'''simple docstring'''




import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


__lowercase          =  logging.get_logger(__name__)






def 				snake_case__							(			_A:					Tuple=None      ,   _A:					List[str]=None )    ->	Tuple:

		'''simple docstring'''


		return field(default_factory=lambda: default      ,   metadata=__SCREAMING_SNAKE_CASE )






@dataclass
class 				a__:


			'''simple docstring'''






			UpperCAmelCase_			:     List[str]      =       list_field(
			    default=[] ,						metadata={
			        '''help''': (
			            '''Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'''
			            ''' of all available models'''
			        )
			    } ,						)

			UpperCAmelCase_			:     List[int]      =       list_field(
			    default=[8] ,						metadata={'''help''': '''List of batch sizes for which memory and time performance will be evaluated'''}	)

			UpperCAmelCase_			:     List[int]      =       list_field(
			    default=[8, 3_2, 1_2_8, 5_1_2] ,						metadata={'''help''': '''List of sequence lengths for which memory and time performance will be evaluated'''} ,						)

			UpperCAmelCase_			:     bool      =       field(
			    default=__snake_case ,						metadata={'''help''': '''Whether to benchmark inference of model. Inference can be disabled via --no-inference.'''} ,						)
			UpperCAmelCase_			:     bool      =       field(
			    default=__snake_case ,						metadata={'''help''': '''Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'''} ,						)
			UpperCAmelCase_			:     bool      =       field(
			    default=__snake_case ,						metadata={'''help''': '''Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'''}	)
			UpperCAmelCase_			:     bool      =       field(default=__snake_case ,						metadata={'''help''': '''Use FP16 to accelerate inference.'''}	)
			UpperCAmelCase_			:     bool      =       field(default=__snake_case ,						metadata={'''help''': '''Benchmark training of model'''}	)
			UpperCAmelCase_			:     bool      =       field(default=__snake_case ,						metadata={'''help''': '''Verbose memory tracing'''}	)
			UpperCAmelCase_			:     bool      =       field(
			    default=__snake_case ,						metadata={'''help''': '''Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'''} ,						)
			UpperCAmelCase_			:     bool      =       field(
			    default=__snake_case ,						metadata={
			        '''help''': '''Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'''
			    } ,						)
			UpperCAmelCase_			:     bool      =       field(default=__snake_case ,						metadata={'''help''': '''Trace memory line by line'''}	)
			UpperCAmelCase_			:     bool      =       field(default=__snake_case ,						metadata={'''help''': '''Save result to a CSV file'''}	)
			UpperCAmelCase_			:     bool      =       field(default=__snake_case ,						metadata={'''help''': '''Save all print statements in a log file'''}	)
			UpperCAmelCase_			:     bool      =       field(default=__snake_case ,						metadata={'''help''': '''Whether to print environment information'''}	)
			UpperCAmelCase_			:     bool      =       field(
			    default=__snake_case ,						metadata={
			        '''help''': (
			            '''Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'''
			            ''' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'''
			            ''' for debugging / testing and on TPU.'''
			        )
			    } ,						)
			UpperCAmelCase_			:     str      =       field(
			    default=F'''inference_time_{round(time()	)}.csv''' ,						metadata={'''help''': '''CSV filename used if saving time results to csv.'''} ,						)
			UpperCAmelCase_			:     str      =       field(
			    default=F'''inference_memory_{round(time()	)}.csv''' ,						metadata={'''help''': '''CSV filename used if saving memory results to csv.'''} ,						)
			UpperCAmelCase_			:     str      =       field(
			    default=F'''train_time_{round(time()	)}.csv''' ,						metadata={'''help''': '''CSV filename used if saving time results to csv for training.'''} ,						)
			UpperCAmelCase_			:     str      =       field(
			    default=F'''train_memory_{round(time()	)}.csv''' ,						metadata={'''help''': '''CSV filename used if saving memory results to csv for training.'''} ,						)
			UpperCAmelCase_			:     str      =       field(
			    default=F'''env_info_{round(time()	)}.csv''' ,						metadata={'''help''': '''CSV filename used if saving environment information.'''} ,						)
			UpperCAmelCase_			:     str      =       field(
			    default=F'''log_{round(time()	)}.csv''' ,						metadata={'''help''': '''Log filename used if print statements are saved in log.'''} ,						)
			UpperCAmelCase_			:     int      =       field(default=3 ,						metadata={'''help''': '''Times an experiment will be run.'''}	)
			UpperCAmelCase_			:     bool      =       field(
			    default=__snake_case ,						metadata={
			        '''help''': (
			            '''Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'''
			            ''' model weights.'''
			        )
			    } ,						)



