





"""simple docstring"""






# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
		import jax
		import jaxlib

__UpperCAmelCase						=			get_logger()

__UpperCAmelCase						=			None



class 					__UpperCAmelCase							(						TensorFormatter[Mapping, "jax.Array", Mapping]  ):
				def __init__(		self    :			Optional[int]	, a_    :			str=None	, a_    :			Optional[Any]=None	, **a_    :			Dict    )	->	Optional[int]:



							'''simple docstring'''



							super().__init__(features=a_    )
							import jax
							from jaxlib.xla_client import Device

							if isinstance(a_	, a_    ):
										raise ValueError(
										    F"Expected {device} to be a `str` not {type(a_    )}, as `jaxlib.xla_extension.Device` "
										    "is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
										    "the device with `str()` to get its string identifier that will be internally mapped "
										    "to the actual `jaxlib.xla_extension.Device`."    )
							a__  :	Any		 =			device if isinstance(a_	, a_    ) else str(jax.devices()[0]    )
							# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
							# with `pickle` nor with `dill`, so we need to use a global variable instead
							global DEVICE_MAPPING
							if DEVICE_MAPPING is None:
										a__  :	Dict		 =			self._map_devices_to_str()
							if self.device not in list(DEVICE_MAPPING.keys()    ):
										logger.warning(
										    F"Device with string identifier {self.device} not listed among the available "
										    F"devices: {list(DEVICE_MAPPING.keys()    )}, so falling back to the default "
										    F"device: {str(jax.devices()[0]    )}."    )
										a__  :	Optional[int]		 =			str(jax.devices()[0]    )
							a__  :	Union[str, Any]		 =			jnp_array_kwargs
				@staticmethod
				def       UpperCAmelCase      (		)	->	Dict[str, "jaxlib.xla_extension.Device"]:



							'''simple docstring'''



							import jax

							return {str(a_    ): device for device in jax.devices()}
				def       UpperCAmelCase      (		self    :			str	, a_    :			Optional[Any]    )	->	str:



							'''simple docstring'''



							import jax
							import jax.numpy as jnp

							if isinstance(a_	, a_    ) and column:
										if all(
										    isinstance(a_	, jax.Array    ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column    ):
													return jnp.stack(a_	, axis=0    )
							return column
				def       UpperCAmelCase      (		self    :			Optional[Any]	, a_    :			Any    )	->	Optional[Any]:



							'''simple docstring'''



							import jax
							import jax.numpy as jnp

							if isinstance(a_	, (str, bytes, type(a_    ))    ):
										return value
							elif isinstance(a_	, (np.character, np.ndarray)    ) and np.issubdtype(value.dtype	, np.character    ):
										return value.tolist()

							a__  :	List[str]		 =			{}

							if isinstance(a_	, (np.number, np.ndarray)    ) and np.issubdtype(value.dtype	, np.integer    ):
										# the default int precision depends on the jax config
										# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
										if jax.config.jax_enable_xaa:
													a__  :	Any		 =			{"dtype": jnp.intaa}
										else:
													a__  :	Any		 =			{"dtype": jnp.intaa}
							elif isinstance(a_	, (np.number, np.ndarray)    ) and np.issubdtype(value.dtype	, np.floating    ):
										a__  :	List[Any]		 =			{"dtype": jnp.floataa}
							elif config.PIL_AVAILABLE and "PIL" in sys.modules:
										import PIL.Image

										if isinstance(a_	, PIL.Image.Image    ):
													a__  :	List[str]		 =			np.asarray(a_    )

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
							global DEVICE_MAPPING
							if DEVICE_MAPPING is None:
										a__  :	List[Any]		 =			self._map_devices_to_str()

							with jax.default_device(DEVICE_MAPPING[self.device]    ):
										# calling jnp.array on a np.ndarray does copy the data
										# see https://github.com/google/jax/issues/4486
										return jnp.array(a_	, **{**default_dtype, **self.jnp_array_kwargs}    )
				def       UpperCAmelCase      (		self    :			str	, a_    :			Optional[int]    )	->	Dict:



							'''simple docstring'''



							import jax

							# support for torch, tf, jax etc.
							if config.TORCH_AVAILABLE and "torch" in sys.modules:
										import torch

										if isinstance(a_	, torch.Tensor    ):
													return self._tensorize(data_struct.detach().cpu().numpy()[()]    )
							if hasattr(a_	, "__array__"    ) and not isinstance(a_	, jax.Array    ):
										a__  :	Optional[Any]		 =			data_struct.__array__()
							# support for nested types like struct of list of struct
							if isinstance(a_	, np.ndarray    ):
										if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
													return self._consolidate([self.recursive_tensorize(a_    ) for substruct in data_struct]    )
							elif isinstance(a_	, (list, tuple)    ):
										return self._consolidate([self.recursive_tensorize(a_    ) for substruct in data_struct]    )
							return self._tensorize(a_    )
				def       UpperCAmelCase      (		self    :			Optional[Any]	, a_    :			dict    )	->	Optional[Any]:



							'''simple docstring'''



							return map_nested(self._recursive_tensorize	, a_	, map_list=a_    )
				def       UpperCAmelCase      (		self    :			Optional[int]	, a_    :			pa.Table    )	->	Mapping:



							'''simple docstring'''



							a__  :	Union[str, Any]		 =			self.numpy_arrow_extractor().extract_row(a_    )
							a__  :	List[Any]		 =			self.python_features_decoder.decode_row(a_    )
							return self.recursive_tensorize(a_    )
				def       UpperCAmelCase      (		self    :			Optional[int]	, a_    :			pa.Table    )	->	"jax.Array":



							'''simple docstring'''



							a__  :	List[Any]		 =			self.numpy_arrow_extractor().extract_column(a_    )
							a__  :	Optional[Any]		 =			self.python_features_decoder.decode_column(a_	, pa_table.column_names[0]    )
							a__  :	Tuple		 =			self.recursive_tensorize(a_    )
							a__  :	Tuple		 =			self._consolidate(a_    )
							return column






				def       UpperCAmelCase      (		self    :			Union[str, Any]	, a_    :			pa.Table    )	->	Mapping:



							'''simple docstring'''



							a__  :	Optional[int]		 =			self.numpy_arrow_extractor().extract_batch(a_    )
							a__  :	Optional[int]		 =			self.python_features_decoder.decode_batch(a_    )
							a__  :	List[Any]		 =			self.recursive_tensorize(a_    )
							for column_name in batch:
										a__  :	List[Any]		 =			self._consolidate(batch[column_name]    )
							return batch






"""simple docstring"""






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							(						_UpperCamelCase  ):
				# to overwrite at feature extractactor specific tests
				__lowerCamelCase				:	int				     =				None
				__lowerCamelCase				:	Tuple				     =				None
				@property
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	Optional[int]:



							'''simple docstring'''



							return self.feat_extract_tester.prepare_feat_extract_dict()
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	Dict:



							'''simple docstring'''



							a__  :	List[str]		 =			self.feature_extraction_class(**self.feat_extract_dict    )
							self.assertTrue(hasattr(a_	, "feature_size"    )    )
							self.assertTrue(hasattr(a_	, "sampling_rate"    )    )
							self.assertTrue(hasattr(a_	, "padding_value"    )    )
				def       UpperCAmelCase      (		self    :			List[str]    )	->	Union[str, Any]:



							'''simple docstring'''



							a__  :	Any		 =			self.feat_extract_tester.prepare_inputs_for_common()
							a__  :	Union[str, Any]		 =			self.feature_extraction_class(**self.feat_extract_dict    )
							a__  :	List[str]		 =			feat_extract.model_input_names[0]

							a__  :	Tuple		 =			BatchFeature({input_name: speech_inputs}    )

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

							a__  :	Any		 =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=a_    )
							a__  :	List[Any]		 =			BatchFeature({input_name: speech_inputs}	, tensor_type="np"    )

							a__  :	Union[str, Any]		 =			processed_features[input_name]

							if len(batch_features_input.shape    ) < 3:
										a__  :	List[Any]		 =			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    :			Optional[Any]    )	->	Tuple:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=a_    )
							a__  :	List[Any]		 =			self.feature_extraction_class(**self.feat_extract_dict    )
							a__  :	Optional[Any]		 =			feat_extract.model_input_names[0]

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

							a__  :	Optional[int]		 =			processed_features[input_name]

							if len(batch_features_input.shape    ) < 3:
										a__  :	Any		 =			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    :			Optional[Any]    )	->	str:



							'''simple docstring'''



							a__  :	Optional[int]		 =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=a_    )
							a__  :	Optional[int]		 =			self.feature_extraction_class(**self.feat_extract_dict    )
							a__  :	str		 =			feat_extract.model_input_names[0]

							a__  :	List[str]		 =			BatchFeature({input_name: speech_inputs}	, tensor_type="tf"    )

							a__  :	Tuple		 =			processed_features[input_name]

							if len(batch_features_input.shape    ) < 3:
										a__  :	List[str]		 =			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    :			Dict	, a_    :			Optional[Any]=False    )	->	List[Any]:



							'''simple docstring'''



							def _inputs_have_equal_length(a_    :			int    ):
										a__  :	Any		 =			len(input[0]    )
										for input_slice in input[1:]:
													if len(a_    ) != length:
																return False
										return True

							def _inputs_are_equal(a_    :			List[Any]	, a_    :			str    ):
										if len(a_    ) != len(a_    ):
													return False

