
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


_lowerCAmelCase						:   Any		=   datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
  import pyspark
@dataclass
class 			_UpperCamelCase      (				datasets.BuilderConfig		):
      UpperCAmelCase_        =		None



def 						lowerCAmelCase		(		_lowerCAmelCase							:   "pyspark.sql.DataFrame"						,							_lowerCAmelCase							:   List[int]						,							):




 """simple docstring"""







 import pyspark

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

 return generate_fn
class 			_UpperCamelCase      (				_BaseExamplesIterable		):
      def __init__(			self   :List[str] ,      lowerCamelCase   :"pyspark.sql.DataFrame" ,      lowerCamelCase   :str=None ,      )     ->			Optional[int]:
       UpperCAmelCase__							 =						df
       UpperCAmelCase__							 =						partition_order or range(self.df.rdd.getNumPartitions()      )
       UpperCAmelCase__							 =						_generate_iterable_examples(self.df ,      self.partition_order      )
      def __iter__(			self   :List[Any]      )     ->			Tuple:
       yield from self.generate_examples_fn()
      def 	UpperCAmelCase_						(			self   :List[Any] ,      lowerCamelCase   :np.random.Generator      )     ->			"SparkExamplesIterable":
       UpperCAmelCase__							 =						list(range(self.df.rdd.getNumPartitions()      )      )
       generator.shuffle(lowerCamelCase      )
       return SparkExamplesIterable(self.df ,      partition_order=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Any ,      lowerCamelCase   :int ,      lowerCamelCase   :int      )     ->			"SparkExamplesIterable":
       UpperCAmelCase__							 =						self.split_shard_indices_by_worker(lowerCamelCase ,      lowerCamelCase      )
       return SparkExamplesIterable(self.df ,      partition_order=lowerCamelCase      )







      @property
      def 	UpperCAmelCase_						(			self   :Any      )     ->			int:
       return len(self.partition_order      )



class 			_UpperCamelCase      (				datasets.DatasetBuilder		):
      UpperCAmelCase_        =		SparkConfig
      def __init__(			self   :str ,      lowerCamelCase   :"pyspark.sql.DataFrame" ,      lowerCamelCase   :str = None ,      lowerCamelCase   :str = None ,      **lowerCamelCase   :List[str] ,      )     ->			Any:
       import pyspark

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

       super().__init__(
           cache_dir=lowerCamelCase ,      config_name=str(self.df.semanticHash()      ) ,      **lowerCamelCase ,      )
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			Any:
       # Returns the path of the created file.
       def create_cache_and_write_probe(lowerCamelCase   :List[Any]      ):
        # 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=lowerCamelCase      )
        UpperCAmelCase__							 =						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(lowerCamelCase ,      "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:
        UpperCAmelCase__							 =						(
            self._spark.sparkContext.parallelize(range(1      ) ,      1      ).mapPartitions(lowerCamelCase      ).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 	UpperCAmelCase_						(			self   :Union[str, Any]      )     ->			List[str]:
       return datasets.DatasetInfo(features=self.config.features      )
      def 	UpperCAmelCase_						(			self   :Tuple ,      lowerCamelCase   :datasets.download.download_manager.DownloadManager      )     ->			Union[str, Any]:
       return [datasets.SplitGenerator(name=datasets.Split.TRAIN      )]
      def 	UpperCAmelCase_						(			self   :Optional[Any] ,      lowerCamelCase   :List[Any]      )     ->			Tuple:
       import pyspark

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

       UpperCAmelCase__							 =						self.df.count()
       UpperCAmelCase__							 =						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.
       UpperCAmelCase__							 =						(
           self.df.limit(lowerCamelCase      )
           .repartition(1      )
           .mapInArrow(lowerCamelCase ,      "batch_bytes: long"      )
           .agg(pyspark.sql.functions.sum("batch_bytes"      ).alias("sample_bytes"      )      )
           .collect()[0]
           .sample_bytes
           / sample_num_rows
       )
       UpperCAmelCase__							 =						approx_bytes_per_row * df_num_rows
       if approx_total_size > max_shard_size:
        # Make sure there is at least one row per partition.
        UpperCAmelCase__							 =						min(lowerCamelCase ,      int(approx_total_size / max_shard_size      )      )
        UpperCAmelCase__							 =						self.df.repartition(lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Union[str, Any] ,      lowerCamelCase   :str ,      lowerCamelCase   :str ,      lowerCamelCase   :int ,      )     ->			Iterable[Tuple[int, bool, Union[int, tuple]]]:
       import pyspark