			def 							a_       (   self):




					"""simple docstring"""


					warnings.warn(
					    f"The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"
					    """ are deprecated in general and it is advised to use external Benchmarking libraries """
					    """ to benchmark Transformer models."""       ,    UpperCamelCase__       ,    )



			def 							a_       (   self):




					"""simple docstring"""


					return json.dumps(dataclasses.asdict(self)       ,    indent=2)



			@property
			def 							a_       (   self):




					"""simple docstring"""


					if len(self.models) <= 0:
							raise ValueError(
							    """Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
							    """ bert-base-cased` or `args.models = ['bert-base-cased'].""")
					return self.models







			@property
			def 							a_       (   self):




					"""simple docstring"""


					if not self.multi_process:
							return False
					elif self.is_tpu:
							logger.info("""Multiprocessing is currently not possible on TPU.""")
							return False
					else:
							return True







"""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     snake_case	(				__snake_case  ):
				# to overwrite at feature extractactor specific tests
				SCREAMING_SNAKE_CASE_   :      List[str]         =  None
				SCREAMING_SNAKE_CASE_   :      Tuple         =  None

				@property
				def 							lowercase_     (	self							:				str)->   Any:



						'''simple docstring'''


						return self.feat_extract_tester.prepare_feat_extract_dict()

				def 							lowercase_     (	self							:				Tuple)->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			self.feature_extraction_class(**self.feat_extract_dict)
						self.assertTrue(hasattr(UpperCamelCase__     ,  "feature_size"))
						self.assertTrue(hasattr(UpperCamelCase__     ,  "sampling_rate"))
						self.assertTrue(hasattr(UpperCamelCase__     ,  "padding_value"))

				def 							lowercase_     (	self							:				Dict)->   Any:



						'''simple docstring'''


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

						__lowerCAmelCase:       List[Any]          =			BatchFeature({input_name: speech_inputs})

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

						__lowerCAmelCase:       Tuple          =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase__)
						__lowerCAmelCase:       Optional[int]          =			BatchFeature({input_name: speech_inputs}     ,  tensor_type="np")

						__lowerCAmelCase:       Optional[int]          =			processed_features[input_name]

						if len(batch_features_input.shape) < 3:
								__lowerCAmelCase:       Dict          =			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 							lowercase_     (	self							:				Optional[int])->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase__)
						__lowerCAmelCase:       int          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Any          =			feat_extract.model_input_names[0]

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

						__lowerCAmelCase:       str          =			processed_features[input_name]

						if len(batch_features_input.shape) < 3:
								__lowerCAmelCase:       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 							lowercase_     (	self							:				str)->   Optional[Any]:



						'''simple docstring'''


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

						__lowerCAmelCase:       Optional[int]          =			BatchFeature({input_name: speech_inputs}     ,  tensor_type="tf")

						__lowerCAmelCase:       int          =			processed_features[input_name]

						if len(batch_features_input.shape) < 3:
								__lowerCAmelCase:       Dict          =			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 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				List[str]=False)->   Optional[int]:



