


import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)






def 	__lowerCamelCase						(							lowerCamelCase__		:						Dict  ,							lowerCamelCase__		:						Optional[Any]=False      ):





			'''simple docstring'''




			try:
						lowerCamelCase    		=  os.environ[key]
			except KeyError:
						# KEY isn't set, default to `default`.
						lowerCamelCase    		=  default
			else:
						# KEY is set, convert it to True or False.
						try:
									lowerCamelCase    		=  strtobool(lowerCamelCase__      )
						except ValueError:
									# More values are supported, but let's keep the message simple.
									raise ValueError(f'If set, {key} must be yes or no.'      )
			return _value


UpperCAmelCase   :    List[Any]     =		parse_flag_from_env("RUN_SLOW", default=False)






def 	__lowerCamelCase						(							lowerCamelCase__		:						Tuple      ):





			'''simple docstring'''




			return unittest.skip("""Test was skipped"""      )(lowerCamelCase__      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						List[Any]      ):





			'''simple docstring'''




			return unittest.skipUnless(_run_slow_tests  ,							"""test is slow"""      )(lowerCamelCase__      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						str      ):





			'''simple docstring'''




			return unittest.skipUnless(not torch.cuda.is_available()  ,							"""test requires only a CPU"""      )(lowerCamelCase__      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						int      ):





			'''simple docstring'''




			return unittest.skipUnless(torch.cuda.is_available()  ,							"""test requires a GPU"""      )(lowerCamelCase__      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						List[str]      ):





			'''simple docstring'''




			return unittest.skipUnless(is_xpu_available()  ,							"""test requires a XPU"""      )(lowerCamelCase__      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						List[str]      ):





			'''simple docstring'''




			return unittest.skipUnless(is_mps_available()  ,							"""test requires a `mps` backend support in `torch`"""      )(lowerCamelCase__      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						Optional[Any]      ):





			'''simple docstring'''




			return unittest.skipUnless(
			    is_transformers_available() and is_datasets_available()  ,							"""test requires the Hugging Face suite"""      )(lowerCamelCase__      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						Union[str, Any]      ):





			'''simple docstring'''




			return unittest.skipUnless(is_bnb_available()  ,							"""test requires the bitsandbytes library"""      )(lowerCamelCase__      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						Optional[int]      ):





			'''simple docstring'''




			return unittest.skipUnless(is_tpu_available()  ,							"""test requires TPU"""      )(lowerCamelCase__      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						List[Any]      ):





			'''simple docstring'''




			return unittest.skipUnless(torch.cuda.device_count() == 1  ,							"""test requires a GPU"""      )(lowerCamelCase__      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						List[Any]      ):





			'''simple docstring'''




			return unittest.skipUnless(torch.xpu.device_count() == 1  ,							"""test requires a XPU"""      )(lowerCamelCase__      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						int      ):





			'''simple docstring'''




			return unittest.skipUnless(torch.cuda.device_count() > 1  ,							"""test requires multiple GPUs"""      )(lowerCamelCase__      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						Optional[int]      ):





			'''simple docstring'''




			return unittest.skipUnless(torch.xpu.device_count() > 1  ,							"""test requires multiple XPUs"""      )(lowerCamelCase__      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						Optional[Any]      ):





			'''simple docstring'''




			return unittest.skipUnless(is_safetensors_available()  ,							"""test requires safetensors"""      )(lowerCamelCase__      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						List[Any]      ):





			'''simple docstring'''




			return unittest.skipUnless(is_deepspeed_available()  ,							"""test requires DeepSpeed"""      )(lowerCamelCase__      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						Union[str, Any]      ):





			'''simple docstring'''




			return unittest.skipUnless(is_torch_version(""">="""  ,							"""1.12.0"""      )  ,							"""test requires torch version >= 1.12.0"""      )(lowerCamelCase__      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						Optional[int]=None  ,							lowerCamelCase__		:						List[str]=None      ):





