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 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case						):
					# Load configuration defined in the metadata file
					with open(__snake_case						) as metadata_file:
										_UpperCamelCase			     =     json.load(__snake_case						)
					_UpperCamelCase			     =     LukeConfig(use_entity_aware_attention=__snake_case  ,     **metadata['''model_config''']						)

					# Load in the weights from the checkpoint_path
					_UpperCamelCase			     =     torch.load(__snake_case  ,     map_location='''cpu'''						)['''module''']

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

					_UpperCamelCase			     =     XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']						)

					# Add special tokens to the token vocabulary for downstream tasks
					_UpperCamelCase			     =     AddedToken('''<ent>'''  ,     lstrip=__snake_case  ,     rstrip=__snake_case						)
					_UpperCamelCase			     =     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:
										_UpperCamelCase			     =     json.load(__snake_case						)
					_UpperCamelCase			     =     '''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						)

					_UpperCamelCase			     =     MLukeTokenizer.from_pretrained(__snake_case						)

					# Initialize the embeddings of the special tokens
					_UpperCamelCase			     =     tokenizer.convert_tokens_to_ids(['''@''']						)[0]
					_UpperCamelCase			     =     tokenizer.convert_tokens_to_ids(['''#''']						)[0]

					_UpperCamelCase			     =     state_dict['''embeddings.word_embeddings.weight''']
					_UpperCamelCase			     =     word_emb[ent_init_index].unsqueeze(0						)
					_UpperCamelCase			     =     word_emb[enta_init_index].unsqueeze(0						)
					_UpperCamelCase			     =     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"]:
										_UpperCamelCase			     =     state_dict[bias_name]
										_UpperCamelCase			     =     decoder_bias[ent_init_index].unsqueeze(0						)
										_UpperCamelCase			     =     decoder_bias[enta_init_index].unsqueeze(0						)
										_UpperCamelCase			     =     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"]:
															_UpperCamelCase			     =     f"""encoder.layer.{layer_index}.attention.self."""
															_UpperCamelCase			     =     state_dict[prefix + matrix_name]
															_UpperCamelCase			     =     state_dict[prefix + matrix_name]
															_UpperCamelCase			     =     state_dict[prefix + matrix_name]

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

					_UpperCamelCase			     =     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'''						)
					_UpperCamelCase			     =     OrderedDict()
					for key, value in state_dict.items():
										if not (key.startswith('''lm_head'''						) or key.startswith('''entity_predictions'''						)):
															_UpperCamelCase			     =     state_dict[key]
										else:
															_UpperCamelCase			     =     state_dict[key]

					_UpperCamelCase     ,    _UpperCamelCase			     =     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
					_UpperCamelCase			     =     MLukeTokenizer.from_pretrained(__snake_case  ,     task='''entity_classification'''						)

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

					_UpperCamelCase			     =     model(**__snake_case						)

					# Verify word hidden states
					if model_size == "large":
										raise NotImplementedError
					else:  # base
										_UpperCamelCase			     =     torch.Size((1, 33, 768)						)
										_UpperCamelCase			     =     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
										_UpperCamelCase			     =     torch.Size((1, 1, 768)						)
										_UpperCamelCase			     =     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
					_UpperCamelCase			     =     MLukeTokenizer.from_pretrained(__snake_case						)
					_UpperCamelCase			     =     '''Tokyo is the capital of <mask>.'''
					_UpperCamelCase			     =     (24, 30)
					_UpperCamelCase			     =     tokenizer(__snake_case  ,     entity_spans=[span]  ,     return_tensors='''pt'''						)

					_UpperCamelCase			     =     model(**__snake_case						)

					_UpperCamelCase			     =     encoding['''input_ids'''][0].tolist()
					_UpperCamelCase			     =     input_ids.index(tokenizer.convert_tokens_to_ids('''<mask>'''						)						)
					_UpperCamelCase			     =     outputs.logits[0][mask_position_id].argmax(dim=-1						)
					assert "Japan" == tokenizer.decode(__snake_case						)

					_UpperCamelCase			     =     outputs.entity_logits[0][0].argmax().item()
					_UpperCamelCase			     =     [
					    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 					_snake_case	(  __snake_case						):
					_UpperCamelCase			     =     ['''[MASK]''', '''[PAD]''', '''[UNK]''']

					_UpperCamelCase			     =     [json.loads(__snake_case						) for line in open(__snake_case						)]

					_UpperCamelCase			     =     {}
					for entry in data:
										_UpperCamelCase			     =     entry['''id''']
										for entity_name, language in entry["entities"]:
															if entity_name in SPECIAL_TOKENS:
																				_UpperCamelCase			     =     entity_id
																				break
															_UpperCamelCase			     =     f"""{language}:{entity_name}"""
															_UpperCamelCase			     =     entity_id
					return new_mapping


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"""
from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


UpperCAmelCase				=						"""\
@misc{wu2016googles,
      title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
              and Jeffrey Dean},
      year={2016},
      eprint={1609.08144},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
"""

UpperCAmelCase				=						"""\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the 'GLEU score'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score's range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
"""

UpperCAmelCase				=						"""\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.

Args:
    predictions (list of str): list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references (list of list of str): list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.

Returns:
    'google_bleu': google_bleu score

Examples:
    Example 1:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results[\"google_bleu\"], 2))
        0.44

    Example 2:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results[\"google_bleu\"], 2))
        0.61

    Example 3:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
        >>> print(round(results[\"google_bleu\"], 2))
        0.53

    Example 4:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
        >>> print(round(results[\"google_bleu\"], 2))
        0.4
"""




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION				)
class 	lowercase__				(					datasets.Metric				):





   def 		UpperCamelCase_  (							self)  ->  MetricInfo:
      return datasets.MetricInfo(
          description=_DESCRIPTION      ,   citation=_CITATION      ,   inputs_description=_KWARGS_DESCRIPTION      ,   features=datasets.Features(
              {
                  """predictions""": datasets.Sequence(datasets.Value("""string"""      ,   id="""token""")      ,   id="""sequence"""),
                  """references""": datasets.Sequence(
                      datasets.Sequence(datasets.Value("""string"""      ,   id="""token""")      ,   id="""sequence""")      ,   id="""references"""),
              })      ,   )





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = 1      ,   SCREAMING_SNAKE_CASE = 4      ,   )  ->  Dict[str, float]:
      return {
          "google_bleu": gleu_score.corpus_gleu(
              list_of_references=SCREAMING_SNAKE_CASE      ,   hypotheses=SCREAMING_SNAKE_CASE      ,   min_len=SCREAMING_SNAKE_CASE      ,   max_len=SCREAMING_SNAKE_CASE)
      }