       UpperCAmelCase__							 =						ParquetWriter if file_format == "parquet" else ArrowWriter
       UpperCAmelCase__							 =						os.path.join(self._working_dir ,      os.path.basename(lowerCamelCase      )      ) if self._working_dir else fpath
       UpperCAmelCase__							 =						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.
       UpperCAmelCase__							 =						self.config.features
       UpperCAmelCase__							 =						self._writer_batch_size
       UpperCAmelCase__							 =						self._fs.storage_options

       def write_arrow(lowerCamelCase   :Dict      ):
        # Within the same SparkContext, no two task attempts will share the same attempt ID.
        UpperCAmelCase__							 =						pyspark.TaskContext().taskAttemptId()
        UpperCAmelCase__							 =						next(lowerCamelCase ,      lowerCamelCase      )
        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"] ,      )
        UpperCAmelCase__							 =						0
        UpperCAmelCase__							 =						writer_class(
            features=lowerCamelCase ,      path=working_fpath.replace("SSSSS" ,      f'''{shard_id:05d}'''      ).replace("TTTTT" ,      f'''{task_id:05d}'''      ) ,      writer_batch_size=lowerCamelCase ,      storage_options=lowerCamelCase ,      embed_local_files=lowerCamelCase ,      )
        UpperCAmelCase__							 =						pa.Table.from_batches([first_batch]      )
        writer.write_table(lowerCamelCase      )
        for batch in it:
         if max_shard_size is not None and writer._num_bytes >= max_shard_size:
          UpperCAmelCase__ , UpperCAmelCase__							 =						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
          UpperCAmelCase__							 =						writer_class(
              features=writer._features ,      path=working_fpath.replace("SSSSS" ,      f'''{shard_id:05d}'''      ).replace("TTTTT" ,      f'''{task_id:05d}'''      ) ,      writer_batch_size=lowerCamelCase ,      storage_options=lowerCamelCase ,      embed_local_files=lowerCamelCase ,      )
         UpperCAmelCase__							 =						pa.Table.from_batches([batch]      )
         writer.write_table(lowerCamelCase      )

        if writer._num_bytes > 0:
         UpperCAmelCase__ , UpperCAmelCase__							 =						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(lowerCamelCase      )      ):
          UpperCAmelCase__							 =						os.path.join(os.path.dirname(lowerCamelCase      ) ,      os.path.basename(lowerCamelCase      )      )
          shutil.move(lowerCamelCase ,      lowerCamelCase      )

       UpperCAmelCase__							 =						(
           self.df.mapInArrow(lowerCamelCase ,      "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 	UpperCAmelCase_						(			self   :Tuple ,      lowerCamelCase   :"datasets.SplitGenerator" ,      lowerCamelCase   :str = "arrow" ,      lowerCamelCase   :Optional[Union[str, int]] = None ,      lowerCamelCase   :Optional[int] = None ,      **lowerCamelCase   :List[Any] ,      )     ->			List[str]:
       self._validate_cache_dir()

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

       UpperCAmelCase__							 =						"-TTTTT-SSSSS-of-NNNNN"
       UpperCAmelCase__							 =						f'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
       UpperCAmelCase__							 =						path_join(self._output_dir ,      lowerCamelCase      )

       UpperCAmelCase__							 =						0
       UpperCAmelCase__							 =						0
       UpperCAmelCase__							 =						0
       UpperCAmelCase__							 =						[]
       UpperCAmelCase__							 =						[]

       for task_id, content in self._prepare_split_single(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase      ):
        (
            (
            UpperCAmelCase__ 
        ) , (
            UpperCAmelCase__ 
        ) , (
            UpperCAmelCase__ 
        ) , (
            UpperCAmelCase__ 
        ) ,  
        )							 =						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(lowerCamelCase      )

       UpperCAmelCase__							 =						total_num_examples
       UpperCAmelCase__							 =						total_num_bytes

       # should rename everything at the end
       logger.debug(f'''Renaming {total_shards} shards.'''      )
       if total_shards > 1:
        UpperCAmelCase__							 =						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.
        UpperCAmelCase__							 =						self._fs

        # use the -SSSSS-of-NNNNN pattern
        def _rename_shard(
            lowerCamelCase   :int ,      lowerCamelCase   :int ,      lowerCamelCase   :int ,      ):
         rename(
             lowerCamelCase ,      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}'''      ) ,      )