			'''simple docstring'''




			if test_case is None:
						return partial(lowerCamelCase__  ,							version=lowerCamelCase__      )
			return unittest.skipUnless(is_torch_version(""">="""  ,							lowerCamelCase__      )  ,							f'test requires torch version >= {version}'      )(lowerCamelCase__      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						Optional[int]      ):





			'''simple docstring'''




			return unittest.skipUnless(is_tensorboard_available()  ,							"""test requires Tensorboard"""      )(lowerCamelCase__      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						Any      ):





			'''simple docstring'''




			return unittest.skipUnless(is_wandb_available()  ,							"""test requires wandb"""      )(lowerCamelCase__      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						str      ):





			'''simple docstring'''




			return unittest.skipUnless(is_comet_ml_available()  ,							"""test requires comet_ml"""      )(lowerCamelCase__      )


UpperCAmelCase   :    Tuple     =		(
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)






def 	__lowerCamelCase						(							lowerCamelCase__		:						Union[str, Any]      ):





			'''simple docstring'''




			return unittest.skipUnless(
			    _atleast_one_tracker_available  ,							"""test requires at least one tracker to be available and for `comet_ml` to not be installed"""  ,							)(lowerCamelCase__      )







class        __lowercase       (							unittest.TestCase  ):



		"""simple docstring"""

		UpperCamelCase     :					Union[str, Any]			       =		True







		@classmethod
		def 					__A   ( cls	)  -> int:







					'''simple docstring'''







					lowerCamelCase    		=  tempfile.mkdtemp()







		@classmethod
		def 					__A   ( cls	)  -> int:







					'''simple docstring'''







					if os.path.exists(cls.tmpdir	):
								shutil.rmtree(cls.tmpdir	)







		def 					__A   ( self	)  -> Tuple:







					'''simple docstring'''







					if self.clear_on_setup:
								for path in Path(self.tmpdir	).glob("""**/*"""	):
											if path.is_file():
														path.unlink()
											elif path.is_dir():
														shutil.rmtree(A	)







class        __lowercase       (							unittest.TestCase  ):



		"""simple docstring"""

		def 					__A   ( self	)  -> List[Any]:







					'''simple docstring'''







					super().tearDown()
					# Reset the state of the AcceleratorState singleton.
					AcceleratorState._reset_state()
					PartialState._reset_state()







class        __lowercase       (							unittest.TestCase  ):



		"""simple docstring"""

		def 					__A   ( self		,      A	)  -> List[str]:







					'''simple docstring'''







					lowerCamelCase    		=  mocks if isinstance(A		,      (tuple, list)	) else [mocks]
					for m in self.mocks:
								m.start()
								self.addCleanup(m.stop	)






def 	__lowerCamelCase						(							lowerCamelCase__		:						List[Any]      ):





			'''simple docstring'''




			lowerCamelCase    		=  AcceleratorState()
			lowerCamelCase    		=  tensor[None].clone().to(state.device      )
			lowerCamelCase    		=  gather(lowerCamelCase__      ).cpu()
			lowerCamelCase    		=  tensor[0].cpu()
			for i in range(tensors.shape[0]      ):
						if not torch.equal(tensors[i]  ,							lowerCamelCase__      ):
									return False
			return True







class        __lowercase       :



		"""simple docstring"""

		def __init__( self		,      A		,      A		,      A	)  -> Any:







					'''simple docstring'''







					lowerCamelCase    		=  returncode
					lowerCamelCase    		=  stdout
					lowerCamelCase    		=  stderr






async def 	__lowerCamelCase						(							lowerCamelCase__		:						Union[str, Any]  ,							lowerCamelCase__		:						int      ):





