




"""simple docstring"""




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

import flax
import jax.numpy as jnp
from scipy import integrate

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

@flax.struct.dataclass
class        UpperCAmelCase_  :
		UpperCamelCase									=42

		# setable values
		UpperCamelCase									=42
		UpperCamelCase									=42
		UpperCamelCase									=42
		UpperCamelCase									=None

		# running values
		UpperCamelCase									=None
		@classmethod
		def 					_lowerCamelCase    (		cls							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )		->   Union[str, Any]:
					return cls(common=UpperCamelCase_							,		init_noise_sigma=UpperCamelCase_							,		timesteps=UpperCamelCase_							,		sigmas=UpperCamelCase_   )


@dataclass
class        UpperCAmelCase_  (	UpperCAmelCase__						):
		UpperCamelCase									=42


class        UpperCAmelCase_  (	UpperCAmelCase__ ,			UpperCAmelCase__						):
		UpperCamelCase									=[e.name for e in FlaxKarrasDiffusionSchedulers]

		UpperCamelCase									=42
		@property
		def 					_lowerCamelCase    (		self   )		->   Any:
					return True
		@register_to_config
		def __init__(		self							,		UpperCamelCase_ = 10_00							,		UpperCamelCase_ = 0.0_0_0_1							,		UpperCamelCase_ = 0.0_2							,		UpperCamelCase_ = "linear"							,		UpperCamelCase_ = None							,		UpperCamelCase_ = "epsilon"							,		UpperCamelCase_ = jnp.floataa							,		)		->   List[Any]:
					__lowercase	:       int				=   dtype
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_ = None   )		->   LMSDiscreteSchedulerState:




					if common is None:
								__lowercase	:       Optional[Any]				=   CommonSchedulerState.create(self   )

					__lowercase	:       Optional[Any]				=   jnp.arange(0							,		self.config.num_train_timesteps   ).round()[::-1]
					__lowercase	:       Union[str, Any]				=   ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

					# standard deviation of the initial noise distribution
					__lowercase	:       List[Any]				=   sigmas.max()

					return LMSDiscreteSchedulerState.create(
					    common=UpperCamelCase_							,		init_noise_sigma=UpperCamelCase_							,		timesteps=UpperCamelCase_							,		sigmas=UpperCamelCase_							,		)
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )		->   jnp.ndarray:
					(__lowercase						)	:       Optional[Any]				=   jnp.where(state.timesteps == timestep							,		size=1   )
					__lowercase	:       List[Any]				=   step_index[0]

					__lowercase	:       Optional[Any]				=   state.sigmas[step_index]
					__lowercase	:       int				=   sample / ((sigma**2 + 1) ** 0.5)
					return sample
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )		->   int:




					def lms_derivative(UpperCamelCase_   ):
								__lowercase	:       Union[str, Any]				=   1.0
								for k in range(UpperCamelCase_   ):
											if current_order == k:
														continue
											prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
								return prod

					__lowercase	:       Union[str, Any]				=   integrate.quad(UpperCamelCase_							,		state.sigmas[t]							,		state.sigmas[t + 1]							,		epsrel=1E-4   )[0]

					return integrated_coeff
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_ = ()   )		->   LMSDiscreteSchedulerState:
					__lowercase	:       Tuple				=   jnp.linspace(self.config.num_train_timesteps - 1							,		0							,		UpperCamelCase_							,		dtype=self.dtype   )

					__lowercase	:       List[str]				=   jnp.floor(UpperCamelCase_   ).astype(jnp.intaa   )
					__lowercase	:       Optional[Any]				=   jnp.ceil(UpperCamelCase_   ).astype(jnp.intaa   )

					__lowercase	:       int				=   jnp.mod(UpperCamelCase_							,		1.0   )

					__lowercase	:       str				=   ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
					__lowercase	:       Optional[int]				=   (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
					__lowercase	:       List[str]				=   jnp.concatenate([sigmas, jnp.array([0.0]							,		dtype=self.dtype   )]   )

					__lowercase	:       Union[str, Any]				=   timesteps.astype(jnp.intaa   )

					# initial running values
					__lowercase	:       Tuple				=   jnp.zeros((0,) + shape							,		dtype=self.dtype   )

					return state.replace(
					    timesteps=UpperCamelCase_							,		sigmas=UpperCamelCase_							,		num_inference_steps=UpperCamelCase_							,		derivatives=UpperCamelCase_							,		)
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_ = 4							,		UpperCamelCase_ = True							,		)		->   Union[FlaxLMSSchedulerOutput, Tuple]:




					if state.num_inference_steps is None:
								raise ValueError(
								    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''   )

					__lowercase	:       Tuple				=   state.sigmas[timestep]

					# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
					if self.config.prediction_type == "epsilon":
								__lowercase	:       Optional[Any]				=   sample - sigma * model_output
					elif self.config.prediction_type == "v_prediction":
								# * c_out + input * c_skip
								__lowercase	:       Optional[Any]				=   model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
					else:
								raise ValueError(
								    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""   )

