# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput


@dataclass
class 			lowerCamelCase_  (     UpperCamelCase_				):






    '''simple docstring'''






    a__			:		Tuple      			=   4_2
    a__			:		Union[str, Any]      			=   4_2


class 			lowerCamelCase_  (     UpperCamelCase_		,			UpperCamelCase_				):






    '''simple docstring'''






    a__			:		Optional[int]      			=   1







    @register_to_config
    def __init__(			self				,				__lowercase = 2_000				,				__lowercase = 0.15				,				__lowercase = 0.01				,				__lowercase = 1_348.0				,				__lowercase = 1E-5				,				__lowercase = 1				,				) ->			Union[str, Any]:
        # standard deviation of the initial noise distribution
        __UpperCamelCase  :Tuple    						=	sigma_max

        # setable values
        __UpperCamelCase  :Union[str, Any]    						=	None

        self.set_sigmas(__A				,				__A				,				__A				,				__A)







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase = None) ->			Union[str, Any]:
        return sample







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase = None				,				__lowercase = None) ->			Any:
        __UpperCamelCase  :List[Any]    						=	sampling_eps if sampling_eps is not None else self.config.sampling_eps

        __UpperCamelCase  :Union[str, Any]    						=	torch.linspace(1				,				__A				,				__A				,				device=__A)







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase = None				,				__lowercase = None				,				__lowercase = None) ->			Any:
        __UpperCamelCase  :List[Any]    						=	sigma_min if sigma_min is not None else self.config.sigma_min
        __UpperCamelCase  :Tuple    						=	sigma_max if sigma_max is not None else self.config.sigma_max
        __UpperCamelCase  :Union[str, Any]    						=	sampling_eps if sampling_eps is not None else self.config.sampling_eps
        if self.timesteps is None:
            self.set_timesteps(__A				,				__A)

        __UpperCamelCase  :str    						=	sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
        __UpperCamelCase  :List[str]    						=	torch.exp(torch.linspace(math.log(__A)				,				math.log(__A)				,				__A))
        __UpperCamelCase  :Union[str, Any]    						=	torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps])







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase) ->			Union[str, Any]:
        return torch.where(
            timesteps == 0				,				torch.zeros_like(t.to(timesteps.device))				,				self.discrete_sigmas[timesteps - 1].to(timesteps.device)				,				)







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase = None				,				__lowercase = True				,				) ->			List[str]:
        if self.timesteps is None:
            raise ValueError(
                '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler''')

        __UpperCamelCase  :Dict    						=	timestep * torch.ones(
            sample.shape[0]				,				device=sample.device)  # torch.repeat_interleave(timestep, sample.shape[0])
        __UpperCamelCase  :str    						=	(timestep * (len(self.timesteps) - 1)).long()

        # mps requires indices to be in the same device, so we use cpu as is the default with cuda
        __UpperCamelCase  :Optional[int]    						=	timesteps.to(self.discrete_sigmas.device)

        __UpperCamelCase  :Dict    						=	self.discrete_sigmas[timesteps].to(sample.device)
        __UpperCamelCase  :List[Any]    						=	self.get_adjacent_sigma(__A				,				__A).to(sample.device)
        __UpperCamelCase  :Any    						=	torch.zeros_like(__A)
        __UpperCamelCase  :int    						=	(sigma**2 - adjacent_sigma**2) ** 0.5

        # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
        # also equation 47 shows the analog from SDE models to ancestral sampling methods
        __UpperCamelCase  :Any    						=	diffusion.flatten()
        while len(diffusion.shape) < len(sample.shape):
            __UpperCamelCase  :Optional[int]    						=	diffusion.unsqueeze(-1)
        __UpperCamelCase  :List[Any]    						=	drift - diffusion**2 * model_output

        #  equation 6: sample noise for the diffusion term of
        __UpperCamelCase  :List[str]    						=	randn_tensor(
            sample.shape				,				layout=sample.layout				,				generator=__A				,				device=sample.device				,				dtype=sample.dtype)
        __UpperCamelCase  :Tuple    						=	sample - drift  # subtract because `dt` is a small negative timestep
        # TODO is the variable diffusion the correct scaling term for the noise?
        __UpperCamelCase  :Tuple    						=	prev_sample_mean + diffusion * noise  # add impact of diffusion field g

        if not return_dict:
            return (prev_sample, prev_sample_mean)

        return SdeVeOutput(prev_sample=__A				,				prev_sample_mean=__A)







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase				,				__lowercase = None				,				__lowercase = True				,				) ->			List[str]:
        if self.timesteps is None:
            raise ValueError(
                '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler''')

        # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
        # sample noise for correction
        __UpperCamelCase  :Optional[int]    						=	randn_tensor(sample.shape				,				layout=sample.layout				,				generator=__A).to(sample.device)

        # compute step size from the model_output, the noise, and the snr
        __UpperCamelCase  :Dict    						=	torch.norm(model_output.reshape(model_output.shape[0]				,				-1)				,				dim=-1).mean()
        __UpperCamelCase  :Any    						=	torch.norm(noise.reshape(noise.shape[0]				,				-1)				,				dim=-1).mean()
        __UpperCamelCase  :Dict    						=	(self.config.snr * noise_norm / grad_norm) ** 2 * 2
        __UpperCamelCase  :Optional[int]    						=	step_size * torch.ones(sample.shape[0]).to(sample.device)
        # self.repeat_scalar(step_size, sample.shape[0])

        # compute corrected sample: model_output term and noise term
        __UpperCamelCase  :Optional[int]    						=	step_size.flatten()
        while len(step_size.shape) < len(sample.shape):
            __UpperCamelCase  :List[Any]    						=	step_size.unsqueeze(-1)
        __UpperCamelCase  :List[str]    						=	sample + step_size * model_output
        __UpperCamelCase  :Optional[int]    						=	prev_sample_mean + ((step_size * 2) ** 0.5) * noise

        if not return_dict:
            return (prev_sample,)

        return SchedulerOutput(prev_sample=__A)







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase				,				__lowercase				,				) ->			Tuple:
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        __UpperCamelCase  :Tuple    						=	timesteps.to(original_samples.device)
        __UpperCamelCase  :Union[str, Any]    						=	self.discrete_sigmas.to(original_samples.device)[timesteps]
        __UpperCamelCase  :List[Any]    						=	(
            noise * sigmas[:, None, None, None]
            if noise is not None
            else torch.randn_like(__A) * sigmas[:, None, None, None]
        )
        __UpperCamelCase  :str    						=	noise + original_samples
        return noisy_samples







    def __len__(			self) ->			Optional[int]:
        return self.config.num_train_timesteps
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 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    a__			:		Union[str, Any]      			=   None
    a__			:		Tuple      			=   None







    @property
    def     UpperCamelCase__     (			self) ->			Any:
        return self.feat_extract_tester.prepare_feat_extract_dict()







    def     UpperCamelCase__     (			self) ->			List[str]:
        __UpperCamelCase  :str    						=	self.feature_extraction_class(**self.feat_extract_dict)
        self.assertTrue(hasattr(__lowercase				,				'''feature_size'''))
        self.assertTrue(hasattr(__lowercase				,				'''sampling_rate'''))
        self.assertTrue(hasattr(__lowercase				,				'''padding_value'''))







    def     UpperCamelCase__     (			self) ->			Tuple:
        __UpperCamelCase  :str    						=	self.feat_extract_tester.prepare_inputs_for_common()
        __UpperCamelCase  :Optional[int]    						=	self.feature_extraction_class(**self.feat_extract_dict)
        __UpperCamelCase  :Optional[Any]    						=	feat_extract.model_input_names[0]

        __UpperCamelCase  :Union[str, Any]    						=	BatchFeature({input_name: speech_inputs})

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

        __UpperCamelCase  :Any    						=	self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowercase)
        __UpperCamelCase  :Optional[int]    						=	BatchFeature({input_name: speech_inputs}				,				tensor_type='''np''')

        __UpperCamelCase  :Any    						=	processed_features[input_name]

        if len(batch_features_input.shape) < 3:
            __UpperCamelCase  :Optional[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) ->			int:
        __UpperCamelCase  :Dict    						=	self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowercase)
        __UpperCamelCase  :Tuple    						=	self.feature_extraction_class(**self.feat_extract_dict)
        __UpperCamelCase  :str    						=	feat_extract.model_input_names[0]

        __UpperCamelCase  :Optional[Any]    						=	BatchFeature({input_name: speech_inputs}				,				tensor_type='''pt''')

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

        if len(batch_features_input.shape) < 3:
            __UpperCamelCase  :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))







    @require_tf
    def     UpperCamelCase__     (			self) ->			Optional[Any]:
        __UpperCamelCase  :str    						=	self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowercase)
        __UpperCamelCase  :str    						=	self.feature_extraction_class(**self.feat_extract_dict)
        __UpperCamelCase  :Union[str, Any]    						=	feat_extract.model_input_names[0]

