




"""simple docstring"""





import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
					import torch

if is_datasets_available():
					from datasets import load_dataset

A   =       random.Random()

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase=1.0				,       UpperCamelCase=None				,       UpperCamelCase=None							)			->			List[Any]:


			"""simple docstring"""
			if rng is None:
						__UpperCAmelCase	: Dict		=					global_rng

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

			return values



class      a__				(	unittest.TestCase   ):


			def __init__(      self      :    Optional[Any]      ,  UpperCamelCase_      :    List[str]      ,  UpperCamelCase_      :    Dict=7      ,  UpperCamelCase_      :    Optional[Any]=400      ,  UpperCamelCase_      :    List[Any]=2000      ,  UpperCamelCase_      :    Tuple=2048      ,  UpperCamelCase_      :    List[Any]=128      ,  UpperCamelCase_      :    Any=1      ,  UpperCamelCase_      :    str=512      ,  UpperCamelCase_      :    Tuple=30      ,  UpperCamelCase_      :    Dict=44100      ,  ):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[int]		=					parent
						__UpperCAmelCase	: Dict		=					batch_size
						__UpperCAmelCase	: List[Any]		=					min_seq_length
						__UpperCAmelCase	: Tuple		=					max_seq_length
						__UpperCAmelCase	: Optional[int]		=					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
						__UpperCAmelCase	: Dict		=					spectrogram_length
						__UpperCAmelCase	: Dict		=					feature_size
						__UpperCAmelCase	: Tuple		=					num_audio_channels
						__UpperCAmelCase	: Optional[Any]		=					hop_length
						__UpperCAmelCase	: List[Any]		=					chunk_length
						__UpperCAmelCase	: Union[str, Any]		=					sampling_rate


			def       a_     (      self      :    List[Any]):


						"""simple docstring"""




						return {
						    "spectrogram_length": self.spectrogram_length,
						    "feature_size": self.feature_size,
						    "num_audio_channels": self.num_audio_channels,
						    "hop_length": self.hop_length,
						    "chunk_length": self.chunk_length,
						    "sampling_rate": self.sampling_rate,
						}


			def       a_     (      self      :    Optional[Any]      ,  UpperCamelCase_      :    Any=False      ,  UpperCamelCase_      :    Union[str, Any]=False):


						"""simple docstring"""




						def _flatten(UpperCamelCase_      :    Optional[Any]):
									return list(itertools.chain(*UpperCamelCase_))

						if equal_length:
									__UpperCAmelCase	: Any		=					[floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
						else:
									# make sure that inputs increase in size
									__UpperCAmelCase	: Optional[Any]		=					[
									    floats_list((x, self.feature_size))
									    for x in range(self.min_seq_length      ,  self.max_seq_length      ,  self.seq_length_diff)
									]
						if numpify:
									__UpperCAmelCase	: Tuple		=					[np.asarray(UpperCamelCase_) for x in speech_inputs]
						return speech_inputs






@require_torch
@require_torchaudio
class      a__				(	__magic_name__							,    unittest.TestCase   ):
			lowercase_    =	TvltFeatureExtractor


			def       a_     (      self      :    List[Any]):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[int]		=					TvltFeatureExtractionTester(self)


			def       a_     (      self      :    str):


						"""simple docstring"""




						__UpperCAmelCase	: Any		=					self.feature_extraction_class(**self.feat_extract_dict)
						self.assertTrue(hasattr(UpperCamelCase_      ,  "spectrogram_length"))
						self.assertTrue(hasattr(UpperCamelCase_      ,  "feature_size"))
						self.assertTrue(hasattr(UpperCamelCase_      ,  "num_audio_channels"))
						self.assertTrue(hasattr(UpperCamelCase_      ,  "hop_length"))
						self.assertTrue(hasattr(UpperCamelCase_      ,  "chunk_length"))
						self.assertTrue(hasattr(UpperCamelCase_      ,  "sampling_rate"))


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: int		=					self.feature_extraction_class(**self.feat_extract_dict)

						with tempfile.TemporaryDirectory() as tmpdirname:
									__UpperCAmelCase	: int		=					feat_extract_first.save_pretrained(UpperCamelCase_)[0]
									check_json_file_has_correct_format(UpperCamelCase_)
									__UpperCAmelCase	: int		=					self.feature_extraction_class.from_pretrained(UpperCamelCase_)

