

"""simple docstring"""




import os
import re
import shutil
import sys
import tempfile
import unittest

import black


A_							:			Optional[Any]											= os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
A_							:			Any											= "    \"\"\"\n    Output class for the scheduler's step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"\"\"\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n"





class    lowerCamelCase						(unittest.TestCase			):

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      tempfile.mkdtemp()
						os.makedirs(os.path.join(self.diffusers_dir    ,				"""schedulers/"""       )       )
						SCREAMING_SNAKE_CASE__								=      self.diffusers_dir
						shutil.copy(
						    os.path.join(__UpperCAmelCase    ,				"""src/diffusers/schedulers/scheduling_ddpm.py"""       )    ,				os.path.join(self.diffusers_dir    ,				"""schedulers/scheduling_ddpm.py"""       )    ,				)

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      """src/diffusers"""
						shutil.rmtree(self.diffusers_dir       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						List[str]    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						str=None       )   ->    List[Any]:
						SCREAMING_SNAKE_CASE__								=      comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
						if overwrite_result is not None:
									SCREAMING_SNAKE_CASE__								=      comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
						SCREAMING_SNAKE_CASE__								=      black.Mode(target_versions={black.TargetVersion.PYaa}    ,				line_length=1_1_9       )
						SCREAMING_SNAKE_CASE__								=      black.format_str(__UpperCAmelCase    ,				mode=__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      os.path.join(self.diffusers_dir    ,				"""new_code.py"""       )
						with open(__UpperCAmelCase    ,				"""w"""    ,				newline="""\n"""       ) as f:
									f.write(__UpperCAmelCase       )
						if overwrite_result is None:
									self.assertTrue(len(check_copies.is_copy_consistent(__UpperCAmelCase       )       ) == 0       )
						else:
									check_copies.is_copy_consistent(f.name    ,				overwrite=__UpperCAmelCase       )
									with open(__UpperCAmelCase    ,				"""r"""       ) as f:
												self.assertTrue(f.read()    ,				__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      check_copies.find_code_in_diffusers("""schedulers.scheduling_ddpm.DDPMSchedulerOutput"""       )
						self.assertEqual(__UpperCAmelCase    ,				__UpperCAmelCase       )





			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    List[Any]:
						# Base copy consistency
						self.check_copy_consistency(
						    """# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput"""    ,				"""DDPMSchedulerOutput"""    ,				REFERENCE_CODE + """\n"""    ,				)

						# With no empty line at the end
						self.check_copy_consistency(
						    """# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput"""    ,				"""DDPMSchedulerOutput"""    ,				__UpperCAmelCase    ,				)

						# Copy consistency with rename
						self.check_copy_consistency(
						    """# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test"""    ,				"""TestSchedulerOutput"""    ,				re.sub("""DDPM"""    ,				"""Test"""    ,				__UpperCAmelCase       )    ,				)

						# Copy consistency with a really long name
						SCREAMING_SNAKE_CASE__								=      """TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"""
						self.check_copy_consistency(
						    F"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}"""    ,				F"""{long_class_name}SchedulerOutput"""    ,				re.sub("""Bert"""    ,				__UpperCAmelCase    ,				__UpperCAmelCase       )    ,				)

						# Copy consistency with overwrite
						self.check_copy_consistency(
						    """# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test"""    ,				"""TestSchedulerOutput"""    ,				__UpperCAmelCase    ,				overwrite_result=re.sub("""DDPM"""    ,				"""Test"""    ,				__UpperCAmelCase       )    ,				)





"""simple docstring"""




import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
						import torch


A_							:			List[str]											= random.Random()

def 	A       (     snake_case__		,				snake_case__=1.0		,				snake_case__=None		,				snake_case__=None    ):




			'''simple docstring'''


			if rng is None:
						SCREAMING_SNAKE_CASE__								=      global_rng

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

			return values





@require_torch
class    lowerCamelCase						(unittest.TestCase			):

