
import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
		from transformers import SpeechaTextFeatureExtractor

_UpperCAmelCase      :				int          = random.Random()


def    A (      lowercase					, lowercase=1.0					, lowercase=None					, lowercase=None				)     ->						List[Any]:
		'''simple docstring'''

		if rng is None:
				UpperCamelCase						=  global_rng

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

		return values


@require_torch
@require_torchaudio
class        lowercase      (	unittest.TestCase     ):



		def __init__(				self       ,   A_       ,   A_=7       ,   A_=400       ,   A_=2_000       ,   A_=24       ,   A_=24       ,   A_=0.0       ,   A_=16_000       ,   A_=True       ,   A_=True       ,   )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  parent
				UpperCamelCase						=  batch_size
				UpperCamelCase						=  min_seq_length
				UpperCamelCase						=  max_seq_length
				UpperCamelCase						=  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
				UpperCamelCase						=  feature_size
				UpperCamelCase						=  num_mel_bins
				UpperCamelCase						=  padding_value
				UpperCamelCase						=  sampling_rate
				UpperCamelCase						=  return_attention_mask
				UpperCamelCase						=  do_normalize



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				return {
				    "feature_size": self.feature_size,
				    "num_mel_bins": self.num_mel_bins,
				    "padding_value": self.padding_value,
				    "sampling_rate": self.sampling_rate,
				    "return_attention_mask": self.return_attention_mask,
				    "do_normalize": self.do_normalize,
				}



		def __UpperCamelCase				(				self       ,   A_=False       ,   A_=False )						->      Optional[int]:
				"""simple docstring"""
				def _flatten(A_ ):
						return list(itertools.chain(*A_ ) )

				if equal_length:
						UpperCamelCase						=  [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
				else:
						# make sure that inputs increase in size
						UpperCamelCase						=  [
						    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						=  [np.asarray(A_ ) for x in speech_inputs]
				return speech_inputs






@require_torch
@require_torchaudio
class        lowercase      (	_SCREAMING_SNAKE_CASE						,     unittest.TestCase     ):
		__lowercase : Optional[int]			  =       SpeechaTextFeatureExtractor if is_speech_available() else None



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  SpeechaTextFeatureExtractionTester(self )



		def __UpperCamelCase				(				self       ,   A_ )						->      Optional[Any]:
				"""simple docstring"""
				self.assertTrue(np.all(np.mean(A_       ,   axis=0 ) < 1e-3 ) )
				self.assertTrue(np.all(np.abs(np.var(A_       ,   axis=0 ) - 1 ) < 1e-3 ) )



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
				# create three inputs of length 800, 1000, and 1200
				UpperCamelCase						=  [floats_list((1, x) )[0] for x in range(800       ,   1_400       ,   200 )]
				UpperCamelCase						=  [np.asarray(A_ ) for speech_input in speech_inputs]

				# Test feature size
				UpperCamelCase						=  feature_extractor(A_       ,   padding=A_       ,   return_tensors='np' ).input_features
				self.assertTrue(input_features.ndim == 3 )
				self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size )

				# Test not batched input
				UpperCamelCase						=  feature_extractor(speech_inputs[0]       ,   return_tensors='np' ).input_features
				UpperCamelCase						=  feature_extractor(np_speech_inputs[0]       ,   return_tensors='np' ).input_features
				self.assertTrue(np.allclose(A_       ,   A_       ,   atol=1e-3 ) )

				# Test batched
				UpperCamelCase						=  feature_extractor(A_       ,   return_tensors='np' ).input_features
				UpperCamelCase						=  feature_extractor(A_       ,   return_tensors='np' ).input_features
				for enc_seq_a, enc_seq_a in zip(A_       ,   A_ ):
						self.assertTrue(np.allclose(A_       ,   A_       ,   atol=1e-3 ) )

				# Test 2-D numpy arrays are batched.
				UpperCamelCase						=  [floats_list((1, x) )[0] for x in (800, 800, 800)]
				UpperCamelCase						=  np.asarray(A_ )
				UpperCamelCase						=  feature_extractor(A_       ,   return_tensors='np' ).input_features
				UpperCamelCase						=  feature_extractor(A_       ,   return_tensors='np' ).input_features
				for enc_seq_a, enc_seq_a in zip(A_       ,   A_ ):
						self.assertTrue(np.allclose(A_       ,   A_       ,   atol=1e-3 ) )



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
				UpperCamelCase						=  [floats_list((1, x) )[0] for x in range(800       ,   1_400       ,   200 )]

				UpperCamelCase						=  ['longest', 'max_length', 'do_not_pad']
				UpperCamelCase						=  [None, 16, None]
				for max_length, padding in zip(A_       ,   A_ ):
						UpperCamelCase						=  feature_extractor(
						    A_       ,   padding=A_       ,   max_length=A_       ,   return_attention_mask=A_ )
						UpperCamelCase						=  inputs.input_features
						UpperCamelCase						=  inputs.attention_mask
						UpperCamelCase						=  [np.sum(A_ ) for x in attention_mask]

						self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
						self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
						self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
				UpperCamelCase						=  [floats_list((1, x) )[0] for x in range(800       ,   1_400       ,   200 )]