						__UpperCAmelCase	: Tuple		=					feat_extract_first.to_dict()
						__UpperCAmelCase	: str		=					feat_extract_second.to_dict()
						__UpperCAmelCase	: List[str]		=					dict_first.pop("mel_filters")
						__UpperCAmelCase	: Optional[Any]		=					dict_second.pop("mel_filters")
						self.assertTrue(np.allclose(UpperCamelCase_      ,  UpperCamelCase_))
						self.assertEqual(UpperCamelCase_      ,  UpperCamelCase_)


			def       a_     (      self      :    Any):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					self.feature_extraction_class(**self.feat_extract_dict)

						with tempfile.TemporaryDirectory() as tmpdirname:
									__UpperCAmelCase	: Optional[int]		=					os.path.join(UpperCamelCase_      ,  "feat_extract.json")
									feat_extract_first.to_json_file(UpperCamelCase_)
									__UpperCAmelCase	: Union[str, Any]		=					self.feature_extraction_class.from_json_file(UpperCamelCase_)

						__UpperCAmelCase	: Any		=					feat_extract_first.to_dict()
						__UpperCAmelCase	: Dict		=					feat_extract_second.to_dict()
						__UpperCAmelCase	: Optional[Any]		=					dict_first.pop("mel_filters")
						__UpperCAmelCase	: str		=					dict_second.pop("mel_filters")
						self.assertTrue(np.allclose(UpperCamelCase_      ,  UpperCamelCase_))
						self.assertEqual(UpperCamelCase_      ,  UpperCamelCase_)


			def       a_     (      self      :    Tuple):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					self.feature_extraction_class(**self.feat_extract_dict)

						# create three inputs of length 800, 1000, and 1200
						__UpperCAmelCase	: Union[str, Any]		=					[floats_list((1, x))[0] for x in range(800      ,  1400      ,  200)]
						__UpperCAmelCase	: str		=					[np.asarray(UpperCamelCase_) for speech_input in speech_inputs]

						# Test not batched input
						__UpperCAmelCase	: Optional[Any]		=					feature_extractor(np_speech_inputs[0]      ,  return_tensors="np"      ,  sampling_rate=44100).audio_values

						self.assertTrue(encoded_audios.ndim == 4)
						self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
						self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
						self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)

						# Test batched
						__UpperCAmelCase	: Union[str, Any]		=					feature_extractor(UpperCamelCase_      ,  return_tensors="np"      ,  sampling_rate=44100).audio_values

						self.assertTrue(encoded_audios.ndim == 4)
						self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
						self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
						self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)

						# Test audio masking
						__UpperCAmelCase	: Optional[Any]		=					feature_extractor(
						    UpperCamelCase_      ,  return_tensors="np"      ,  sampling_rate=44100      ,  mask_audio=UpperCamelCase_).audio_values

						self.assertTrue(encoded_audios.ndim == 4)
						self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
						self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
						self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)

						# Test 2-D numpy arrays are batched.
						__UpperCAmelCase	: Union[str, Any]		=					[floats_list((1, x))[0] for x in (800, 800, 800)]
						__UpperCAmelCase	: Union[str, Any]		=					np.asarray(UpperCamelCase_)
						__UpperCAmelCase	: str		=					feature_extractor(UpperCamelCase_      ,  return_tensors="np"      ,  sampling_rate=44100).audio_values
						self.assertTrue(encoded_audios.ndim == 4)
						self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
						self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
						self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)


			def       a_     (      self      :    List[str]      ,  UpperCamelCase_      :    Optional[Any]):


						"""simple docstring"""




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

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


			def       a_     (      self      :    List[Any]):


						"""simple docstring"""




						__UpperCAmelCase	: Union[str, Any]		=					self._load_datasamples(1)
						__UpperCAmelCase	: Dict		=					TvltFeatureExtractor()
						__UpperCAmelCase	: Any		=					feature_extractor(UpperCamelCase_      ,  return_tensors="pt").audio_values

						self.assertEquals(audio_values.shape      ,  (1, 1, 192, 128))

						__UpperCAmelCase	: Any		=					torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]])
						self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]      ,  UpperCamelCase_      ,  atol=1e-4))








"""simple docstring"""





# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)



@flax.struct.dataclass
class      a__				:
			lowercase_    =	42

			# setable values
			lowercase_    =	42
			lowercase_    =	42
			lowercase_    =	None


			@classmethod
			def       a_     (      cls      :    List[str]      ,  UpperCamelCase_      :    CommonSchedulerState      ,  UpperCamelCase_      :    jnp.ndarray      ,  UpperCamelCase_      :    jnp.ndarray):


