


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   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     str							,					UpperCamelCase__:     Optional[Any]							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     Any						) ->   Union[str, Any]:





	"""simple docstring"""







	with open(__a						) as metadata_file:
		A								=				json.load(__a						)
	A								=				LukeConfig(use_entity_aware_attention=__a							,					**metadata["""model_config"""]						)

	# Load in the weights from the checkpoint_path
	A								=				torch.load(__a							,					map_location="""cpu"""						)

	# Load the entity vocab file
	A								=				load_entity_vocab(__a						)

	A								=				RobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]						)

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

	A								=				LukeTokenizer.from_pretrained(__a						)

	# Initialize the embeddings of the special tokens
	A								=				state_dict["""embeddings.word_embeddings.weight"""]
	A								=				word_emb[tokenizer.convert_tokens_to_ids(["""@"""]						)[0]].unsqueeze(0						)
	A								=				word_emb[tokenizer.convert_tokens_to_ids(["""#"""]						)[0]].unsqueeze(0						)
	A								=				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"]:
			A								=				f'encoder.layer.{layer_index}.attention.self.'
			A								=				state_dict[prefix + matrix_name]
			A								=				state_dict[prefix + matrix_name]
			A								=				state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
	A								=				state_dict["""entity_embeddings.entity_embeddings.weight"""]
	A								=				entity_emb[entity_vocab["""[MASK]"""]]

	A								=				LukeModel(config=__a						).eval()

	A					,			A								=				model.load_state_dict(__a							,					strict=__a						)
	if not (len(__a						) == 1 and missing_keys[0] == "embeddings.position_ids"):
		raise ValueError(f'Missing keys {", ".join(__a						)}. 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
	A								=				LukeTokenizer.from_pretrained(__a							,					task="""entity_classification"""						)

	A								=				(
	    """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 ."""
	)
	A								=				(39, 42)
	A								=				tokenizer(__a							,					entity_spans=[span]							,					add_prefix_space=__a							,					return_tensors="""pt"""						)

	A								=				model(**__a						)

	# Verify word hidden states
	if model_size == "large":
		A								=				torch.Size((1, 42, 10_24)						)
		A								=				torch.tensor(
		    [[0.01_33, 0.08_65, 0.00_95], [0.30_93, -0.25_76, -0.74_18], [-0.17_20, -0.21_17, -0.28_69]]						)
	else:  # base
		A								=				torch.Size((1, 42, 7_68)						)
		A								=				torch.tensor([[0.00_37, 0.13_68, -0.00_91], [0.10_99, 0.33_29, -0.10_95], [0.07_65, 0.53_35, 0.11_79]]						)

	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]							,					__a							,					atol=1e-4						):
		raise ValueError

	# Verify entity hidden states
	if model_size == "large":
		A								=				torch.Size((1, 1, 10_24)						)
		A								=				torch.tensor([[0.04_66, -0.01_06, -0.01_79]]						)
	else:  # base
		A								=				torch.Size((1, 1, 7_68)						)
		A								=				torch.tensor([[0.14_57, 0.10_44, 0.01_74]]						)

	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]							,					__a							,					atol=1e-4						):
		raise ValueError

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





def   _lowerCAmelCase				(   UpperCamelCase__:     List[Any]						) ->   List[str]:





	"""simple docstring"""







	A								=				{}
	with open(__a							,					"""r"""							,					encoding="""utf-8"""						) as f:
		for index, line in enumerate(__a						):
			A					,			A								=				line.rstrip().split("""\t"""						)
			A								=				index

	return entity_vocab


if __name__ == "__main__":
				_lowercase       :      int	=	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."
				)
				_lowercase       :      int	=	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 __future__ import annotations

from typing import Any






class       _UpperCamelCase    :

				"""simple docstring"""







				def __init__(						self				,   a__				,   a__				,   a__ = 0   )	->				None:
					A					,			A								=				row, column
					A								=				[[default_value for c in range(a__   )] for r in range(a__   )]

				def __str__(						self   )	->				str:
					A								=				f'Matrix consist of {self.row} rows and {self.column} columns\n'

