ == "__main__":
				__UpperCAmelCase       =  argparse.ArgumentParser()
				# Required parameters
				parser.add_argument('--checkpoint_path', type=str, help='Path to a pytorch_model.bin file.')
				parser.add_argument(
				    '--metadata_path', default=None, type=str, help='Path to a metadata.json file, defining the configuration.'
				)
				parser.add_argument(
				    '--entity_vocab_path',
				    default=None,
				    type=str,
				    help='Path to an entity_vocab.tsv file, containing the entity vocabulary.',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, help='Path to where to dump the output PyTorch model.'
				)
				parser.add_argument(
				    '--model_size', default='base', type=str, choices=['base', 'large'], help='Size of the model to be converted.'
				)
				__UpperCAmelCase       =  parser.parse_args()
				convert_luke_checkpoint(
				    args.checkpoint_path,
				    args.metadata_path,
				    args.entity_vocab_path,
				    args.pytorch_dump_folder_path,
				    args.model_size,
				)














from math import factorial

UpperCAmelCase__				   =		{str(digit): factorial(digit) for digit in range(10)}
def 						_a    (      a :int )							->			int:
 if not isinstance(a	,							a ):
  raise TypeError('''Parameter number must be int''' )

 if number < 0:
  raise ValueError('''Parameter number must be greater than or equal to 0''' )

 # Converts number in string to iterate on its digits and adds its factorial.
 return sum(DIGIT_FACTORIAL[digit] for digit in str(a ) )
def 						_a    (      a :int = 60	,							a :int = 1_000_000 )							->			int:

 if not isinstance(a	,							a ) or not isinstance(a	,							a ):
  raise TypeError('''Parameters chain_length and number_limit must be int''' )

 if chain_length <= 0 or number_limit <= 0:
  raise ValueError(
      '''Parameters chain_length and number_limit must be greater than 0''' )

 # the counter for the chains with the exact desired length
 a		  =      0
 # the cached sizes of the previous chains
 a		  =      {}

 for start_chain_element in range(1	,							a ):
  # The temporary set will contain the elements of the chain
  a		  =      set()
  a		  =      0

  # Stop computing the chain when you find a cached size, a repeating item or the
  # length is greater then the desired one.
  a		  =      start_chain_element
  while (
      chain_element not in chain_sets_lengths
      and chain_element not in chain_set
      and chain_set_length <= chain_length
  ):
   chain_set.add(a )
   chain_set_length += 1
   a		  =      digit_factorial_sum(a )

  if chain_element in chain_sets_lengths:
   chain_set_length += chain_sets_lengths[chain_element]

  a		  =      chain_set_length

  # If chain contains the exact amount of elements increase the counter
  if chain_set_length == chain_length:
   chains_counter += 1

 return chains_counter


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(f"""{solution()}""")


