


'''simple docstring'''
import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		List[str]     				=    (DDIMParallelScheduler,)
 __lowercase					:		Optional[int]     				=    (('''eta''', 0.0), ('''num_inference_steps''', 5_0))


 def 			snake_case__ (   self   ,   **__lowercase    ):

  """simple docstring"""







  __A	:		Dict	  =      {
      'num_train_timesteps': 1_000,
      'beta_start': 0.0_0_0_1,
      'beta_end': 0.0_2,
      'beta_schedule': 'linear',
      'clip_sample': True,
  }

  config.update(**__lowercase    )
  return config


 def 			snake_case__ (   self   ,   **__lowercase    ):

  """simple docstring"""







  __A	:		Optional[int]	  =      self.scheduler_classes[0]
  __A	:		Any	  =      self.get_scheduler_config(**__lowercase    )
  __A	:		Tuple	  =      scheduler_class(**__lowercase    )

  __A  ,__A	:		Optional[Any]	  =      10, 0.0

  __A	:		Dict	  =      self.dummy_model()
  __A	:		Tuple	  =      self.dummy_sample_deter

  scheduler.set_timesteps(__lowercase    )

  for t in scheduler.timesteps:
   __A	:		Optional[Any]	  =      model(__lowercase   ,   __lowercase    )
   __A	:		Tuple	  =      scheduler.step(__lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase    ).prev_sample

  return sample


 def 			snake_case__ (   self    ):

  """simple docstring"""







  for timesteps in [100, 500, 1_000]:
   self.check_over_configs(num_train_timesteps=__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  for steps_offset in [0, 1]:
   self.check_over_configs(steps_offset=__lowercase    )

  __A	:		int	  =      self.scheduler_classes[0]
  __A	:		Tuple	  =      self.get_scheduler_config(steps_offset=1    )
  __A	:		Any	  =      scheduler_class(**__lowercase    )
  scheduler.set_timesteps(5    )
  assert torch.equal(scheduler.timesteps   ,   torch.LongTensor([801, 601, 401, 201, 1]    )    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]   ,   [0.0_0_2, 0.0_2, 0.2, 2]    ):
   self.check_over_configs(beta_start=__lowercase   ,   beta_end=__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  for schedule in ["linear", "squaredcos_cap_v2"]:
   self.check_over_configs(beta_schedule=__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  for prediction_type in ["epsilon", "v_prediction"]:
   self.check_over_configs(prediction_type=__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  for clip_sample in [True, False]:
   self.check_over_configs(clip_sample=__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  for timestep_spacing in ["trailing", "leading"]:
   self.check_over_configs(timestep_spacing=__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  for rescale_betas_zero_snr in [True, False]:
   self.check_over_configs(rescale_betas_zero_snr=__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  self.check_over_configs(thresholding=__lowercase    )
  for threshold in [0.5, 1.0, 2.0]:
   for prediction_type in ["epsilon", "v_prediction"]:
    self.check_over_configs(
        thresholding=__lowercase   ,   prediction_type=__lowercase   ,   sample_max_value=__lowercase   ,   )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  for t in [1, 10, 49]:
   self.check_over_forward(time_step=__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  for t, num_inference_steps in zip([1, 10, 50]   ,   [10, 50, 500]    ):
   self.check_over_forward(time_step=__lowercase   ,   num_inference_steps=__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  for t, eta in zip([1, 10, 49]   ,   [0.0, 0.5, 1.0]    ):
   self.check_over_forward(time_step=__lowercase   ,   eta=__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		List[str]	  =      self.scheduler_classes[0]
  __A	:		str	  =      self.get_scheduler_config()
  __A	:		Optional[int]	  =      scheduler_class(**__lowercase    )

  assert torch.sum(torch.abs(scheduler._get_variance(0   ,   0    ) - 0.0    )    ) < 1E-5
  assert torch.sum(torch.abs(scheduler._get_variance(420   ,   400    ) - 0.1_4_7_7_1    )    ) < 1E-5
  assert torch.sum(torch.abs(scheduler._get_variance(980   ,   960    ) - 0.3_2_4_6_0    )    ) < 1E-5
  assert torch.sum(torch.abs(scheduler._get_variance(0   ,   0    ) - 0.0    )    ) < 1E-5
  assert torch.sum(torch.abs(scheduler._get_variance(487   ,   486    ) - 0.0_0_9_7_9    )    ) < 1E-5
  assert torch.sum(torch.abs(scheduler._get_variance(999   ,   998    ) - 0.0_2    )    ) < 1E-5


