


'''simple docstring'''
from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


UpperCamelCase__          = logging.get_logger(__name__)




class      lowerCamelCase_  (      __a ):
      lowerCAmelCase__							=	['input_features', 'attention_mask']






      def __init__(   self							:       Any		,		_A							:       Tuple=80		,		_A							:       Optional[int]=16_000		,		_A							:       Optional[Any]=80		,		_A							:       List[Any]=0.0		,		_A							:       List[Any]=True		,		_A							:       List[str]=True		,		_A							:       Any=True		,		**_A							:       Tuple		,		):


          '''simple docstring'''


          super().__init__(feature_size=_A		,		sampling_rate=_A		,		padding_value=_A		,		**_A					)
          UpperCAmelCase__     :						int						=							num_mel_bins
          UpperCAmelCase__     :						Optional[Any]						=							do_ceptral_normalize
          UpperCAmelCase__     :						List[str]						=							normalize_means
          UpperCAmelCase__     :						Optional[Any]						=							normalize_vars
          UpperCAmelCase__     :						int						=							True






      def lowercase_				(   self							:       int		,		_A							:       np.ndarray		,		):


          '''simple docstring'''


          UpperCAmelCase__     :						Optional[int]						=							waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
          UpperCAmelCase__     :						Optional[int]						=							torch.from_numpy(_A					).unsqueeze(0					)
          UpperCAmelCase__     :						Union[str, Any]						=							ta_kaldi.fbank(_A		,		num_mel_bins=self.num_mel_bins		,		sample_frequency=self.sampling_rate					)
          return features.numpy()






      @staticmethod
      def lowercase_				(   _A							:       np.ndarray		,		_A							:       int		,		_A							:       Optional[bool] = True		,		_A							:       Optional[bool] = True		,		_A							:       float = 0.0		,		):


          '''simple docstring'''


          if normalize_means:
              UpperCAmelCase__     :						Optional[Any]						=							x[:input_length].mean(axis=0					)
              UpperCAmelCase__     :						Dict						=							np.subtract(_A		,		_A					)
          if normalize_vars:
              UpperCAmelCase__     :						Union[str, Any]						=							x[:input_length].std(axis=0					)
              UpperCAmelCase__     :						str						=							np.divide(_A		,		_A					)

          if input_length < x.shape[0]:
              UpperCAmelCase__     :						str						=							padding_value

          # make sure array is in float32
          UpperCAmelCase__     :						int						=							x.astype(np.floataa					)

          return x






      def lowercase_				(   self							:       str		,		_A							:       List[np.ndarray]		,		_A							:       Optional[np.ndarray] = None					):


          '''simple docstring'''


          UpperCAmelCase__     :						List[Any]						=							attention_mask.sum(-1					) if attention_mask is not None else [x.shape[0] for x in input_features]
          return [
              self.utterance_cmvn(_A		,		_A		,		self.normalize_means		,		self.normalize_vars		,		self.padding_value					)
              for x, n in zip(_A		,		_A					)
          ]






      def __call__(   self							:       Optional[int]		,		_A							:       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]		,		_A							:       Union[bool, str, PaddingStrategy] = False		,		_A							:       Optional[int] = None		,		_A							:       bool = False		,		_A							:       Optional[int] = None		,		_A							:       Optional[Union[str, TensorType]] = None		,		_A							:       Optional[int] = None		,		_A							:       Optional[bool] = None		,		**_A							:       Any		,		):


          '''simple docstring'''


          if sampling_rate is not None:
              if sampling_rate != self.sampling_rate:
                  raise ValueError(
                      f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
                      f""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
                      f""" {self.sampling_rate} and not {sampling_rate}."""					)
          else:
              logger.warning(
                  '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
                  '''Failing to do so can result in silent errors that might be hard to debug.'''					)

          UpperCAmelCase__     :						Any						=							isinstance(_A		,		np.ndarray					) and len(raw_speech.shape					) > 1
          if is_batched_numpy and len(raw_speech.shape					) > 2:
              raise ValueError(f"""Only mono-channel audio is supported for input to {self}"""					)
          UpperCAmelCase__     :						Tuple						=							is_batched_numpy or (
              isinstance(_A		,		(list, tuple)					) and (isinstance(raw_speech[0]		,		(np.ndarray, tuple, list)					))
          )

          if is_batched:
              UpperCAmelCase__     :						Any						=							[np.asarray(_A		,		dtype=np.floataa					) for speech in raw_speech]
          elif not is_batched and not isinstance(_A		,		np.ndarray					):
              UpperCAmelCase__     :						Any						=							np.asarray(_A		,		dtype=np.floataa					)
          elif isinstance(_A		,		np.ndarray					) and raw_speech.dtype is np.dtype(np.floataa					):
              UpperCAmelCase__     :						List[str]						=							raw_speech.astype(np.floataa					)

