





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   __A   :				Union[str, Any]         =  (UnCLIPScheduler,)

   def 							__lowercase		(					self					,  **lowercase)			->						Optional[Any]:







          '''simple docstring'''

          a__						:	Optional[int] =				{
              'num_train_timesteps': 1000,
              'variance_type': 'fixed_small_log',
              'clip_sample': True,
              'clip_sample_range': 1.0,
              'prediction_type': 'epsilon',
          }

          config.update(**lowercase)
          return config

   def 							__lowercase		(					self)			->						Optional[Any]:







          '''simple docstring'''

          for timesteps in [1, 5, 100, 1000]:
                 self.check_over_configs(num_train_timesteps=lowercase)

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          for variance in ["fixed_small_log", "learned_range"]:
                 self.check_over_configs(variance_type=lowercase)

   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

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

   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          for clip_sample_range in [1, 5, 10, 20]:
                 self.check_over_configs(clip_sample_range=lowercase)

   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(prediction_type=lowercase)

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          for time_step in [0, 500, 999]:
                 for prev_timestep in [None, 5, 100, 250, 500, 750]:
                        if prev_timestep is not None and prev_timestep >= time_step:
                               continue

                        self.check_over_forward(time_step=lowercase					,  prev_timestep=lowercase)

   def 							__lowercase		(					self)			->						Optional[Any]:







          '''simple docstring'''

          a__						:	Optional[Any] =				self.scheduler_classes[0]
          a__						:	Tuple =				self.get_scheduler_config(variance_type='fixed_small_log')
          a__						:	Dict =				scheduler_class(**lowercase)

          assert torch.sum(torch.abs(scheduler._get_variance(0) - 1.0000e-10)) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.0_54_96_25)) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.9_99_49_87)) < 1e-5

   def 							__lowercase		(					self)			->						Optional[Any]:







          '''simple docstring'''

          a__						:	Union[str, Any] =				self.scheduler_classes[0]
          a__						:	List[str] =				self.get_scheduler_config(variance_type='learned_range')
          a__						:	Optional[Any] =				scheduler_class(**lowercase)

          a__						:	Optional[Any] =				0.5

          assert scheduler._get_variance(1					,  predicted_variance=lowercase) - -10.1_71_27_90 < 1e-5
          assert scheduler._get_variance(487					,  predicted_variance=lowercase) - -5.7_99_80_52 < 1e-5
          assert scheduler._get_variance(999					,  predicted_variance=lowercase) - -0.0_01_00_11 < 1e-5

   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          a__						:	Dict =				self.scheduler_classes[0]
          a__						:	List[str] =				self.get_scheduler_config()
          a__						:	Tuple =				scheduler_class(**lowercase)

          a__						:	List[str] =				scheduler.timesteps

          a__						:	Optional[Any] =				self.dummy_model()
          a__						:	int =				self.dummy_sample_deter
          a__						:	Tuple =				torch.manual_seed(0)

          for i, t in enumerate(lowercase):
                 # 1. predict noise residual
                 a__						:	Tuple =				model(lowercase					,  lowercase)

                 # 2. predict previous mean of sample x_t-1
                 a__						:	Union[str, Any] =				scheduler.step(lowercase					,  lowercase					,  lowercase					,  generator=lowercase).prev_sample

                 a__						:	Tuple =				pred_prev_sample

          a__						:	Tuple =				torch.sum(torch.abs(lowercase))
          a__						:	List[str] =				torch.mean(torch.abs(lowercase))

          assert abs(result_sum.item() - 2_52.2_68_24_95) < 1e-2
          assert abs(result_mean.item() - 0.3_28_47_43) < 1e-3

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	Optional[int] =				self.scheduler_classes[0]
          a__						:	Optional[int] =				self.get_scheduler_config()
          a__						:	Any =				scheduler_class(**lowercase)

          scheduler.set_timesteps(25)

          a__						:	List[Any] =				scheduler.timesteps

          a__						:	List[str] =				self.dummy_model()
          a__						:	Optional[Any] =				self.dummy_sample_deter
          a__						:	Optional[Any] =				torch.manual_seed(0)

          for i, t in enumerate(lowercase):
                 # 1. predict noise residual
                 a__						:	Dict =				model(lowercase					,  lowercase)

                 if i + 1 == timesteps.shape[0]:
                        a__						:	Optional[int] =				None
                 else:
                        a__						:	str =				timesteps[i + 1]