			def __init__(					self				:						Tuple    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						List[str]=7    ,				__UpperCAmelCase				:						List[str]=4_0_0    ,				__UpperCAmelCase				:						Tuple=2_0_0_0    ,				__UpperCAmelCase				:						Any=1    ,				__UpperCAmelCase				:						str=0.0    ,				__UpperCAmelCase				:						int=1_6_0_0_0    ,				__UpperCAmelCase				:						str=True    ,				__UpperCAmelCase				:						List[str]=8_0    ,				__UpperCAmelCase				:						Dict=1_6    ,				__UpperCAmelCase				:						int=6_4    ,				__UpperCAmelCase				:						Any="hann_window"    ,				__UpperCAmelCase				:						Tuple=8_0    ,				__UpperCAmelCase				:						Tuple=7_6_0_0    ,				__UpperCAmelCase				:						List[Any]=1e-10    ,				__UpperCAmelCase				:						Tuple=True    ,				)   ->    int:
						SCREAMING_SNAKE_CASE__								=      parent
						SCREAMING_SNAKE_CASE__								=      batch_size
						SCREAMING_SNAKE_CASE__								=      min_seq_length
						SCREAMING_SNAKE_CASE__								=      max_seq_length
						SCREAMING_SNAKE_CASE__								=      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
						SCREAMING_SNAKE_CASE__								=      feature_size
						SCREAMING_SNAKE_CASE__								=      padding_value
						SCREAMING_SNAKE_CASE__								=      sampling_rate
						SCREAMING_SNAKE_CASE__								=      do_normalize
						SCREAMING_SNAKE_CASE__								=      num_mel_bins
						SCREAMING_SNAKE_CASE__								=      hop_length
						SCREAMING_SNAKE_CASE__								=      win_length
						SCREAMING_SNAKE_CASE__								=      win_function
						SCREAMING_SNAKE_CASE__								=      fmin
						SCREAMING_SNAKE_CASE__								=      fmax
						SCREAMING_SNAKE_CASE__								=      mel_floor
						SCREAMING_SNAKE_CASE__								=      return_attention_mask

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    Any:
						return {
						    "feature_size": self.feature_size,
						    "padding_value": self.padding_value,
						    "sampling_rate": self.sampling_rate,
						    "do_normalize": self.do_normalize,
						    "num_mel_bins": self.num_mel_bins,
						    "hop_length": self.hop_length,
						    "win_length": self.win_length,
						    "win_function": self.win_function,
						    "fmin": self.fmin,
						    "fmax": self.fmax,
						    "mel_floor": self.mel_floor,
						    "return_attention_mask": self.return_attention_mask,
						}

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				__UpperCAmelCase				:						Dict=False    ,				__UpperCAmelCase				:						List[str]=False       )   ->    Optional[Any]:
						def _flatten(__UpperCAmelCase				:						List[Any]       ):
									return list(itertools.chain(*__UpperCAmelCase       )       )

						if equal_length:
									SCREAMING_SNAKE_CASE__								=      floats_list((self.batch_size, self.max_seq_length)       )
						else:
									# make sure that inputs increase in size
									SCREAMING_SNAKE_CASE__								=      [
									    _flatten(floats_list((x, self.feature_size)       )       )
									    for x in range(self.min_seq_length    ,				self.max_seq_length    ,				self.seq_length_diff       )
									]

						if numpify:
									SCREAMING_SNAKE_CASE__								=      [np.asarray(__UpperCAmelCase       ) for x in speech_inputs]

						return speech_inputs





			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						Union[str, Any]=False    ,				__UpperCAmelCase				:						Optional[int]=False       )   ->    str:
						if equal_length:
									SCREAMING_SNAKE_CASE__								=      [floats_list((self.max_seq_length, self.num_mel_bins)       ) for _ in range(self.batch_size       )]
						else:
									# make sure that inputs increase in size
									SCREAMING_SNAKE_CASE__								=      [
									    floats_list((x, self.num_mel_bins)       )
									    for x in range(self.min_seq_length    ,				self.max_seq_length    ,				self.seq_length_diff       )
									]

						if numpify:
									SCREAMING_SNAKE_CASE__								=      [np.asarray(__UpperCAmelCase       ) for x in speech_inputs]

						return speech_inputs





@require_torch
class    lowerCamelCase						(A__			,unittest.TestCase			):
			lowerCamelCase__   :		Any							=							SpeechTaFeatureExtractor

