



'''simple docstring'''







import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

def  lowerCamelCase_					(       A_    ,				A_=0.999    ,				A_="cosine"    ,				):
	if alpha_transform_type == "cosine":

		def alpha_bar_fn(A_				):
			return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

	elif alpha_transform_type == "exp":

		def alpha_bar_fn(A_				):
			return math.exp(t * -12.0				)

	else:
		raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''				)

	__lowerCamelCase		       =		[]
	for i in range(lowerCamelCase_				):
		__lowerCamelCase		       =		i / num_diffusion_timesteps
		__lowerCamelCase		       =		(i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar_fn(lowerCamelCase_				) / alpha_bar_fn(lowerCamelCase_				)    ,				lowerCamelCase_				)				)
	return torch.tensor(lowerCamelCase_    ,				dtype=torch.floataa				)







class 						_SCREAMING_SNAKE_CASE      ( A__				,  A__						):






			"""simple docstring"""







			SCREAMING_SNAKE_CASE_   			=				[e.name for e in KarrasDiffusionSchedulers]
			SCREAMING_SNAKE_CASE_   			=				2


			@register_to_config
			def __init__(   self       , _snake_case = 10_00       , _snake_case = 0.0_0_0_8_5       , _snake_case = 0.0_1_2       , _snake_case = "linear"       , _snake_case = None       , _snake_case = "epsilon"       , _snake_case = "linspace"       , _snake_case = 0       , ):







				"""simple docstring"""



				if trained_betas is not None:
					__lowerCamelCase		       =		torch.tensor(_snake_case       , dtype=torch.floataa    )
				elif beta_schedule == "linear":
					__lowerCamelCase		       =		torch.linspace(_snake_case       , _snake_case       , _snake_case       , dtype=torch.floataa    )
				elif beta_schedule == "scaled_linear":
					# this schedule is very specific to the latent diffusion model.
					__lowerCamelCase		       =		(
					    torch.linspace(beta_start**0.5       , beta_end**0.5       , _snake_case       , dtype=torch.floataa    ) ** 2
					)
				elif beta_schedule == "squaredcos_cap_v2":
					# Glide cosine schedule
					__lowerCamelCase		       =		betas_for_alpha_bar(_snake_case    )
				else:
					raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''    )

				__lowerCamelCase		       =		1.0 - self.betas
				__lowerCamelCase		       =		torch.cumprod(self.alphas       , dim=0    )

				#  set all values
				self.set_timesteps(_snake_case       , _snake_case       , _snake_case    )


			def 						_lowerCamelCase       (   self       , _snake_case       , _snake_case=None    ):







				"""simple docstring"""



				if schedule_timesteps is None:
					__lowerCamelCase		       =		self.timesteps

				__lowerCamelCase		       =		(schedule_timesteps == timestep).nonzero()

				# The sigma index that is taken for the **very** first `step`
				# is always the second index (or the last index if there is only 1)
				# This way we can ensure we don't accidentally skip a sigma in
				# case we start in the middle of the denoising schedule (e.g. for image-to-image)
				if len(self._index_counter    ) == 0:
					__lowerCamelCase		       =		1 if len(_snake_case    ) > 1 else 0
				else:
					__lowerCamelCase		       =		timestep.cpu().item() if torch.is_tensor(_snake_case    ) else timestep
					__lowerCamelCase		       =		self._index_counter[timestep_int]

				return indices[pos].item()


			@property
			def 						_lowerCamelCase       (   self    ):







				"""simple docstring"""



				if self.config.timestep_spacing in ["linspace", "trailing"]:
					return self.sigmas.max()

				return (self.sigmas.max() ** 2 + 1) ** 0.5


			def 						_lowerCamelCase       (   self       , _snake_case       , _snake_case       , ):







				"""simple docstring"""



				__lowerCamelCase		       =		self.index_for_timestep(_snake_case    )

				if self.state_in_first_order:
					__lowerCamelCase		       =		self.sigmas[step_index]
				else:
					__lowerCamelCase		       =		self.sigmas_interpol[step_index]

				__lowerCamelCase		       =		sample / ((sigma**2 + 1) ** 0.5)
				return sample


			def 						_lowerCamelCase       (   self       , _snake_case       , _snake_case = None       , _snake_case = None       , ):







				"""simple docstring"""



