





import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
						pass

if is_speech_available():
						from transformers import EncodecFeatureExtractor, MusicgenProcessor


A_        =  random.Random()





def  __UpperCamelCase   (     a,  a=1.0,  a=None,  a=None)			->Dict:
					if rng is None:
										lowerCamelCase__      =							global_rng

					lowerCamelCase__      =							[]
					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_sentencepiece
class SCREAMING_SNAKE_CASE_			(       unittest.TestCase ):





					"""simple docstring"""


					def        __magic_name__			(			self						):
										lowerCamelCase__      =							"facebook/musicgen-small"
										lowerCamelCase__      =							tempfile.mkdtemp()





					def        __magic_name__			(			self						,	**_lowerCAmelCase						):
										return TaTokenizer.from_pretrained(self.checkpoint						,	**_lowerCAmelCase						)





					def        __magic_name__			(			self						,	**_lowerCAmelCase						):
										return EncodecFeatureExtractor.from_pretrained(self.checkpoint						,	**_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										shutil.rmtree(self.tmpdirname						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.get_tokenizer()
										lowerCamelCase__      =							self.get_feature_extractor()

										lowerCamelCase__      =							MusicgenProcessor(tokenizer=_lowerCAmelCase						,	feature_extractor=_lowerCAmelCase						)

										processor.save_pretrained(self.tmpdirname						)
										lowerCamelCase__      =							MusicgenProcessor.from_pretrained(self.tmpdirname						)

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

										self.assertEqual(processor.feature_extractor.to_json_string()						,	feature_extractor.to_json_string()						)
										self.assertIsInstance(processor.feature_extractor						,	_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							MusicgenProcessor(tokenizer=self.get_tokenizer()						,	feature_extractor=self.get_feature_extractor()						)
										processor.save_pretrained(self.tmpdirname						)

										lowerCamelCase__      =							self.get_tokenizer(bos_token="(BOS)"						,	eos_token="(EOS)"						)
										lowerCamelCase__      =							self.get_feature_extractor(do_normalize=_lowerCAmelCase						,	padding_value=1.0						)

										lowerCamelCase__      =							MusicgenProcessor.from_pretrained(
										    self.tmpdirname						,	bos_token="(BOS)"						,	eos_token="(EOS)"						,	do_normalize=_lowerCAmelCase						,	padding_value=1.0						)

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

										self.assertEqual(processor.feature_extractor.to_json_string()						,	feature_extractor_add_kwargs.to_json_string()						)
										self.assertIsInstance(processor.feature_extractor						,	_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.get_feature_extractor()
										lowerCamelCase__      =							self.get_tokenizer()

										lowerCamelCase__      =							MusicgenProcessor(tokenizer=_lowerCAmelCase						,	feature_extractor=_lowerCAmelCase						)

										lowerCamelCase__      =							floats_list((3, 1000)						)

										lowerCamelCase__      =							feature_extractor(_lowerCAmelCase						,	return_tensors="np"						)
										lowerCamelCase__      =							processor(_lowerCAmelCase						,	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        __magic_name__			(			self						):
										lowerCamelCase__      =							self.get_feature_extractor()
										lowerCamelCase__      =							self.get_tokenizer()

										lowerCamelCase__      =							MusicgenProcessor(tokenizer=_lowerCAmelCase						,	feature_extractor=_lowerCAmelCase						)

										lowerCamelCase__      =							"This is a test string"

										lowerCamelCase__      =							processor(text=_lowerCAmelCase						)

										lowerCamelCase__      =							tokenizer(_lowerCAmelCase						)

										for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key]						,	encoded_processor[key]						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.get_feature_extractor()
										lowerCamelCase__      =							self.get_tokenizer()

										lowerCamelCase__      =							MusicgenProcessor(tokenizer=_lowerCAmelCase						,	feature_extractor=_lowerCAmelCase						)

										lowerCamelCase__      =							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										lowerCamelCase__      =							processor.batch_decode(sequences=_lowerCAmelCase						)
										lowerCamelCase__      =							tokenizer.batch_decode(_lowerCAmelCase						)

										self.assertListEqual(_lowerCAmelCase						,	_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.get_feature_extractor()
										lowerCamelCase__      =							self.get_tokenizer()

										lowerCamelCase__      =							MusicgenProcessor(tokenizer=_lowerCAmelCase						,	feature_extractor=_lowerCAmelCase						)

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






					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.get_feature_extractor(padding_side="left"						)
										lowerCamelCase__      =							self.get_tokenizer()

										lowerCamelCase__      =							MusicgenProcessor(tokenizer=_lowerCAmelCase						,	feature_extractor=_lowerCAmelCase						)

										lowerCamelCase__      =							[floats_list((1, x)						)[0] for x in range(5						,	20						,	5						)]
										lowerCamelCase__      =							processor(_lowerCAmelCase						).padding_mask

										lowerCamelCase__      =							np.asarray(floats_list((3, 20)						)						)[:, None, :]
										lowerCamelCase__      =							processor.batch_decode(_lowerCAmelCase						,	padding_mask=_lowerCAmelCase						)

										self.assertIsInstance(_lowerCAmelCase						,	_lowerCAmelCase						)

										for audio in decoded_audios:
															self.assertIsInstance(_lowerCAmelCase						,	np.ndarray						)

										self.assertTrue(decoded_audios[0].shape == (1, 10)						)
										self.assertTrue(decoded_audios[1].shape == (1, 15)						)
										self.assertTrue(decoded_audios[2].shape == (1, 20)						)








