
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


SCREAMING_SNAKE_CASE    =					random.Random()







def snake_case_    (				lowercase__      ,					lowercase__=1.0      ,					lowercase__=None      ,					lowercase__=None		):
  if rng is None:
    UpperCAmelCase__			:				Optional[int]												=       global_rng

  UpperCAmelCase__			:				List[str]												=       []
  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        UpperCAmelCase_       (						unittest.TestCase							):

 '''simple docstring'''







 def        UpperCamelCase     (				self		:     Optional[int]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Tuple												=       "facebook/musicgen-small"
   UpperCAmelCase__			:				Union[str, Any]												=       tempfile.mkdtemp()







 def        UpperCamelCase     (				self		:     Union[str, Any] ,							**snake_case__		:     Dict   ):



   '''simple docstring'''




   return TaTokenizer.from_pretrained(self.checkpoint ,							**snake_case__   )







 def        UpperCamelCase     (				self		:     Optional[int] ,							**snake_case__		:     str   ):



   '''simple docstring'''




   return EncodecFeatureExtractor.from_pretrained(self.checkpoint ,							**snake_case__   )







 def        UpperCamelCase     (				self		:     List[str]   ):



   '''simple docstring'''




   shutil.rmtree(self.tmpdirname   )







 def        UpperCamelCase     (				self		:     Any   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Any												=       self.get_tokenizer()
   UpperCAmelCase__			:				Tuple												=       self.get_feature_extractor()

   UpperCAmelCase__			:				int												=       MusicgenProcessor(tokenizer=snake_case__ ,							feature_extractor=snake_case__   )

   processor.save_pretrained(self.tmpdirname   )
   UpperCAmelCase__			:				Dict												=       MusicgenProcessor.from_pretrained(self.tmpdirname   )

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

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







 def        UpperCamelCase     (				self		:     Tuple   ):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       MusicgenProcessor(tokenizer=self.get_tokenizer() ,							feature_extractor=self.get_feature_extractor()   )
   processor.save_pretrained(self.tmpdirname   )

   UpperCAmelCase__			:				List[Any]												=       self.get_tokenizer(bos_token="(BOS)" ,							eos_token="(EOS)"   )
   UpperCAmelCase__			:				int												=       self.get_feature_extractor(do_normalize=snake_case__ ,							padding_value=1.0   )

   UpperCAmelCase__			:				Optional[Any]												=       MusicgenProcessor.from_pretrained(
       self.tmpdirname ,							bos_token="(BOS)" ,							eos_token="(EOS)" ,							do_normalize=snake_case__ ,							padding_value=1.0   )

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

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







 def        UpperCamelCase     (				self		:     List[Any]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       self.get_feature_extractor()
   UpperCAmelCase__			:				int												=       self.get_tokenizer()

   UpperCAmelCase__			:				List[str]												=       MusicgenProcessor(tokenizer=snake_case__ ,							feature_extractor=snake_case__   )

   UpperCAmelCase__			:				Optional[Any]												=       floats_list((3, 10_00)   )

   UpperCAmelCase__			:				List[Any]												=       feature_extractor(snake_case__ ,							return_tensors="np"   )
   UpperCAmelCase__			:				List[Any]												=       processor(snake_case__ ,							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        UpperCamelCase     (				self		:     List[str]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Union[str, Any]												=       self.get_feature_extractor()
   UpperCAmelCase__			:				Union[str, Any]												=       self.get_tokenizer()

   UpperCAmelCase__			:				Optional[int]												=       MusicgenProcessor(tokenizer=snake_case__ ,							feature_extractor=snake_case__   )

   UpperCAmelCase__			:				List[Any]												=       "This is a test string"

   UpperCAmelCase__			:				Dict												=       processor(text=snake_case__   )

   UpperCAmelCase__			:				Any												=       tokenizer(snake_case__   )

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







 def        UpperCamelCase     (				self		:     int   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Union[str, Any]												=       self.get_feature_extractor()
   UpperCAmelCase__			:				str												=       self.get_tokenizer()

   UpperCAmelCase__			:				List[Any]												=       MusicgenProcessor(tokenizer=snake_case__ ,							feature_extractor=snake_case__   )

   UpperCAmelCase__			:				Optional[int]												=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

   UpperCAmelCase__			:				Any												=       processor.batch_decode(sequences=snake_case__   )
   UpperCAmelCase__			:				Optional[Any]												=       tokenizer.batch_decode(snake_case__   )

   self.assertListEqual(snake_case__ ,							snake_case__   )







 def        UpperCamelCase     (				self		:     Tuple   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Any												=       self.get_feature_extractor()
   UpperCAmelCase__			:				List[Any]												=       self.get_tokenizer()