				__lowerCamelCase		       =		num_inference_steps

				__lowerCamelCase		       =		num_train_timesteps or self.config.num_train_timesteps

				# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
				if self.config.timestep_spacing == "linspace":
					__lowerCamelCase		       =		np.linspace(0       , num_train_timesteps - 1       , _snake_case       , dtype=_snake_case    )[::-1].copy()
				elif self.config.timestep_spacing == "leading":
					__lowerCamelCase		       =		num_train_timesteps // self.num_inference_steps
					# creates integer timesteps by multiplying by ratio
					# casting to int to avoid issues when num_inference_step is power of 3
					__lowerCamelCase		       =		(np.arange(0       , _snake_case    ) * step_ratio).round()[::-1].copy().astype(_snake_case    )
					timesteps += self.config.steps_offset
				elif self.config.timestep_spacing == "trailing":
					__lowerCamelCase		       =		num_train_timesteps / self.num_inference_steps
					# creates integer timesteps by multiplying by ratio
					# casting to int to avoid issues when num_inference_step is power of 3
					__lowerCamelCase		       =		(np.arange(_snake_case       , 0       , -step_ratio    )).round().copy().astype(_snake_case    )
					timesteps -= 1
				else:
					raise ValueError(
					    F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''    )

				__lowerCamelCase		       =		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
				__lowerCamelCase		       =		torch.from_numpy(np.log(_snake_case    )    ).to(_snake_case    )

				__lowerCamelCase		       =		np.interp(_snake_case       , np.arange(0       , len(_snake_case    )    )       , _snake_case    )
				__lowerCamelCase		       =		np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )
				__lowerCamelCase		       =		torch.from_numpy(_snake_case    ).to(device=_snake_case    )

				# interpolate sigmas
				__lowerCamelCase		       =		sigmas.log().lerp(sigmas.roll(1    ).log()       , 0.5    ).exp()

				__lowerCamelCase		       =		torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2    ), sigmas[-1:]]    )
				__lowerCamelCase		       =		torch.cat(
				    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2    ), sigmas_interpol[-1:]]    )

				if str(_snake_case    ).startswith('''mps'''    ):
					# mps does not support float64
					__lowerCamelCase		       =		torch.from_numpy(_snake_case    ).to(_snake_case       , dtype=torch.floataa    )
				else:
					__lowerCamelCase		       =		torch.from_numpy(_snake_case    ).to(_snake_case    )

				# interpolate timesteps
				__lowerCamelCase		       =		self.sigma_to_t(_snake_case    ).to(_snake_case       , dtype=timesteps.dtype    )
				__lowerCamelCase		       =		torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])       , dim=-1    ).flatten()

				__lowerCamelCase		       =		torch.cat([timesteps[:1], interleaved_timesteps]    )

				__lowerCamelCase		       =		None

				# for exp beta schedules, such as the one for `pipeline_shap_e.py`
				# we need an index counter
				__lowerCamelCase		       =		defaultdict(_snake_case    )


			def 						_lowerCamelCase       (   self       , _snake_case    ):







				"""simple docstring"""



				__lowerCamelCase		       =		sigma.log()

				# get distribution
				__lowerCamelCase		       =		log_sigma - self.log_sigmas[:, None]

				# get sigmas range
				__lowerCamelCase		       =		dists.ge(0    ).cumsum(dim=0    ).argmax(dim=0    ).clamp(max=self.log_sigmas.shape[0] - 2    )
				__lowerCamelCase		       =		low_idx + 1

				__lowerCamelCase		       =		self.log_sigmas[low_idx]
				__lowerCamelCase		       =		self.log_sigmas[high_idx]

				# interpolate sigmas
				__lowerCamelCase		       =		(low - log_sigma) / (low - high)
				__lowerCamelCase		       =		w.clamp(0       , 1    )

				# transform interpolation to time range
				__lowerCamelCase		       =		(1 - w) * low_idx + w * high_idx
				__lowerCamelCase		       =		t.view(sigma.shape    )
				return t


			@property
			def 						_lowerCamelCase       (   self    ):







				"""simple docstring"""



				return self.sample is None


			def 						_lowerCamelCase       (   self       , _snake_case       , _snake_case       , _snake_case       , _snake_case = True       , ):







				"""simple docstring"""



