





import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union

import numpy as np
import pyarrow as pa

import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
    is_remote_filesystem,
    rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int


__A							:			List[str]         =			datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
 import pyspark







@dataclass
class      lowerCamelCase(						datasets.BuilderConfig	):
     '''simple docstring'''


     __magic_name__   =			None





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     )							-> Tuple:




    """simple docstring"""




    import pyspark

    def generate_fn():
        _A     =							df.select('*'							,     pyspark.sql.functions.spark_partition_id().alias('part_id'				)				)
        for partition_id in partition_order:
            _A     =							df_with_partition_id.select('*'				).where(F"part_id = {partition_id}"				).drop('part_id'				)
            _A     =							partition_df.collect()
            _A     =							0
            for row in rows:
                yield F"{partition_id}_{row_id}", row.asDict()
                row_id += 1

    return generate_fn







class      lowerCamelCase(						_BaseExamplesIterable	):
     '''simple docstring'''


     def __init__(    self				,     snake_case_				,     snake_case_=None				,     ):
         _A     =							df
         _A     =							partition_order or range(self.df.rdd.getNumPartitions()							)
         _A     =							_generate_iterable_examples(self.df				,     self.partition_order							)




     def __iter__(    self							):
         yield from self.generate_examples_fn()




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         _A     =							list(range(self.df.rdd.getNumPartitions()							)							)
         generator.shuffle(snake_case_							)
         return SparkExamplesIterable(self.df				,     partition_order=snake_case_							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_							):
         _A     =							self.split_shard_indices_by_worker(snake_case_				,     snake_case_							)
         return SparkExamplesIterable(self.df				,     partition_order=snake_case_							)




     @property
     def        lowerCAmelCase__		(    self							):
         return len(self.partition_order							)







class      lowerCamelCase(						datasets.DatasetBuilder	):
     '''simple docstring'''


     __magic_name__   =			SparkConfig




     def __init__(    self				,     snake_case_				,     snake_case_ = None				,     snake_case_ = None				,     **snake_case_				,     ):
         import pyspark

         _A     =							pyspark.sql.SparkSession.builder.getOrCreate()
         _A     =							df
         _A     =							working_dir

         super().__init__(
             cache_dir=snake_case_				,     config_name=str(self.df.semanticHash()							)				,     **snake_case_				,     )




     def        lowerCAmelCase__		(    self							):
         # Returns the path of the created file.
         def create_cache_and_write_probe(snake_case_							):
             # makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
             # already exist.
             os.makedirs(self._cache_dir				,     exist_ok=snake_case_							)
             _A     =							os.path.join(self._cache_dir				,     'fs_test' + uuid.uuida().hex							)
             # Opening the file in append mode will create a new file unless it already exists, in which case it will not
             # change the file contents.
             open(snake_case_				,     'a'							)
             return [probe_file]

         if self._spark.conf.get('spark.master'				,     ''							).startswith('local'							):
             return

         # If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
         # accessible to the driver.
         # TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
         if self._cache_dir:
             _A     =							(
                 self._spark.sparkContext.parallelize(range(1							)				,     1							).mapPartitions(snake_case_							).collect()
             )
             if os.path.isfile(probe[0]							):
                 return

         raise ValueError(
             'When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir'							)




     def        lowerCAmelCase__		(    self							):
         return datasets.DatasetInfo(features=self.config.features							)




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN							)]




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         import pyspark

         def get_arrow_batch_size(snake_case_							):
             for batch in it:
                 yield pa.RecordBatch.from_pydict({'batch_bytes': [batch.nbytes]}							)

         _A     =							self.df.count()
         _A     =							df_num_rows if df_num_rows <= 100 else 100
         # Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
         _A     =							(
             self.df.limit(snake_case_							)
             .repartition(1							)
             .mapInArrow(snake_case_				,     'batch_bytes: long'							)
             .agg(pyspark.sql.functions.sum('batch_bytes'							).alias('sample_bytes'							)							)
             .collect()[0]
             .sample_bytes
             / sample_num_rows
         )
         _A     =							approx_bytes_per_row * df_num_rows
         if approx_total_size > max_shard_size:
             # Make sure there is at least one row per partition.
             _A     =							min(snake_case_				,     int(approx_total_size / max_shard_size							)							)
             _A     =							self.df.repartition(snake_case_							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     ):
         import pyspark

         _A     =							ParquetWriter if file_format == 'parquet' else ArrowWriter
         _A     =							os.path.join(self._working_dir				,     os.path.basename(snake_case_							)							) if self._working_dir else fpath
         _A     =							file_format == 'parquet'

         # Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
         # pickling the SparkContext.
         _A     =							self.config.features
         _A     =							self._writer_batch_size
         _A     =							self._fs.storage_options

         def write_arrow(snake_case_							):
             # Within the same SparkContext, no two task attempts will share the same attempt ID.
             _A     =							pyspark.TaskContext().taskAttemptId()
             _A     =							next(snake_case_				,     snake_case_							)
             if first_batch is None:
                 # Some partitions might not receive any data.
                 return pa.RecordBatch.from_arrays(
                     [[task_id], [0], [0]]				,     names=['task_id', 'num_examples', 'num_bytes']				,     )
             _A     =							0
             _A     =							writer_class(
                 features=snake_case_				,     path=working_fpath.replace('SSSSS'				,     F"{shard_id:05d}"							).replace('TTTTT'				,     F"{task_id:05d}"							)				,     writer_batch_size=snake_case_				,     storage_options=snake_case_				,     embed_local_files=snake_case_				,     )
             _A     =							pa.Table.from_batches([first_batch]							)
             writer.write_table(snake_case_							)
             for batch in it:
                 if max_shard_size is not None and writer._num_bytes >= max_shard_size:
                     _A,			_A     =							writer.finalize()
                     writer.close()
                     yield pa.RecordBatch.from_arrays(
                         [[task_id], [num_examples], [num_bytes]]				,     names=['task_id', 'num_examples', 'num_bytes']				,     )
                     shard_id += 1
                     _A     =							writer_class(
                         features=writer._features				,     path=working_fpath.replace('SSSSS'				,     F"{shard_id:05d}"							).replace('TTTTT'				,     F"{task_id:05d}"							)				,     writer_batch_size=snake_case_				,     storage_options=snake_case_				,     embed_local_files=snake_case_				,     )
                 _A     =							pa.Table.from_batches([batch]							)
                 writer.write_table(snake_case_							)

             if writer._num_bytes > 0:
                 _A,			_A     =							writer.finalize()
                 writer.close()
                 yield pa.RecordBatch.from_arrays(
                     [[task_id], [num_examples], [num_bytes]]				,     names=['task_id', 'num_examples', 'num_bytes']				,     )

             if working_fpath != fpath:
                 for file in os.listdir(os.path.dirname(snake_case_							)							):
                     _A     =							os.path.join(os.path.dirname(snake_case_							)				,     os.path.basename(snake_case_							)							)
                     shutil.move(snake_case_				,     snake_case_							)

         _A     =							(
             self.df.mapInArrow(snake_case_				,     'task_id: long, num_examples: long, num_bytes: long'							)
             .groupBy('task_id'							)
             .agg(
                 pyspark.sql.functions.sum('num_examples'							).alias('total_num_examples'							)				,     pyspark.sql.functions.sum('num_bytes'							).alias('total_num_bytes'							)				,     pyspark.sql.functions.count('num_bytes'							).alias('num_shards'							)				,     pyspark.sql.functions.collect_list('num_examples'							).alias('shard_lengths'							)				,     )
             .collect()
         )
         for row in stats:
             yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = "arrow"				,     snake_case_ = None				,     snake_case_ = None				,     **snake_case_				,     ):
         self._validate_cache_dir()

         _A     =							convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE							)
         self._repartition_df_if_needed(snake_case_							)
         _A     =							not is_remote_filesystem(self._fs							)
         _A     =							os.path.join if is_local else posixpath.join

         _A     =							'-TTTTT-SSSSS-of-NNNNN'
         _A     =							F"{self.name}-{split_generator.name}{SUFFIX}.{file_format}"
         _A     =							path_join(self._output_dir				,     snake_case_							)

         _A     =							0
         _A     =							0
         _A     =							0
         _A     =							[]
         _A     =							[]

         for task_id, content in self._prepare_split_single(snake_case_				,     snake_case_				,     snake_case_							):
             (
                 (
                 _A
             ),			(
                 _A
             ),			(
                 _A
             ),			(
                 _A
             ),			
             )     =							content
             if num_bytes > 0:
                 total_num_examples += num_examples
                 total_num_bytes += num_bytes
                 total_shards += num_shards
                 task_id_and_num_shards.append((task_id, num_shards)							)
                 all_shard_lengths.extend(snake_case_							)

         _A     =							total_num_examples
         _A     =							total_num_bytes

         # should rename everything at the end
         logger.debug(F"Renaming {total_shards} shards."							)
         if total_shards > 1:
             _A     =							all_shard_lengths

             # Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
             # pickling error due to pickling the SparkContext.
             _A     =							self._fs

             # use the -SSSSS-of-NNNNN pattern
             def _rename_shard(
                 snake_case_				,     snake_case_				,     snake_case_				,     ):
                 rename(
                     snake_case_				,     fpath.replace('SSSSS'				,     F"{shard_id:05d}"							).replace('TTTTT'				,     F"{task_id:05d}"							)				,     fpath.replace('TTTTT-SSSSS'				,     F"{global_shard_id:05d}"							).replace('NNNNN'				,     F"{total_shards:05d}"							)				,     )