			'''simple docstring'''




			while True:
						lowerCamelCase    		=  await stream.readline()
						if line:
									callback(lowerCamelCase__      )
						else:
									break






async def 	__lowerCamelCase						(							lowerCamelCase__		:						int  ,							lowerCamelCase__		:						Union[str, Any]=None  ,							lowerCamelCase__		:						List[Any]=None  ,							lowerCamelCase__		:						str=None  ,							lowerCamelCase__		:						Dict=False  ,							lowerCamelCase__		:						str=False      ):





			'''simple docstring'''




			if echo:
						print("""\nRunning: """  ,							""" """.join(lowerCamelCase__      )      )

			lowerCamelCase    		=  await asyncio.create_subprocess_exec(
			    cmd[0]  ,							*cmd[1:]  ,							stdin=lowerCamelCase__  ,							stdout=asyncio.subprocess.PIPE  ,							stderr=asyncio.subprocess.PIPE  ,							env=lowerCamelCase__  ,							)

			# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
			# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
			#
			# If it starts hanging, will need to switch to the following code. The problem is that no data
			# will be seen until it's done and if it hangs for example there will be no debug info.
			# out, err = await p.communicate()
			# return _RunOutput(p.returncode, out, err)

			lowerCamelCase    		=  []
			lowerCamelCase    		=  []

			def tee(lowerCamelCase__		:						List[str]  ,							lowerCamelCase__		:						Dict  ,							lowerCamelCase__		:						Optional[int]  ,							lowerCamelCase__		:						Tuple=""      ):
						lowerCamelCase    		=  line.decode("""utf-8"""      ).rstrip()
						sink.append(lowerCamelCase__      )
						if not quiet:
									print(lowerCamelCase__  ,							lowerCamelCase__  ,							file=lowerCamelCase__      )

    # XXX: the timeout doesn't seem to make any difference here
			await asyncio.wait(
			    [
			        asyncio.create_task(_read_stream(p.stdout  ,							lambda lowerCamelCase__      : tee(lowerCamelCase__  ,							lowerCamelCase__  ,							sys.stdout  ,							label="""stdout:"""      )      )      ),
			        asyncio.create_task(_read_stream(p.stderr  ,							lambda lowerCamelCase__      : tee(lowerCamelCase__  ,							lowerCamelCase__  ,							sys.stderr  ,							label="""stderr:"""      )      )      ),
			    ]  ,							timeout=lowerCamelCase__  ,							)
			return _RunOutput(await p.wait()  ,							lowerCamelCase__  ,							lowerCamelCase__      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						Any  ,							lowerCamelCase__		:						List[Any]=None  ,							lowerCamelCase__		:						List[Any]=None  ,							lowerCamelCase__		:						Dict=180  ,							lowerCamelCase__		:						List[str]=False  ,							lowerCamelCase__		:						List[Any]=True      ):





			'''simple docstring'''




			lowerCamelCase    		=  asyncio.get_event_loop()
			lowerCamelCase    		=  loop.run_until_complete(
			    _stream_subprocess(lowerCamelCase__  ,							env=lowerCamelCase__  ,							stdin=lowerCamelCase__  ,							timeout=lowerCamelCase__  ,							quiet=lowerCamelCase__  ,							echo=lowerCamelCase__      )      )

			lowerCamelCase    		=  """ """.join(lowerCamelCase__      )
			if result.returncode > 0:
						lowerCamelCase    		=  """\n""".join(result.stderr      )
						raise RuntimeError(
						    f'\'{cmd_str}\' failed with returncode {result.returncode}\n\n'
						    f'The combined stderr from workers follows:\n{stderr}'      )

			return result







class        __lowercase       (							a_  ):



		"""simple docstring"""

		pass






def 	__lowerCamelCase						(							lowerCamelCase__		:						List[str]  ,							lowerCamelCase__		:						Optional[int]=False      ):