        __UpperCamelCase  :Optional[Any]    						=	BatchFeature({input_name: speech_inputs}				,				tensor_type='''tf''')

        __UpperCamelCase  :int    						=	processed_features[input_name]

        if len(batch_features_input.shape) < 3:
            __UpperCamelCase  :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))







    def     UpperCamelCase__     (			self				,				__lowercase=False) ->			Dict:
        def _inputs_have_equal_length(__lowercase):
            __UpperCamelCase  :List[str]    						=	len(input[0])
            for input_slice in input[1:]:
                if len(__lowercase) != length:
                    return False
            return True

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

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

        __UpperCamelCase  :List[str]    						=	self.feature_extraction_class(**self.feat_extract_dict)
        __UpperCamelCase  :Optional[int]    						=	self.feat_extract_tester.prepare_inputs_for_common(numpify=__lowercase)
        __UpperCamelCase  :Any    						=	feat_extract.model_input_names[0]

        __UpperCamelCase  :Optional[Any]    						=	BatchFeature({input_name: speech_inputs})

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

        # test padding for List[int] + numpy
        __UpperCamelCase  :List[Any]    						=	feat_extract.pad(__lowercase				,				padding=__lowercase)
        __UpperCamelCase  :Tuple    						=	input_a[input_name]

        __UpperCamelCase  :int    						=	feat_extract.pad(__lowercase				,				padding='''longest''')
        __UpperCamelCase  :int    						=	input_a[input_name]

        __UpperCamelCase  :Optional[Any]    						=	feat_extract.pad(__lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[-1]))
        __UpperCamelCase  :Dict    						=	input_a[input_name]

        __UpperCamelCase  :List[Any]    						=	feat_extract.pad(__lowercase				,				padding='''longest'''				,				return_tensors='''np''')
        __UpperCamelCase  :Optional[int]    						=	input_a[input_name]

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

        __UpperCamelCase  :Any    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=__lowercase				,				return_tensors='''np''')
        __UpperCamelCase  :Tuple    						=	input_a[input_name]

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

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

        # test padding for `pad_to_multiple_of` for List[int] + numpy
        __UpperCamelCase  :int    						=	feat_extract.pad(__lowercase				,				pad_to_multiple_of=10)
        __UpperCamelCase  :Tuple    						=	input_a[input_name]

        __UpperCamelCase  :Optional[int]    						=	feat_extract.pad(__lowercase				,				padding='''longest'''				,				pad_to_multiple_of=10)
        __UpperCamelCase  :Tuple    						=	input_a[input_name]

        __UpperCamelCase  :str    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				pad_to_multiple_of=10				,				max_length=__lowercase)
        __UpperCamelCase  :Any    						=	input_a[input_name]

        __UpperCamelCase  :List[str]    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				pad_to_multiple_of=10				,				max_length=__lowercase				,				return_tensors='''np'''				,				)
        __UpperCamelCase  :List[str]    						=	input_a[input_name]

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

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

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

        # Check padding value is correct
        __UpperCamelCase  :Optional[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     UpperCamelCase__     (			self				,				__lowercase=False) ->			Dict:
        def _inputs_have_equal_length(__lowercase):
            __UpperCamelCase  :Dict    						=	len(input[0])
            for input_slice in input[1:]:
                if len(__lowercase) != length:
                    return False
            return True

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

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

        __UpperCamelCase  :str    						=	self.feature_extraction_class(**self.feat_extract_dict)
        __UpperCamelCase  :Union[str, Any]    						=	self.feat_extract_tester.prepare_inputs_for_common(numpify=__lowercase)
        __UpperCamelCase  :Tuple    						=	feat_extract.model_input_names[0]

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

        # truncate to smallest
        __UpperCamelCase  :List[Any]    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[0])				,				truncation=__lowercase)
        __UpperCamelCase  :str    						=	input_a[input_name]

        __UpperCamelCase  :Optional[int]    						=	feat_extract.pad(__lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[0]))
        __UpperCamelCase  :List[str]    						=	input_a[input_name]

        self.assertTrue(_inputs_have_equal_length(__lowercase))
        self.assertFalse(_inputs_have_equal_length(__lowercase))

        # truncate to smallest with np
        __UpperCamelCase  :Union[str, Any]    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[0])				,				return_tensors='''np'''				,				truncation=__lowercase				,				)
        __UpperCamelCase  :List[Any]    						=	input_a[input_name]

        __UpperCamelCase  :int    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[0])				,				return_tensors='''np''')
        __UpperCamelCase  :Optional[Any]    						=	input_a[input_name]

        self.assertTrue(_inputs_have_equal_length(__lowercase))
        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(__lowercase))

        # truncate to middle
        __UpperCamelCase  :Optional[Any]    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[1])				,				truncation=__lowercase				,				return_tensors='''np'''				,				)
        __UpperCamelCase  :Dict    						=	input_a[input_name]

