





'''simple docstring'''






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  lowerCAmelCase_					=				(UniPCMultistepScheduler,)
  lowerCAmelCase_					=				(("""num_inference_steps""", 25),)


  def   UpperCamelCase__      (				self    :					int							,	**UpperCamelCase__    :					Optional[int]  )     ->						Optional[int]:
    _UpperCamelCase       			={
        '''num_train_timesteps''': 1000,
        '''beta_start''': 0.0001,
        '''beta_end''': 0.02,
        '''beta_schedule''': '''linear''',
        '''solver_order''': 2,
        '''solver_type''': '''bh2''',
    }

    config.update(**UpperCamelCase__  )
    return config


  def   UpperCamelCase__      (				self    :					Dict							,	UpperCamelCase__    :					str=0							,	**UpperCamelCase__    :					Optional[Any]  )     ->						int:
    _UpperCamelCase       			=dict(self.forward_default_kwargs  )
    _UpperCamelCase       			=kwargs.pop('''num_inference_steps'''							,	UpperCamelCase__  )
    _UpperCamelCase       			=self.dummy_sample
    _UpperCamelCase       			=0.1 * sample
    _UpperCamelCase       			=[residual + 0.2, residual + 0.15, residual + 0.10]

    for scheduler_class in self.scheduler_classes:
      _UpperCamelCase       			=self.get_scheduler_config(**UpperCamelCase__  )
      _UpperCamelCase       			=scheduler_class(**UpperCamelCase__  )
      scheduler.set_timesteps(UpperCamelCase__  )
      # copy over dummy past residuals
      _UpperCamelCase       			=dummy_past_residuals[: scheduler.config.solver_order]

      with tempfile.TemporaryDirectory() as tmpdirname:
        scheduler.save_config(UpperCamelCase__  )
        _UpperCamelCase       			=scheduler_class.from_pretrained(UpperCamelCase__  )
        new_scheduler.set_timesteps(UpperCamelCase__  )
        # copy over dummy past residuals
        _UpperCamelCase       			=dummy_past_residuals[: new_scheduler.config.solver_order]

      _UpperCamelCase			,     _UpperCamelCase       			=sample, sample
      for t in range(UpperCamelCase__							,	time_step + scheduler.config.solver_order + 1  ):
        _UpperCamelCase       			=scheduler.step(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__							,	**UpperCamelCase__  ).prev_sample
        _UpperCamelCase       			=new_scheduler.step(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__							,	**UpperCamelCase__  ).prev_sample

        assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"


  def   UpperCamelCase__      (				self    :					int							,	UpperCamelCase__    :					Tuple=0							,	**UpperCamelCase__    :					Optional[Any]  )     ->						Optional[int]:
    _UpperCamelCase       			=dict(self.forward_default_kwargs  )
    _UpperCamelCase       			=kwargs.pop('''num_inference_steps'''							,	UpperCamelCase__  )
    _UpperCamelCase       			=self.dummy_sample
    _UpperCamelCase       			=0.1 * sample
    _UpperCamelCase       			=[residual + 0.2, residual + 0.15, residual + 0.10]

    for scheduler_class in self.scheduler_classes:
      _UpperCamelCase       			=self.get_scheduler_config()
      _UpperCamelCase       			=scheduler_class(**UpperCamelCase__  )
      scheduler.set_timesteps(UpperCamelCase__  )

      # copy over dummy past residuals (must be after setting timesteps)
      _UpperCamelCase       			=dummy_past_residuals[: scheduler.config.solver_order]

      with tempfile.TemporaryDirectory() as tmpdirname:
        scheduler.save_config(UpperCamelCase__  )
        _UpperCamelCase       			=scheduler_class.from_pretrained(UpperCamelCase__  )
        # copy over dummy past residuals
        new_scheduler.set_timesteps(UpperCamelCase__  )

        # copy over dummy past residual (must be after setting timesteps)
        _UpperCamelCase       			=dummy_past_residuals[: new_scheduler.config.solver_order]