						'''simple docstring'''


						def _inputs_have_equal_length(UpperCamelCase__							:				Tuple):
								__lowerCAmelCase:       Optional[int]          =			len(input[0])
								for input_slice in input[1:]:
										if len(UpperCamelCase__) != length:
												return False
								return True

						def _inputs_are_equal(UpperCamelCase__							:				str     ,  UpperCamelCase__							:				str):
								if len(UpperCamelCase__) != len(UpperCamelCase__):
										return False

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

						__lowerCAmelCase:       List[str]          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Optional[Any]          =			self.feat_extract_tester.prepare_inputs_for_common(numpify=UpperCamelCase__)
						__lowerCAmelCase:       Tuple          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       List[Any]          =			BatchFeature({input_name: speech_inputs})

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

						# test padding for List[int] + numpy
						__lowerCAmelCase:       Optional[int]          =			feat_extract.pad(UpperCamelCase__     ,  padding=UpperCamelCase__)
						__lowerCAmelCase:       Optional[int]          =			input_a[input_name]

						__lowerCAmelCase:       List[Any]          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest")
						__lowerCAmelCase:       int          =			input_a[input_name]

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

						__lowerCAmelCase:       Union[str, Any]          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  return_tensors="np")
						__lowerCAmelCase:       Optional[int]          =			input_a[input_name]

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

						__lowerCAmelCase:       Any          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=UpperCamelCase__     ,  return_tensors="np")
						__lowerCAmelCase:       Optional[Any]          =			input_a[input_name]

						self.assertFalse(_inputs_have_equal_length(UpperCamelCase__))
						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						self.assertTrue(_inputs_are_equal(UpperCamelCase__     ,  UpperCamelCase__))
						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
						__lowerCAmelCase:       Dict          =			feat_extract.pad(UpperCamelCase__     ,  pad_to_multiple_of=1_0)
						__lowerCAmelCase:       Tuple          =			input_a[input_name]

						__lowerCAmelCase:       Tuple          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  pad_to_multiple_of=1_0)
						__lowerCAmelCase:       List[str]          =			input_a[input_name]

						__lowerCAmelCase:       str          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  pad_to_multiple_of=1_0     ,  max_length=UpperCamelCase__)
						__lowerCAmelCase:       str          =			input_a[input_name]

						__lowerCAmelCase:       Dict          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  pad_to_multiple_of=1_0     ,  max_length=UpperCamelCase__     ,  return_tensors="np"     ,  )
						__lowerCAmelCase:       List[str]          =			input_a[input_name]

						self.assertTrue(all(len(UpperCamelCase__) % 1_0 == 0 for x in input_a))
						self.assertTrue(_inputs_are_equal(UpperCamelCase__     ,  UpperCamelCase__))

						__lowerCAmelCase:       Optional[Any]          =			pad_max_length if pad_max_length % 1_0 == 0 else (pad_max_length // 1_0 + 1) * 1_0
						self.assertTrue(all(len(UpperCamelCase__) == 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
						__lowerCAmelCase:       Any          =			(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 							lowercase_     (	self							:				Optional[int]     ,  UpperCamelCase__							:				Optional[int]=False)->   str:



						'''simple docstring'''


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

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

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

						__lowerCAmelCase:       Dict          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Any          =			self.feat_extract_tester.prepare_inputs_for_common(numpify=UpperCamelCase__)
						__lowerCAmelCase:       Any          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       Optional[int]          =			BatchFeature({input_name: speech_inputs})

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

						__lowerCAmelCase:       str          =			feat_extract.pad(UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0]))
						__lowerCAmelCase:       Tuple          =			input_a[input_name]

						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						self.assertFalse(_inputs_have_equal_length(UpperCamelCase__))

						# truncate to smallest with np
						__lowerCAmelCase:       Optional[int]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0])     ,  return_tensors="np"     ,  truncation=UpperCamelCase__     ,  )
						__lowerCAmelCase:       List[str]          =			input_a[input_name]

						__lowerCAmelCase:       List[str]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0])     ,  return_tensors="np")
						__lowerCAmelCase:       Any          =			input_a[input_name]