				UpperCamelCase						=  ['longest', 'max_length', 'do_not_pad']
				UpperCamelCase						=  [None, 16, None]
				for max_length, padding in zip(A_       ,   A_ ):
						UpperCamelCase						=  feature_extractor(
						    A_       ,   max_length=A_       ,   padding=A_       ,   return_tensors='np'       ,   return_attention_mask=A_ )
						UpperCamelCase						=  inputs.input_features
						UpperCamelCase						=  inputs.attention_mask
						UpperCamelCase						=  [np.sum(A_ ) for x in attention_mask]

						self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
						self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6 )
						self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
						self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6 )
						self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
				UpperCamelCase						=  [floats_list((1, x) )[0] for x in range(800       ,   1_400       ,   200 )]
				UpperCamelCase						=  feature_extractor(
				    A_       ,   padding='max_length'       ,   max_length=4       ,   truncation=A_       ,   return_tensors='np'       ,   return_attention_mask=A_       ,   )
				UpperCamelCase						=  inputs.input_features
				UpperCamelCase						=  inputs.attention_mask
				UpperCamelCase						=  np.sum(attention_mask == 1       ,   axis=1 )

				self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
				self._check_zero_mean_unit_variance(input_features[1] )
				self._check_zero_mean_unit_variance(input_features[2] )



		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
				UpperCamelCase						=  [floats_list((1, x) )[0] for x in range(800       ,   1_400       ,   200 )]
				UpperCamelCase						=  feature_extractor(
				    A_       ,   padding='longest'       ,   max_length=4       ,   truncation=A_       ,   return_tensors='np'       ,   return_attention_mask=A_       ,   )
				UpperCamelCase						=  inputs.input_features
				UpperCamelCase						=  inputs.attention_mask
				UpperCamelCase						=  np.sum(attention_mask == 1       ,   axis=1 )

				self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
				self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
				self._check_zero_mean_unit_variance(input_features[2] )

				# make sure that if max_length < longest -> then pad to max_length
				self.assertEqual(input_features.shape       ,   (3, 4, 24) )

				UpperCamelCase						=  [floats_list((1, x) )[0] for x in range(800       ,   1_400       ,   200 )]
				UpperCamelCase						=  feature_extractor(
				    A_       ,   padding='longest'       ,   max_length=16       ,   truncation=A_       ,   return_tensors='np'       ,   return_attention_mask=A_       ,   )
				UpperCamelCase						=  inputs.input_features
				UpperCamelCase						=  inputs.attention_mask
				UpperCamelCase						=  np.sum(attention_mask == 1       ,   axis=1 )

				self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
				self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
				self._check_zero_mean_unit_variance(input_features[2] )

				# make sure that if max_length < longest -> then pad to max_length
				self.assertEqual(input_features.shape       ,   (3, 6, 24) )



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				import torch

				UpperCamelCase						=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
				UpperCamelCase						=  np.random.rand(100       ,   32 ).astype(np.floataa )
				UpperCamelCase						=  np_speech_inputs.tolist()

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



		def __UpperCamelCase				(				self       ,   A_ )						->      Optional[Any]:
				"""simple docstring"""
				from datasets import load_dataset

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

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



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  np.array([
				    -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
				    -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
				    -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
				] )
				# fmt: on

				UpperCamelCase						=  self._load_datasamples(1 )
				UpperCamelCase						=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
				UpperCamelCase						=  feature_extractor(A_       ,   return_tensors='pt' ).input_features
				self.assertEquals(input_features.shape       ,   (1, 584, 24) )
				self.assertTrue(np.allclose(input_features[0, 0, :30]       ,   A_       ,   atol=1e-4 ) )


import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


_UpperCAmelCase      :				str          = "scheduler_config.json"


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Tuple			  =       1
		__lowercase : int			  =       2
		__lowercase : List[Any]			  =       3
		__lowercase : str			  =       4
		__lowercase : Optional[Any]			  =       5






@dataclass
class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : jnp.ndarray






class        lowercase      :
		__lowercase : Union[str, Any]			  =       SCHEDULER_CONFIG_NAME
		__lowercase : Dict			  =       ["dtype"]
		__lowercase : List[Any]			  =       []
		__lowercase : Dict			  =       True



