



"""simple docstring"""






import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken


@torch.no_grad()
def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case   ,__snake_case   ,__snake_case      )		->  int:
			# Load configuration defined in the metadata file
			with open(__snake_case      ) as metadata_file:
						__lowerCAmelCase :    Optional[Any]				=			json.load(__snake_case      )
			__lowerCAmelCase :    Optional[int]				=			LukeConfig(use_entity_aware_attention=__snake_case   ,**metadata["model_config"]      )

			# Load in the weights from the checkpoint_path
			__lowerCAmelCase :    int				=			torch.load(__snake_case   ,map_location="cpu"      )["module"]

			# Load the entity vocab file
			__lowerCAmelCase :    Optional[int]				=			load_original_entity_vocab(__snake_case      )
			# add an entry for [MASK2]
			__lowerCAmelCase :    str				=			max(entity_vocab.values()      ) + 1
			config.entity_vocab_size += 1

			__lowerCAmelCase :    List[Any]				=			XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]      )

			# Add special tokens to the token vocabulary for downstream tasks
			__lowerCAmelCase :    Any				=			AddedToken("<ent>"   ,lstrip=__snake_case   ,rstrip=__snake_case      )
			__lowerCAmelCase :    Optional[int]				=			AddedToken("<ent2>"   ,lstrip=__snake_case   ,rstrip=__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(__snake_case      )
			with open(os.path.join(__snake_case   ,"tokenizer_config.json"      )   ,"r"      ) as f:
						__lowerCAmelCase :    Optional[int]				=			json.load(__snake_case      )
			__lowerCAmelCase :    Tuple				=			"MLukeTokenizer"
			with open(os.path.join(__snake_case   ,"tokenizer_config.json"      )   ,"w"      ) as f:
						json.dump(__snake_case   ,__snake_case      )

			with open(os.path.join(__snake_case   ,MLukeTokenizer.vocab_files_names["entity_vocab_file"]      )   ,"w"      ) as f:
						json.dump(__snake_case   ,__snake_case      )

			__lowerCAmelCase :    Tuple				=			MLukeTokenizer.from_pretrained(__snake_case      )

			# Initialize the embeddings of the special tokens
			__lowerCAmelCase :    List[Any]				=			tokenizer.convert_tokens_to_ids(["@"]      )[0]
			__lowerCAmelCase :    Optional[Any]				=			tokenizer.convert_tokens_to_ids(["#"]      )[0]

			__lowerCAmelCase :    str				=			state_dict["embeddings.word_embeddings.weight"]
			__lowerCAmelCase :    Tuple				=			word_emb[ent_init_index].unsqueeze(0      )
			__lowerCAmelCase :    Any				=			word_emb[enta_init_index].unsqueeze(0      )
			__lowerCAmelCase :    str				=			torch.cat([word_emb, ent_emb, enta_emb]      )
			# add special tokens for 'entity_predictions.bias'
			for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
						__lowerCAmelCase :    str				=			state_dict[bias_name]
						__lowerCAmelCase :    Any				=			decoder_bias[ent_init_index].unsqueeze(0      )
						__lowerCAmelCase :    List[Any]				=			decoder_bias[enta_init_index].unsqueeze(0      )
						__lowerCAmelCase :    Optional[int]				=			torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]      )

			# 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 :    Optional[int]				=			F"""encoder.layer.{layer_index}.attention.self."""
									__lowerCAmelCase :    Tuple				=			state_dict[prefix + matrix_name]
									__lowerCAmelCase :    List[str]				=			state_dict[prefix + matrix_name]
									__lowerCAmelCase :    Dict				=			state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
			__lowerCAmelCase :    List[Any]				=			state_dict["entity_embeddings.entity_embeddings.weight"]
			__lowerCAmelCase :    int				=			entity_emb[entity_vocab["[MASK]"]].unsqueeze(0      )
			__lowerCAmelCase :    Any				=			torch.cat([entity_emb, entity_mask_emb]      )
			# add [MASK2] for 'entity_predictions.bias'
			__lowerCAmelCase :    Dict				=			state_dict["entity_predictions.bias"]
			__lowerCAmelCase :    Dict				=			entity_prediction_bias[entity_vocab["[MASK]"]].unsqueeze(0      )
			__lowerCAmelCase :    Optional[Any]				=			torch.cat([entity_prediction_bias, entity_mask_bias]      )

