


"""simple docstring"""



import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
  from transformers import SpeechaTextFeatureExtractor

__snake_case             =   random.Random()







def   __lowerCAmelCase						(						lowercase  :  Dict						,							lowercase  :  int=1.0						,							lowercase  :  Dict=None						,							lowercase  :  Any=None			)     ->						Union[str, Any]:


    """simple docstring"""





    if rng is None:
        snake_case   :				Optional[Any]				 =   global_rng

    snake_case   :				str				 =   []
    for batch_idx in range(shape[0]			):
        values.append([]			)
        for _ in range(shape[1]			):
            values[-1].append(rng.random() * scale			)

    return values







@require_torch
@require_torchaudio
class _lowerCAmelCase    (				unittest.TestCase     ):







   def __init__(     self						,     UpperCamelCase__						,     UpperCamelCase__=7						,     UpperCamelCase__=400						,     UpperCamelCase__=2000						,     UpperCamelCase__=24						,     UpperCamelCase__=24						,     UpperCamelCase__=0.0						,     UpperCamelCase__=1_6000						,     UpperCamelCase__=True						,     UpperCamelCase__=True						,     )     ->		str:

       '''simple docstring'''




       snake_case   :				Dict				 =   parent
       snake_case   :				Optional[Any]				 =   batch_size
       snake_case   :				Optional[Any]				 =   min_seq_length
       snake_case   :				Tuple				 =   max_seq_length
       snake_case   :				Optional[Any]				 =   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       snake_case   :				str				 =   feature_size
       snake_case   :				int				 =   num_mel_bins
       snake_case   :				str				 =   padding_value
       snake_case   :				Dict				 =   sampling_rate
       snake_case   :				Tuple				 =   return_attention_mask
       snake_case   :				str				 =   do_normalize







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

       '''simple docstring'''




       return {
           "feature_size": self.feature_size,
           "num_mel_bins": self.num_mel_bins,
           "padding_value": self.padding_value,
           "sampling_rate": self.sampling_rate,
           "return_attention_mask": self.return_attention_mask,
           "do_normalize": self.do_normalize,
       }







   def 			lowerCamelCase		(     self						,     UpperCamelCase__=False						,     UpperCamelCase__=False						)     ->		Any:

       '''simple docstring'''




       def _flatten(UpperCamelCase__						):
           return list(itertools.chain(*UpperCamelCase__						)						)

       if equal_length:
           snake_case   :				List[str]				 =   [floats_list((self.max_seq_length, self.feature_size)						) for _ in range(self.batch_size						)]
       else:
           # make sure that inputs increase in size
           snake_case   :				List[str]				 =   [
               floats_list((x, self.feature_size)						)
               for x in range(self.min_seq_length						,     self.max_seq_length						,     self.seq_length_diff						)
           ]
       if numpify:
           snake_case   :				str				 =   [np.asarray(UpperCamelCase__						) for x in speech_inputs]
       return speech_inputs







@require_torch
@require_torchaudio
class _lowerCAmelCase    (				snake_case_   ,							unittest.TestCase     ):
   __UpperCAmelCase			:	Optional[int]							  =  SpeechaTextFeatureExtractor if is_speech_available() else None







   def 			lowerCamelCase		(     self						)     ->		Optional[Any]:

       '''simple docstring'''




       snake_case   :				Optional[int]				 =   SpeechaTextFeatureExtractionTester(self						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		Optional[Any]:

       '''simple docstring'''




       self.assertTrue(np.all(np.mean(UpperCamelCase__						,     axis=0						) < 1e-3						)						)
       self.assertTrue(np.all(np.abs(np.var(UpperCamelCase__						,     axis=0						) - 1						) < 1e-3						)						)







   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       snake_case   :				List[Any]				 =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
       # create three inputs of length 800, 1000, and 1200
       snake_case   :				Union[str, Any]				 =   [floats_list((1, x)						)[0] for x in range(800						,     1400						,     200						)]
       snake_case   :				List[Any]				 =   [np.asarray(UpperCamelCase__						) for speech_input in speech_inputs]