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







      def 	UpperCAmelCase_						(			self   :str ,      lowerCamelCase   :"datasets.SplitGenerator" ,      )     ->			SparkExamplesIterable:
       return SparkExamplesIterable(self.df      )











import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class 			_UpperCamelCase      (				lowerCAmelCase		):
      # to overwrite at feature extractactor specific tests
      UpperCAmelCase_        =		None
      UpperCAmelCase_        =		None
      @property
      def 	UpperCAmelCase_						(			self   :int      )     ->			int:
       return self.feat_extract_tester.prepare_feat_extract_dict()
      def 	UpperCAmelCase_						(			self   :Any      )     ->			str:
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       self.assertTrue(hasattr(lowerCamelCase ,      "feature_size"      )      )
       self.assertTrue(hasattr(lowerCamelCase ,      "sampling_rate"      )      )
       self.assertTrue(hasattr(lowerCamelCase ,      "padding_value"      )      )
      def 	UpperCAmelCase_						(			self   :str      )     ->			int:
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common()
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )

       self.assertTrue(all(len(lowerCamelCase      ) == len(lowerCamelCase      ) for x, y in zip(lowerCamelCase ,      processed_features[input_name]      )      )      )

       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCamelCase      )
       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs} ,      tensor_type="np"      )

       UpperCAmelCase__							 =						processed_features[input_name]

       if len(batch_features_input.shape      ) < 3:
        UpperCAmelCase__							 =						batch_features_input[:, :, None]

       self.assertTrue(
           batch_features_input.shape
           == (self.feat_extract_tester.batch_size, len(speech_inputs[0]      ), self.feat_extract_tester.feature_size)      )
      @require_torch
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			Union[str, Any]:
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCamelCase      )
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs} ,      tensor_type="pt"      )

       UpperCAmelCase__							 =						processed_features[input_name]

       if len(batch_features_input.shape      ) < 3:
        UpperCAmelCase__							 =						batch_features_input[:, :, None]

       self.assertTrue(
           batch_features_input.shape
           == (self.feat_extract_tester.batch_size, len(speech_inputs[0]      ), self.feat_extract_tester.feature_size)      )
      @require_tf
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Dict:
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCamelCase      )
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs} ,      tensor_type="tf"      )

       UpperCAmelCase__							 =						processed_features[input_name]

       if len(batch_features_input.shape      ) < 3:
        UpperCAmelCase__							 =						batch_features_input[:, :, None]

       self.assertTrue(
           batch_features_input.shape
           == (self.feat_extract_tester.batch_size, len(speech_inputs[0]      ), self.feat_extract_tester.feature_size)      )
      def 	UpperCAmelCase_						(			self   :int ,      lowerCamelCase   :int=False      )     ->			str:
       def _inputs_have_equal_length(lowerCamelCase   :Union[str, Any]      ):
        UpperCAmelCase__							 =						len(input[0]      )
        for input_slice in input[1:]:
         if len(lowerCamelCase      ) != length:
          return False
        return True

       def _inputs_are_equal(lowerCamelCase   :Dict ,      lowerCamelCase   :Optional[Any]      ):
        if len(lowerCamelCase      ) != len(lowerCamelCase      ):
         return False

        for input_slice_a, input_slice_a in zip(lowerCamelCase ,      lowerCamelCase      ):
         if not np.allclose(np.asarray(lowerCamelCase      ) ,      np.asarray(lowerCamelCase      ) ,      atol=1e-3      ):
          return False
        return True

       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common(numpify=lowerCamelCase      )
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )

       UpperCAmelCase__							 =						self.feat_extract_tester.seq_length_diff
       UpperCAmelCase__							 =						self.feat_extract_tester.max_seq_length + pad_diff
       UpperCAmelCase__							 =						self.feat_extract_tester.min_seq_length
       UpperCAmelCase__							 =						self.feat_extract_tester.batch_size
       UpperCAmelCase__							 =						self.feat_extract_tester.feature_size

       # test padding for List[int] + numpy
       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding=lowerCamelCase      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest"      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[-1]      )      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      return_tensors="np"      )
       UpperCAmelCase__							 =						input_a[input_name]