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Any:
						SCREAMING_SNAKE_CASE__								=      SpeechTaFeatureExtractionTester(self       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]    ,				__UpperCAmelCase				:						Any       )   ->    Tuple:
						self.assertTrue(np.all(np.mean(__UpperCAmelCase    ,				axis=0       ) < 1e-3       )       )
						self.assertTrue(np.all(np.abs(np.var(__UpperCAmelCase    ,				axis=0       ) - 1       ) < 1e-3       )       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    Optional[int]:
						# Tests that all call wrap to encode_plus and batch_encode_plus
						SCREAMING_SNAKE_CASE__								=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
						# create three inputs of length 800, 1000, and 1200
						SCREAMING_SNAKE_CASE__								=      [floats_list((1, x)       )[0] for x in range(8_0_0    ,				1_4_0_0    ,				2_0_0       )]
						SCREAMING_SNAKE_CASE__								=      [np.asarray(__UpperCAmelCase       ) for speech_input in speech_inputs]

						# Test not batched input
						SCREAMING_SNAKE_CASE__								=      feat_extract(speech_inputs[0]    ,				return_tensors="""np"""       ).input_values
						SCREAMING_SNAKE_CASE__								=      feat_extract(np_speech_inputs[0]    ,				return_tensors="""np"""       ).input_values
						self.assertTrue(np.allclose(__UpperCAmelCase    ,				__UpperCAmelCase    ,				atol=1e-3       )       )

						# Test batched
						SCREAMING_SNAKE_CASE__								=      feat_extract(__UpperCAmelCase    ,				return_tensors="""np"""       ).input_values
						SCREAMING_SNAKE_CASE__								=      feat_extract(__UpperCAmelCase    ,				return_tensors="""np"""       ).input_values
						for enc_seq_a, enc_seq_a in zip(__UpperCAmelCase    ,				__UpperCAmelCase       ):
									self.assertTrue(np.allclose(__UpperCAmelCase    ,				__UpperCAmelCase    ,				atol=1e-3       )       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
						SCREAMING_SNAKE_CASE__								=      [floats_list((1, x)       )[0] for x in range(8_0_0    ,				1_4_0_0    ,				2_0_0       )]

						SCREAMING_SNAKE_CASE__								=      ["""longest""", """max_length""", """do_not_pad"""]
						SCREAMING_SNAKE_CASE__								=      [None, 1_6_0_0, None]
						for max_length, padding in zip(__UpperCAmelCase    ,				__UpperCAmelCase       ):
									SCREAMING_SNAKE_CASE__								=      feat_extract(__UpperCAmelCase    ,				padding=__UpperCAmelCase    ,				max_length=__UpperCAmelCase    ,				return_tensors="""np"""       )
									SCREAMING_SNAKE_CASE__								=      processed.input_values

									self._check_zero_mean_unit_variance(input_values[0][:8_0_0]       )
									self.assertTrue(input_values[0][8_0_0:].sum() < 1e-6       )
									self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]       )
									self.assertTrue(input_values[0][1_0_0_0:].sum() < 1e-6       )
									self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
						SCREAMING_SNAKE_CASE__								=      range(8_0_0    ,				1_4_0_0    ,				2_0_0       )
						SCREAMING_SNAKE_CASE__								=      [floats_list((1, x)       )[0] for x in lengths]

						SCREAMING_SNAKE_CASE__								=      ["""longest""", """max_length""", """do_not_pad"""]
						SCREAMING_SNAKE_CASE__								=      [None, 1_6_0_0, None]

						for max_length, padding in zip(__UpperCAmelCase    ,				__UpperCAmelCase       ):
									SCREAMING_SNAKE_CASE__								=      feat_extract(__UpperCAmelCase    ,				max_length=__UpperCAmelCase    ,				padding=__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      processed.input_values

									self._check_zero_mean_unit_variance(input_values[0][:8_0_0]       )
									self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]       )
									self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
						SCREAMING_SNAKE_CASE__								=      [floats_list((1, x)       )[0] for x in range(8_0_0    ,				1_4_0_0    ,				2_0_0       )]
						SCREAMING_SNAKE_CASE__								=      feat_extract(
						    __UpperCAmelCase    ,				truncation=__UpperCAmelCase    ,				max_length=1_0_0_0    ,				padding="""max_length"""    ,				return_tensors="""np"""       )
						SCREAMING_SNAKE_CASE__								=      processed.input_values

						self._check_zero_mean_unit_variance(input_values[0, :8_0_0]       )
						self._check_zero_mean_unit_variance(input_values[1]       )
						self._check_zero_mean_unit_variance(input_values[2]       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
						SCREAMING_SNAKE_CASE__								=      [floats_list((1, x)       )[0] for x in range(8_0_0    ,				1_4_0_0    ,				2_0_0       )]
						SCREAMING_SNAKE_CASE__								=      feat_extract(
						    __UpperCAmelCase    ,				truncation=__UpperCAmelCase    ,				max_length=1_0_0_0    ,				padding="""longest"""    ,				return_tensors="""np"""       )
						SCREAMING_SNAKE_CASE__								=      processed.input_values

						self._check_zero_mean_unit_variance(input_values[0, :8_0_0]       )
						self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]       )
						self._check_zero_mean_unit_variance(input_values[2]       )