				__lowerCamelCase		       =		self.index_for_timestep(_snake_case    )

				# advance index counter by 1
				__lowerCamelCase		       =		timestep.cpu().item() if torch.is_tensor(_snake_case    ) else timestep
				self._index_counter[timestep_int] += 1

				if self.state_in_first_order:
					__lowerCamelCase		       =		self.sigmas[step_index]
					__lowerCamelCase		       =		self.sigmas_interpol[step_index + 1]
					__lowerCamelCase		       =		self.sigmas[step_index + 1]
				else:
					# 2nd order / KDPM2's method
					__lowerCamelCase		       =		self.sigmas[step_index - 1]
					__lowerCamelCase		       =		self.sigmas_interpol[step_index]
					__lowerCamelCase		       =		self.sigmas[step_index]

				# currently only gamma=0 is supported. This usually works best anyways.
				# We can support gamma in the future but then need to scale the timestep before
				# passing it to the model which requires a change in API
				__lowerCamelCase		       =		0
				__lowerCamelCase		       =		sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

				# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
				if self.config.prediction_type == "epsilon":
					__lowerCamelCase		       =		sigma_hat if self.state_in_first_order else sigma_interpol
					__lowerCamelCase		       =		sample - sigma_input * model_output
				elif self.config.prediction_type == "v_prediction":
					__lowerCamelCase		       =		sigma_hat if self.state_in_first_order else sigma_interpol
					__lowerCamelCase		       =		model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
					    sample / (sigma_input**2 + 1)
					)
				elif self.config.prediction_type == "sample":
					raise NotImplementedError('''prediction_type not implemented yet: sample'''    )
				else:
					raise ValueError(
					    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''    )

				if self.state_in_first_order:
					# 2. Convert to an ODE derivative for 1st order
					__lowerCamelCase		       =		(sample - pred_original_sample) / sigma_hat
					# 3. delta timestep
					__lowerCamelCase		       =		sigma_interpol - sigma_hat

					# store for 2nd order step
					__lowerCamelCase		       =		sample
				else:
					# DPM-Solver-2
					# 2. Convert to an ODE derivative for 2nd order
					__lowerCamelCase		       =		(sample - pred_original_sample) / sigma_interpol

					# 3. delta timestep
					__lowerCamelCase		       =		sigma_next - sigma_hat

					__lowerCamelCase		       =		self.sample
					__lowerCamelCase		       =		None

				__lowerCamelCase		       =		sample + derivative * dt

				if not return_dict:
					return (prev_sample,)

				return SchedulerOutput(prev_sample=_snake_case    )


			def 						_lowerCamelCase       (   self       , _snake_case       , _snake_case       , _snake_case       , ):







				"""simple docstring"""



				__lowerCamelCase		       =		self.sigmas.to(device=original_samples.device       , dtype=original_samples.dtype    )
				if original_samples.device.type == "mps" and torch.is_floating_point(_snake_case    ):
					# mps does not support float64
					__lowerCamelCase		       =		self.timesteps.to(original_samples.device       , dtype=torch.floataa    )
					__lowerCamelCase		       =		timesteps.to(original_samples.device       , dtype=torch.floataa    )
				else:
					__lowerCamelCase		       =		self.timesteps.to(original_samples.device    )
					__lowerCamelCase		       =		timesteps.to(original_samples.device    )

				__lowerCamelCase		       =		[self.index_for_timestep(_snake_case       , _snake_case    ) for t in timesteps]

				__lowerCamelCase		       =		sigmas[step_indices].flatten()
				while len(sigma.shape    ) < len(original_samples.shape    ):
					__lowerCamelCase		       =		sigma.unsqueeze(-1    )

				__lowerCamelCase		       =		original_samples + noise * sigma
				return noisy_samples


			def __len__(   self    ):







				"""simple docstring"""



				return self.config.num_train_timesteps









import unittest

import numpy as np

from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torchaudio,
    slow,
)

from .test_pipelines_common import ANY
@is_pipeline_test
class  _UpperCAmelCase   (			unittest.TestCase      ):

				"""simple docstring"""




				lowercase__											=			MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
				lowercase__											=			TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING




				def 							lowercase__  (	self		:       Any,    lowerCamelCase		:       Optional[Any],    lowerCamelCase		:       Any,    lowerCamelCase		:       Tuple    ):

								'''simple docstring'''
								lowercase__  					=     AudioClassificationPipeline(model=lowerCamelCase,    feature_extractor=lowerCamelCase    )

								# test with a raw waveform
								lowercase__  					=     np.zeros((34_000,)    )
								lowercase__  					=     np.zeros((14_000,)    )
								return audio_classifier, [audioa, audio]




				def 							lowercase__  (	self		:       str,    lowerCamelCase		:       Dict,    lowerCamelCase		:       Union[str, Any]    ):

