





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 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		# Load configuration defined in the metadata file
		with open(lowerCAmelCase__						) as metadata_file:
				lowerCamelCase_	:     Optional[Any] 						=      json.load(lowerCAmelCase__						)
		lowerCamelCase_	:     List[Any] 						=      LukeConfig(use_entity_aware_attention=lowerCAmelCase__							,**metadata['model_config']						)

		# Load in the weights from the checkpoint_path
		lowerCamelCase_	:     Optional[Any] 						=      torch.load(lowerCAmelCase__							,map_location='cpu'						)['module']

		# Load the entity vocab file
		lowerCamelCase_	:     Optional[Any] 						=      load_original_entity_vocab(lowerCAmelCase__						)
		# add an entry for [MASK2]
		lowerCamelCase_	:     List[Any] 						=      max(entity_vocab.values()						) + 1
		config.entity_vocab_size += 1

		lowerCamelCase_	:     Optional[int] 						=      XLMRobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']						)

		# Add special tokens to the token vocabulary for downstream tasks
		lowerCamelCase_	:     Union[str, Any] 						=      AddedToken('<ent>'							,lstrip=lowerCAmelCase__							,rstrip=lowerCAmelCase__						)
		lowerCamelCase_	:     Tuple 						=      AddedToken('<ent2>'							,lstrip=lowerCAmelCase__							,rstrip=lowerCAmelCase__						)
		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(lowerCAmelCase__						)
		with open(os.path.join(lowerCAmelCase__							,'tokenizer_config.json'						)							,'r'						) as f:
				lowerCamelCase_	:     Dict 						=      json.load(lowerCAmelCase__						)
		lowerCamelCase_	:     str 						=      'MLukeTokenizer'
		with open(os.path.join(lowerCAmelCase__							,'tokenizer_config.json'						)							,'w'						) as f:
				json.dump(lowerCAmelCase__							,lowerCAmelCase__						)

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

		lowerCamelCase_	:     Tuple 						=      MLukeTokenizer.from_pretrained(lowerCAmelCase__						)

		# Initialize the embeddings of the special tokens
		lowerCamelCase_	:     int 						=      tokenizer.convert_tokens_to_ids(['@']						)[0]
		lowerCamelCase_	:     Any 						=      tokenizer.convert_tokens_to_ids(['#']						)[0]

		lowerCamelCase_	:     int 						=      state_dict['embeddings.word_embeddings.weight']
		lowerCamelCase_	:     str 						=      word_emb[ent_init_index].unsqueeze(0						)
		lowerCamelCase_	:     Any 						=      word_emb[enta_init_index].unsqueeze(0						)
		lowerCamelCase_	:     Any 						=      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_	:     Any 						=      state_dict[bias_name]
				lowerCamelCase_	:     Optional[Any] 						=      decoder_bias[ent_init_index].unsqueeze(0						)
				lowerCamelCase_	:     List[Any] 						=      decoder_bias[enta_init_index].unsqueeze(0						)
				lowerCamelCase_	:     str 						=      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_	:     Union[str, Any] 						=      F"encoder.layer.{layer_index}.attention.self."
						lowerCamelCase_	:     Any 						=      state_dict[prefix + matrix_name]
						lowerCamelCase_	:     int 						=      state_dict[prefix + matrix_name]
						lowerCamelCase_	:     Union[str, Any] 						=      state_dict[prefix + matrix_name]

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

		lowerCamelCase_	:     Dict 						=      LukeForMaskedLM(config=lowerCAmelCase__						).eval()

		state_dict.pop('entity_predictions.decoder.weight'						)
		state_dict.pop('lm_head.decoder.weight'						)
		state_dict.pop('lm_head.decoder.bias'						)
		lowerCamelCase_	:     List[str] 						=      OrderedDict()
		for key, value in state_dict.items():
				if not (key.startswith('lm_head'						) or key.startswith('entity_predictions'						)):
						lowerCamelCase_	:     int 						=      state_dict[key]
				else:
						lowerCamelCase_	:     Optional[Any] 						=      state_dict[key]

		lowerCamelCase_						,      lowerCamelCase_	:     str 						=      model.load_state_dict(lowerCAmelCase__							,strict=lowerCAmelCase__						)

		if set(lowerCAmelCase__						) != {"luke.embeddings.position_ids"}:
				raise ValueError(F"Unexpected unexpected_keys: {unexpected_keys}"						)
		if set(lowerCAmelCase__						) != {
		    "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_	:     Dict 						=      MLukeTokenizer.from_pretrained(lowerCAmelCase__							,task='entity_classification'						)

		lowerCamelCase_	:     Tuple 						=      'ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'
		lowerCamelCase_	:     int 						=      (0, 9)
		lowerCamelCase_	:     Optional[Any] 						=      tokenizer(lowerCAmelCase__							,entity_spans=[span]							,return_tensors='pt'						)

		lowerCamelCase_	:     Dict 						=      model(**lowerCAmelCase__						)

		# Verify word hidden states
		if model_size == "large":
				raise NotImplementedError
		else:  # base
				lowerCamelCase_	:     Dict 						=      torch.Size((1, 33, 7_68)						)
				lowerCamelCase_	:     int 						=      torch.tensor([[0.0_892, 0.0_596, -0.2_819], [0.0_134, 0.1_199, 0.0_573], [-0.0_169, 0.0_927, 0.0_644]]						)