       # Test feature size
       snake_case   :				Union[str, Any]				 =   feature_extractor(UpperCamelCase__						,     padding=UpperCamelCase__						,     return_tensors="np"						).input_features
       self.assertTrue(input_features.ndim == 3						)
       self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size						)

       # Test not batched input
       snake_case   :				Any				 =   feature_extractor(speech_inputs[0]						,     return_tensors="np"						).input_features
       snake_case   :				Dict				 =   feature_extractor(np_speech_inputs[0]						,     return_tensors="np"						).input_features
       self.assertTrue(np.allclose(UpperCamelCase__						,     UpperCamelCase__						,     atol=1e-3						)						)

       # Test batched
       snake_case   :				Tuple				 =   feature_extractor(UpperCamelCase__						,     return_tensors="np"						).input_features
       snake_case   :				Any				 =   feature_extractor(UpperCamelCase__						,     return_tensors="np"						).input_features
       for enc_seq_a, enc_seq_a in zip(UpperCamelCase__						,     UpperCamelCase__						):
           self.assertTrue(np.allclose(UpperCamelCase__						,     UpperCamelCase__						,     atol=1e-3						)						)

       # Test 2-D numpy arrays are batched.
       snake_case   :				List[Any]				 =   [floats_list((1, x)						)[0] for x in (800, 800, 800)]
       snake_case   :				int				 =   np.asarray(UpperCamelCase__						)
       snake_case   :				Dict				 =   feature_extractor(UpperCamelCase__						,     return_tensors="np"						).input_features
       snake_case   :				str				 =   feature_extractor(UpperCamelCase__						,     return_tensors="np"						).input_features
       for enc_seq_a, enc_seq_a in zip(UpperCamelCase__						,     UpperCamelCase__						):
           self.assertTrue(np.allclose(UpperCamelCase__						,     UpperCamelCase__						,     atol=1e-3						)						)







   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       snake_case   :				int				 =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
       snake_case   :				Tuple				 =   [floats_list((1, x)						)[0] for x in range(800						,     1400						,     200						)]

       snake_case   :				int				 =   ["longest", "max_length", "do_not_pad"]
       snake_case   :				Union[str, Any]				 =   [None, 16, None]
       for max_length, padding in zip(UpperCamelCase__						,     UpperCamelCase__						):
           snake_case   :				str				 =   feature_extractor(
               UpperCamelCase__						,     padding=UpperCamelCase__						,     max_length=UpperCamelCase__						,     return_attention_mask=UpperCamelCase__						)
           snake_case   :				List[str]				 =   inputs.input_features
           snake_case   :				Any				 =   inputs.attention_mask
           snake_case   :				List[Any]				 =   [np.sum(UpperCamelCase__						) for x in attention_mask]

           self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]						)
           self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]						)
           self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]						)







   def 			lowerCamelCase		(     self						)     ->		Tuple:

       '''simple docstring'''




       snake_case   :				str				 =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
       snake_case   :				List[str]				 =   [floats_list((1, x)						)[0] for x in range(800						,     1400						,     200						)]

       snake_case   :				Optional[int]				 =   ["longest", "max_length", "do_not_pad"]
       snake_case   :				Dict				 =   [None, 16, None]
       for max_length, padding in zip(UpperCamelCase__						,     UpperCamelCase__						):
           snake_case   :				int				 =   feature_extractor(
               UpperCamelCase__						,     max_length=UpperCamelCase__						,     padding=UpperCamelCase__						,     return_tensors="np"						,     return_attention_mask=UpperCamelCase__						)
           snake_case   :				str				 =   inputs.input_features
           snake_case   :				Tuple				 =   inputs.attention_mask
           snake_case   :				str				 =   [np.sum(UpperCamelCase__						) for x in attention_mask]

           self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]						)
           self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6						)
           self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]						)
           self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6						)
           self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]						)







