

'''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  a__       (    _SCREAMING_SNAKE_CASE   :       Tuple	,				_SCREAMING_SNAKE_CASE   :       List[str]	,				_SCREAMING_SNAKE_CASE   :       Dict	,				_SCREAMING_SNAKE_CASE   :       Optional[int]	,				_SCREAMING_SNAKE_CASE   :       str				)     ->					Optional[int]:


	"""simple docstring"""





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

	# Load in the weights from the checkpoint_path
	UpperCAmelCase_     :      Any						=  torch.load(_SCREAMING_SNAKE_CASE	,				map_location="cpu"				)

	# Load the entity vocab file
	UpperCAmelCase_     :      Optional[Any]						=  load_entity_vocab(_SCREAMING_SNAKE_CASE				)

	UpperCAmelCase_     :      Optional[Any]						=  RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]				)

	# Add special tokens to the token vocabulary for downstream tasks
	UpperCAmelCase_     :      List[Any]						=  AddedToken("<ent>"	,				lstrip=_SCREAMING_SNAKE_CASE	,				rstrip=_SCREAMING_SNAKE_CASE				)
	UpperCAmelCase_     :      str						=  AddedToken("<ent2>"	,				lstrip=_SCREAMING_SNAKE_CASE	,				rstrip=_SCREAMING_SNAKE_CASE				)
	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(_SCREAMING_SNAKE_CASE				)
	with open(os.path.join(_SCREAMING_SNAKE_CASE	,				LukeTokenizer.vocab_files_names["entity_vocab_file"]				)	,				"w"				) as f:
		json.dump(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)

	UpperCAmelCase_     :      List[str]						=  LukeTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE				)

	# Initialize the embeddings of the special tokens
	UpperCAmelCase_     :      str						=  state_dict["embeddings.word_embeddings.weight"]
	UpperCAmelCase_     :      Dict						=  word_emb[tokenizer.convert_tokens_to_ids(["@"]				)[0]].unsqueeze(0				)
	UpperCAmelCase_     :      Any						=  word_emb[tokenizer.convert_tokens_to_ids(["#"]				)[0]].unsqueeze(0				)
	UpperCAmelCase_     :      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"]:
			UpperCAmelCase_     :      List[str]						=  F'''encoder.layer.{layer_index}.attention.self.'''
			UpperCAmelCase_     :      Tuple						=  state_dict[prefix + matrix_name]
			UpperCAmelCase_     :      Union[str, Any]						=  state_dict[prefix + matrix_name]
			UpperCAmelCase_     :      Optional[int]						=  state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
	UpperCAmelCase_     :      List[str]						=  state_dict["entity_embeddings.entity_embeddings.weight"]
	UpperCAmelCase_     :      int						=  entity_emb[entity_vocab["[MASK]"]]

	UpperCAmelCase_     :      Dict						=  LukeModel(config=_SCREAMING_SNAKE_CASE				).eval()

	UpperCAmelCase_					,					UpperCAmelCase_     :      List[Any]						=  model.load_state_dict(_SCREAMING_SNAKE_CASE	,				strict=_SCREAMING_SNAKE_CASE				)
	if not (len(_SCREAMING_SNAKE_CASE				) == 1 and missing_keys[0] == "embeddings.position_ids"):
		raise ValueError(F'''Missing keys {", ".join(_SCREAMING_SNAKE_CASE				)}. 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
	UpperCAmelCase_     :      Optional[Any]						=  LukeTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE	,				task="entity_classification"				)

	UpperCAmelCase_     :      Tuple						=  (
	    "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 ."
	)
	UpperCAmelCase_     :      Optional[Any]						=  (39, 42)
	UpperCAmelCase_     :      List[str]						=  tokenizer(_SCREAMING_SNAKE_CASE	,				entity_spans=[span]	,				add_prefix_space=_SCREAMING_SNAKE_CASE	,				return_tensors="pt"				)

	UpperCAmelCase_     :      Dict						=  model(**_SCREAMING_SNAKE_CASE				)

	# Verify word hidden states
	if model_size == "large":
		UpperCAmelCase_     :      str						=  torch.Size((1, 42, 10_24)				)
		UpperCAmelCase_     :      List[Any]						=  torch.tensor(
		    [[0.0_133, 0.0_865, 0.0_095], [0.3_093, -0.2_576, -0.7_418], [-0.1_720, -0.2_117, -0.2_869]]				)
	else:  # base
		UpperCAmelCase_     :      int						=  torch.Size((1, 42, 7_68)				)
		UpperCAmelCase_     :      Dict						=  torch.tensor([[0.0_037, 0.1_368, -0.0_091], [0.1_099, 0.3_329, -0.1_095], [0.0_765, 0.5_335, 0.1_179]]				)

	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]	,				_SCREAMING_SNAKE_CASE	,				atol=1E-4				):
		raise ValueError

	# Verify entity hidden states
	if model_size == "large":
		UpperCAmelCase_     :      int						=  torch.Size((1, 1, 10_24)				)
		UpperCAmelCase_     :      Optional[Any]						=  torch.tensor([[0.0_466, -0.0_106, -0.0_179]]				)
	else:  # base
		UpperCAmelCase_     :      int						=  torch.Size((1, 1, 7_68)				)
		UpperCAmelCase_     :      Optional[Any]						=  torch.tensor([[0.1_457, 0.1_044, 0.0_174]]				)

	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]	,				_SCREAMING_SNAKE_CASE	,				atol=1E-4				):
		raise ValueError

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


def  a__       (    _SCREAMING_SNAKE_CASE   :       Optional[Any]				)     ->					List[Any]:


	"""simple docstring"""





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

	return entity_vocab


if __name__ == "__main__":
					_lowerCamelCase      					=     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."""
					)
					_lowerCamelCase      					=     parser.parse_args()
					convert_luke_checkpoint(
					    args.checkpoint_path,
					    args.metadata_path,
					    args.entity_vocab_path,
					    args.pytorch_dump_folder_path,
					    args.model_size,
					)











'''simple docstring'''

__lowerCAmelCase				:      List[str]						      ="Alexander Joslin"

import operator as op

from .stack import Stack





def 		UpperCamelCase							(				_lowerCamelCase   :   str			):
    A__							=     {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}

    A__							=     Stack()
    A__							=     Stack()

    for i in equation:
        if i.isdigit():
            # RULE 1
            operand_stack.push(int(_lowerCamelCase			)			)
        elif i in operators:
            # RULE 2
            operator_stack.push(_lowerCamelCase			)
        elif i == ")":
            # RULE 4
            A__							=     operator_stack.peek()
            operator_stack.pop()
            A__							=     operand_stack.peek()
            operand_stack.pop()
            A__							=     operand_stack.peek()
            operand_stack.pop()

            A__							=     operators[opr](_lowerCamelCase		,			_lowerCamelCase			)
            operand_stack.push(_lowerCamelCase			)

    # RULE 5
    return operand_stack.peek()


if __name__ == "__main__":
  __lowerCAmelCase				:      Dict						      ="(5 + ((4 * 2) * (2 + 3)))"
  # answer = 45
  print(f"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")