      _UpperCamelCase       			=scheduler.step(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__							,	**UpperCamelCase__  ).prev_sample
      _UpperCamelCase       			=new_scheduler.step(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__							,	**UpperCamelCase__  ).prev_sample

      assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"


  def   UpperCamelCase__      (				self    :					List[Any]							,	UpperCamelCase__    :					List[Any]=None							,	**UpperCamelCase__    :					Optional[int]  )     ->						Any:
    if scheduler is None:
      _UpperCamelCase       			=self.scheduler_classes[0]
      _UpperCamelCase       			=self.get_scheduler_config(**UpperCamelCase__  )
      _UpperCamelCase       			=scheduler_class(**UpperCamelCase__  )

    _UpperCamelCase       			=self.scheduler_classes[0]
    _UpperCamelCase       			=self.get_scheduler_config(**UpperCamelCase__  )
    _UpperCamelCase       			=scheduler_class(**UpperCamelCase__  )

    _UpperCamelCase       			=10
    _UpperCamelCase       			=self.dummy_model()
    _UpperCamelCase       			=self.dummy_sample_deter
    scheduler.set_timesteps(UpperCamelCase__  )

    for i, t in enumerate(scheduler.timesteps  ):
      _UpperCamelCase       			=model(UpperCamelCase__							,	UpperCamelCase__  )
      _UpperCamelCase       			=scheduler.step(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__  ).prev_sample

    return sample


  def   UpperCamelCase__      (				self    :					List[str]  )     ->						str:
    _UpperCamelCase       			=dict(self.forward_default_kwargs  )

    _UpperCamelCase       			=kwargs.pop('''num_inference_steps'''							,	UpperCamelCase__  )

    for scheduler_class in self.scheduler_classes:
      _UpperCamelCase       			=self.get_scheduler_config()
      _UpperCamelCase       			=scheduler_class(**UpperCamelCase__  )

      _UpperCamelCase       			=self.dummy_sample
      _UpperCamelCase       			=0.1 * sample

      if num_inference_steps is not None and hasattr(UpperCamelCase__							,	'''set_timesteps'''  ):
        scheduler.set_timesteps(UpperCamelCase__  )
      elif num_inference_steps is not None and not hasattr(UpperCamelCase__							,	'''set_timesteps'''  ):
        _UpperCamelCase       			=num_inference_steps

      # copy over dummy past residuals (must be done after set_timesteps)
      _UpperCamelCase       			=[residual + 0.2, residual + 0.15, residual + 0.10]
      _UpperCamelCase       			=dummy_past_residuals[: scheduler.config.solver_order]

      _UpperCamelCase       			=scheduler.timesteps[5]
      _UpperCamelCase       			=scheduler.timesteps[6]

      _UpperCamelCase       			=scheduler.step(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__							,	**UpperCamelCase__  ).prev_sample
      _UpperCamelCase       			=scheduler.step(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__							,	**UpperCamelCase__  ).prev_sample

      self.assertEqual(output_a.shape							,	sample.shape  )
      self.assertEqual(output_a.shape							,	output_a.shape  )


  def   UpperCamelCase__      (				self    :					Any  )     ->						Dict:
    # make sure that iterating over schedulers with same config names gives same results
    # for defaults
    _UpperCamelCase       			=UniPCMultistepScheduler(**self.get_scheduler_config()  )
    _UpperCamelCase       			=self.full_loop(scheduler=UpperCamelCase__  )
    _UpperCamelCase       			=torch.mean(torch.abs(UpperCamelCase__  )  )

    assert abs(result_mean.item() - 0.2464  ) < 1E-3

    _UpperCamelCase       			=DPMSolverSinglestepScheduler.from_config(scheduler.config  )
    _UpperCamelCase       			=DEISMultistepScheduler.from_config(scheduler.config  )
    _UpperCamelCase       			=DPMSolverMultistepScheduler.from_config(scheduler.config  )
    _UpperCamelCase       			=UniPCMultistepScheduler.from_config(scheduler.config  )