			'''simple docstring'''




			try:
						lowerCamelCase    		=  subprocess.check_output(lowerCamelCase__  ,							stderr=subprocess.STDOUT      )
						if return_stdout:
									if hasattr(lowerCamelCase__  ,							"""decode"""      ):
												lowerCamelCase    		=  output.decode("""utf-8"""      )
									return output
			except subprocess.CalledProcessError as e:
						raise SubprocessCallException(
						    f'Command `{" ".join(lowerCamelCase__      )}` failed with the following error:\n\n{e.output.decode()}'      ) from e





import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
		from transformers import SpeechaTextFeatureExtractor

UpperCAmelCase   :    Any     =		random.Random()






def 	__lowerCamelCase						(							lowerCamelCase__		:						int  ,							lowerCamelCase__		:						Union[str, Any]=1.0  ,							lowerCamelCase__		:						Union[str, Any]=None  ,							lowerCamelCase__		:						int=None      ):





			'''simple docstring'''




			if rng is None:
						lowerCamelCase    		=  global_rng

			lowerCamelCase    		=  []
			for batch_idx in range(shape[0]      ):
						values.append([]      )
						for _ in range(shape[1]      ):
									values[-1].append(rng.random() * scale      )

			return values







@require_torch
@require_torchaudio
class        __lowercase       (							unittest.TestCase  ):



		"""simple docstring"""

		def __init__( self		,      A		,      A=7		,      A=4_00		,      A=20_00		,      A=24		,      A=24		,      A=0.0		,      A=1_60_00		,      A=True		,      A=True		,      )  -> str:







					'''simple docstring'''







					lowerCamelCase    		=  parent
					lowerCamelCase    		=  batch_size
					lowerCamelCase    		=  min_seq_length
					lowerCamelCase    		=  max_seq_length
					lowerCamelCase    		=  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
					lowerCamelCase    		=  feature_size
					lowerCamelCase    		=  num_mel_bins
					lowerCamelCase    		=  padding_value
					lowerCamelCase    		=  sampling_rate
					lowerCamelCase    		=  return_attention_mask
					lowerCamelCase    		=  do_normalize







		def 					__A   ( self	)  -> List[str]:







					'''simple docstring'''







					return {
					    "feature_size": self.feature_size,
					    "num_mel_bins": self.num_mel_bins,
					    "padding_value": self.padding_value,
					    "sampling_rate": self.sampling_rate,
					    "return_attention_mask": self.return_attention_mask,
					    "do_normalize": self.do_normalize,
					}







		def 					__A   ( self		,      A=False		,      A=False	)  -> Tuple:







					'''simple docstring'''







					def _flatten(A	):
								return list(itertools.chain(*A	)	)

					if equal_length:
								lowerCamelCase    		=  [floats_list((self.max_seq_length, self.feature_size)	) for _ in range(self.batch_size	)]
					else:
								# make sure that inputs increase in size
								lowerCamelCase    		=  [
								    floats_list((x, self.feature_size)	)
								    for x in range(self.min_seq_length		,      self.max_seq_length		,      self.seq_length_diff	)
								]
					if numpify:
								lowerCamelCase    		=  [np.asarray(A	) for x in speech_inputs]
					return speech_inputs







@require_torch
@require_torchaudio
class        __lowercase       (							a_			,							unittest.TestCase  ):



		"""simple docstring"""

		UpperCamelCase     :					str			       =		SpeechaTextFeatureExtractor if is_speech_available() else None







		def 					__A   ( self	)  -> int:







					'''simple docstring'''







					lowerCamelCase    		=  SpeechaTextFeatureExtractionTester(self	)







		def 					__A   ( self		,      A	)  -> List[Any]:







					'''simple docstring'''







					self.assertTrue(np.all(np.mean(A		,      axis=0	) < 1e-3	)	)
					self.assertTrue(np.all(np.abs(np.var(A		,      axis=0	) - 1	) < 1e-3	)	)







		def 					__A   ( self	)  -> Optional[int]:







					'''simple docstring'''







					lowerCamelCase    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
					# create three inputs of length 800, 1000, and 1200
					lowerCamelCase    		=  [floats_list((1, x)	)[0] for x in range(8_00		,      14_00		,      2_00	)]
					lowerCamelCase    		=  [np.asarray(A	) for speech_input in speech_inputs]

					# Test feature size
					lowerCamelCase    		=  feature_extractor(A		,      padding=A		,      return_tensors="""np"""	).input_features
					self.assertTrue(input_features.ndim == 3	)
					self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size	)

					# Test not batched input
					lowerCamelCase    		=  feature_extractor(speech_inputs[0]		,      return_tensors="""np"""	).input_features
					lowerCamelCase    		=  feature_extractor(np_speech_inputs[0]		,      return_tensors="""np"""	).input_features
					self.assertTrue(np.allclose(A		,      A		,      atol=1e-3	)	)

					# Test batched
					lowerCamelCase    		=  feature_extractor(A		,      return_tensors="""np"""	).input_features
					lowerCamelCase    		=  feature_extractor(A		,      return_tensors="""np"""	).input_features
					for enc_seq_a, enc_seq_a in zip(A		,      A	):
								self.assertTrue(np.allclose(A		,      A		,      atol=1e-3	)	)

					# Test 2-D numpy arrays are batched.
					lowerCamelCase    		=  [floats_list((1, x)	)[0] for x in (8_00, 8_00, 8_00)]
					lowerCamelCase    		=  np.asarray(A	)
					lowerCamelCase    		=  feature_extractor(A		,      return_tensors="""np"""	).input_features
					lowerCamelCase    		=  feature_extractor(A		,      return_tensors="""np"""	).input_features
					for enc_seq_a, enc_seq_a in zip(A		,      A	):
								self.assertTrue(np.allclose(A		,      A		,      atol=1e-3	)	)







		def 					__A   ( self	)  -> Optional[Any]:







					'''simple docstring'''







					lowerCamelCase    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
					lowerCamelCase    		=  [floats_list((1, x)	)[0] for x in range(8_00		,      14_00		,      2_00	)]

					lowerCamelCase    		=  ["""longest""", """max_length""", """do_not_pad"""]
					lowerCamelCase    		=  [None, 16, None]
					for max_length, padding in zip(A		,      A	):
								lowerCamelCase    		=  feature_extractor(
								    A		,      padding=A		,      max_length=A		,      return_attention_mask=A	)
								lowerCamelCase    		=  inputs.input_features
								lowerCamelCase    		=  inputs.attention_mask
								lowerCamelCase    		=  [np.sum(A	) for x in attention_mask]

								self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]	)
								self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]	)
								self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]	)







		def 					__A   ( self	)  -> List[Any]:







					'''simple docstring'''







					lowerCamelCase    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
					lowerCamelCase    		=  [floats_list((1, x)	)[0] for x in range(8_00		,      14_00		,      2_00	)]

					lowerCamelCase    		=  ["""longest""", """max_length""", """do_not_pad"""]
					lowerCamelCase    		=  [None, 16, None]
					for max_length, padding in zip(A		,      A	):
								lowerCamelCase    		=  feature_extractor(
								    A		,      max_length=A		,      padding=A		,      return_tensors="""np"""		,      return_attention_mask=A	)
								lowerCamelCase    		=  inputs.input_features
								lowerCamelCase    		=  inputs.attention_mask
								lowerCamelCase    		=  [np.sum(A	) for x in attention_mask]

								self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]	)
								self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6	)
								self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]	)
								self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6	)
								self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]	)







		def 					__A   ( self	)  -> int:







					'''simple docstring'''







					lowerCamelCase    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
					lowerCamelCase    		=  [floats_list((1, x)	)[0] for x in range(8_00		,      14_00		,      2_00	)]
					lowerCamelCase    		=  feature_extractor(
					    A		,      padding="""max_length"""		,      max_length=4		,      truncation=A		,      return_tensors="""np"""		,      return_attention_mask=A		,      )
					lowerCamelCase    		=  inputs.input_features
					lowerCamelCase    		=  inputs.attention_mask
					lowerCamelCase    		=  np.sum(attention_mask == 1		,      axis=1	)

					self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]	)
					self._check_zero_mean_unit_variance(input_features[1]	)
					self._check_zero_mean_unit_variance(input_features[2]	)







		def 					__A   ( self	)  -> List[Any]:







					'''simple docstring'''







					lowerCamelCase    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
					lowerCamelCase    		=  [floats_list((1, x)	)[0] for x in range(8_00		,      14_00		,      2_00	)]
					lowerCamelCase    		=  feature_extractor(
					    A		,      padding="""longest"""		,      max_length=4		,      truncation=A		,      return_tensors="""np"""		,      return_attention_mask=A		,      )
					lowerCamelCase    		=  inputs.input_features
					lowerCamelCase    		=  inputs.attention_mask
					lowerCamelCase    		=  np.sum(attention_mask == 1		,      axis=1	)

					self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]	)
					self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]	)
					self._check_zero_mean_unit_variance(input_features[2]	)

					# make sure that if max_length < longest -> then pad to max_length
					self.assertEqual(input_features.shape		,      (3, 4, 24)	)

					lowerCamelCase    		=  [floats_list((1, x)	)[0] for x in range(8_00		,      14_00		,      2_00	)]
					lowerCamelCase    		=  feature_extractor(
					    A		,      padding="""longest"""		,      max_length=16		,      truncation=A		,      return_tensors="""np"""		,      return_attention_mask=A		,      )
					lowerCamelCase    		=  inputs.input_features
					lowerCamelCase    		=  inputs.attention_mask
					lowerCamelCase    		=  np.sum(attention_mask == 1		,      axis=1	)

					self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]	)
					self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]	)
					self._check_zero_mean_unit_variance(input_features[2]	)

					# make sure that if max_length < longest -> then pad to max_length
					self.assertEqual(input_features.shape		,      (3, 6, 24)	)







		def 					__A   ( self	)  -> Optional[int]:







					'''simple docstring'''







					import torch

					lowerCamelCase    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
					lowerCamelCase    		=  np.random.rand(1_00		,      32	).astype(np.floataa	)
					lowerCamelCase    		=  np_speech_inputs.tolist()

					for inputs in [py_speech_inputs, np_speech_inputs]:
								lowerCamelCase    		=  feature_extractor.pad([{"""input_features""": inputs}]		,      return_tensors="""np"""	)
								self.assertTrue(np_processed.input_features.dtype == np.floataa	)
								lowerCamelCase    		=  feature_extractor.pad([{"""input_features""": inputs}]		,      return_tensors="""pt"""	)
								self.assertTrue(pt_processed.input_features.dtype == torch.floataa	)







		def 					__A   ( self		,      A	)  -> Any:







					'''simple docstring'''







					from datasets import load_dataset

					lowerCamelCase    		=  load_dataset("""hf-internal-testing/librispeech_asr_dummy"""		,      """clean"""		,      split="""validation"""	)
					# automatic decoding with librispeech
					lowerCamelCase    		=  ds.sort("""id"""	).select(range(A	)	)[:num_samples]["""audio"""]

					return [x["array"] for x in speech_samples]







		def 					__A   ( self	)  -> Any:







					'''simple docstring'''







					lowerCamelCase    		=  np.array([
					    -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
					    -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
					    -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
					]	)
					# fmt: on

					lowerCamelCase    		=  self._load_datasamples(1	)
					lowerCamelCase    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
					lowerCamelCase    		=  feature_extractor(A		,      return_tensors="""pt"""	).input_features
					self.assertEquals(input_features.shape		,      (1, 5_84, 24)	)
					self.assertTrue(np.allclose(input_features[0, 0, :30]		,      A		,      atol=1e-4	)	)