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Tuple	  =      self.scheduler_classes[0]
  __A	:		Tuple	  =      self.get_scheduler_config()
  __A	:		Union[str, Any]	  =      scheduler_class(**__lowercase    )

  __A  ,__A	:		Union[str, Any]	  =      10, 0.0
  scheduler.set_timesteps(__lowercase    )

  __A	:		Optional[int]	  =      self.dummy_model()
  __A	:		Dict	  =      self.dummy_sample_deter
  __A	:		int	  =      self.dummy_sample_deter + 0.1
  __A	:		Tuple	  =      self.dummy_sample_deter - 0.1

  __A	:		Any	  =      samplea.shape[0]
  __A	:		str	  =      torch.stack([samplea, samplea, samplea]   ,   dim=0    )
  __A	:		Optional[int]	  =      torch.arange(__lowercase    )[0:3, None].repeat(1   ,   __lowercase    )

  __A	:		str	  =      model(samples.flatten(0   ,   1    )   ,   timesteps.flatten(0   ,   1    )    )
  __A	:		Union[str, Any]	  =      scheduler.batch_step_no_noise(__lowercase   ,   timesteps.flatten(0   ,   1    )   ,   samples.flatten(0   ,   1    )   ,   __lowercase    )

  __A	:		Optional[int]	  =      torch.sum(torch.abs(__lowercase    )    )
  __A	:		Union[str, Any]	  =      torch.mean(torch.abs(__lowercase    )    )

  assert abs(result_sum.item() - 1_1_4_7.7_9_0_4    ) < 1E-2
  assert abs(result_mean.item() - 0.4_9_8_2    ) < 1E-3


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Optional[Any]	  =      self.full_loop()

  __A	:		Tuple	  =      torch.sum(torch.abs(__lowercase    )    )
  __A	:		Optional[int]	  =      torch.mean(torch.abs(__lowercase    )    )

  assert abs(result_sum.item() - 1_7_2.0_0_6_7    ) < 1E-2
  assert abs(result_mean.item() - 0.2_2_3_9_6_7    ) < 1E-3


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Union[str, Any]	  =      self.full_loop(prediction_type='v_prediction'    )

  __A	:		Any	  =      torch.sum(torch.abs(__lowercase    )    )
  __A	:		Optional[int]	  =      torch.mean(torch.abs(__lowercase    )    )

  assert abs(result_sum.item() - 5_2.5_3_0_2    ) < 1E-2
  assert abs(result_mean.item() - 0.0_6_8_4    ) < 1E-3


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		List[str]	  =      self.full_loop(set_alpha_to_one=__lowercase   ,   beta_start=0.0_1    )
  __A	:		Union[str, Any]	  =      torch.sum(torch.abs(__lowercase    )    )
  __A	:		List[Any]	  =      torch.mean(torch.abs(__lowercase    )    )

  assert abs(result_sum.item() - 1_4_9.8_2_9_5    ) < 1E-2
  assert abs(result_mean.item() - 0.1_9_5_1    ) < 1E-3







 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Union[str, Any]	  =      self.full_loop(set_alpha_to_one=__lowercase   ,   beta_start=0.0_1    )
  __A	:		Dict	  =      torch.sum(torch.abs(__lowercase    )    )
  __A	:		Dict	  =      torch.mean(torch.abs(__lowercase    )    )

  assert abs(result_sum.item() - 1_4_9.0_7_8_4    ) < 1E-2
  assert abs(result_mean.item() - 0.1_9_4_1    ) < 1E-3










'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase_			:  Any										=   logging.get_logger(__name__)

UpperCAmelCase_			:  Tuple										=   {'vocab_file': 'spm_char.model'}

UpperCAmelCase_			:  int										=   {
    'vocab_file': {
        'microsoft/speecht5_asr': 'https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model',
        'microsoft/speecht5_tts': 'https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model',
        'microsoft/speecht5_vc': 'https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model',
    }
}