          # always return batch
          if not is_batched:
              UpperCAmelCase__     :						Optional[Any]						=							[raw_speech]

          # extract fbank features
          UpperCAmelCase__     :						Optional[Any]						=							[self._extract_fbank_features(_A					) for waveform in raw_speech]

          # convert into correct format for padding
          UpperCAmelCase__     :						List[Any]						=							BatchFeature({'''input_features''': features}					)

          UpperCAmelCase__     :						int						=							self.pad(
              _A		,		padding=_A		,		max_length=_A		,		truncation=_A		,		pad_to_multiple_of=_A		,		return_attention_mask=_A		,		**_A		,		)

          # make sure list is in array format
          UpperCAmelCase__     :						List[Any]						=							padded_inputs.get('''input_features'''					)
          if isinstance(input_features[0]		,		_A					):
              UpperCAmelCase__     :						Optional[int]						=							[np.asarray(_A		,		dtype=np.floataa					) for feature in input_features]

          UpperCAmelCase__     :						List[Any]						=							padded_inputs.get('''attention_mask'''					)
          if attention_mask is not None:
              UpperCAmelCase__     :						Dict						=							[np.asarray(_A		,		dtype=np.intaa					) for array in attention_mask]

          # Utterance-level cepstral mean and variance normalization
          if self.do_ceptral_normalize:
              UpperCAmelCase__     :						List[str]						=							(
                  np.array(_A		,		dtype=np.intaa					)
                  if self._get_padding_strategies(_A		,		max_length=_A					) is not PaddingStrategy.DO_NOT_PAD
                  else None
              )
              UpperCAmelCase__     :						str						=							self.normalize(
                  padded_inputs['''input_features''']		,		attention_mask=_A					)

          if return_tensors is not None:
              UpperCAmelCase__     :						Optional[Any]						=							padded_inputs.convert_to_tensors(_A					)

          return padded_inputs



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   __UpperCAmelCase					(      __a :     Tuple						,__a :     Dict						,__a :     List[str]						,__a :     Optional[Any]						,__a :     Tuple      )     ->	Dict:

					"""simple docstring"""

					with open(__a      ) as metadata_file:
										_a      :       Optional[Any]   =						json.load(__a      )
					_a      :       List[Any]   =						LukeConfig(use_entity_aware_attention=__a						,**metadata['''model_config''']      )

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

					# Load the entity vocab file
					_a      :       Any   =						load_original_entity_vocab(__a      )
					# add an entry for [MASK2]
					_a      :       Union[str, Any]   =						max(entity_vocab.values()      ) + 1
					config.entity_vocab_size += 1

					_a      :       Dict   =						XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']      )

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

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

					_a      :       Optional[int]   =						MLukeTokenizer.from_pretrained(__a      )

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

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

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

					_a      :       Optional[int]   =						LukeForMaskedLM(config=__a      ).eval()

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

					_a ,   _a      :       int   =						model.load_state_dict(__a						,strict=__a      )

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

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

					_a      :       int   =						model(**__a      )

					# Verify word hidden states
					if model_size == "large":
										raise NotImplementedError
					else:  # base
										_a      :       List[str]   =						torch.Size((1, 33, 768)      )
										_a      :       Union[str, Any]   =						torch.tensor([[0.08_92, 0.05_96, -0.28_19], [0.01_34, 0.11_99, 0.05_73], [-0.01_69, 0.09_27, 0.06_44]]      )

					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":
										raise NotImplementedError
					else:  # base
										_a      :       str   =						torch.Size((1, 1, 768)      )
										_a      :       List[Any]   =						torch.tensor([[-0.14_82, 0.06_09, 0.03_22]]      )

					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

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

					_a      :       Optional[Any]   =						model(**__a      )

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

					_a      :       Any   =						outputs.entity_logits[0][0].argmax().item()
					_a      :       Optional[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(__a      )      )
					model.save_pretrained(__a      )



def   __UpperCAmelCase					(      __a :     List[Any]      )     ->	int:

					"""simple docstring"""

					_a      :       Union[str, Any]   =						['''[MASK]''', '''[PAD]''', '''[UNK]''']

					_a      :       int   =						[json.loads(__a      ) for line in open(__a      )]

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


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