		@classmethod
		def __UpperCamelCase				(				cls       ,   A_ = None       ,   A_ = None       ,   A_=False       ,   **A_       ,   )						->      Optional[Any]:
				"""simple docstring"""
				UpperCamelCase			,						UpperCamelCase						=  cls.load_config(
				    pretrained_model_name_or_path=A_       ,   subfolder=A_       ,   return_unused_kwargs=A_       ,   **A_       ,   )
				UpperCamelCase			,						UpperCamelCase						=  cls.from_config(A_       ,   return_unused_kwargs=A_       ,   **A_ )

				if hasattr(A_       ,   'create_state' ) and getattr(A_       ,   'has_state'       ,   A_ ):
						UpperCamelCase						=  scheduler.create_state()

				if return_unused_kwargs:
						return scheduler, state, unused_kwargs

				return scheduler, state



		def __UpperCamelCase				(				self       ,   A_       ,   A_ = False       ,   **A_ )						->      str:
				"""simple docstring"""
				self.save_config(save_directory=A_       ,   push_to_hub=A_       ,   **A_ )



		@property
		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				return self._get_compatibles()



		@classmethod
		def __UpperCamelCase				(				cls )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  list(set([cls.__name__] + cls._compatibles ) )
				UpperCamelCase						=  importlib.import_module(__name__.split('.' )[0] )
				UpperCamelCase						=  [
				    getattr(A_       ,   A_ ) for c in compatible_classes_str if hasattr(A_       ,   A_ )
				]
				return compatible_classes






def    A (      lowercase					, lowercase				)     ->						jnp.ndarray:
		'''simple docstring'''

		assert len(lowercase				) >= x.ndim
		return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowercase				) - x.ndim)				)					, lowercase				)


def    A (      lowercase					, lowercase=0.9_9_9					, lowercase=jnp.floataa				)     ->						jnp.ndarray:
		'''simple docstring'''

		def alpha_bar(lowercase				):
				return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2				) ** 2

		UpperCamelCase						=  []
		for i in range(lowercase				):
				UpperCamelCase						=  i / num_diffusion_timesteps
				UpperCamelCase						=  (i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar(lowercase				) / alpha_bar(lowercase				)					, lowercase				)				)
		return jnp.array(lowercase					, dtype=lowercase				)


@flax.struct.dataclass
class        lowercase      :
		__lowercase : jnp.ndarray
		__lowercase : jnp.ndarray
		__lowercase : jnp.ndarray



		@classmethod
		def __UpperCamelCase				(				cls       ,   A_ )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  scheduler.config

				if config.trained_betas is not None:
						UpperCamelCase						=  jnp.asarray(config.trained_betas       ,   dtype=scheduler.dtype )
				elif config.beta_schedule == "linear":
						UpperCamelCase						=  jnp.linspace(config.beta_start       ,   config.beta_end       ,   config.num_train_timesteps       ,   dtype=scheduler.dtype )
				elif config.beta_schedule == "scaled_linear":
						# this schedule is very specific to the latent diffusion model.
						UpperCamelCase						=  (
						    jnp.linspace(
						        config.beta_start**0.5       ,   config.beta_end**0.5       ,   config.num_train_timesteps       ,   dtype=scheduler.dtype )
						    ** 2
						)
				elif config.beta_schedule == "squaredcos_cap_v2":
						# Glide cosine schedule
						UpperCamelCase						=  betas_for_alpha_bar(config.num_train_timesteps       ,   dtype=scheduler.dtype )
				else:
						raise NotImplementedError(
						    F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}''' )

				UpperCamelCase						=  1.0 - betas

				UpperCamelCase						=  jnp.cumprod(A_       ,   axis=0 )

				return cls(
				    alphas=A_       ,   betas=A_       ,   alphas_cumprod=A_       ,   )






def    A (      lowercase					, lowercase					, lowercase					, lowercase				)     ->						List[Any]:
		'''simple docstring'''

		UpperCamelCase						=  state.alphas_cumprod

		UpperCamelCase						=  alphas_cumprod[timesteps] ** 0.5
		UpperCamelCase						=  sqrt_alpha_prod.flatten()
		UpperCamelCase						=  broadcast_to_shape_from_left(lowercase					, original_samples.shape				)

		UpperCamelCase						=  (1 - alphas_cumprod[timesteps]) ** 0.5
		UpperCamelCase						=  sqrt_one_minus_alpha_prod.flatten()
		UpperCamelCase						=  broadcast_to_shape_from_left(lowercase					, original_samples.shape				)

		return sqrt_alpha_prod, sqrt_one_minus_alpha_prod


def    A (      lowercase					, lowercase					, lowercase					, lowercase				)     ->						Dict:
		'''simple docstring'''

		UpperCamelCase			,						UpperCamelCase						=  get_sqrt_alpha_prod(lowercase					, lowercase					, lowercase					, lowercase				)
		UpperCamelCase						=  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
		return noisy_samples


def    A (      lowercase					, lowercase					, lowercase					, lowercase				)     ->						int:
		'''simple docstring'''

		UpperCamelCase			,						UpperCamelCase						=  get_sqrt_alpha_prod(lowercase					, lowercase					, lowercase					, lowercase				)
		UpperCamelCase						=  sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
		return velocity