   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       snake_case   :				Optional[Any]				 =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
       snake_case   :				List[str]				 =   [floats_list((1, x)						)[0] for x in range(800						,     1400						,     200						)]
       snake_case   :				Union[str, Any]				 =   feature_extractor(
           UpperCamelCase__						,     padding="max_length"						,     max_length=4						,     truncation=UpperCamelCase__						,     return_tensors="np"						,     return_attention_mask=UpperCamelCase__						,     )
       snake_case   :				str				 =   inputs.input_features
       snake_case   :				Optional[Any]				 =   inputs.attention_mask
       snake_case   :				str				 =   np.sum(attention_mask == 1						,     axis=1						)

       self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]						)
       self._check_zero_mean_unit_variance(input_features[1]						)
       self._check_zero_mean_unit_variance(input_features[2]						)







   def 			lowerCamelCase		(     self						)     ->		int:

       '''simple docstring'''




       snake_case   :				Union[str, Any]				 =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
       snake_case   :				int				 =   [floats_list((1, x)						)[0] for x in range(800						,     1400						,     200						)]
       snake_case   :				Tuple				 =   feature_extractor(
           UpperCamelCase__						,     padding="longest"						,     max_length=4						,     truncation=UpperCamelCase__						,     return_tensors="np"						,     return_attention_mask=UpperCamelCase__						,     )
       snake_case   :				int				 =   inputs.input_features
       snake_case   :				str				 =   inputs.attention_mask
       snake_case   :				Union[str, Any]				 =   np.sum(attention_mask == 1						,     axis=1						)

       self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]						)
       self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]						)
       self._check_zero_mean_unit_variance(input_features[2]						)

       # make sure that if max_length < longest -> then pad to max_length
       self.assertEqual(input_features.shape						,     (3, 4, 24)						)

       snake_case   :				Dict				 =   [floats_list((1, x)						)[0] for x in range(800						,     1400						,     200						)]
       snake_case   :				Optional[Any]				 =   feature_extractor(
           UpperCamelCase__						,     padding="longest"						,     max_length=16						,     truncation=UpperCamelCase__						,     return_tensors="np"						,     return_attention_mask=UpperCamelCase__						,     )
       snake_case   :				Any				 =   inputs.input_features
       snake_case   :				List[str]				 =   inputs.attention_mask
       snake_case   :				int				 =   np.sum(attention_mask == 1						,     axis=1						)

       self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]						)
       self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]						)
       self._check_zero_mean_unit_variance(input_features[2]						)

       # make sure that if max_length < longest -> then pad to max_length
       self.assertEqual(input_features.shape						,     (3, 6, 24)						)







   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       import torch

       snake_case   :				Any				 =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
       snake_case   :				Optional[Any]				 =   np.random.rand(100						,     32						).astype(np.floataa						)
       snake_case   :				Dict				 =   np_speech_inputs.tolist()

       for inputs in [py_speech_inputs, np_speech_inputs]:
           snake_case   :				Optional[int]				 =   feature_extractor.pad([{"input_features": inputs}]						,     return_tensors="np"						)
           self.assertTrue(np_processed.input_features.dtype == np.floataa						)
           snake_case   :				int				 =   feature_extractor.pad([{"input_features": inputs}]						,     return_tensors="pt"						)
           self.assertTrue(pt_processed.input_features.dtype == torch.floataa						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		str:

       '''simple docstring'''




       from datasets import load_dataset

       snake_case   :				Any				 =   load_dataset("hf-internal-testing/librispeech_asr_dummy"						,     "clean"						,     split="validation"						)
       # automatic decoding with librispeech
       snake_case   :				Optional[int]				 =   ds.sort("id"						).select(range(UpperCamelCase__						)						)[:num_samples]["audio"]

       return [x["array"] for x in speech_samples]







   def 			lowerCamelCase		(     self						)     ->		int:

       '''simple docstring'''




       snake_case   :				Tuple				 =   np.array([
           -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
           -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
           -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
       ]						)
       # fmt: on

       snake_case   :				List[Any]				 =   self._load_datasamples(1						)
       snake_case   :				Any				 =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
       snake_case   :				List[Any]				 =   feature_extractor(UpperCamelCase__						,     return_tensors="pt"						).input_features
       self.assertEquals(input_features.shape						,     (1, 584, 24)						)
       self.assertTrue(np.allclose(input_features[0, 0, :30]						,     UpperCamelCase__						,     atol=1e-4						)						)