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

							a__  :	int		 =			self.feature_extraction_class(**self.feat_extract_dict    )
							a__  :	int		 =			self.feat_extract_tester.prepare_inputs_for_common(numpify=a_    )
							a__  :	Tuple		 =			feat_extract.model_input_names[0]

							a__  :	List[str]		 =			BatchFeature({input_name: speech_inputs}    )

							a__  :	Optional[int]		 =			self.feat_extract_tester.seq_length_diff
							a__  :	Optional[Any]		 =			self.feat_extract_tester.max_seq_length + pad_diff
							a__  :	Union[str, Any]		 =			self.feat_extract_tester.min_seq_length
							a__  :	Tuple		 =			self.feat_extract_tester.batch_size
							a__  :	str		 =			self.feat_extract_tester.feature_size

							# test padding for List[int] + numpy
							a__  :	Any		 =			feat_extract.pad(a_	, padding=a_    )
							a__  :	List[str]		 =			input_a[input_name]

							a__  :	int		 =			feat_extract.pad(a_	, padding="longest"    )
							a__  :	Optional[Any]		 =			input_a[input_name]

							a__  :	Optional[Any]		 =			feat_extract.pad(a_	, padding="max_length"	, max_length=len(speech_inputs[-1]    )    )
							a__  :	Any		 =			input_a[input_name]

							a__  :	Tuple		 =			feat_extract.pad(a_	, padding="longest"	, return_tensors="np"    )
							a__  :	Optional[Any]		 =			input_a[input_name]

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

							a__  :	Tuple		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=a_	, return_tensors="np"    )
							a__  :	Optional[Any]		 =			input_a[input_name]

							self.assertFalse(_inputs_have_equal_length(a_    )    )
							self.assertTrue(_inputs_have_equal_length(a_    )    )
							self.assertTrue(_inputs_have_equal_length(a_    )    )
							self.assertTrue(_inputs_are_equal(a_	, a_    )    )
							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
							a__  :	Optional[int]		 =			feat_extract.pad(a_	, pad_to_multiple_of=10    )
							a__  :	Optional[Any]		 =			input_a[input_name]

							a__  :	List[str]		 =			feat_extract.pad(a_	, padding="longest"	, pad_to_multiple_of=10    )
							a__  :	Dict		 =			input_a[input_name]

							a__  :	List[Any]		 =			feat_extract.pad(
							    a_	, padding="max_length"	, pad_to_multiple_of=10	, max_length=a_    )
							a__  :	Tuple		 =			input_a[input_name]

							a__  :	List[Any]		 =			feat_extract.pad(
							    a_	, padding="max_length"	, pad_to_multiple_of=10	, max_length=a_	, return_tensors="np"	, )
							a__  :	Optional[int]		 =			input_a[input_name]

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

							a__  :	List[str]		 =			pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
							self.assertTrue(all(len(a_    ) == 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
							a__  :	Optional[int]		 =			(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    :			Dict	, a_    :			Optional[int]=False    )	->	List[str]:



							'''simple docstring'''



							def _inputs_have_equal_length(a_    :			List[Any]    ):
										a__  :	List[str]		 =			len(input[0]    )
										for input_slice in input[1:]:
													if len(a_    ) != length:
																return False
										return True

							def _inputs_are_equal(a_    :			Optional[int]	, a_    :			List[Any]    ):
										if len(a_    ) != len(a_    ):
													return False

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

							a__  :	Dict		 =			self.feature_extraction_class(**self.feat_extract_dict    )
							a__  :	Union[str, Any]		 =			self.feat_extract_tester.prepare_inputs_for_common(numpify=a_    )
							a__  :	List[str]		 =			feat_extract.model_input_names[0]

							a__  :	str		 =			BatchFeature({input_name: speech_inputs}    )

							# truncate to smallest
							a__  :	int		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=len(speech_inputs[0]    )	, truncation=a_    )
							a__  :	Optional[Any]		 =			input_a[input_name]

							a__  :	int		 =			feat_extract.pad(a_	, padding="max_length"	, max_length=len(speech_inputs[0]    )    )
							a__  :	List[Any]		 =			input_a[input_name]

							self.assertTrue(_inputs_have_equal_length(a_    )    )
							self.assertFalse(_inputs_have_equal_length(a_    )    )

							# truncate to smallest with np
							a__  :	Union[str, Any]		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=len(speech_inputs[0]    )	, return_tensors="np"	, truncation=a_	, )
							a__  :	Tuple		 =			input_a[input_name]

							a__  :	str		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=len(speech_inputs[0]    )	, return_tensors="np"    )
							a__  :	Union[str, Any]		 =			input_a[input_name]

							self.assertTrue(_inputs_have_equal_length(a_    )    )
							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(a_    )    )