        __UpperCamelCase  :str    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[1])				,				truncation=__lowercase)
        __UpperCamelCase  :Union[str, Any]    						=	input_a[input_name]

        __UpperCamelCase  :Optional[Any]    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[1])				,				return_tensors='''np''')
        __UpperCamelCase  :Union[str, Any]    						=	input_a[input_name]

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

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

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

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

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

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

        # test truncation for `pad_to_multiple_of` for List[int] + numpy
        __UpperCamelCase  :Dict    						=	12
        __UpperCamelCase  :str    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[0])				,				pad_to_multiple_of=__lowercase				,				truncation=__lowercase				,				)
        __UpperCamelCase  :List[Any]    						=	input_a[input_name]

        __UpperCamelCase  :Tuple    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[0])				,				pad_to_multiple_of=__lowercase				,				)
        __UpperCamelCase  :Any    						=	input_a[input_name]

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







    def     UpperCamelCase__     (			self) ->			Any:
        self._check_padding(numpify=__lowercase)







    def     UpperCamelCase__     (			self) ->			Dict:
        self._check_padding(numpify=__lowercase)







    def     UpperCamelCase__     (			self) ->			Any:
        self._check_truncation(numpify=__lowercase)







    def     UpperCamelCase__     (			self) ->			Union[str, Any]:
        self._check_truncation(numpify=__lowercase)







    @require_torch
    def     UpperCamelCase__     (			self) ->			Tuple:
        __UpperCamelCase  :int    						=	self.feature_extraction_class(**self.feat_extract_dict)
        __UpperCamelCase  :Union[str, Any]    						=	self.feat_extract_tester.prepare_inputs_for_common()
        __UpperCamelCase  :str    						=	feat_extract.model_input_names[0]

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

        __UpperCamelCase  :List[str]    						=	feat_extract.pad(__lowercase				,				padding='''longest'''				,				return_tensors='''np''')[input_name]
        __UpperCamelCase  :List[Any]    						=	feat_extract.pad(__lowercase				,				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) ->			Tuple:
        __UpperCamelCase  :int    						=	self.feature_extraction_class(**self.feat_extract_dict)
        __UpperCamelCase  :Any    						=	self.feat_extract_tester.prepare_inputs_for_common()
        __UpperCamelCase  :Any    						=	feat_extract.model_input_names[0]

        __UpperCamelCase  :Union[str, Any]    						=	BatchFeature({input_name: speech_inputs})

        __UpperCamelCase  :str    						=	feat_extract.pad(__lowercase				,				padding='''longest'''				,				return_tensors='''np''')[input_name]
        __UpperCamelCase  :Optional[Any]    						=	feat_extract.pad(__lowercase				,				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) ->			str:
        __UpperCamelCase  :List[Any]    						=	self.feat_extract_dict
        __UpperCamelCase  :Dict    						=	True
        __UpperCamelCase  :Dict    						=	self.feature_extraction_class(**__lowercase)
        __UpperCamelCase  :List[Any]    						=	self.feat_extract_tester.prepare_inputs_for_common()
        __UpperCamelCase  :Any    						=	[len(__lowercase) for x in speech_inputs]
        __UpperCamelCase  :int    						=	feat_extract.model_input_names[0]

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

        __UpperCamelCase  :Union[str, Any]    						=	feat_extract.pad(__lowercase				,				padding='''longest'''				,				return_tensors='''np''')
        self.assertIn('''attention_mask'''				,				__lowercase)
        self.assertListEqual(list(processed.attention_mask.shape)				,				list(processed[input_name].shape[:2]))
        self.assertListEqual(processed.attention_mask.sum(-1).tolist()				,				__lowercase)







    def     UpperCamelCase__     (			self) ->			List[str]:
        __UpperCamelCase  :Optional[int]    						=	self.feat_extract_dict
        __UpperCamelCase  :Optional[int]    						=	True
        __UpperCamelCase  :Dict    						=	self.feature_extraction_class(**__lowercase)
        __UpperCamelCase  :List[str]    						=	self.feat_extract_tester.prepare_inputs_for_common()
        __UpperCamelCase  :List[Any]    						=	[len(__lowercase) for x in speech_inputs]
        __UpperCamelCase  :List[Any]    						=	feat_extract.model_input_names[0]

        __UpperCamelCase  :int    						=	BatchFeature({input_name: speech_inputs})
        __UpperCamelCase  :Dict    						=	min(__lowercase)

        __UpperCamelCase  :Union[str, Any]    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=__lowercase				,				truncation=__lowercase				,				return_tensors='''np''')
        self.assertIn('''attention_mask'''				,				__lowercase)
        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])