			__lowerCAmelCase :    Union[str, Any]				=			LukeForMaskedLM(config=__snake_case      ).eval()

			state_dict.pop("entity_predictions.decoder.weight"      )
			state_dict.pop("lm_head.decoder.weight"      )
			state_dict.pop("lm_head.decoder.bias"      )
			__lowerCAmelCase :    Dict				=			OrderedDict()
			for key, value in state_dict.items():
						if not (key.startswith("lm_head"      ) or key.startswith("entity_predictions"      )):
									__lowerCAmelCase :    Optional[int]				=			state_dict[key]
						else:
									__lowerCAmelCase :    int				=			state_dict[key]

			__lowerCAmelCase		,      __lowerCAmelCase :    int				=			model.load_state_dict(__snake_case   ,strict=__snake_case      )

			if set(__snake_case      ) != {"luke.embeddings.position_ids"}:
						raise ValueError(F"""Unexpected unexpected_keys: {unexpected_keys}"""      )
			if set(__snake_case      ) != {
			    "lm_head.decoder.weight",
			    "lm_head.decoder.bias",
			    "entity_predictions.decoder.weight",
			}:
						raise ValueError(F"""Unexpected missing_keys: {missing_keys}"""      )

			model.tie_weights()
			assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
			assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

			# Check outputs
			__lowerCAmelCase :    Any				=			MLukeTokenizer.from_pretrained(__snake_case   ,task="entity_classification"      )

			__lowerCAmelCase :    Union[str, Any]				=			"ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
			__lowerCAmelCase :    Optional[Any]				=			(0, 9)
			__lowerCAmelCase :    Any				=			tokenizer(__snake_case   ,entity_spans=[span]   ,return_tensors="pt"      )

			__lowerCAmelCase :    Tuple				=			model(**__snake_case      )

			# Verify word hidden states
			if model_size == "large":
						raise NotImplementedError
			else:  # base
						__lowerCAmelCase :    Dict				=			torch.Size((1, 33, 768)      )
						__lowerCAmelCase :    str				=			torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]]      )

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

			# Verify entity hidden states
			if model_size == "large":
						raise NotImplementedError
			else:  # base
						__lowerCAmelCase :    Dict				=			torch.Size((1, 1, 768)      )
						__lowerCAmelCase :    Tuple				=			torch.tensor([[-0.1482, 0.0609, 0.0322]]      )

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

			# Verify masked word/entity prediction
			__lowerCAmelCase :    int				=			MLukeTokenizer.from_pretrained(__snake_case      )
			__lowerCAmelCase :    List[str]				=			"Tokyo is the capital of <mask>."
			__lowerCAmelCase :    List[Any]				=			(24, 30)
			__lowerCAmelCase :    Tuple				=			tokenizer(__snake_case   ,entity_spans=[span]   ,return_tensors="pt"      )

			__lowerCAmelCase :    Any				=			model(**__snake_case      )

			__lowerCAmelCase :    List[Any]				=			encoding["input_ids"][0].tolist()
			__lowerCAmelCase :    List[str]				=			input_ids.index(tokenizer.convert_tokens_to_ids("<mask>"      )      )
			__lowerCAmelCase :    Optional[int]				=			outputs.logits[0][mask_position_id].argmax(dim=-1      )
			assert "Japan" == tokenizer.decode(__snake_case      )

			__lowerCAmelCase :    int				=			outputs.entity_logits[0][0].argmax().item()
			__lowerCAmelCase :    int				=			[
			    entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
			]
			assert [e for e in multilingual_predicted_entities if e.startswith("en:"      )][0] == "en:Japan"

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


def      _lowercase							(    __snake_case      )		->  List[str]:
			__lowerCAmelCase :    Optional[int]				=			["[MASK]", "[PAD]", "[UNK]"]

			__lowerCAmelCase :    List[Any]				=			[json.loads(__snake_case      ) for line in open(__snake_case      )]

			__lowerCAmelCase :    Dict				=			{}
			for entry in data:
						__lowerCAmelCase :    List[Any]				=			entry["id"]
						for entity_name, language in entry["entities"]:
									if entity_name in SPECIAL_TOKENS:
												__lowerCAmelCase :    List[str]				=			entity_id
												break
									__lowerCAmelCase :    Tuple				=			F"""{language}:{entity_name}"""
									__lowerCAmelCase :    Union[str, Any]				=			entity_id
			return new_mapping


if __name__ == "__main__":
	__snake_case				:  Any =       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.'
	)
	__snake_case				:  List[Any] =       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"""






from math import factorial



class      A__  :




	'''simple docstring'''





	def __init__(	self:						str			,     _SCREAMING_SNAKE_CASE:						Tuple			,     _SCREAMING_SNAKE_CASE:						Any)							->			str:

				"""simple docstring"""