"""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   __lowerCAmelCase						(						lowercase  :  List[str]						,							lowercase  :  int						,							lowercase  :  Dict						,							lowercase  :  Dict						,							lowercase  :  int			)     ->						int:


    """simple docstring"""





    with open(lowercase			) as metadata_file:
        snake_case   :				str				 =   json.load(lowercase			)
    snake_case   :				Optional[Any]				 =   LukeConfig(use_entity_aware_attention=lowercase						,							**metadata["model_config"]			)

    # Load in the weights from the checkpoint_path
    snake_case   :				Tuple				 =   torch.load(lowercase						,							map_location="cpu"			)["module"]

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

    snake_case   :				Union[str, Any]				 =   XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]			)

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

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

    snake_case   :				Dict				 =   MLukeTokenizer.from_pretrained(lowercase			)

    # Initialize the embeddings of the special tokens
    snake_case   :				Tuple				 =   tokenizer.convert_tokens_to_ids(["@"]			)[0]
    snake_case   :				str				 =   tokenizer.convert_tokens_to_ids(["#"]			)[0]

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

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

    snake_case   :				Union[str, Any]				 =   LukeForMaskedLM(config=lowercase			).eval()

    state_dict.pop("entity_predictions.decoder.weight"			)
    state_dict.pop("lm_head.decoder.weight"			)
    state_dict.pop("lm_head.decoder.bias"			)
    snake_case   :				Tuple				 =   OrderedDict()
    for key, value in state_dict.items():
        if not (key.startswith("lm_head"			) or key.startswith("entity_predictions"			)):
            snake_case   :				Any				 =   state_dict[key]
        else:
            snake_case   :				Tuple				 =   state_dict[key]

    snake_case ,snake_case   :				Optional[Any]				 =   model.load_state_dict(lowercase						,							strict=lowercase			)

    if set(lowercase			) != {"luke.embeddings.position_ids"}:
        raise ValueError(F'Unexpected unexpected_keys: {unexpected_keys}'			)
    if set(lowercase			) != {
        "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
    snake_case   :				Optional[Any]				 =   MLukeTokenizer.from_pretrained(lowercase						,							task="entity_classification"			)

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

    snake_case   :				int				 =   model(**lowercase			)

    # Verify word hidden states
    if model_size == "large":
        raise NotImplementedError
    else:  # base
        snake_case   :				int				 =   torch.Size((1, 33, 768)			)
        snake_case   :				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]						,							lowercase						,							atol=1e-4			):
        raise ValueError

    # Verify entity hidden states
    if model_size == "large":
        raise NotImplementedError
    else:  # base
        snake_case   :				Any				 =   torch.Size((1, 1, 768)			)
        snake_case   :				List[Any]				 =   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]						,							lowercase						,							atol=1e-4			):
        raise ValueError

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

    snake_case   :				Any				 =   model(**lowercase			)

    snake_case   :				int				 =   encoding["input_ids"][0].tolist()
    snake_case   :				str				 =   input_ids.index(tokenizer.convert_tokens_to_ids("<mask>"			)			)
    snake_case   :				Tuple				 =   outputs.logits[0][mask_position_id].argmax(dim=-1			)
    assert "Japan" == tokenizer.decode(lowercase			)

    snake_case   :				Tuple				 =   outputs.entity_logits[0][0].argmax().item()
    snake_case   :				Optional[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(lowercase			)			)
    model.save_pretrained(lowercase			)







def   __lowerCAmelCase						(						lowercase  :  Union[str, Any]			)     ->						Dict:


    """simple docstring"""





    snake_case   :				Tuple				 =   ["[MASK]", "[PAD]", "[UNK]"]

    snake_case   :				Optional[Any]				 =   [json.loads(lowercase			) for line in open(lowercase			)]

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


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