								'''simple docstring'''
								lowercase__					,		lowercase__  					=     examples
								lowercase__  					=     audio_classifier(lowerCamelCase    )
								# by default a model is initialized with num_labels=2
								self.assertEqual(
								    lowerCamelCase,    [
								        {'''score''': ANY(lowerCamelCase    ), '''label''': ANY(lowerCamelCase    )},
								        {'''score''': ANY(lowerCamelCase    ), '''label''': ANY(lowerCamelCase    )},
								    ],    )
								lowercase__  					=     audio_classifier(lowerCamelCase,    top_k=1    )
								self.assertEqual(
								    lowerCamelCase,    [
								        {'''score''': ANY(lowerCamelCase    ), '''label''': ANY(lowerCamelCase    )},
								    ],    )

								self.run_torchaudio(lowerCamelCase    )




				@require_torchaudio
				def 							lowercase__  (	self		:       Optional[int],    lowerCamelCase		:       List[Any]    ):

								'''simple docstring'''
								import datasets

								# test with a local file
								lowercase__  					=     datasets.load_dataset('''hf-internal-testing/librispeech_asr_dummy''',    '''clean''',    split='''validation'''    )
								lowercase__  					=     dataset[0]['''audio''']['''array''']
								lowercase__  					=     audio_classifier(lowerCamelCase    )
								self.assertEqual(
								    lowerCamelCase,    [
								        {'''score''': ANY(lowerCamelCase    ), '''label''': ANY(lowerCamelCase    )},
								        {'''score''': ANY(lowerCamelCase    ), '''label''': ANY(lowerCamelCase    )},
								    ],    )




				@require_torch
				def 							lowercase__  (	self		:       int    ):

								'''simple docstring'''
								lowercase__  					=     '''anton-l/wav2vec2-random-tiny-classifier'''

								lowercase__  					=     pipeline('''audio-classification''',    model=lowerCamelCase    )

								lowercase__  					=     np.ones((8_000,)    )
								lowercase__  					=     audio_classifier(lowerCamelCase,    top_k=4    )

								lowercase__  					=     [
								    {'''score''': 0.0842, '''label''': '''no'''},
								    {'''score''': 0.0838, '''label''': '''up'''},
								    {'''score''': 0.0837, '''label''': '''go'''},
								    {'''score''': 0.0834, '''label''': '''right'''},
								]
								lowercase__  					=     [
								    {'''score''': 0.0845, '''label''': '''stop'''},
								    {'''score''': 0.0844, '''label''': '''on'''},
								    {'''score''': 0.0841, '''label''': '''right'''},
								    {'''score''': 0.0834, '''label''': '''left'''},
								]
								self.assertIn(nested_simplify(lowerCamelCase,    decimals=4    ),    [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]    )

								lowercase__  					=     {'''array''': np.ones((8_000,)    ), '''sampling_rate''': audio_classifier.feature_extractor.sampling_rate}
								lowercase__  					=     audio_classifier(lowerCamelCase,    top_k=4    )
								self.assertIn(nested_simplify(lowerCamelCase,    decimals=4    ),    [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]    )




				@require_torch
				@slow
				def 							lowercase__  (	self		:       List[str]    ):

								'''simple docstring'''
								import datasets

								lowercase__  					=     '''superb/wav2vec2-base-superb-ks'''

								lowercase__  					=     pipeline('''audio-classification''',    model=lowerCamelCase    )
								lowercase__  					=     datasets.load_dataset('''anton-l/superb_dummy''',    '''ks''',    split='''test'''    )

								lowercase__  					=     np.array(dataset[3]['''speech'''],    dtype=np.floataa    )
								lowercase__  					=     audio_classifier(lowerCamelCase,    top_k=4    )
								self.assertEqual(
								    nested_simplify(lowerCamelCase,    decimals=3    ),    [
								        {'''score''': 0.981, '''label''': '''go'''},
								        {'''score''': 0.007, '''label''': '''up'''},
								        {'''score''': 0.006, '''label''': '''_unknown_'''},
								        {'''score''': 0.001, '''label''': '''down'''},
								    ],    )




				@require_tf
				@unittest.skip('''Audio classification is not implemented for TF'''    )
				def 							lowercase__  (	self		:       Optional[int]    ):

								'''simple docstring'''
								pass