							# truncate to middle
							a__  :	str		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=len(speech_inputs[1]    )	, truncation=a_	, return_tensors="np"	, )
							a__  :	List[Any]		 =			input_a[input_name]

							a__  :	List[str]		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=len(speech_inputs[1]    )	, truncation=a_    )
							a__  :	str		 =			input_a[input_name]

							a__  :	Any		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=len(speech_inputs[1]    )	, return_tensors="np"    )
							a__  :	Optional[int]		 =			input_a[input_name]

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

							# 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(a_    )    )
							self.assertTrue(len(input_a[-1]    ) == len(speech_inputs[-1]    )    )

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

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

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

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

							# test truncation for `pad_to_multiple_of` for List[int] + numpy
							a__  :	Tuple		 =			12
							a__  :	Dict		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=len(speech_inputs[0]    )	, pad_to_multiple_of=a_	, truncation=a_	, )
							a__  :	Optional[int]		 =			input_a[input_name]

							a__  :	List[Any]		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=len(speech_inputs[0]    )	, pad_to_multiple_of=a_	, )
							a__  :	str		 =			input_a[input_name]

							# retrieve expected_length as multiple of pad_to_multiple_of
							a__  :	Dict		 =			len(speech_inputs[0]    )
							if expected_length % pad_to_multiple_of != 0:
										a__  :	Tuple		 =			((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(a_    )    )
							self.assertFalse(_inputs_have_equal_length(a_    )    )
				def       UpperCAmelCase      (		self    :			List[str]    )	->	List[str]:



							'''simple docstring'''



							self._check_padding(numpify=a_    )
				def       UpperCAmelCase      (		self    :			Tuple    )	->	Tuple:



							'''simple docstring'''



							self._check_padding(numpify=a_    )
				def       UpperCAmelCase      (		self    :			int    )	->	Optional[Any]:



							'''simple docstring'''



							self._check_truncation(numpify=a_    )
				def       UpperCAmelCase      (		self    :			str    )	->	int:



							'''simple docstring'''



							self._check_truncation(numpify=a_    )
				@require_torch
				def       UpperCAmelCase      (		self    :			List[Any]    )	->	Tuple:



							'''simple docstring'''



							a__  :	Tuple		 =			self.feature_extraction_class(**self.feat_extract_dict    )
							a__  :	Tuple		 =			self.feat_extract_tester.prepare_inputs_for_common()
							a__  :	int		 =			feat_extract.model_input_names[0]

							a__  :	str		 =			BatchFeature({input_name: speech_inputs}    )

							a__  :	int		 =			feat_extract.pad(a_	, padding="longest"	, return_tensors="np"    )[input_name]
							a__  :	str		 =			feat_extract.pad(a_	, 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    :			Optional[Any]    )	->	str:



							'''simple docstring'''



							a__  :	int		 =			self.feature_extraction_class(**self.feat_extract_dict    )
							a__  :	Optional[int]		 =			self.feat_extract_tester.prepare_inputs_for_common()
							a__  :	Optional[Any]		 =			feat_extract.model_input_names[0]

							a__  :	Dict		 =			BatchFeature({input_name: speech_inputs}    )

							a__  :	List[Any]		 =			feat_extract.pad(a_	, padding="longest"	, return_tensors="np"    )[input_name]
							a__  :	List[str]		 =			feat_extract.pad(a_	, 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    :			Optional[Any]    )	->	List[str]:



							'''simple docstring'''



							a__  :	Union[str, Any]		 =			self.feat_extract_dict
							a__  :	Dict		 =			True
							a__  :	Dict		 =			self.feature_extraction_class(**a_    )
							a__  :	List[Any]		 =			self.feat_extract_tester.prepare_inputs_for_common()
							a__  :	List[Any]		 =			[len(a_    ) for x in speech_inputs]
							a__  :	List[str]		 =			feat_extract.model_input_names[0]

							a__  :	int		 =			BatchFeature({input_name: speech_inputs}    )

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






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



							'''simple docstring'''



							a__  :	List[str]		 =			self.feat_extract_dict
							a__  :	Any		 =			True
							a__  :	Optional[Any]		 =			self.feature_extraction_class(**a_    )
							a__  :	List[Any]		 =			self.feat_extract_tester.prepare_inputs_for_common()
							a__  :	Optional[int]		 =			[len(a_    ) for x in speech_inputs]
							a__  :	Tuple		 =			feat_extract.model_input_names[0]

							a__  :	int		 =			BatchFeature({input_name: speech_inputs}    )
							a__  :	Tuple		 =			min(a_    )

							a__  :	int		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=a_	, truncation=a_	, return_tensors="np"    )
							self.assertIn("attention_mask"	, a_    )
							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]    )