





import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class   lowercase__     (				__A	):
  __UpperCamelCase						  =	(EulerAncestralDiscreteScheduler,)
  __UpperCamelCase						  =	10







  def 						UpperCAmelCase__		(						self     ,    **_lowercase    ):
       lowerCAmelCase_       :							Optional[int]			 =		{
           '''num_train_timesteps''': 1_100,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
       }

       config.update(**_lowercase    )
       return config







  def 						UpperCAmelCase__		(						self    ):







       for timesteps in [10, 50, 100, 1_000]:
            self.check_over_configs(num_train_timesteps=_lowercase    )







  def 						UpperCAmelCase__		(						self    ):







       for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]     ,    [0.0002, 0.002, 0.02]    ):
            self.check_over_configs(beta_start=_lowercase     ,    beta_end=_lowercase    )







  def 						UpperCAmelCase__		(						self    ):







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







  def 						UpperCAmelCase__		(						self    ):







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







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							int			 =		self.scheduler_classes[0]
       lowerCAmelCase_       :							Dict			 =		self.get_scheduler_config()
       lowerCAmelCase_       :							Tuple			 =		scheduler_class(**_lowercase    )

       scheduler.set_timesteps(self.num_inference_steps    )

       lowerCAmelCase_       :							Any			 =		torch.manual_seed(0    )

       lowerCAmelCase_       :							Optional[Any]			 =		self.dummy_model()
       lowerCAmelCase_       :							Any			 =		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       lowerCAmelCase_       :							Tuple			 =		sample.to(_lowercase    )

       for i, t in enumerate(scheduler.timesteps    ):
            lowerCAmelCase_       :							Optional[int]			 =		scheduler.scale_model_input(_lowercase     ,    _lowercase    )

            lowerCAmelCase_       :							List[Any]			 =		model(_lowercase     ,    _lowercase    )

            lowerCAmelCase_       :							List[Any]			 =		scheduler.step(_lowercase     ,    _lowercase     ,    _lowercase     ,    generator=_lowercase    )
            lowerCAmelCase_       :							Optional[int]			 =		output.prev_sample

       lowerCAmelCase_       :							int			 =		torch.sum(torch.abs(_lowercase    )    )
       lowerCAmelCase_       :							List[str]			 =		torch.mean(torch.abs(_lowercase    )    )

       assert abs(result_sum.item() - 152.3_192    ) < 1e-2
       assert abs(result_mean.item() - 0.1983    ) < 1e-3







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							Any			 =		self.scheduler_classes[0]
       lowerCAmelCase_       :							List[str]			 =		self.get_scheduler_config(prediction_type="""v_prediction"""    )
       lowerCAmelCase_       :							Optional[int]			 =		scheduler_class(**_lowercase    )

       scheduler.set_timesteps(self.num_inference_steps    )

       lowerCAmelCase_       :							List[Any]			 =		torch.manual_seed(0    )

       lowerCAmelCase_       :							List[Any]			 =		self.dummy_model()
       lowerCAmelCase_       :							int			 =		self.dummy_sample_deter * scheduler.init_noise_sigma
       lowerCAmelCase_       :							Tuple			 =		sample.to(_lowercase    )

       for i, t in enumerate(scheduler.timesteps    ):
            lowerCAmelCase_       :							Dict			 =		scheduler.scale_model_input(_lowercase     ,    _lowercase    )

            lowerCAmelCase_       :							List[Any]			 =		model(_lowercase     ,    _lowercase    )

            lowerCAmelCase_       :							int			 =		scheduler.step(_lowercase     ,    _lowercase     ,    _lowercase     ,    generator=_lowercase    )
            lowerCAmelCase_       :							Optional[int]			 =		output.prev_sample

       lowerCAmelCase_       :							Union[str, Any]			 =		torch.sum(torch.abs(_lowercase    )    )
       lowerCAmelCase_       :							Any			 =		torch.mean(torch.abs(_lowercase    )    )

       assert abs(result_sum.item() - 108.4_439    ) < 1e-2
       assert abs(result_mean.item() - 0.1412    ) < 1e-3







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							int			 =		self.scheduler_classes[0]
       lowerCAmelCase_       :							Tuple			 =		self.get_scheduler_config()
       lowerCAmelCase_       :							List[str]			 =		scheduler_class(**_lowercase    )

       scheduler.set_timesteps(self.num_inference_steps     ,    device=_lowercase    )
       lowerCAmelCase_       :							Tuple			 =		torch.manual_seed(0    )

       lowerCAmelCase_       :							List[Any]			 =		self.dummy_model()
       lowerCAmelCase_       :							List[str]			 =		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       lowerCAmelCase_       :							List[str]			 =		sample.to(_lowercase    )

       for t in scheduler.timesteps:
            lowerCAmelCase_       :							Dict			 =		scheduler.scale_model_input(_lowercase     ,    _lowercase    )

            lowerCAmelCase_       :							Optional[int]			 =		model(_lowercase     ,    _lowercase    )

            lowerCAmelCase_       :							Dict			 =		scheduler.step(_lowercase     ,    _lowercase     ,    _lowercase     ,    generator=_lowercase    )
            lowerCAmelCase_       :							Optional[int]			 =		output.prev_sample

       lowerCAmelCase_       :							Any			 =		torch.sum(torch.abs(_lowercase    )    )
       lowerCAmelCase_       :							Optional[int]			 =		torch.mean(torch.abs(_lowercase    )    )

       assert abs(result_sum.item() - 152.3_192    ) < 1e-2
       assert abs(result_mean.item() - 0.1983    ) < 1e-3