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 SCREAMING_SNAKE_CASE_			:





					"""simple docstring"""


					A__								=     42

					# setable values
					A__								=     42
					A__								=     42
					A__								=     42
					A__								=     None

					# running values
					A__								=     None





					@classmethod
					def        __magic_name__			(			cls						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										return cls(common=_lowerCAmelCase						,	init_noise_sigma=_lowerCAmelCase						,	timesteps=_lowerCAmelCase						,	sigmas=_lowerCAmelCase						)

@dataclass
class SCREAMING_SNAKE_CASE_			(       lowercase_ ):





					"""simple docstring"""


					A__								=     42

class SCREAMING_SNAKE_CASE_			(       lowercase_				,							lowercase_ ):





					"""simple docstring"""


					A__								=     [e.name for e in FlaxKarrasDiffusionSchedulers]

					A__								=     42





					@property
					def        __magic_name__			(			self						):
										return True





					@register_to_config
					def __init__(			self						,	_lowerCAmelCase = 1000						,	_lowerCAmelCase = 0.0_001						,	_lowerCAmelCase = 0.02						,	_lowerCAmelCase = "linear"						,	_lowerCAmelCase = None						,	_lowerCAmelCase = "epsilon"						,	_lowerCAmelCase = jnp.floataa						,	):
										lowerCamelCase__      =							dtype





					def        __magic_name__			(			self						,	_lowerCAmelCase = None						):
										if common is None:
															lowerCamelCase__      =							CommonSchedulerState.create(self						)

										lowerCamelCase__      =							jnp.arange(0						,	self.config.num_train_timesteps						).round()[::-1]
										lowerCamelCase__      =							((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

										# standard deviation of the initial noise distribution
										lowerCamelCase__      =							sigmas.max()

										return LMSDiscreteSchedulerState.create(
										    common=_lowerCAmelCase						,	init_noise_sigma=_lowerCAmelCase						,	timesteps=_lowerCAmelCase						,	sigmas=_lowerCAmelCase						,	)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										((lowerCamelCase__)     ,		)      =							jnp.where(state.timesteps == timestep						,	size=1						)
										lowerCamelCase__      =							step_index[0]

										lowerCamelCase__      =							state.sigmas[step_index]
										lowerCamelCase__      =							sample / ((sigma**2 + 1) ** 0.5)
										return sample





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):

										def lms_derivative(_lowerCAmelCase						):
															lowerCamelCase__      =							1.0
															for k in range(_lowerCAmelCase						):
																				if current_order == k:
																									continue
																				prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
															return prod

										lowerCamelCase__      =							integrate.quad(_lowerCAmelCase						,	state.sigmas[t]						,	state.sigmas[t + 1]						,	epsrel=1E-4						)[0]

										return integrated_coeff





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase = ()						):
										lowerCamelCase__      =							jnp.linspace(self.config.num_train_timesteps - 1						,	0						,	_lowerCAmelCase						,	dtype=self.dtype						)

										lowerCamelCase__      =							jnp.floor(_lowerCAmelCase						).astype(jnp.intaa						)
										lowerCamelCase__      =							jnp.ceil(_lowerCAmelCase						).astype(jnp.intaa						)

										lowerCamelCase__      =							jnp.mod(_lowerCAmelCase						,	1.0						)

										lowerCamelCase__      =							((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
										lowerCamelCase__      =							(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
										lowerCamelCase__      =							jnp.concatenate([sigmas, jnp.array([0.0]						,	dtype=self.dtype						)]						)

										lowerCamelCase__      =							timesteps.astype(jnp.intaa						)

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

										return state.replace(
										    timesteps=_lowerCAmelCase						,	sigmas=_lowerCAmelCase						,	num_inference_steps=_lowerCAmelCase						,	derivatives=_lowerCAmelCase						,	)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase = 4						,	_lowerCAmelCase = True						,	):
										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"						)

										lowerCamelCase__      =							state.sigmas[timestep]

										# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
										if self.config.prediction_type == "epsilon":
															lowerCamelCase__      =							sample - sigma * model_output
										elif self.config.prediction_type == "v_prediction":
															# * c_out + input * c_skip
															lowerCamelCase__      =							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
										lowerCamelCase__      =							(sample - pred_original_sample) / sigma
										lowerCamelCase__      =							state.replace(derivatives=jnp.append(state.derivatives						,	_lowerCAmelCase						)						)
										if len(state.derivatives						) > order:
															lowerCamelCase__      =							state.replace(derivatives=jnp.delete(state.derivatives						,	0						)						)

										# 3. Compute linear multistep coefficients
										lowerCamelCase__      =							min(timestep + 1						,	_lowerCAmelCase						)
										lowerCamelCase__      =							[self.get_lms_coefficient(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						) for curr_order in range(_lowerCAmelCase						)]

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

										if not return_dict:
															return (prev_sample, state)

										return FlaxLMSSchedulerOutput(prev_sample=_lowerCAmelCase						,	state=_lowerCAmelCase						)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	):
										lowerCamelCase__      =							state.sigmas[timesteps].flatten()
										lowerCamelCase__      =							broadcast_to_shape_from_left(_lowerCAmelCase						,	noise.shape						)

										lowerCamelCase__      =							original_samples + noise * sigma

										return noisy_samples






					def __len__(			self						):
										return self.config.num_train_timesteps