   UpperCAmelCase__			:				Union[str, Any]												=       MusicgenProcessor(tokenizer=snake_case__ ,							feature_extractor=snake_case__   )

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







 def        UpperCamelCase     (				self		:     Dict   ):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       self.get_feature_extractor(padding_side="left"   )
   UpperCAmelCase__			:				str												=       self.get_tokenizer()

   UpperCAmelCase__			:				int												=       MusicgenProcessor(tokenizer=snake_case__ ,							feature_extractor=snake_case__   )

   UpperCAmelCase__			:				List[Any]												=       [floats_list((1, x)   )[0] for x in range(5 ,							20 ,							5   )]
   UpperCAmelCase__			:				List[Any]												=       processor(snake_case__   ).padding_mask

   UpperCAmelCase__			:				Dict												=       np.asarray(floats_list((3, 20)   )   )[:, None, :]
   UpperCAmelCase__			:				Optional[Any]												=       processor.batch_decode(snake_case__ ,							padding_mask=snake_case__   )

   self.assertIsInstance(snake_case__ ,							snake_case__   )

   for audio in decoded_audios:
     self.assertIsInstance(snake_case__ ,							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)   )





'''simple docstring'''





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left






@flax.struct.dataclass
class        UpperCAmelCase_       :

 '''simple docstring'''
 # setable values
 lowercase_				:						Optional[jnp.ndarray]				=       None
 lowercase_				:						Optional[jnp.ndarray]				=       None
 lowercase_				:						Optional[jnp.ndarray]				=       None







 @classmethod
 def        UpperCamelCase     (				cls		:     List[str]   ):



   '''simple docstring'''




   return cls()






@dataclass
class        UpperCAmelCase_       (						A							):

 '''simple docstring'''
 lowercase_				:						ScoreSdeVeSchedulerState
 lowercase_				:						jnp.ndarray
 lowercase_				:						Optional[jnp.ndarray]				=       None






class        UpperCAmelCase_       (						A				,							A							):

 '''simple docstring'''







 @property
 def        UpperCamelCase     (				self		:     List[Any]   ):



   '''simple docstring'''




   return True







 @register_to_config
 def __init__(				self		:     Optional[Any] ,							snake_case__		:     int = 20_00 ,							snake_case__		:     float = 0.15 ,							snake_case__		:     float = 0.01 ,							snake_case__		:     float = 1348.0 ,							snake_case__		:     float = 1e-5 ,							snake_case__		:     int = 1 ,							):



   '''simple docstring'''




   pass







 def        UpperCamelCase     (				self		:     Optional[int]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Union[str, Any]												=       ScoreSdeVeSchedulerState.create()
   return self.set_sigmas(
       snake_case__ ,							self.config.num_train_timesteps ,							self.config.sigma_min ,							self.config.sigma_max ,							self.config.sampling_eps ,							)







 def        UpperCamelCase     (				self		:     List[Any] ,							snake_case__		:     ScoreSdeVeSchedulerState ,							snake_case__		:     int ,							snake_case__		:     Tuple = () ,							snake_case__		:     float = None   ):



   '''simple docstring'''




   UpperCAmelCase__			:				str												=       sampling_eps if sampling_eps is not None else self.config.sampling_eps

   UpperCAmelCase__			:				Optional[int]												=       jnp.linspace(1 ,							snake_case__ ,							snake_case__   )
   return state.replace(timesteps=snake_case__   )







 def        UpperCamelCase     (				self		:     str ,							snake_case__		:     ScoreSdeVeSchedulerState ,							snake_case__		:     int ,							snake_case__		:     float = None ,							snake_case__		:     float = None ,							snake_case__		:     float = None ,							):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       sigma_min if sigma_min is not None else self.config.sigma_min
   UpperCAmelCase__			:				Dict												=       sigma_max if sigma_max is not None else self.config.sigma_max
   UpperCAmelCase__			:				Any												=       sampling_eps if sampling_eps is not None else self.config.sampling_eps
   if state.timesteps is None:
     UpperCAmelCase__			:				Optional[int]												=       self.set_timesteps(snake_case__ ,							snake_case__ ,							snake_case__   )

   UpperCAmelCase__			:				int												=       jnp.exp(jnp.linspace(jnp.log(snake_case__   ) ,							jnp.log(snake_case__   ) ,							snake_case__   )   )
   UpperCAmelCase__			:				Optional[int]												=       jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]   )

   return state.replace(discrete_sigmas=snake_case__ ,							sigmas=snake_case__   )







 def        UpperCamelCase     (				self		:     List[str] ,							snake_case__		:     str ,							snake_case__		:     Tuple ,							snake_case__		:     Dict   ):