             _A     =							[]
             _A     =							0
             for i in range(len(snake_case_							)							):
                 _A,			_A     =							task_id_and_num_shards[i]
                 for shard_id in range(snake_case_							):
                     args.append([task_id, shard_id, global_shard_id]							)
                     global_shard_id += 1
             self._spark.sparkContext.parallelize(snake_case_				,     len(snake_case_							)							).map(lambda snake_case_							: _rename_shard(*snake_case_							)							).collect()
         else:
             # don't use any pattern
             _A     =							0
             _A     =							task_id_and_num_shards[0][0]
             self._rename(
                 fpath.replace('SSSSS'				,     F"{shard_id:05d}"							).replace('TTTTT'				,     F"{task_id:05d}"							)				,     fpath.replace(snake_case_				,     ''							)				,     )




     def        lowerCAmelCase__		(    self				,     snake_case_				,     ):
         return SparkExamplesIterable(self.df							)













import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


lowercase    				=   logging.get_logger(__name__)




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					['''input_features''', '''is_longer''']




	def __init__(			self			,     a=64			,     a=4_80_00			,     a=4_80			,     a=10			,     a=10_24			,     a=0.0			,     a=False			,     a = 0			,     a = 1_40_00			,     a = None			,     a = "fusion"			,     a = "repeatpad"			,     **a			,     )			->       List[str]:
								super().__init__(
								    feature_size=a			,     sampling_rate=a			,     padding_value=a			,     return_attention_mask=a			,     **a			,     )
								snake_case_           =				top_db
								snake_case_           =				truncation
								snake_case_           =				padding
								snake_case_           =				fft_window_size
								snake_case_           =				(fft_window_size >> 1) + 1
								snake_case_           =				hop_length
								snake_case_           =				max_length_s
								snake_case_           =				max_length_s * sampling_rate
								snake_case_           =				sampling_rate
								snake_case_           =				frequency_min
								snake_case_           =				frequency_max
								snake_case_           =				mel_filter_bank(
								    num_frequency_bins=self.nb_frequency_bins			,     num_mel_filters=a			,     min_frequency=a			,     max_frequency=a			,     sampling_rate=a			,     norm=a			,     mel_scale='htk'			,     )
								snake_case_           =				mel_filter_bank(
								    num_frequency_bins=self.nb_frequency_bins			,     num_mel_filters=a			,     min_frequency=a			,     max_frequency=a			,     sampling_rate=a			,     norm='slaney'			,     mel_scale='slaney'			,     )




	def 				_UpperCamelCase     (			self   )			->       Dict[str, Any]:
								snake_case_           =				copy.deepcopy(self.__dict__   )
								snake_case_           =				self.__class__.__name__
								if "mel_filters" in output:
															del output["mel_filters"]
								if "mel_filters_slaney" in output:
															del output["mel_filters_slaney"]
								return output




	def 				_UpperCamelCase     (			self			,     a			,     a = None   )			->       np.ndarray:
								snake_case_           =				spectrogram(
								    a			,     window_function(self.fft_window_size			,     'hann'   )			,     frame_length=self.fft_window_size			,     hop_length=self.hop_length			,     power=2.0			,     mel_filters=a			,     log_mel='dB'			,     )
								return log_mel_spectrogram.T




	def 				_UpperCamelCase     (			self			,     a			,     a			,     a   )			->       Tuple:
								snake_case_           =				np.array_split(list(range(0			,     total_frames - chunk_frames + 1   )   )			,     3   )
								if len(ranges[1]   ) == 0:
															# if the audio is too short, we just use the first chunk
															snake_case_           =				[0]
								if len(ranges[2]   ) == 0:
															# if the audio is too short, we just use the first chunk
															snake_case_           =				[0]
								# randomly choose index for each part
								snake_case_           =				np.random.choice(ranges[0]   )
								snake_case_           =				np.random.choice(ranges[1]   )
								snake_case_           =				np.random.choice(ranges[2]   )

								snake_case_           =				mel[idx_front : idx_front + chunk_frames, :]
								snake_case_           =				mel[idx_middle : idx_middle + chunk_frames, :]
								snake_case_           =				mel[idx_back : idx_back + chunk_frames, :]

								snake_case_           =				torch.tensor(mel[None, None, :]   )
								snake_case_           =				torch.nn.functional.interpolate(
								    a			,     size=[chunk_frames, 64]			,     mode='bilinear'			,     align_corners=a   )
								snake_case_           =				mel_shrink[0][0].numpy()
								snake_case_           =				np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]			,     axis=0   )
								return mel_fusion




