
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     __UpperCamelCase :






     """simple docstring"""







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



     def __call__(				self							:							int ):







       """simple docstring"""




       return self.pa_type



     def  UpperCAmelCase__							(				self							:							str     ,						_A							:							Union[str, bytes, dict] ):







       """simple docstring"""




       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
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		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!)
             __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		np.frombuffer(value['''bytes''']     ,						dtype=np.intaa ).astype(np.floataa ) / 3_2767
           else:
             __SCREAMING_SNAKE_CASE      :	Optional[int]					=		np.memmap(value['''path''']     ,						dtype='''h'''     ,						mode='''r''' ).astype(np.floataa ) / 3_2767

           __SCREAMING_SNAKE_CASE      :	str					=		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  UpperCAmelCase__							(				self							:							List[Any]     ,						_A							:							dict     ,						_A							:							Optional[Dict[str, Union[str, bool, None]]] = None ):







       """simple docstring"""




       if not self.decode:
         raise RuntimeError('''Decoding is disabled for this feature. Please use Audio(decode=True) instead.''' )

       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Optional[Any]					=		(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

       __SCREAMING_SNAKE_CASE      :	List[Any]					=		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:
         __SCREAMING_SNAKE_CASE      :	str					=		token_per_repo_id or {}
         __SCREAMING_SNAKE_CASE      :	int					=		path.split('''::''' )[-1]
         try:
           __SCREAMING_SNAKE_CASE      :	int					=		string_to_dict(_A     ,						config.HUB_DATASETS_URL )['''repo_id''']
           __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		token_per_repo_id[repo_id]
         except (ValueError, KeyError):
           __SCREAMING_SNAKE_CASE      :	str					=		None

         with xopen(_A     ,						'''rb'''     ,						use_auth_token=_A ) as f:
           __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		sf.read(_A )

       else:
         __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	int					=		sf.read(_A )

       __SCREAMING_SNAKE_CASE      :	Tuple					=		array.T
       if self.mono:
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		librosa.to_mono(_A )
       if self.sampling_rate and self.sampling_rate != sampling_rate:
         __SCREAMING_SNAKE_CASE      :	Any					=		librosa.resample(_A     ,						orig_sr=_A     ,						target_sr=self.sampling_rate )
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		self.sampling_rate

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



     def  UpperCAmelCase__							(				self							:							Any ):







       """simple docstring"""




       from .features import Value

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



     def  UpperCAmelCase__							(				self							:							Any     ,						_A							:							Union[pa.StringArray, pa.StructArray] ):







       """simple docstring"""




       if pa.types.is_string(storage.type ):
         __SCREAMING_SNAKE_CASE      :	str					=		pa.array([None] * len(_A )     ,						type=pa.binary() )
         __SCREAMING_SNAKE_CASE      :	int					=		pa.StructArray.from_arrays([bytes_array, storage]     ,						['''bytes''', '''path''']     ,						mask=storage.is_null() )
       elif pa.types.is_binary(storage.type ):
         __SCREAMING_SNAKE_CASE      :	Dict					=		pa.array([None] * len(_A )     ,						type=pa.string() )
         __SCREAMING_SNAKE_CASE      :	List[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''' ):
         __SCREAMING_SNAKE_CASE      :	Optional[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:
           __SCREAMING_SNAKE_CASE      :	List[str]					=		storage.field('''bytes''' )
         else:
           __SCREAMING_SNAKE_CASE      :	Optional[int]					=		pa.array([None] * len(_A )     ,						type=pa.binary() )
         if storage.type.get_field_index('''path''' ) >= 0:
           __SCREAMING_SNAKE_CASE      :	Tuple					=		storage.field('''path''' )
         else:
           __SCREAMING_SNAKE_CASE      :	int					=		pa.array([None] * len(_A )     ,						type=pa.string() )
         __SCREAMING_SNAKE_CASE      :	List[str]					=		pa.StructArray.from_arrays([bytes_array, path_array]     ,						['''bytes''', '''path''']     ,						mask=storage.is_null() )
       return array_cast(_A     ,						self.pa_type )




     def  UpperCAmelCase__							(				self							:							Union[str, Any]     ,						_A							:							pa.StructArray ):







       """simple docstring"""




