


import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
       from .features import FeatureType


lowercase_   ,lowercase_   ,lowercase_	   =     False, False, False


@dataclass
class 	A_ :




      '''simple docstring'''


      __snake_case 	=       None
      __snake_case 	=       True
      __snake_case 	=       True
      __snake_case 	=       None
      # Automatically constructed
      __snake_case 	=       "dict"
      __snake_case 	=       pa.struct({"""bytes""": pa.binary(), """path""": pa.string()}   )
      __snake_case 	=       field(default="""Audio""" ,				init=__UpperCamelCase ,				repr=__UpperCamelCase   )




      def __call__(    self:	int    ):
       return self.pa_type




      def 					_snake_case     (    self:	Tuple						,				a:	Union[str, bytes, dict]    ):
       try:
        import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
       except ImportError as err:
        raise ImportError('To support encoding audio data, please install \'soundfile\'.'    ) from err
       if isinstance(a						,				a    ):
        return {"bytes": None, "path": value}
       elif isinstance(a						,				a    ):
        return {"bytes": value, "path": None}
       elif "array" in value:
        # convert the audio array to wav bytes
        __lowerCamelCase		:							List[str]		 =						BytesIO()
        sf.write(a						,				value['array']						,				value['sampling_rate']						,				format='wav'    )
        return {"bytes": buffer.getvalue(), "path": None}
       elif value.get('path'    ) is not None and os.path.isfile(value['path']    ):
        # we set "bytes": None to not duplicate the data if they're already available locally
        if value["path"].endswith('pcm'    ):
         # "PCM" only has raw audio bytes
         if value.get('sampling_rate'    ) is None:
          # At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
          raise KeyError('To use PCM files, please specify a \'sampling_rate\' in Audio object'    )
         if value.get('bytes'    ):
          # If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
          __lowerCamelCase		:							int		 =						np.frombuffer(value['bytes']						,				dtype=np.intaa    ).astype(np.floataa    ) / 3_2767
         else:
          __lowerCamelCase		:							Dict		 =						np.memmap(value['path']						,				dtype='h'						,				mode='r'    ).astype(np.floataa    ) / 3_2767

         __lowerCamelCase		:							Dict		 =						BytesIO(bytes()    )
         sf.write(a						,				a						,				value['sampling_rate']						,				format='wav'    )
         return {"bytes": buffer.getvalue(), "path": None}
        else:
         return {"bytes": None, "path": value.get('path'    )}
       elif value.get('bytes'    ) is not None or value.get('path'    ) is not None:
        # store the audio bytes, and path is used to infer the audio format using the file extension
        return {"bytes": value.get('bytes'    ), "path": value.get('path'    )}
       else:
        raise ValueError(
            F'An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.'    )




      def 					_snake_case     (    self:	Tuple						,				a:	dict						,				a:	Optional[Dict[str, Union[str, bool, None]]] = None    ):
       if not self.decode:
        raise RuntimeError('Decoding is disabled for this feature. Please use Audio(decode=True) instead.'    )

       __lowerCamelCase ,					__lowerCamelCase		:							Optional[int]		 =						(value['path'], BytesIO(value['bytes']    )) if value['bytes'] is not None else (value['path'], None)
       if path is None and file is None:
        raise ValueError(F'An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.'    )

       try:
        import librosa
        import soundfile as sf
       except ImportError as err:
        raise ImportError('To support decoding audio files, please install \'librosa\' and \'soundfile\'.'    ) from err

       __lowerCamelCase		:							Dict		 =						xsplitext(a    )[1][1:].lower() if path is not None else None
       if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
        raise RuntimeError(
            'Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, '
            'You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. '    )
       elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
        raise RuntimeError(
            'Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, '
            'You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. '    )

       if file is None:
        __lowerCamelCase		:							Union[str, Any]		 =						token_per_repo_id or {}
        __lowerCamelCase		:							str		 =						path.split('::'    )[-1]
        try:
         __lowerCamelCase		:							Optional[Any]		 =						string_to_dict(a						,				config.HUB_DATASETS_URL    )['repo_id']
         __lowerCamelCase		:							List[str]		 =						token_per_repo_id[repo_id]
        except (ValueError, KeyError):
         __lowerCamelCase		:							Optional[int]		 =						None

        with xopen(a						,				'rb'						,				use_auth_token=a    ) as f:
         __lowerCamelCase ,					__lowerCamelCase		:							Optional[int]		 =						sf.read(a    )

       else:
        __lowerCamelCase ,					__lowerCamelCase		:							str		 =						sf.read(a    )