				__lowerCAmelCase :    str				=			real
				if isinstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase :    Optional[int]				=			[1] * rank
				else:
							__lowerCAmelCase :    Optional[Any]				=			rank

	def __repr__(	self:						Optional[int])							->			List[str]:

				"""simple docstring"""

				return (
				    F"""{self.real}+"""
				    F"""{'+'.join(str(_SCREAMING_SNAKE_CASE)+'E'+str(n+1)for n,dual in enumerate(self.duals))}"""
				)

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[str])							->			Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			self.duals.copy()
				while cur[-1] == 0:
							cur.pop(-1)
				return Dual(self.real			,     _SCREAMING_SNAKE_CASE)

	def __add__(	self:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						Tuple)							->			Union[str, Any]:

				"""simple docstring"""

				if not isinstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE):
							return Dual(self.real + other			,     self.duals)
				__lowerCAmelCase :    int				=			self.duals.copy()
				__lowerCAmelCase :    List[Any]				=			other.duals.copy()
				if len(_SCREAMING_SNAKE_CASE) > len(_SCREAMING_SNAKE_CASE):
							o_dual.extend([1] * (len(_SCREAMING_SNAKE_CASE) - len(_SCREAMING_SNAKE_CASE)))
				elif len(_SCREAMING_SNAKE_CASE) < len(_SCREAMING_SNAKE_CASE):
							s_dual.extend([1] * (len(_SCREAMING_SNAKE_CASE) - len(_SCREAMING_SNAKE_CASE)))
				__lowerCAmelCase :    Tuple				=			[]
				for i in range(len(_SCREAMING_SNAKE_CASE)):
							new_duals.append(s_dual[i] + o_dual[i])
				return Dual(self.real + other.real			,     _SCREAMING_SNAKE_CASE)

	SCREAMING_SNAKE_CASE        =      __add__

	def __sub__(	self:						Dict			,     _SCREAMING_SNAKE_CASE:						str)							->			Optional[Any]:

				"""simple docstring"""

				return self + other * -1

	def __mul__(	self:						str			,     _SCREAMING_SNAKE_CASE:						Tuple)							->			Optional[int]:

				"""simple docstring"""

				if not isinstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase :    List[Any]				=			[]
							for i in self.duals:
										new_duals.append(i * other)
							return Dual(self.real * other			,     _SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    List[str]				=			[0] * (len(self.duals) + len(other.duals) + 1)
				for i, item in enumerate(self.duals):
							for j, jtem in enumerate(other.duals):
										new_duals[i + j + 1] += item * jtem
				for k in range(len(self.duals)):
							new_duals[k] += self.duals[k] * other.real
				for index in range(len(other.duals)):
							new_duals[index] += other.duals[index] * self.real
				return Dual(self.real * other.real			,     _SCREAMING_SNAKE_CASE)

	SCREAMING_SNAKE_CASE        =      __mul__

	def __truediv__(	self:						Any			,     _SCREAMING_SNAKE_CASE:						str)							->			Dict:

				"""simple docstring"""

				if not isinstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase :    List[str]				=			[]
							for i in self.duals:
										new_duals.append(i / other)
							return Dual(self.real / other			,     _SCREAMING_SNAKE_CASE)
				raise ValueError

	def __floordiv__(	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						Tuple)							->			str:

				"""simple docstring"""

				if not isinstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase :    str				=			[]
							for i in self.duals:
										new_duals.append(i // other)
							return Dual(self.real // other			,     _SCREAMING_SNAKE_CASE)
				raise ValueError







	def __pow__(	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						Tuple)							->			List[Any]:

				"""simple docstring"""

				if n < 0 or isinstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE):
							raise ValueError("power must be a positive integer")
				if n == 0:
							return 1
				if n == 1:
							return self
				__lowerCAmelCase :    List[Any]				=			self
				for _ in range(n - 1):
							x *= self
				return x






def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case      )		->  Any:
			if not callable(__snake_case      ):
						raise ValueError("differentiate() requires a function as input for func"      )
			if not isinstance(__snake_case   ,(float, int)      ):
						raise ValueError("differentiate() requires a float as input for position"      )
			if not isinstance(__snake_case   ,__snake_case      ):
						raise ValueError("differentiate() requires an int as input for order"      )
			__lowerCAmelCase :    Any				=			Dual(__snake_case   ,1      )
			__lowerCAmelCase :    Tuple				=			func(__snake_case      )
			if order == 0:
						return result.real
			return result.duals[order - 1] * factorial(__snake_case      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()


	def      _lowercase							(    __snake_case      )		->  Tuple:
				return y**2 * y**4





	print(differentiate(f, 9, 2))