					# Make string identifier
					A								=				0
					for row_vector in self.array:
						for obj in row_vector:
							A								=				max(a__				,   len(str(a__   )   )   )
					A								=				f'%{max_element_length}s'

					# Make string and return
					def single_line(a__   ) -> str:
						nonlocal string_format_identifier
						A								=				"""["""
						line += ", ".join(string_format_identifier % (obj,) for obj in row_vector   )
						line += "]"
						return line

					s += "\n".join(single_line(a__   ) for row_vector in self.array   )
					return s

				def __repr__(						self   )	->				str:
					return str(self   )

				def      _UpperCAmelCase					(						self				,   a__   )	->				bool:
					if not (isinstance(a__				,   (list, tuple)   ) and len(a__   ) == 2):
						return False
					elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
						return False
					else:
						return True

				def __getitem__(						self				,   a__   )	->				Any:
					assert self.validate_indicies(a__   )
					return self.array[loc[0]][loc[1]]

				def __setitem__(						self				,   a__				,   a__   )	->				None:
					assert self.validate_indicies(a__   )
					A								=				value

				def __add__(						self				,   a__   )	->				Matrix:
					assert isinstance(a__				,   a__   )
					assert self.row == another.row and self.column == another.column

					# Add
					A								=				Matrix(self.row				,   self.column   )
					for r in range(self.row   ):
						for c in range(self.column   ):
							A								=				self[r, c] + another[r, c]
					return result

				def __neg__(						self   )	->				Matrix:
					A								=				Matrix(self.row				,   self.column   )
					for r in range(self.row   ):
						for c in range(self.column   ):
							A								=				-self[r, c]
					return result

				def __sub__(						self				,   a__   )	->				Matrix:
					return self + (-another)

				def __mul__(						self				,   a__   )	->				Matrix:

					if isinstance(a__				,   (int, float)   ):  # Scalar multiplication
						A								=				Matrix(self.row				,   self.column   )
						for r in range(self.row   ):
							for c in range(self.column   ):
								A								=				self[r, c] * another
						return result
					elif isinstance(a__				,   a__   ):  # Matrix multiplication
						assert self.column == another.row
						A								=				Matrix(self.row				,   another.column   )
						for r in range(self.row   ):
							for c in range(another.column   ):
								for i in range(self.column   ):
									result[r, c] += self[r, i] * another[i, c]
						return result
					else:
						A								=				f'Unsupported type given for another ({type(a__   )})'
						raise TypeError(a__   )

				def      _UpperCAmelCase					(						self   )	->				Matrix:
					A								=				Matrix(self.column				,   self.row   )
					for r in range(self.row   ):
						for c in range(self.column   ):
							A								=				self[r, c]
					return result

				def      _UpperCAmelCase					(						self				,   a__				,   a__   )	->				Any:
					assert isinstance(a__				,   a__   ) and isinstance(a__				,   a__   )
					assert self.row == self.column == u.row == v.row  # u, v should be column vector
					assert u.column == v.column == 1  # u, v should be column vector

					# Calculate
					A								=				v.transpose()
					A								=				(v_t * self * u)[0, 0] + 1
					if numerator_factor == 0:
						return None  # It's not invertable
					return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":





				def   _lowerCAmelCase				(   ) ->   None:





					"""simple docstring"""







					A								=				Matrix(3							,					3							,					0						)
					for i in range(3						):
						A								=				1
					print(f'a^(-1) is {ainv}'						)
					# u, v
					A								=				Matrix(3							,					1							,					0						)
					A					,			A					,			A								=				1, 2, -3
					A								=				Matrix(3							,					1							,					0						)
					A					,			A					,			A								=				4, -2, 5
					print(f'u is {u}'						)
					print(f'v is {v}'						)
					print(f'uv^T is {u * v.transpose()}'						)
					# Sherman Morrison
					print(f'(a + uv^T)^(-1) is {ainv.sherman_morrison(UpperCamelCase__							,					UpperCamelCase__						)}'						)





				def   _lowerCAmelCase				(   ) ->   None:





					"""simple docstring"""







					import doctest

					doctest.testmod()
				testa()