					# 2. Convert to an ODE derivative
					__lowercase	:       Optional[Any]				=   (sample - pred_original_sample) / sigma
					__lowercase	:       Tuple				=   state.replace(derivatives=jnp.append(state.derivatives							,		UpperCamelCase_   )   )
					if len(state.derivatives   ) > order:
								__lowercase	:       Optional[Any]				=   state.replace(derivatives=jnp.delete(state.derivatives							,		0   )   )

					# 3. Compute linear multistep coefficients
					__lowercase	:       List[Any]				=   min(timestep + 1							,		UpperCamelCase_   )
					__lowercase	:       Optional[Any]				=   [self.get_lms_coefficient(UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   ) for curr_order in range(UpperCamelCase_   )]

					# 4. Compute previous sample based on the derivatives path
					__lowercase	:       int				=   sample + sum(
					    coeff * derivative for coeff, derivative in zip(UpperCamelCase_							,		reversed(state.derivatives   )   )   )

					if not return_dict:
								return (prev_sample, state)

					return FlaxLMSSchedulerOutput(prev_sample=UpperCamelCase_							,		state=UpperCamelCase_   )
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		)		->   jnp.ndarray:
					__lowercase	:       str				=   state.sigmas[timesteps].flatten()
					__lowercase	:       List[Any]				=   broadcast_to_shape_from_left(UpperCamelCase_							,		noise.shape   )

					__lowercase	:       str				=   original_samples + noise * sigma

					return noisy_samples
		def __len__(		self   )		->   Optional[Any]:
					return self.config.num_train_timesteps









"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
			from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

			from .test_feature_extraction_speechta import floats_list


a_            =   get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')

@require_torch
class        UpperCAmelCase_  (	unittest.TestCase						):
		def 					_lowerCamelCase    (		self   )		->   Dict:
					__lowercase	:       Union[str, Any]				=   tempfile.mkdtemp()

					__lowercase	:       List[str]				=   SpeechTaTokenizer(UpperCamelCase_   )
					tokenizer.save_pretrained(self.tmpdirname   )

					__lowercase	:       Dict				=   {
					    '''feature_size''': 1,
					    '''padding_value''': 0.0,
					    '''sampling_rate''': 1_60_00,
					    '''do_normalize''': False,
					    '''num_mel_bins''': 80,
					    '''hop_length''': 16,
					    '''win_length''': 64,
					    '''win_function''': '''hann_window''',
					    '''fmin''': 80,
					    '''fmax''': 76_00,
					    '''mel_floor''': 1E-10,
					    '''reduction_factor''': 2,
					    '''return_attention_mask''': True,
					}

					__lowercase	:       List[Any]				=   os.path.join(self.tmpdirname							,		UpperCamelCase_   )
					with open(self.feature_extraction_file							,		'''w'''							,		encoding='''utf-8'''   ) as fp:
								fp.write(json.dumps(UpperCamelCase_   ) + '''\n'''   )
		def 					_lowerCamelCase    (		self							,		**UpperCamelCase_   )		->   Union[str, Any]:
					return SpeechTaTokenizer.from_pretrained(self.tmpdirname							,		**UpperCamelCase_   )
		def 					_lowerCamelCase    (		self							,		**UpperCamelCase_   )		->   Optional[Any]:
					return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname							,		**UpperCamelCase_   )
		def 					_lowerCamelCase    (		self   )		->   Any:
					shutil.rmtree(self.tmpdirname   )
		def 					_lowerCamelCase    (		self   )		->   Union[str, Any]:
					__lowercase	:       List[str]				=   self.get_tokenizer()
					__lowercase	:       int				=   self.get_feature_extractor()

					__lowercase	:       Any				=   SpeechTaProcessor(tokenizer=UpperCamelCase_							,		feature_extractor=UpperCamelCase_   )

					processor.save_pretrained(self.tmpdirname   )
					__lowercase	:       List[Any]				=   SpeechTaProcessor.from_pretrained(self.tmpdirname   )

					self.assertEqual(processor.tokenizer.get_vocab()							,		tokenizer.get_vocab()   )
					self.assertIsInstance(processor.tokenizer							,		UpperCamelCase_   )

					self.assertEqual(processor.feature_extractor.to_json_string()							,		feature_extractor.to_json_string()   )
					self.assertIsInstance(processor.feature_extractor							,		UpperCamelCase_   )
		def 					_lowerCamelCase    (		self   )		->   Optional[Any]:
					__lowercase	:       Optional[int]				=   SpeechTaProcessor(tokenizer=self.get_tokenizer()							,		feature_extractor=self.get_feature_extractor()   )
					processor.save_pretrained(self.tmpdirname   )

					__lowercase	:       str				=   self.get_tokenizer(bos_token='''(BOS)'''							,		eos_token='''(EOS)'''   )
					__lowercase	:       Dict				=   self.get_feature_extractor(do_normalize=UpperCamelCase_							,		padding_value=1.0   )