						# make sure that if max_length < longest -> then pad to max_length
						self.assertTrue(input_values.shape == (3, 1_0_0_0)       )

						SCREAMING_SNAKE_CASE__								=      [floats_list((1, x)       )[0] for x in range(8_0_0    ,				1_4_0_0    ,				2_0_0       )]
						SCREAMING_SNAKE_CASE__								=      feat_extract(
						    __UpperCAmelCase    ,				truncation=__UpperCAmelCase    ,				max_length=2_0_0_0    ,				padding="""longest"""    ,				return_tensors="""np"""       )
						SCREAMING_SNAKE_CASE__								=      processed.input_values

						self._check_zero_mean_unit_variance(input_values[0, :8_0_0]       )
						self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]       )
						self._check_zero_mean_unit_variance(input_values[2]       )

						# make sure that if max_length > longest -> then pad to longest
						self.assertTrue(input_values.shape == (3, 1_2_0_0)       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
						SCREAMING_SNAKE_CASE__								=      np.random.rand(1_0_0       ).astype(np.floataa       )
						SCREAMING_SNAKE_CASE__								=      np_speech_inputs.tolist()

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    str:
						# Tests that all call wrap to encode_plus and batch_encode_plus
						SCREAMING_SNAKE_CASE__								=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
						# create three inputs of length 800, 1000, and 1200
						SCREAMING_SNAKE_CASE__								=      [floats_list((1, x)       )[0] for x in range(8_0_0    ,				1_4_0_0    ,				2_0_0       )]
						SCREAMING_SNAKE_CASE__								=      [np.asarray(__UpperCAmelCase       ) for speech_input in speech_inputs]

						# Test feature size
						SCREAMING_SNAKE_CASE__								=      feature_extractor(audio_target=__UpperCAmelCase    ,				padding=__UpperCAmelCase    ,				return_tensors="""np"""       ).input_values
						self.assertTrue(input_values.ndim == 3       )
						self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins       )

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

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

						# Test 2-D numpy arrays are batched.
						SCREAMING_SNAKE_CASE__								=      [floats_list((1, x)       )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
						SCREAMING_SNAKE_CASE__								=      np.asarray(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      feature_extractor(__UpperCAmelCase    ,				return_tensors="""np"""       ).input_values
						SCREAMING_SNAKE_CASE__								=      feature_extractor(__UpperCAmelCase    ,				return_tensors="""np"""       ).input_values
						for enc_seq_a, enc_seq_a in zip(__UpperCAmelCase    ,				__UpperCAmelCase       ):
									self.assertTrue(np.allclose(__UpperCAmelCase    ,				__UpperCAmelCase    ,				atol=1e-3       )       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Any:
						SCREAMING_SNAKE_CASE__								=      self.feat_extract_tester.prepare_inputs_for_target()
						SCREAMING_SNAKE_CASE__								=      self.feature_extraction_class(**self.feat_extract_dict       )
						SCREAMING_SNAKE_CASE__								=      feat_extract.model_input_names[0]

						SCREAMING_SNAKE_CASE__								=      BatchFeature({input_name: speech_inputs}       )

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

						SCREAMING_SNAKE_CASE__								=      self.feat_extract_tester.prepare_inputs_for_target(equal_length=__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      BatchFeature({input_name: speech_inputs}    ,				tensor_type="""np"""       )

						SCREAMING_SNAKE_CASE__								=      processed_features[input_name]

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

						self.assertTrue(
						    batch_features_input.shape
						    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]       ), self.feat_extract_tester.num_mel_bins)       )

			@require_torch
			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      self.feat_extract_tester.prepare_inputs_for_target(equal_length=__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.feature_extraction_class(**self.feat_extract_dict       )
						SCREAMING_SNAKE_CASE__								=      feat_extract.model_input_names[0]

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

						SCREAMING_SNAKE_CASE__								=      processed_features[input_name]

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

						self.assertTrue(
						    batch_features_input.shape
						    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]       ), self.feat_extract_tester.num_mel_bins)       )

			@require_torch
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      self.feature_extraction_class(**self.feat_extract_dict       )
						SCREAMING_SNAKE_CASE__								=      self.feat_extract_tester.prepare_inputs_for_target()
						SCREAMING_SNAKE_CASE__								=      feat_extract.model_input_names[0]

						SCREAMING_SNAKE_CASE__								=      BatchFeature({input_name: speech_inputs}       )

						SCREAMING_SNAKE_CASE__								=      feat_extract.num_mel_bins  # hack!