    _UpperCamelCase       			=self.full_loop(scheduler=UpperCamelCase__  )
    _UpperCamelCase       			=torch.mean(torch.abs(UpperCamelCase__  )  )

    assert abs(result_mean.item() - 0.2464  ) < 1E-3


  def   UpperCamelCase__      (				self    :					List[str]  )     ->						Optional[Any]:
    for timesteps in [25, 50, 100, 999, 1000]:
      self.check_over_configs(num_train_timesteps=UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					Optional[int]  )     ->						Union[str, Any]:
    self.check_over_configs(thresholding=UpperCamelCase__  )
    for order in [1, 2, 3]:
      for solver_type in ["bh1", "bh2"]:
        for threshold in [0.5, 1.0, 2.0]:
          for prediction_type in ["epsilon", "sample"]:
            self.check_over_configs(
                thresholding=UpperCamelCase__							,	prediction_type=UpperCamelCase__							,	sample_max_value=UpperCamelCase__							,	solver_order=UpperCamelCase__							,	solver_type=UpperCamelCase__							,	)


  def   UpperCamelCase__      (				self    :					Dict  )     ->						List[str]:
    for prediction_type in ["epsilon", "v_prediction"]:
      self.check_over_configs(prediction_type=UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					Optional[int]  )     ->						Optional[Any]:
    for solver_type in ["bh1", "bh2"]:
      for order in [1, 2, 3]:
        for prediction_type in ["epsilon", "sample"]:
          self.check_over_configs(
              solver_order=UpperCamelCase__							,	solver_type=UpperCamelCase__							,	prediction_type=UpperCamelCase__							,	)
          _UpperCamelCase       			=self.full_loop(
              solver_order=UpperCamelCase__							,	solver_type=UpperCamelCase__							,	prediction_type=UpperCamelCase__							,	)
          assert not torch.isnan(UpperCamelCase__  ).any(), "Samples have nan numbers"


  def   UpperCamelCase__      (				self    :					int  )     ->						List[str]:
    self.check_over_configs(lower_order_final=UpperCamelCase__  )
    self.check_over_configs(lower_order_final=UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					Optional[Any]  )     ->						Optional[Any]:
    for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
      self.check_over_forward(num_inference_steps=UpperCamelCase__							,	time_step=0  )


  def   UpperCamelCase__      (				self    :					List[Any]  )     ->						Optional[Any]:
    _UpperCamelCase       			=self.full_loop()
    _UpperCamelCase       			=torch.mean(torch.abs(UpperCamelCase__  )  )

    assert abs(result_mean.item() - 0.2464  ) < 1E-3


  def   UpperCamelCase__      (				self    :					List[str]  )     ->						Any:
    _UpperCamelCase       			=self.full_loop(prediction_type='''v_prediction'''  )
    _UpperCamelCase       			=torch.mean(torch.abs(UpperCamelCase__  )  )

    assert abs(result_mean.item() - 0.1014  ) < 1E-3


  def   UpperCamelCase__      (				self    :					List[Any]  )     ->						Optional[int]:
    _UpperCamelCase       			=self.scheduler_classes[0]
    _UpperCamelCase       			=self.get_scheduler_config(thresholding=UpperCamelCase__							,	dynamic_thresholding_ratio=0  )
    _UpperCamelCase       			=scheduler_class(**UpperCamelCase__  )

    _UpperCamelCase       			=10
    _UpperCamelCase       			=self.dummy_model()
    _UpperCamelCase       			=self.dummy_sample_deter.half()
    scheduler.set_timesteps(UpperCamelCase__  )

    for i, t in enumerate(scheduler.timesteps  ):
      _UpperCamelCase       			=model(UpperCamelCase__							,	UpperCamelCase__  )
      _UpperCamelCase       			=scheduler.step(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__  ).prev_sample

    assert sample.dtype == torch.floataa




  def   UpperCamelCase__      (				self    :					Optional[int]							,	**UpperCamelCase__    :					Tuple  )     ->						List[Any]:
    for scheduler_class in self.scheduler_classes:
      _UpperCamelCase       			=self.get_scheduler_config(**UpperCamelCase__  )
      _UpperCamelCase       			=scheduler_class(**UpperCamelCase__  )

      scheduler.set_timesteps(scheduler.config.num_train_timesteps  )
      assert len(scheduler.timesteps.unique()  ) == scheduler.num_inference_steps