       __lowerCamelCase		:							Any		 =						array.T
       if self.mono:
        __lowerCamelCase		:							List[str]		 =						librosa.to_mono(a    )
       if self.sampling_rate and self.sampling_rate != sampling_rate:
        __lowerCamelCase		:							List[Any]		 =						librosa.resample(a						,				orig_sr=a						,				target_sr=self.sampling_rate    )
        __lowerCamelCase		:							Optional[Any]		 =						self.sampling_rate

       return {"path": path, "array": array, "sampling_rate": sampling_rate}




      def 					_snake_case     (    self:	Tuple    ):
       from .features import Value

       if self.decode:
        raise ValueError('Cannot flatten a decoded Audio feature.'    )
       return {
           "bytes": Value('binary'    ),
           "path": Value('string'    ),
       }




      def 					_snake_case     (    self:	Optional[Any]						,				a:	Union[pa.StringArray, pa.StructArray]    ):
       if pa.types.is_string(storage.type    ):
        __lowerCamelCase		:							Dict		 =						pa.array([None] * len(a    )						,				type=pa.binary()    )
        __lowerCamelCase		:							List[str]		 =						pa.StructArray.from_arrays([bytes_array, storage]						,				['bytes', 'path']						,				mask=storage.is_null()    )
       elif pa.types.is_binary(storage.type    ):
        __lowerCamelCase		:							str		 =						pa.array([None] * len(a    )						,				type=pa.string()    )
        __lowerCamelCase		:							Any		 =						pa.StructArray.from_arrays([storage, path_array]						,				['bytes', 'path']						,				mask=storage.is_null()    )
       elif pa.types.is_struct(storage.type    ) and storage.type.get_all_field_indices('array'    ):
        __lowerCamelCase		:							Union[str, Any]		 =						pa.array([Audio().encode_example(a    ) if x is not None else None for x in storage.to_pylist()]    )
       elif pa.types.is_struct(storage.type    ):
        if storage.type.get_field_index('bytes'    ) >= 0:
         __lowerCamelCase		:							Optional[Any]		 =						storage.field('bytes'    )
        else:
         __lowerCamelCase		:							List[str]		 =						pa.array([None] * len(a    )						,				type=pa.binary()    )
        if storage.type.get_field_index('path'    ) >= 0:
         __lowerCamelCase		:							List[str]		 =						storage.field('path'    )
        else:
         __lowerCamelCase		:							Optional[Any]		 =						pa.array([None] * len(a    )						,				type=pa.string()    )
        __lowerCamelCase		:							Optional[Any]		 =						pa.StructArray.from_arrays([bytes_array, path_array]						,				['bytes', 'path']						,				mask=storage.is_null()    )
       return array_cast(a						,				self.pa_type    )




      def 					_snake_case     (    self:	Optional[Any]						,				a:	pa.StructArray    ):

       @no_op_if_value_is_null
       def path_to_bytes(a:	Any    ):
        with xopen(a						,				'rb'    ) as f:
         __lowerCamelCase		:							List[str]		 =						f.read()
        return bytes_

       __lowerCamelCase		:							Dict		 =						pa.array(
           [
               (path_to_bytes(x['path']    ) if x['bytes'] is None else x['bytes']) if x is not None else None
               for x in storage.to_pylist()
           ]						,				type=pa.binary()						,				)
       __lowerCamelCase		:							List[Any]		 =						pa.array(
           [os.path.basename(a    ) if path is not None else None for path in storage.field('path'    ).to_pylist()]						,				type=pa.string()						,				)
       __lowerCamelCase		:							Optional[Any]		 =						pa.StructArray.from_arrays([bytes_array, path_array]						,				['bytes', 'path']						,				mask=bytes_array.is_null()    )
       return array_cast(a						,				self.pa_type    )





import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 # Load configuration defined in the metadata file
 with open(SCREAMING_SNAKE_CASE__						) as metadata_file:
  __lowerCamelCase		:							List[str]		 =						json.load(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							int		 =						LukeConfig(use_entity_aware_attention=SCREAMING_SNAKE_CASE__      ,	**metadata['model_config']						)

 # Load in the weights from the checkpoint_path
 __lowerCamelCase		:							Union[str, Any]		 =						torch.load(SCREAMING_SNAKE_CASE__      ,	map_location='cpu'						)

 # Load the entity vocab file
 __lowerCamelCase		:							List[Any]		 =						load_entity_vocab(SCREAMING_SNAKE_CASE__						)

 __lowerCamelCase		:							Optional[int]		 =						RobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']						)

 # Add special tokens to the token vocabulary for downstream tasks
 __lowerCamelCase		:							str		 =						AddedToken('<ent>'      ,	lstrip=SCREAMING_SNAKE_CASE__      ,	rstrip=SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							Dict		 =						AddedToken('<ent2>'      ,	lstrip=SCREAMING_SNAKE_CASE__      ,	rstrip=SCREAMING_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(SCREAMING_SNAKE_CASE__						)
 with open(os.path.join(SCREAMING_SNAKE_CASE__      ,	LukeTokenizer.vocab_files_names['entity_vocab_file']						)      ,	'w'						) as f:
  json.dump(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)