   '''simple docstring'''




   return jnp.where(timesteps == 0 ,							jnp.zeros_like(snake_case__   ) ,							state.discrete_sigmas[timesteps - 1]   )







 def        UpperCamelCase     (				self		:     Optional[int] ,							snake_case__		:     ScoreSdeVeSchedulerState ,							snake_case__		:     jnp.ndarray ,							snake_case__		:     int ,							snake_case__		:     jnp.ndarray ,							snake_case__		:     random.KeyArray ,							snake_case__		:     bool = True ,							):



   '''simple docstring'''




   if state.timesteps is None:
     raise ValueError(
         "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"   )

   UpperCAmelCase__			:				str												=       timestep * jnp.ones(
       sample.shape[0] ,							)
   UpperCAmelCase__			:				int												=       (timestep * (len(state.timesteps   ) - 1)).long()

   UpperCAmelCase__			:				Optional[int]												=       state.discrete_sigmas[timesteps]
   UpperCAmelCase__			:				str												=       self.get_adjacent_sigma(snake_case__ ,							snake_case__ ,							snake_case__   )
   UpperCAmelCase__			:				Optional[Any]												=       jnp.zeros_like(snake_case__   )
   UpperCAmelCase__			:				List[Any]												=       (sigma**2 - adjacent_sigma**2) ** 0.5

   # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
   # also equation 47 shows the analog from SDE models to ancestral sampling methods
   UpperCAmelCase__			:				Optional[Any]												=       diffusion.flatten()
   UpperCAmelCase__			:				List[str]												=       broadcast_to_shape_from_left(snake_case__ ,							sample.shape   )
   UpperCAmelCase__			:				Optional[Any]												=       drift - diffusion**2 * model_output

   #  equation 6: sample noise for the diffusion term of
   UpperCAmelCase__			:				List[str]												=       random.split(snake_case__ ,							num=1   )
   UpperCAmelCase__			:				Dict												=       random.normal(key=snake_case__ ,							shape=sample.shape   )
   UpperCAmelCase__			:				Optional[int]												=       sample - drift  # subtract because `dt` is a small negative timestep
   # TODO is the variable diffusion the correct scaling term for the noise?
   UpperCAmelCase__			:				int												=       prev_sample_mean + diffusion * noise  # add impact of diffusion field g

   if not return_dict:
     return (prev_sample, prev_sample_mean, state)

   return FlaxSdeVeOutput(prev_sample=snake_case__ ,							prev_sample_mean=snake_case__ ,							state=snake_case__   )







 def        UpperCamelCase     (				self		:     Any ,							snake_case__		:     ScoreSdeVeSchedulerState ,							snake_case__		:     jnp.ndarray ,							snake_case__		:     jnp.ndarray ,							snake_case__		:     random.KeyArray ,							snake_case__		:     bool = True ,							):



   '''simple docstring'''




   if state.timesteps is None:
     raise ValueError(
         "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"   )

   # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
   # sample noise for correction
   UpperCAmelCase__			:				str												=       random.split(snake_case__ ,							num=1   )
   UpperCAmelCase__			:				Tuple												=       random.normal(key=snake_case__ ,							shape=sample.shape   )

   # compute step size from the model_output, the noise, and the snr
   UpperCAmelCase__			:				Union[str, Any]												=       jnp.linalg.norm(snake_case__   )
   UpperCAmelCase__			:				List[str]												=       jnp.linalg.norm(snake_case__   )
   UpperCAmelCase__			:				Optional[Any]												=       (self.config.snr * noise_norm / grad_norm) ** 2 * 2
   UpperCAmelCase__			:				str												=       step_size * jnp.ones(sample.shape[0]   )

   # compute corrected sample: model_output term and noise term
   UpperCAmelCase__			:				Union[str, Any]												=       step_size.flatten()
   UpperCAmelCase__			:				Optional[int]												=       broadcast_to_shape_from_left(snake_case__ ,							sample.shape   )
   UpperCAmelCase__			:				Tuple												=       sample + step_size * model_output
   UpperCAmelCase__			:				Any												=       prev_sample_mean + ((step_size * 2) ** 0.5) * noise

   if not return_dict:
     return (prev_sample, state)

   return FlaxSdeVeOutput(prev_sample=snake_case__ ,							state=snake_case__   )







 def __len__(				self		:     List[str]   ):



   '''simple docstring'''




   return self.config.num_train_timesteps