'''simple docstring'''






import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


__lowerCamelCase						: str		=   '\\n@inproceedings{snover-etal-2006-study,\n    title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n    author = "Snover, Matthew  and\n      Dorr, Bonnie  and\n      Schwartz, Rich  and\n      Micciulla, Linnea  and\n      Makhoul, John",\n    booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",\n    month = aug # " 8-12",\n    year = "2006",\n    address = "Cambridge, Massachusetts, USA",\n    publisher = "Association for Machine Translation in the Americas",\n    url = "https://aclanthology.org/2006.amta-papers.25",\n    pages = "223--231",\n}\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

__lowerCamelCase						: Optional[int]		=   '\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n'

__lowerCamelCase						: str		=   '\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n    predictions (list of str): The system stream (a sequence of segments).\n    references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n                                    Only applies if `normalized = True`. Defaults to `False`.\n    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n    \'score\' (float): TER score (num_edits / sum_ref_lengths * 100)\n    \'num_edits\' (int): The cumulative number of edits\n    \'ref_length\' (float): The cumulative average reference length\n\nExamples:\n    Example 1:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?",\n        ...                     "What did the TER metric user say to the developer?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n        ...             ["Your jokes are...", "...TERrible"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}\n\n    Example 2:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}\n\n    Example 3:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         normalized=True,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}\n\n    Example 4:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}\n\n    Example 5:\n        >>> predictions = ["does this sentence match??",\n        ...                    "what about this sentence?",\n        ...                    "What did the TER metric user say to the developer?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n        ...             ["Your jokes are...", "...TERrible"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}\n'







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


  """simple docstring"""







  def   UpperCamelCase__      (				self    :					str  )     ->						List[str]:
    if version.parse(scb.__version__  ) < version.parse('''1.4.12'''  ):
      raise ImportWarning(
          '''To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'''
          '''You can install it with `pip install "sacrebleu>=1.4.12"`.'''  )
    return datasets.MetricInfo(
        description=_DESCRIPTION							,	citation=_CITATION							,	homepage='''http://www.cs.umd.edu/~snover/tercom/'''							,	inputs_description=_KWARGS_DESCRIPTION							,	features=datasets.Features(
            {
                '''predictions''': datasets.Value('''string'''							,	id='''sequence'''  ),
                '''references''': datasets.Sequence(datasets.Value('''string'''							,	id='''sequence'''  )							,	id='''references'''  ),
            }  )							,	codebase_urls=['''https://github.com/mjpost/sacreBLEU#ter''']							,	reference_urls=[
            '''https://github.com/jhclark/tercom''',
        ]							,	)




  def   UpperCamelCase__      (				self    :					Union[str, Any]							,	UpperCamelCase__    :					Tuple							,	UpperCamelCase__    :					Optional[Any]							,	UpperCamelCase__    :					bool = False							,	UpperCamelCase__    :					bool = False							,	UpperCamelCase__    :					bool = False							,	UpperCamelCase__    :					bool = False							,	)     ->						str:
    _UpperCamelCase       			=len(references[0]  )
    if any(len(UpperCamelCase__  ) != references_per_prediction for refs in references  ):
      raise ValueError('''Sacrebleu requires the same number of references for each prediction'''  )
    _UpperCamelCase       			=[[refs[i] for refs in references] for i in range(UpperCamelCase__  )]

    _UpperCamelCase       			=TER(
        normalized=UpperCamelCase__							,	no_punct=UpperCamelCase__							,	asian_support=UpperCamelCase__							,	case_sensitive=UpperCamelCase__							,	)
    _UpperCamelCase       			=sb_ter.corpus_score(UpperCamelCase__							,	UpperCamelCase__  )

    return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}