						SCREAMING_SNAKE_CASE__								=      feat_extract.pad(__UpperCAmelCase    ,				padding="""longest"""    ,				return_tensors="""np"""       )[input_name]
						SCREAMING_SNAKE_CASE__								=      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       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      self.feat_extract_dict
						SCREAMING_SNAKE_CASE__								=      True
						SCREAMING_SNAKE_CASE__								=      self.feature_extraction_class(**__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.feat_extract_tester.prepare_inputs_for_target()
						SCREAMING_SNAKE_CASE__								=      [len(__UpperCAmelCase       ) for x in speech_inputs]
						SCREAMING_SNAKE_CASE__								=      feat_extract.model_input_names[0]

						SCREAMING_SNAKE_CASE__								=      BatchFeature({input_name: speech_inputs}       )

						SCREAMING_SNAKE_CASE__								=      feat_extract.num_mel_bins  # hack!

						SCREAMING_SNAKE_CASE__								=      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 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      self.feat_extract_dict
						SCREAMING_SNAKE_CASE__								=      True
						SCREAMING_SNAKE_CASE__								=      self.feature_extraction_class(**__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.feat_extract_tester.prepare_inputs_for_target()
						SCREAMING_SNAKE_CASE__								=      [len(__UpperCAmelCase       ) for x in speech_inputs]
						SCREAMING_SNAKE_CASE__								=      feat_extract.model_input_names[0]

						SCREAMING_SNAKE_CASE__								=      BatchFeature({input_name: speech_inputs}       )
						SCREAMING_SNAKE_CASE__								=      min(__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      feat_extract.num_mel_bins  # hack!

						SCREAMING_SNAKE_CASE__								=      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]       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]    ,				__UpperCAmelCase				:						int       )   ->    str:
						from datasets import load_dataset

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

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    Any:
						# fmt: off
						SCREAMING_SNAKE_CASE__								=      torch.tensor(
						    [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
						     3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
						     2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
						     4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
						     7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
						     4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]       )
						# fmt: on

						SCREAMING_SNAKE_CASE__								=      self._load_datasamples(1       )
						SCREAMING_SNAKE_CASE__								=      SpeechTaFeatureExtractor()
						SCREAMING_SNAKE_CASE__								=      feature_extractor(__UpperCAmelCase    ,				return_tensors="""pt"""       ).input_values
						self.assertEquals(input_values.shape    ,				(1, 9_3_6_8_0)       )
						self.assertTrue(torch.allclose(input_values[0, :3_0]    ,				__UpperCAmelCase    ,				atol=1e-6       )       )





			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Union[str, Any]:
						# fmt: off
						SCREAMING_SNAKE_CASE__								=      torch.tensor(
						    [-2.6_870, -3.0_104, -3.1_356, -3.5_352, -3.0_044, -3.0_353, -3.4_719, -3.6_777,
						     -3.1_520, -2.9_435, -2.6_553, -2.8_795, -2.9_944, -2.5_921, -3.0_279, -3.0_386,
						     -3.0_864, -3.1_291, -3.2_353, -2.7_444, -2.6_831, -2.7_287, -3.1_761, -3.1_571,
						     -3.2_726, -3.0_582, -3.1_007, -3.4_533, -3.4_695, -3.0_998]       )
						# fmt: on

						SCREAMING_SNAKE_CASE__								=      self._load_datasamples(1       )
						SCREAMING_SNAKE_CASE__								=      SpeechTaFeatureExtractor()
						SCREAMING_SNAKE_CASE__								=      feature_extractor(audio_target=__UpperCAmelCase    ,				return_tensors="""pt"""       ).input_values
						self.assertEquals(input_values.shape    ,				(1, 3_6_6, 8_0)       )
						self.assertTrue(torch.allclose(input_values[0, 0, :3_0]    ,				__UpperCAmelCase    ,				atol=1e-4       )       )