UpperCAmelCase_			:  List[Any]										=   {
    'microsoft/speecht5_asr': 1_0_2_4,
    'microsoft/speecht5_tts': 1_0_2_4,
    'microsoft/speecht5_vc': 1_0_2_4,
}

class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		Optional[Any]     				=    VOCAB_FILES_NAMES
 __lowercase					:		int     				=    PRETRAINED_VOCAB_FILES_MAP
 __lowercase					:		int     				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase					:		Optional[int]     				=    ['''input_ids''', '''attention_mask''']


 def __init__(   self   ,   __lowercase   ,   __lowercase="<s>"   ,   __lowercase="</s>"   ,   __lowercase="<unk>"   ,   __lowercase="<pad>"   ,   __lowercase = None   ,   **__lowercase   ,   ):

  """simple docstring"""







  __A	:		Dict	  =      {} if sp_model_kwargs is None else sp_model_kwargs

  super().__init__(
      bos_token=__lowercase   ,   eos_token=__lowercase   ,   unk_token=__lowercase   ,   pad_token=__lowercase   ,   sp_model_kwargs=self.sp_model_kwargs   ,   **__lowercase   ,   )

  __A	:		Optional[Any]	  =      vocab_file

  __A	:		Any	  =      spm.SentencePieceProcessor(**self.sp_model_kwargs    )
  self.sp_model.Load(__lowercase    )


 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.sp_model.get_piece_size()


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Tuple	  =      {self.convert_ids_to_tokens(__lowercase    ): i for i in range(self.vocab_size    )}
  vocab.update(self.added_tokens_encoder    )
  return vocab


 def __getstate__(   self    ):

  """simple docstring"""







  __A	:		List[Any]	  =      self.__dict__.copy()
  __A	:		str	  =      None
  return state


 def __setstate__(   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		List[str]	  =      d

  # for backward compatibility
  if not hasattr(self   ,   'sp_model_kwargs'    ):
   __A	:		Any	  =      {}

  __A	:		List[str]	  =      spm.SentencePieceProcessor(**self.sp_model_kwargs    )
  self.sp_model.Load(self.vocab_file    )


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  return self.sp_model.encode(__lowercase   ,   out_type=__lowercase    )


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  return self.sp_model.piece_to_id(__lowercase    )


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Optional[Any]	  =      self.sp_model.IdToPiece(__lowercase    )
  return token


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		str	  =      []
  __A	:		str	  =      ''
  for token in tokens:
   # make sure that special tokens are not decoded using sentencepiece model
   if token in self.all_special_tokens:
    out_string += self.sp_model.decode(__lowercase    ) + token
    __A	:		Any	  =      []
   else:
    current_sub_tokens.append(__lowercase    )
  out_string += self.sp_model.decode(__lowercase    )
  return out_string.strip()


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase=None    ):

  """simple docstring"""







  if token_ids_a is None:
   return token_ids_a + [self.eos_token_id]
  # We don't expect to process pairs, but leave the pair logic for API consistency
  return token_ids_a + token_ids_a + [self.eos_token_id]


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase = None   ,   __lowercase = False    ):

  """simple docstring"""







  if already_has_special_tokens:
   return super().get_special_tokens_mask(
       token_ids_a=__lowercase   ,   token_ids_a=__lowercase   ,   already_has_special_tokens=__lowercase    )

  __A	:		Dict	  =      [1]
  if token_ids_a is None:
   return ([0] * len(__lowercase    )) + suffix_ones
  return ([0] * len(__lowercase    )) + ([0] * len(__lowercase    )) + suffix_ones







 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase = None    ):

  """simple docstring"""







  if not os.path.isdir(__lowercase    ):
   logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""    )
   return
  __A	:		Optional[int]	  =      os.path.join(
      __lowercase   ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )

  if os.path.abspath(self.vocab_file    ) != os.path.abspath(__lowercase    ) and os.path.isfile(self.vocab_file    ):
   copyfile(self.vocab_file   ,   __lowercase    )
  elif not os.path.isfile(self.vocab_file    ):
   with open(__lowercase   ,   'wb'    ) as fi:
    __A	:		Tuple	  =      self.sp_model.serialized_model_proto()
    fi.write(__lowercase    )

  return (out_vocab_file,)