						"""simple docstring"""




						return cls(common=UpperCamelCase_      ,  init_noise_sigma=UpperCamelCase_      ,  timesteps=UpperCamelCase_)






@dataclass
class      a__				(	__magic_name__   ):
			lowercase_    =	42






class      a__				(	__magic_name__							,    __magic_name__   ):
			lowercase_    =	[e.name for e in FlaxKarrasDiffusionSchedulers]

			lowercase_    =	42


			@property
			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						return True


			@register_to_config
			def __init__(      self      :    str      ,  UpperCamelCase_      :    int = 1000      ,  UpperCamelCase_      :    float = 0.0001      ,  UpperCamelCase_      :    float = 0.02      ,  UpperCamelCase_      :    str = "linear"      ,  UpperCamelCase_      :    Optional[jnp.ndarray] = None      ,  UpperCamelCase_      :    str = "fixed_small"      ,  UpperCamelCase_      :    bool = True      ,  UpperCamelCase_      :    str = "epsilon"      ,  UpperCamelCase_      :    jnp.dtype = jnp.floataa      ,  ):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[int]		=					dtype


			def       a_     (      self      :    Optional[Any]      ,  UpperCamelCase_      :    Optional[CommonSchedulerState] = None):


						"""simple docstring"""




						if common is None:
									__UpperCAmelCase	: Tuple		=					CommonSchedulerState.create(self)

						# standard deviation of the initial noise distribution
						__UpperCAmelCase	: Tuple		=					jnp.array(1.0      ,  dtype=self.dtype)

						__UpperCAmelCase	: Any		=					jnp.arange(0      ,  self.config.num_train_timesteps).round()[::-1]

						return DDPMSchedulerState.create(
						    common=UpperCamelCase_      ,  init_noise_sigma=UpperCamelCase_      ,  timesteps=UpperCamelCase_      ,  )


			def       a_     (      self      :    Optional[Any]      ,  UpperCamelCase_      :    DDPMSchedulerState      ,  UpperCamelCase_      :    jnp.ndarray      ,  UpperCamelCase_      :    Optional[int] = None):


						"""simple docstring"""




						return sample


			def       a_     (      self      :    Any      ,  UpperCamelCase_      :    DDPMSchedulerState      ,  UpperCamelCase_      :    int      ,  UpperCamelCase_      :    Tuple = ()):


						"""simple docstring"""




						__UpperCAmelCase	: List[str]		=					self.config.num_train_timesteps // num_inference_steps
						# creates integer timesteps by multiplying by ratio
						# rounding to avoid issues when num_inference_step is power of 3
						__UpperCAmelCase	: List[str]		=					(jnp.arange(0      ,  UpperCamelCase_) * step_ratio).round()[::-1]

						return state.replace(
						    num_inference_steps=UpperCamelCase_      ,  timesteps=UpperCamelCase_      ,  )


			def       a_     (      self      :    Any      ,  UpperCamelCase_      :    DDPMSchedulerState      ,  UpperCamelCase_      :    Optional[Any]      ,  UpperCamelCase_      :    Union[str, Any]=None      ,  UpperCamelCase_      :    Optional[int]=None):


						"""simple docstring"""




						__UpperCAmelCase	: List[str]		=					state.common.alphas_cumprod[t]
						__UpperCAmelCase	: Optional[int]		=					jnp.where(t > 0      ,  state.common.alphas_cumprod[t - 1]      ,  jnp.array(1.0      ,  dtype=self.dtype))

						# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
						# and sample from it to get previous sample
						# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
						__UpperCAmelCase	: Tuple		=					(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

						if variance_type is None:
									__UpperCAmelCase	: Optional[Any]		=					self.config.variance_type

						# hacks - were probably added for training stability
						if variance_type == "fixed_small":
									__UpperCAmelCase	: str		=					jnp.clip(UpperCamelCase_      ,  a_min=1e-20)
						# for rl-diffuser https://arxiv.org/abs/2205.09991
						elif variance_type == "fixed_small_log":
									__UpperCAmelCase	: Optional[int]		=					jnp.log(jnp.clip(UpperCamelCase_      ,  a_min=1e-20))
						elif variance_type == "fixed_large":
									__UpperCAmelCase	: Tuple		=					state.common.betas[t]
						elif variance_type == "fixed_large_log":
									# Glide max_log
									__UpperCAmelCase	: str		=					jnp.log(state.common.betas[t])
						elif variance_type == "learned":
									return predicted_variance
						elif variance_type == "learned_range":
									__UpperCAmelCase	: Any		=					variance
									__UpperCAmelCase	: Union[str, Any]		=					state.common.betas[t]
									__UpperCAmelCase	: List[str]		=					(predicted_variance + 1) / 2
									__UpperCAmelCase	: int		=					frac * max_log + (1 - frac) * min_log

						return variance


			def       a_     (      self      :    Optional[int]      ,  UpperCamelCase_      :    DDPMSchedulerState      ,  UpperCamelCase_      :    jnp.ndarray      ,  UpperCamelCase_      :    int      ,  UpperCamelCase_      :    jnp.ndarray      ,  UpperCamelCase_      :    Optional[jax.random.KeyArray] = None      ,  UpperCamelCase_      :    bool = True      ,  ):