                 # 2. predict previous mean of sample x_t-1
                 a__						:	int =				scheduler.step(
                     lowercase					,  lowercase					,  lowercase					,  prev_timestep=lowercase					,  generator=lowercase).prev_sample

                 a__						:	str =				pred_prev_sample

          a__						:	Dict =				torch.sum(torch.abs(lowercase))
          a__						:	Optional[int] =				torch.mean(torch.abs(lowercase))

          assert abs(result_sum.item() - 2_58.2_04_49_83) < 1e-2
          assert abs(result_mean.item() - 0.3_36_20_38) < 1e-3

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          pass

   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          pass









from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse("""3.8"""):
    import importlib_metadata
else:
    import importlib.metadata as importlib_metadata


lowercase      :       Tuple									=  """"""


if version.parse(importlib_metadata.version("""jiwer""")) < version.parse("""2.3.0"""):





    class  A__    (							tr.AbstractTransform	):




       """simple docstring"""



       def __init__(					self					,  lowercase = " ")			->						Tuple:







              '''simple docstring'''

              a__						:	Tuple =				sentence_delimiter

       def 							__lowercase		(					self					,  lowercase)			->						Optional[int]:







              '''simple docstring'''

              return list(lowercase)

       def 							__lowercase		(					self					,  lowercase)			->						Dict:







              '''simple docstring'''

              a__						:	Tuple =				[]
              for sent_idx, sentence in enumerate(lowercase):
                     chars.extend(self.process_string(lowercase))
                     if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(lowercase) - 1:
                            chars.append(self.sentence_delimiter)
              return chars

    lowercase      :       Union[str, Any]									=  tr.Compose(
        [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
    )
else:
    lowercase      :       List[str]									=  tr.Compose(
        [
            tr.RemoveMultipleSpaces(),
            tr.Strip(),
            tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
            tr.ReduceToListOfListOfChars(),
        ]
    )


lowercase      :       List[Any]									=  """\
@inproceedings{inproceedings,
    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
    year = {2004},
    month = {01},
    pages = {},
    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
"""

lowercase      :       Optional[int]									=  """\
Character error rate (CER) is a common metric of the performance of an automatic speech recognition system.

CER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.

Character error rate can be computed as:

CER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct characters,
N is the number of characters in the reference (N=S+D+C).

CER's output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the
performance of the ASR system with a CER of 0 being a perfect score.
"""

lowercase      :       Optional[Any]									=  """
Computes CER score of transcribed segments against references.
Args:
    references: list of references for each speech input.
    predictions: list of transcribtions to score.
    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.
Returns:
    (float): the character error rate

Examples:

    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]
    >>> references = [\"this is the reference\", \"there is another one\"]
    >>> cer = datasets.load_metric(\"cer\")
    >>> cer_score = cer.compute(predictions=predictions, references=references)
    >>> print(cer_score)
    0.34146341463414637
"""





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,  _KWARGS_DESCRIPTION	)
class  A__    (							datasets.Metric	):




   """simple docstring"""



   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

          return datasets.MetricInfo(
              description=_DESCRIPTION					,  citation=_CITATION					,  inputs_description=_KWARGS_DESCRIPTION					,  features=datasets.Features(
                  {
                      'predictions': datasets.Value('string'					,  id='sequence'),
                      'references': datasets.Value('string'					,  id='sequence'),
                  })					,  codebase_urls=['https://github.com/jitsi/jiwer/']					,  reference_urls=[
                  'https://en.wikipedia.org/wiki/Word_error_rate',
                  'https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates',
              ]					,  )

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase=False)			->						Any:







          '''simple docstring'''

          if concatenate_texts:
                 return jiwer.compute_measures(
                     lowercase					,  lowercase					,  truth_transform=lowercase					,  hypothesis_transform=lowercase					,  )["wer"]

          a__						:	Optional[int] =				0
          a__						:	str =				0
          for prediction, reference in zip(lowercase					,  lowercase):
                 a__						:	Optional[int] =				jiwer.compute_measures(
                     lowercase					,  lowercase					,  truth_transform=lowercase					,  hypothesis_transform=lowercase					,  )
                 incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
                 total += measures["substitutions"] + measures["deletions"] + measures["hits"]

          return incorrect / total