       # max_length parameter has to be provided when setting `padding="max_length"`
       with self.assertRaises(lowerCamelCase      ):
        feat_extract.pad(lowerCamelCase ,      padding="max_length"      )[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=lowerCamelCase ,      return_tensors="np"      )
       UpperCAmelCase__							 =						input_a[input_name]

       self.assertFalse(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(_inputs_are_equal(lowerCamelCase ,      lowerCamelCase      )      )
       self.assertTrue(len(input_a[0]      ) == pad_min_length      )
       self.assertTrue(len(input_a[1]      ) == pad_min_length + pad_diff      )
       self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]      ))      )
       self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)      )

       if feature_size > 1:
        self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size      )

       # test padding for `pad_to_multiple_of` for List[int] + numpy
       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      pad_to_multiple_of=10      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      pad_to_multiple_of=10      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      pad_to_multiple_of=10 ,      max_length=lowerCamelCase      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      pad_to_multiple_of=10 ,      max_length=lowerCamelCase ,      return_tensors="np" ,      )
       UpperCAmelCase__							 =						input_a[input_name]

       self.assertTrue(all(len(lowerCamelCase      ) % 10 == 0 for x in input_a      )      )
       self.assertTrue(_inputs_are_equal(lowerCamelCase ,      lowerCamelCase      )      )

       UpperCAmelCase__							 =						pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
       self.assertTrue(all(len(lowerCamelCase      ) == expected_mult_pad_length for x in input_a      )      )
       self.assertEqual(input_a.shape[:2] ,      (batch_size, expected_mult_pad_length)      )

       if feature_size > 1:
        self.assertTrue(input_a.shape[2] == feature_size      )

       # Check padding value is correct
       UpperCAmelCase__							 =						(np.ones(self.feat_extract_tester.feature_size      ) * feat_extract.padding_value).sum()
       self.assertTrue(
           abs(np.asarray(input_a[0]      )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)      )
           < 1e-3      )
       self.assertTrue(
           abs(
               np.asarray(input_a[1]      )[pad_min_length + pad_diff :].sum()
               - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)      )
           < 1e-3      )
       self.assertTrue(
           abs(
               np.asarray(input_a[2]      )[pad_min_length + 2 * pad_diff :].sum()
               - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)      )
           < 1e-3      )
       self.assertTrue(
           abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)      ) < 1e-3      )
       self.assertTrue(
           abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)      )
           < 1e-3      )
      def 	UpperCAmelCase_						(			self   :List[str] ,      lowerCamelCase   :int=False      )     ->			str:
       def _inputs_have_equal_length(lowerCamelCase   :Any      ):
        UpperCAmelCase__							 =						len(input[0]      )
        for input_slice in input[1:]:
         if len(lowerCamelCase      ) != length:
          return False
        return True

       def _inputs_are_equal(lowerCamelCase   :Optional[int] ,      lowerCamelCase   :str      ):
        if len(lowerCamelCase      ) != len(lowerCamelCase      ):
         return False

        for input_slice_a, input_slice_a in zip(lowerCamelCase ,      lowerCamelCase      ):
         if not np.allclose(np.asarray(lowerCamelCase      ) ,      np.asarray(lowerCamelCase      ) ,      atol=1e-3      ):
          return False
        return True

       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common(numpify=lowerCamelCase      )
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )

       # truncate to smallest
       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[0]      ) ,      truncation=lowerCamelCase      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[0]      )      )
       UpperCAmelCase__							 =						input_a[input_name]

       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertFalse(_inputs_have_equal_length(lowerCamelCase      )      )

       # truncate to smallest with np
       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[0]      ) ,      return_tensors="np" ,      truncation=lowerCamelCase ,      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[0]      ) ,      return_tensors="np"      )
       UpperCAmelCase__							 =						input_a[input_name]

       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(input_a.shape[1] == len(speech_inputs[0]      )      )

       # since truncation forces padding to be smaller than longest input
       # function can't return `np.ndarray`, but has to return list
       self.assertFalse(_inputs_have_equal_length(lowerCamelCase      )      )

       # truncate to middle
       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[1]      ) ,      truncation=lowerCamelCase ,      return_tensors="np" ,      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[1]      ) ,      truncation=lowerCamelCase      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[1]      ) ,      return_tensors="np"      )
       UpperCAmelCase__							 =						input_a[input_name]

       self.assertTrue(input_a.shape[1] == len(speech_inputs[1]      )      )
       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(_inputs_are_equal(lowerCamelCase ,      lowerCamelCase      )      )

       # since truncation forces padding to be smaller than longest input
       # function can't return `np.ndarray`, but has to return list
       self.assertFalse(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(len(input_a[-1]      ) == len(speech_inputs[-1]      )      )