					__lowercase	:       Tuple				=   SpeechTaProcessor.from_pretrained(
					    self.tmpdirname							,		bos_token='''(BOS)'''							,		eos_token='''(EOS)'''							,		do_normalize=UpperCamelCase_							,		padding_value=1.0   )

					self.assertEqual(processor.tokenizer.get_vocab()							,		tokenizer_add_kwargs.get_vocab()   )
					self.assertIsInstance(processor.tokenizer							,		UpperCamelCase_   )

					self.assertEqual(processor.feature_extractor.to_json_string()							,		feature_extractor_add_kwargs.to_json_string()   )
					self.assertIsInstance(processor.feature_extractor							,		UpperCamelCase_   )
		def 					_lowerCamelCase    (		self   )		->   List[str]:
					__lowercase	:       Optional[Any]				=   self.get_feature_extractor()
					__lowercase	:       Optional[Any]				=   self.get_tokenizer()

					__lowercase	:       Union[str, Any]				=   SpeechTaProcessor(tokenizer=UpperCamelCase_							,		feature_extractor=UpperCamelCase_   )

					__lowercase	:       int				=   floats_list((3, 10_00)   )

					__lowercase	:       str				=   feature_extractor(audio=UpperCamelCase_							,		return_tensors='''np'''   )
					__lowercase	:       Dict				=   processor(audio=UpperCamelCase_							,		return_tensors='''np'''   )

					for key in input_feat_extract.keys():
								self.assertAlmostEqual(input_feat_extract[key].sum()							,		input_processor[key].sum()							,		delta=1E-2   )
		def 					_lowerCamelCase    (		self   )		->   List[Any]:
					__lowercase	:       str				=   self.get_feature_extractor()
					__lowercase	:       str				=   self.get_tokenizer()

					__lowercase	:       Union[str, Any]				=   SpeechTaProcessor(tokenizer=UpperCamelCase_							,		feature_extractor=UpperCamelCase_   )

					__lowercase	:       Optional[int]				=   floats_list((3, 10_00)   )

					__lowercase	:       List[str]				=   feature_extractor(audio_target=UpperCamelCase_							,		return_tensors='''np'''   )
					__lowercase	:       List[str]				=   processor(audio_target=UpperCamelCase_							,		return_tensors='''np'''   )

					for key in input_feat_extract.keys():
								self.assertAlmostEqual(input_feat_extract[key].sum()							,		input_processor[key].sum()							,		delta=1E-2   )
		def 					_lowerCamelCase    (		self   )		->   Any:
					__lowercase	:       List[Any]				=   self.get_feature_extractor()
					__lowercase	:       List[str]				=   self.get_tokenizer()

					__lowercase	:       Union[str, Any]				=   SpeechTaProcessor(tokenizer=UpperCamelCase_							,		feature_extractor=UpperCamelCase_   )

					__lowercase	:       List[str]				=   '''This is a test string'''

					__lowercase	:       Any				=   processor(text=UpperCamelCase_   )
					__lowercase	:       Optional[int]				=   tokenizer(UpperCamelCase_   )

					for key in encoded_tok.keys():
								self.assertListEqual(encoded_tok[key]							,		encoded_processor[key]   )
		def 					_lowerCamelCase    (		self   )		->   str:
					__lowercase	:       int				=   self.get_feature_extractor()
					__lowercase	:       List[str]				=   self.get_tokenizer()

					__lowercase	:       List[Any]				=   SpeechTaProcessor(tokenizer=UpperCamelCase_							,		feature_extractor=UpperCamelCase_   )

					__lowercase	:       Union[str, Any]				=   '''This is a test string'''

					__lowercase	:       Tuple				=   processor(text_target=UpperCamelCase_   )
					__lowercase	:       Optional[int]				=   tokenizer(UpperCamelCase_   )

					for key in encoded_tok.keys():
								self.assertListEqual(encoded_tok[key]							,		encoded_processor[key]   )
		def 					_lowerCamelCase    (		self   )		->   Optional[Any]:
					__lowercase	:       Optional[int]				=   self.get_feature_extractor()
					__lowercase	:       str				=   self.get_tokenizer()

					__lowercase	:       int				=   SpeechTaProcessor(tokenizer=UpperCamelCase_							,		feature_extractor=UpperCamelCase_   )

					__lowercase	:       Optional[Any]				=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					__lowercase	:       str				=   processor.batch_decode(UpperCamelCase_   )
					__lowercase	:       int				=   tokenizer.batch_decode(UpperCamelCase_   )

					self.assertListEqual(UpperCamelCase_							,		UpperCamelCase_   )
		def 					_lowerCamelCase    (		self   )		->   Optional[int]:
					__lowercase	:       Union[str, Any]				=   self.get_feature_extractor()
					__lowercase	:       int				=   self.get_tokenizer()

					__lowercase	:       Optional[int]				=   SpeechTaProcessor(tokenizer=UpperCamelCase_							,		feature_extractor=UpperCamelCase_   )

					self.assertListEqual(
					    processor.model_input_names							,		feature_extractor.model_input_names							,		msg='''`processor` and `feature_extractor` model input names do not match'''							,		)