       @no_op_if_value_is_null
       def path_to_bytes(_A							:							Tuple ):
         with xopen(_A     ,						'''rb''' ) as f:
           __SCREAMING_SNAKE_CASE      :	List[Any]					=		f.read()
         return bytes_

       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		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()     ,						)
       __SCREAMING_SNAKE_CASE      :	Tuple					=		pa.array(
           [os.path.basename(_A ) if path is not None else None for path in storage.field('''path''' ).to_pylist()]     ,						type=pa.string()     ,						)
       __SCREAMING_SNAKE_CASE      :	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 os
from collections import deque

import torch
from torch.utils.data import Dataset


class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		def __init__(							self    ,a_=""    ,a_="train"						):







						"""simple docstring"""
						assert os.path.isdir(a_						)

						lowerCAmelCase__		  =  []
						lowerCAmelCase__		  =  os.listdir(a_						)
						for story_filename in story_filenames_list:
										if "summary" in story_filename:
														continue
										lowerCAmelCase__		  =  os.path.join(a_    ,a_						)
										if not os.path.isfile(a_						):
														continue
										self.documents.append(a_						)
		def __len__(							self						):







						"""simple docstring"""
						return len(self.documents						)



		def __getitem__(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.documents[idx]
						lowerCAmelCase__		  =  document_path.split('/'						)[-1]
						with open(a_    ,encoding='utf-8'						) as source:
										lowerCAmelCase__		  =  source.read()
										lowerCAmelCase__  ,   lowerCAmelCase__		  =  process_story(a_						)
						return document_name, story_lines, summary_lines







def     UpperCAmelCase_		(					snake_case__					)	->     str:



				"""simple docstring"""





				lowerCAmelCase__		  =  list(filter(lambda snake_case__					: len(snake_case__					) != 0    ,			[line.strip() for line in raw_story.split('\n'					)]					)					)

				# for some unknown reason some lines miss a period, add it
				lowerCAmelCase__		  =  [_add_missing_period(snake_case__					) for line in nonempty_lines]

				# gather article lines
				lowerCAmelCase__		  =  []
				lowerCAmelCase__		  =  deque(snake_case__					)
				while True:
								try:
												lowerCAmelCase__		  =  lines.popleft()
												if element.startswith('@highlight'					):
																break
												story_lines.append(snake_case__					)
								except IndexError:
												# if "@highlight" is absent from the file we pop
												# all elements until there is None, raising an exception.
												return story_lines, []

    # gather summary lines
				lowerCAmelCase__		  =  list(filter(lambda snake_case__					: not t.startswith('@highlight'					)    ,			snake_case__					)					)

				return story_lines, summary_lines







def     UpperCAmelCase_		(					snake_case__					)	->     Optional[Any]:



				"""simple docstring"""





				lowerCAmelCase__		  =  ['.', '!', '?', '...', '\'', '`', '"', '\u2019', '\u2019', ')']
				if line.startswith('@highlight'					):
								return line
				if line[-1] in END_TOKENS:
								return line
				return line + "."







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     str:



				"""simple docstring"""





				if len(snake_case__					) > block_size:
								return sequence[:block_size]
				else:
								sequence.extend([pad_token_id] * (block_size - len(snake_case__					))					)
								return sequence







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Optional[int]:



				"""simple docstring"""





				lowerCAmelCase__		  =  torch.ones_like(snake_case__					)
				lowerCAmelCase__		  =  sequence == pad_token_id
				lowerCAmelCase__		  =  0
				return mask







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     Dict:



				"""simple docstring"""





				lowerCAmelCase__		  =  [tokenizer.encode(snake_case__					) for line in story_lines]
				lowerCAmelCase__		  =  [token for sentence in story_lines_token_ids for token in sentence]
				lowerCAmelCase__		  =  [tokenizer.encode(snake_case__					) for line in summary_lines]
				lowerCAmelCase__		  =  [token for sentence in summary_lines_token_ids for token in sentence]

				return story_token_ids, summary_token_ids







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Union[str, Any]:



				"""simple docstring"""





				lowerCAmelCase__		  =  []
				for sequence in batch:
								lowerCAmelCase__		  =  -1
								lowerCAmelCase__		  =  []
								for s in sequence:
												if s == separator_token_id:
																sentence_num += 1
												embeddings.append(sentence_num % 2					)
								batch_embeddings.append(snake_case__					)
				return torch.tensor(snake_case__					)