       # padding has to be max_length when setting `truncation=True`
       with self.assertRaises(lowerCamelCase      ):
        feat_extract.pad(lowerCamelCase ,      truncation=lowerCamelCase      )[input_name]

       # padding has to be max_length when setting `truncation=True`
       with self.assertRaises(lowerCamelCase      ):
        feat_extract.pad(lowerCamelCase ,      padding="longest" ,      truncation=lowerCamelCase      )[input_name]

       # padding has to be max_length when setting `truncation=True`
       with self.assertRaises(lowerCamelCase      ):
        feat_extract.pad(lowerCamelCase ,      padding="longest" ,      truncation=lowerCamelCase      )[input_name]

       # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
       with self.assertRaises(lowerCamelCase      ):
        feat_extract.pad(lowerCamelCase ,      padding="max_length" ,      truncation=lowerCamelCase      )[input_name]

       # test truncation for `pad_to_multiple_of` for List[int] + numpy
       UpperCAmelCase__							 =						12
       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[0]      ) ,      pad_to_multiple_of=lowerCamelCase ,      truncation=lowerCamelCase ,      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[0]      ) ,      pad_to_multiple_of=lowerCamelCase ,      )
       UpperCAmelCase__							 =						input_a[input_name]

       # retrieve expected_length as multiple of pad_to_multiple_of
       UpperCAmelCase__							 =						len(speech_inputs[0]      )
       if expected_length % pad_to_multiple_of != 0:
        UpperCAmelCase__							 =						((len(speech_inputs[0]      ) // pad_to_multiple_of) + 1) * pad_to_multiple_of

       self.assertTrue(len(input_a[0]      ) == expected_length      )
       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertFalse(_inputs_have_equal_length(lowerCamelCase      )      )
      def 	UpperCAmelCase_						(			self   :int      )     ->			List[str]:
       self._check_padding(numpify=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :List[Any]      )     ->			int:
       self._check_padding(numpify=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :str      )     ->			str:
       self._check_truncation(numpify=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			str:
       self._check_truncation(numpify=lowerCamelCase      )
      @require_torch
      def 	UpperCAmelCase_						(			self   :int      )     ->			Any:
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common()
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      return_tensors="np"      )[input_name]
       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      return_tensors="pt"      )[input_name]

       self.assertTrue(abs(input_np.astype(np.floataa      ).sum() - input_pt.numpy().astype(np.floataa      ).sum()      ) < 1e-2      )
      @require_tf
      def 	UpperCAmelCase_						(			self   :List[Any]      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common()
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      return_tensors="np"      )[input_name]
       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      return_tensors="tf"      )[input_name]

       self.assertTrue(abs(input_np.astype(np.floataa      ).sum() - input_tf.numpy().astype(np.floataa      ).sum()      ) < 1e-2      )
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			str:
       UpperCAmelCase__							 =						self.feat_extract_dict
       UpperCAmelCase__							 =						True
       UpperCAmelCase__							 =						self.feature_extraction_class(**lowerCamelCase      )
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common()
       UpperCAmelCase__							 =						[len(lowerCamelCase      ) for x in speech_inputs]
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      return_tensors="np"      )
       self.assertIn("attention_mask" ,      lowerCamelCase      )
       self.assertListEqual(list(processed.attention_mask.shape      ) ,      list(processed[input_name].shape[:2]      )      )
       self.assertListEqual(processed.attention_mask.sum(-1      ).tolist() ,      lowerCamelCase      )







      def 	UpperCAmelCase_						(			self   :int      )     ->			int:
       UpperCAmelCase__							 =						self.feat_extract_dict
       UpperCAmelCase__							 =						True
       UpperCAmelCase__							 =						self.feature_extraction_class(**lowerCamelCase      )
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common()
       UpperCAmelCase__							 =						[len(lowerCamelCase      ) for x in speech_inputs]
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )
       UpperCAmelCase__							 =						min(lowerCamelCase      )

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=lowerCamelCase ,      truncation=lowerCamelCase ,      return_tensors="np"      )
       self.assertIn("attention_mask" ,      lowerCamelCase      )
       self.assertListEqual(
           list(processed_pad.attention_mask.shape      ) ,      [processed_pad[input_name].shape[0], max_length]      )
       self.assertListEqual(
           processed_pad.attention_mask[:, :max_length].sum(-1      ).tolist() ,      [max_length for x in speech_inputs]      )