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

		# Verify entity hidden states
		if model_size == "large":
				raise NotImplementedError
		else:  # base
				lowerCamelCase_	:     List[str] 						=      torch.Size((1, 1, 7_68)						)
				lowerCamelCase_	:     str 						=      torch.tensor([[-0.1_482, 0.0_609, 0.0_322]]						)

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

		# Verify masked word/entity prediction
		lowerCamelCase_	:     List[Any] 						=      MLukeTokenizer.from_pretrained(lowerCAmelCase__						)
		lowerCamelCase_	:     List[Any] 						=      'Tokyo is the capital of <mask>.'
		lowerCamelCase_	:     Optional[Any] 						=      (24, 30)
		lowerCamelCase_	:     List[str] 						=      tokenizer(lowerCAmelCase__							,entity_spans=[span]							,return_tensors='pt'						)

		lowerCamelCase_	:     Optional[int] 						=      model(**lowerCAmelCase__						)

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

		lowerCamelCase_	:     str 						=      outputs.entity_logits[0][0].argmax().item()
		lowerCamelCase_	:     List[Any] 						=      [
		    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(lowerCAmelCase__						)						)
		model.save_pretrained(lowerCAmelCase__						)


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):
		lowerCamelCase_	:     Dict 						=      ['[MASK]', '[PAD]', '[UNK]']

		lowerCamelCase_	:     Dict 						=      [json.loads(lowerCAmelCase__						) for line in open(lowerCAmelCase__						)]

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


if __name__ == "__main__":
		_lowercase		:  List[str]			       =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		:  Tuple			       =parser.parse_args()
		convert_luke_checkpoint(
		    args.checkpoint_path,
		    args.metadata_path,
		    args.entity_vocab_path,
		    args.pytorch_dump_folder_path,
		    args.model_size,
		)







def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):
		lowerCamelCase_	:     Union[str, Any] 						=      []
		lowerCamelCase_	:     Tuple 						=      []
		lowerCamelCase_	:     Dict 						=      {
		    '^': 3,
		    '*': 2,
		    '/': 2,
		    '%': 2,
		    '+': 1,
		    '-': 1,
		}  # Priority of each operator
		lowerCamelCase_	:     Optional[int] 						=      len(lowerCAmelCase__						) if (len(lowerCAmelCase__						) > 7) else 7

		# Print table header for output
		print(
		    'Symbol'.center(8						)							,'Stack'.center(lowerCAmelCase__						)							,'Postfix'.center(lowerCAmelCase__						)							,sep=' | '							,)
		print('-' * (print_width * 3 + 7)						)

		for x in infix:
				if x.isalpha() or x.isdigit():
						post_fix.append(lowerCAmelCase__						)  # if x is Alphabet / Digit, add it to Postfix
				elif x == "(":
						stack.append(lowerCAmelCase__						)  # if x is "(" push to Stack
				elif x == ")":  # if x is ")" pop stack until "(" is encountered
						while stack[-1] != "(":
								post_fix.append(stack.pop()						)  # Pop stack & add the content to Postfix
						stack.pop()
				else:
						if len(lowerCAmelCase__						) == 0:
								stack.append(lowerCAmelCase__						)  # If stack is empty, push x to stack
						else:  # while priority of x is not > priority of element in the stack
								while len(lowerCAmelCase__						) > 0 and priority[x] <= priority[stack[-1]]:
										post_fix.append(stack.pop()						)  # pop stack & add to Postfix
								stack.append(lowerCAmelCase__						)  # push x to stack

				print(
				    x.center(8						)							,(''.join(lowerCAmelCase__						)).ljust(lowerCAmelCase__						)							,(''.join(lowerCAmelCase__						)).ljust(lowerCAmelCase__						)							,sep=' | '							,)  # Output in tabular format

		while len(lowerCAmelCase__						) > 0:  # while stack is not empty
				post_fix.append(stack.pop()						)  # pop stack & add to Postfix
				print(
				    ' '.center(8						)							,(''.join(lowerCAmelCase__						)).ljust(lowerCAmelCase__						)							,(''.join(lowerCAmelCase__						)).ljust(lowerCAmelCase__						)							,sep=' | '							,)  # Output in tabular format

		return "".join(lowerCAmelCase__						)  # return Postfix as str


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):
		lowerCamelCase_	:     Dict 						=      list(infix[::-1]						)  # reverse the infix equation

		for i in range(len(lowerCAmelCase__						)						):
				if infix[i] == "(":
						lowerCamelCase_	:     str 						=      ')'  # change "(" to ")"
				elif infix[i] == ")":
						lowerCamelCase_	:     Optional[Any] 						=      '('  # change ")" to "("

		return (infix_2_postfix(''.join(lowerCAmelCase__						)						))[
		    ::-1
		]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
		_lowercase		:  int			       =input("""\nEnter an Infix Equation = """)  # Input an Infix equation
		_lowercase		:  Optional[Any]			       ="""""".join(Infix.split())  # Remove spaces from the input
		print("""\n\t""", Infix, """(Infix) -> """, infix_2_prefix(Infix), """(Prefix)""")