						"""simple docstring"""




						__UpperCAmelCase	: Dict		=					timestep

						if key is None:
									__UpperCAmelCase	: List[str]		=					jax.random.PRNGKey(0)

						if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
									__UpperCAmelCase	,     __UpperCAmelCase	: int		=					jnp.split(UpperCamelCase_      ,  sample.shape[1]      ,  axis=1)
						else:
									__UpperCAmelCase	: List[str]		=					None

						# 1. compute alphas, betas
						__UpperCAmelCase	: str		=					state.common.alphas_cumprod[t]
						__UpperCAmelCase	: str		=					jnp.where(t > 0      ,  state.common.alphas_cumprod[t - 1]      ,  jnp.array(1.0      ,  dtype=self.dtype))
						__UpperCAmelCase	: Tuple		=					1 - alpha_prod_t
						__UpperCAmelCase	: int		=					1 - alpha_prod_t_prev

						# 2. compute predicted original sample from predicted noise also called
						# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
						if self.config.prediction_type == "epsilon":
									__UpperCAmelCase	: Any		=					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
						elif self.config.prediction_type == "sample":
									__UpperCAmelCase	: Optional[int]		=					model_output
						elif self.config.prediction_type == "v_prediction":
									__UpperCAmelCase	: Optional[int]		=					(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
						else:
									raise ValueError(
									    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` "
									    " for the FlaxDDPMScheduler.")

						# 3. Clip "predicted x_0"
						if self.config.clip_sample:
									__UpperCAmelCase	: List[Any]		=					jnp.clip(UpperCamelCase_      ,  -1      ,  1)

						# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
						# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
						__UpperCAmelCase	: Union[str, Any]		=					(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
						__UpperCAmelCase	: Union[str, Any]		=					state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

						# 5. Compute predicted previous sample µ_t
						# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
						__UpperCAmelCase	: Optional[Any]		=					pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

						# 6. Add noise
						def random_variance():
									__UpperCAmelCase	: int		=					jax.random.split(UpperCamelCase_      ,  num=1)
									__UpperCAmelCase	: Any		=					jax.random.normal(UpperCamelCase_      ,  shape=model_output.shape      ,  dtype=self.dtype)
									return (self._get_variance(UpperCamelCase_      ,  UpperCamelCase_      ,  predicted_variance=UpperCamelCase_) ** 0.5) * noise

						__UpperCAmelCase	: Tuple		=					jnp.where(t > 0      ,  random_variance()      ,  jnp.zeros(model_output.shape      ,  dtype=self.dtype))

						__UpperCAmelCase	: Tuple		=					pred_prev_sample + variance

						if not return_dict:
									return (pred_prev_sample, state)

						return FlaxDDPMSchedulerOutput(prev_sample=UpperCamelCase_      ,  state=UpperCamelCase_)


			def       a_     (      self      :    Optional[Any]      ,  UpperCamelCase_      :    DDPMSchedulerState      ,  UpperCamelCase_      :    jnp.ndarray      ,  UpperCamelCase_      :    jnp.ndarray      ,  UpperCamelCase_      :    jnp.ndarray      ,  ):


						"""simple docstring"""




						return add_noise_common(state.common      ,  UpperCamelCase_      ,  UpperCamelCase_      ,  UpperCamelCase_)


			def       a_     (      self      :    Union[str, Any]      ,  UpperCamelCase_      :    DDPMSchedulerState      ,  UpperCamelCase_      :    jnp.ndarray      ,  UpperCamelCase_      :    jnp.ndarray      ,  UpperCamelCase_      :    jnp.ndarray      ,  ):


						"""simple docstring"""




						return get_velocity_common(state.common      ,  UpperCamelCase_      ,  UpperCamelCase_      ,  UpperCamelCase_)


			def __len__(      self      :    int):


						"""simple docstring"""




						return self.config.num_train_timesteps