 __lowerCamelCase		:							str		 =						LukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__						)

 # Initialize the embeddings of the special tokens
 __lowerCamelCase		:							Union[str, Any]		 =						state_dict['embeddings.word_embeddings.weight']
 __lowerCamelCase		:							Tuple		 =						word_emb[tokenizer.convert_tokens_to_ids(['@']						)[0]].unsqueeze(0						)
 __lowerCamelCase		:							Any		 =						word_emb[tokenizer.convert_tokens_to_ids(['#']						)[0]].unsqueeze(0						)
 __lowerCamelCase		:							List[str]		 =						torch.cat([word_emb, ent_emb, enta_emb]						)

 # 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		:							Dict		 =						state_dict[prefix + matrix_name]
   __lowerCamelCase		:							List[Any]		 =						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[int]		 =						state_dict['entity_embeddings.entity_embeddings.weight']
 __lowerCamelCase		:							Union[str, Any]		 =						entity_emb[entity_vocab['[MASK]']]

 __lowerCamelCase		:							Optional[Any]		 =						LukeModel(config=SCREAMING_SNAKE_CASE__						).eval()

 __lowerCamelCase ,					__lowerCamelCase		:							List[Any]		 =						model.load_state_dict(SCREAMING_SNAKE_CASE__      ,	strict=SCREAMING_SNAKE_CASE__						)
 if not (len(SCREAMING_SNAKE_CASE__						) == 1 and missing_keys[0] == "embeddings.position_ids"):
  raise ValueError(f'Missing keys {", ".join(SCREAMING_SNAKE_CASE__						)}. Expected only missing embeddings.position_ids'						)
 if not (all(key.startswith('entity_predictions'						) or key.startswith('lm_head'						) for key in unexpected_keys						)):
  raise ValueError(
      'Unexpected keys'
      f' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions"						) or key.startswith("lm_head"						))]						)}'						)

 # Check outputs
 __lowerCamelCase		:							Optional[Any]		 =						LukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__      ,	task='entity_classification'						)

 __lowerCamelCase		:							Dict		 =						(
     'Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the'
     ' new world number one avoid a humiliating second- round exit at Wimbledon .'
 )
 __lowerCamelCase		:							Union[str, Any]		 =						(39, 42)
 __lowerCamelCase		:							Optional[Any]		 =						tokenizer(SCREAMING_SNAKE_CASE__      ,	entity_spans=[span]      ,	add_prefix_space=SCREAMING_SNAKE_CASE__      ,	return_tensors='pt'						)

 __lowerCamelCase		:							List[str]		 =						model(**SCREAMING_SNAKE_CASE__						)

 # Verify word hidden states
 if model_size == "large":
  __lowerCamelCase		:							Dict		 =						torch.Size((1, 42, 1_024)						)
  __lowerCamelCase		:							int		 =						torch.tensor(
      [[0.0_133, 0.0_865, 0.0_095], [0.3_093, -0.2_576, -0.7_418], [-0.1_720, -0.2_117, -0.2_869]]						)
 else:  # base
  __lowerCamelCase		:							Union[str, Any]		 =						torch.Size((1, 42, 768)						)
  __lowerCamelCase		:							Tuple		 =						torch.tensor([[0.0_037, 0.1_368, -0.0_091], [0.1_099, 0.3_329, -0.1_095], [0.0_765, 0.5_335, 0.1_179]]						)

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

 # Verify entity hidden states
 if model_size == "large":
  __lowerCamelCase		:							Union[str, Any]		 =						torch.Size((1, 1, 1_024)						)
  __lowerCamelCase		:							Dict		 =						torch.tensor([[0.0_466, -0.0_106, -0.0_179]]						)
 else:  # base
  __lowerCamelCase		:							int		 =						torch.Size((1, 1, 768)						)
  __lowerCamelCase		:							Dict		 =						torch.tensor([[0.1_457, 0.1_044, 0.0_174]]						)

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

 # Finally, save our PyTorch model and tokenizer
 print('Saving PyTorch model to {}'.format(SCREAMING_SNAKE_CASE__						)						)
 model.save_pretrained(SCREAMING_SNAKE_CASE__						)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Tuple		 =						{}
 with open(SCREAMING_SNAKE_CASE__      ,	'r'      ,	encoding='utf-8'						) as f:
  for index, line in enumerate(SCREAMING_SNAKE_CASE__						):
   __lowerCamelCase ,					__lowerCamelCase		:							List[Any]		 =						line.rstrip().split('\t'						)
   __lowerCamelCase		:							Any		 =						index

 return entity_vocab


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