"""simple docstring"""





import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


UpperCamelCase_						:					Optional[int]      				=			"""\
@inproceedings{ramshaw-marcus-1995-text,
    title = \"Text Chunking using Transformation-Based Learning\",
    author = \"Ramshaw, Lance  and
      Marcus, Mitch\",
    booktitle = \"Third Workshop on Very Large Corpora\",
    year = \"1995\",
    url = \"https://www.aclweb.org/anthology/W95-0107\",
}
@misc{seqeval,
  title={{seqeval}: A Python framework for sequence labeling evaluation},
  url={https://github.com/chakki-works/seqeval},
  note={Software available from https://github.com/chakki-works/seqeval},
  author={Hiroki Nakayama},
  year={2018},
}
"""

UpperCamelCase_						:					Any      				=			"""\
seqeval is a Python framework for sequence labeling evaluation.
seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.

This is well-tested by using the Perl script conlleval, which can be used for
measuring the performance of a system that has processed the CoNLL-2000 shared task data.

seqeval supports following formats:
IOB1
IOB2
IOE1
IOE2
IOBES

See the [README.md] file at https://github.com/chakki-works/seqeval for more information.
"""

UpperCamelCase_						:					int      				=			"""
Produces labelling scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)
    references: List of List of reference labels (Ground truth (correct) target values)
    suffix: True if the IOB prefix is after type, False otherwise. default: False
    scheme: Specify target tagging scheme. Should be one of [\"IOB1\", \"IOB2\", \"IOE1\", \"IOE2\", \"IOBES\", \"BILOU\"].
        default: None
    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.
        If you want to only count exact matches, pass mode=\"strict\". default: None.
    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None
    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,
        \"warn\". \"warn\" acts as 0, but the warning is raised.

Returns:
    'scores': dict. Summary of the scores for overall and per type
        Overall:
            'accuracy': accuracy,
            'precision': precision,
            'recall': recall,
            'f1': F1 score, also known as balanced F-score or F-measure,
        Per type:
            'precision': precision,
            'recall': recall,
            'f1': F1 score, also known as balanced F-score or F-measure
Examples:

    >>> predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
    >>> references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
    >>> seqeval = datasets.load_metric(\"seqeval\")
    >>> results = seqeval.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['MISC', 'PER', 'overall_precision', 'overall_recall', 'overall_f1', 'overall_accuracy']
    >>> print(results[\"overall_f1\"])
    0.5
    >>> print(results[\"PER\"][\"f1\"])
    1.0
"""


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


      def _UpperCamelCase    (     self					:						int    ):





            """simple docstring"""







            return datasets.MetricInfo(
                description=_DESCRIPTION				, citation=_CITATION				, homepage='''https://github.com/chakki-works/seqeval'''				, inputs_description=_KWARGS_DESCRIPTION				, features=datasets.Features(
                    {
                        '''predictions''': datasets.Sequence(datasets.Value('''string'''				, id='''label'''    )				, id='''sequence'''    ),
                        '''references''': datasets.Sequence(datasets.Value('''string'''				, id='''label'''    )				, id='''sequence'''    ),
                    }    )				, codebase_urls=['''https://github.com/chakki-works/seqeval''']				, reference_urls=['''https://github.com/chakki-works/seqeval''']				, )







      def _UpperCamelCase    (     self					:						List[Any]				, a					:						List[str]				, a					:						Optional[Any]				, a					:						bool = False				, a					:						Optional[str] = None				, a					:						Optional[str] = None				, a					:						Optional[List[int]] = None				, a					:						Union[str, int] = "warn"				, ):





            """simple docstring"""







            if scheme is not None:
                  try:
                        __snake_case   :     List[str]	      =importlib.import_module('''seqeval.scheme'''    )
                        __snake_case   :     int	      =getattr(a				, a    )
                  except AttributeError:
                        raise ValueError(f'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}'''    )
            __snake_case   :     Optional[Any]	      =classification_report(
                y_true=a				, y_pred=a				, suffix=a				, output_dict=a				, scheme=a				, mode=a				, sample_weight=a				, zero_division=a				, )
            report.pop('''macro avg'''    )
            report.pop('''weighted avg'''    )
            __snake_case   :     Union[str, Any]	      =report.pop('''micro avg'''    )

            __snake_case   :     str	      ={
                type_name: {
                    '''precision''': score['''precision'''],
                    '''recall''': score['''recall'''],
                    '''f1''': score['''f1-score'''],
                    '''number''': score['''support'''],
                }
                for type_name, score in report.items()
            }
            __snake_case   :     str	      =overall_score['''precision''']
            __snake_case   :     int	      =overall_score['''recall''']
            __snake_case   :     str	      =overall_score['''f1-score''']
            __snake_case   :     Union[str, Any]	      =accuracy_score(y_true=a				, y_pred=a    )

            return scores