	def 				_UpperCamelCase     (			self			,     a			,     a			,     a			,     a   )			->       np.array:
								if waveform.shape[0] > max_length:
															if truncation == "rand_trunc":
																						snake_case_           =				True
																						# random crop to max_length (for compatibility) -> this should be handled by self.pad
																						snake_case_           =				len(a   ) - max_length
																						snake_case_           =				np.random.randint(0			,     overflow + 1   )
																						snake_case_           =				waveform[idx : idx + max_length]
																						snake_case_           =				self._np_extract_fbank_features(a			,     self.mel_filters_slaney   )[None, :]
															elif truncation == "fusion":
																						snake_case_           =				self._np_extract_fbank_features(a			,     self.mel_filters   )
																						snake_case_           =				max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																						snake_case_           =				mel.shape[0]
																						if chunk_frames == total_frames:
																													# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
																													# In this case, we just use the whole audio.
																													snake_case_           =				np.stack([mel, mel, mel, mel]			,     axis=0   )
																													snake_case_           =				False
																						else:
																													snake_case_           =				self._random_mel_fusion(a			,     a			,     a   )
																													snake_case_           =				True
															else:
																						raise NotImplementedError(F'''data_truncating {truncation} not implemented'''   )

								else:
															snake_case_           =				False
															# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
															if waveform.shape[0] < max_length:
																						if padding == "repeat":
																													snake_case_           =				int(max_length / len(a   )   )
																													snake_case_           =				np.stack(np.tile(a			,     n_repeat + 1   )   )[:max_length]
																						if padding == "repeatpad":
																													snake_case_           =				int(max_length / len(a   )   )
																													snake_case_           =				np.stack(np.tile(a			,     a   )   )
																						snake_case_           =				np.pad(a			,     (0, max_length - waveform.shape[0])			,     mode='constant'			,     constant_values=0   )

															if truncation == "fusion":
																						snake_case_           =				self._np_extract_fbank_features(a			,     self.mel_filters   )
																						snake_case_           =				np.stack([input_mel, input_mel, input_mel, input_mel]			,     axis=0   )
															else:
																						snake_case_           =				self._np_extract_fbank_features(a			,     self.mel_filters_slaney   )[None, :]

								return input_mel, longer






	def __call__(			self			,     a			,     a = None			,     a = None			,     a = None			,     a = None			,     a = None			,     **a			,     )			->       BatchFeature:
								snake_case_           =				truncation if truncation is not None else self.truncation
								snake_case_           =				padding if padding else self.padding

								if sampling_rate is not None:
															if sampling_rate != self.sampling_rate:
																						raise ValueError(
																						    F'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
																						    F''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
																						    F''' was sampled with {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.'   )

								snake_case_           =				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}'''   )
								snake_case_           =				is_batched_numpy or (
								    isinstance(a			,     (list, tuple)   ) and (isinstance(raw_speech[0]			,     (np.ndarray, tuple, list)   ))
								)

								if is_batched:
															snake_case_           =				[np.asarray(a			,     dtype=np.floataa   ) for speech in raw_speech]
								elif not is_batched and not isinstance(a			,     np.ndarray   ):
															snake_case_           =				np.asarray(a			,     dtype=np.floataa   )
								elif isinstance(a			,     np.ndarray   ) and raw_speech.dtype is np.dtype(np.floataa   ):
															snake_case_           =				raw_speech.astype(np.floataa   )

								# always return batch
								if not is_batched:
															snake_case_           =				[np.asarray(a   )]

								# convert to mel spectrogram, truncate and pad if needed.
								snake_case_           =				[
								    self._get_input_mel(a			,     max_length if max_length else self.nb_max_samples			,     a			,     a   )
								    for waveform in raw_speech
								]

								snake_case_           =				[]
								snake_case_           =				[]
								for mel, longer in padded_inputs:
															input_mel.append(a   )
															is_longer.append(a   )

								if truncation == "fusion" and sum(a   ) == 0:
															# if no audio is longer than 10s, then randomly select one audio to be longer
															snake_case_           =				np.random.randint(0			,     len(a   )   )
															snake_case_           =				True

								if isinstance(input_mel[0]			,     a   ):
															snake_case_           =				[np.asarray(a			,     dtype=np.floataa   ) for feature in input_mel]

								# is_longer is a list of bool
								snake_case_           =				[[longer] for longer in is_longer]

								snake_case_           =				{'input_features': input_mel, 'is_longer': is_longer}
								snake_case_           =				BatchFeature(a   )

								if return_tensors is not None:
															snake_case_           =				input_features.convert_to_tensors(a   )

								return input_features