						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						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(UpperCamelCase__))

						# truncate to middle
						__lowerCAmelCase:       Union[str, Any]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[1])     ,  truncation=UpperCamelCase__     ,  return_tensors="np"     ,  )
						__lowerCAmelCase:       int          =			input_a[input_name]

						__lowerCAmelCase:       List[Any]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[1])     ,  truncation=UpperCamelCase__)
						__lowerCAmelCase:       Dict          =			input_a[input_name]

						__lowerCAmelCase:       List[Any]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[1])     ,  return_tensors="np")
						__lowerCAmelCase:       str          =			input_a[input_name]

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

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

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

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

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

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

						# test truncation for `pad_to_multiple_of` for List[int] + numpy
						__lowerCAmelCase:       Tuple          =			1_2
						__lowerCAmelCase:       Optional[int]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0])     ,  pad_to_multiple_of=UpperCamelCase__     ,  truncation=UpperCamelCase__     ,  )
						__lowerCAmelCase:       Dict          =			input_a[input_name]

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

						# retrieve expected_length as multiple of pad_to_multiple_of
						__lowerCAmelCase:       Optional[Any]          =			len(speech_inputs[0])
						if expected_length % pad_to_multiple_of != 0:
								__lowerCAmelCase:       Optional[Any]          =			((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(UpperCamelCase__))
						self.assertFalse(_inputs_have_equal_length(UpperCamelCase__))

				def 							lowercase_     (	self							:				Union[str, Any])->   Optional[Any]:



						'''simple docstring'''


						self._check_padding(numpify=UpperCamelCase__)

				def 							lowercase_     (	self							:				Optional[Any])->   List[str]:



						'''simple docstring'''


						self._check_padding(numpify=UpperCamelCase__)

				def 							lowercase_     (	self							:				List[Any])->   Tuple:



						'''simple docstring'''


						self._check_truncation(numpify=UpperCamelCase__)

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



						'''simple docstring'''


						self._check_truncation(numpify=UpperCamelCase__)

				@require_torch
				def 							lowercase_     (	self							:				Dict)->   Dict:



						'''simple docstring'''


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

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

						__lowerCAmelCase:       str          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  return_tensors="np")[input_name]
						__lowerCAmelCase:       Dict          =			feat_extract.pad(UpperCamelCase__     ,  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 							lowercase_     (	self							:				Tuple)->   Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Dict          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Tuple          =			self.feat_extract_tester.prepare_inputs_for_common()
						__lowerCAmelCase:       List[Any]          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       Any          =			BatchFeature({input_name: speech_inputs})

						__lowerCAmelCase:       Dict          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  return_tensors="np")[input_name]
						__lowerCAmelCase:       Any          =			feat_extract.pad(UpperCamelCase__     ,  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 							lowercase_     (	self							:				Optional[int])->   List[Any]:



						'''simple docstring'''


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

						__lowerCAmelCase:       Optional[int]          =			BatchFeature({input_name: speech_inputs})

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




				def 							lowercase_     (	self							:				List[str])->   Any:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			self.feat_extract_dict
						__lowerCAmelCase:       str          =			True
						__lowerCAmelCase:       int          =			self.feature_extraction_class(**UpperCamelCase__)
						__lowerCAmelCase:       int          =			self.feat_extract_tester.prepare_inputs_for_common()
						__lowerCAmelCase:       str          =			[len(UpperCamelCase__) for x in speech_inputs]
						__lowerCAmelCase:       Any          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       Optional[int]          =			BatchFeature({input_name: speech_inputs})
						__lowerCAmelCase:       List[str]          =			min(UpperCamelCase__)

						__lowerCAmelCase:       List[str]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=UpperCamelCase__     ,  truncation=UpperCamelCase__     ,  return_tensors="np")
						self.assertIn("attention_mask"     ,  UpperCamelCase__)
						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])


