


'''simple docstring'''


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

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin





class     lowercase__  (						_snake_case     ,     _snake_case					):





							'''simple docstring'''


							A_							:      str      	=  1






							@register_to_config
							def __init__(    self    ,					__snake_case=2000    ,					__snake_case=0.1    ,					__snake_case=20    ,					__snake_case=1e-3  ):
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      None
											_SCREAMING_SNAKE_CASE :       Dict     		=      None
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      None






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case = None  ):
											_SCREAMING_SNAKE_CASE :       Any     		=      torch.linspace(1    ,					self.config.sampling_eps    ,					__snake_case    ,					device=__snake_case  )






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

											# TODO(Patrick) better comments + non-PyTorch
											# postprocess model score
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      (
											    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
											)
											_SCREAMING_SNAKE_CASE :       str     		=      torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff  )  )
											_SCREAMING_SNAKE_CASE :       List[str]     		=      std.flatten()
											while len(std.shape  ) < len(score.shape  ):
															_SCREAMING_SNAKE_CASE :       int     		=      std.unsqueeze(-1  )
											_SCREAMING_SNAKE_CASE :       Tuple     		=      -score / std

											# compute
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      -1.0 / len(self.timesteps  )

											_SCREAMING_SNAKE_CASE :       str     		=      self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      beta_t.flatten()
											while len(beta_t.shape  ) < len(x.shape  ):
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      beta_t.unsqueeze(-1  )
											_SCREAMING_SNAKE_CASE :       Any     		=      -0.5 * beta_t * x

											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      torch.sqrt(__snake_case  )
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      drift - diffusion**2 * score
											_SCREAMING_SNAKE_CASE :       str     		=      x + drift * dt

											# add noise
											_SCREAMING_SNAKE_CASE :       Dict     		=      randn_tensor(x.shape    ,					layout=x.layout    ,					generator=__snake_case    ,					device=x.device    ,					dtype=x.dtype  )
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      x_mean + diffusion * math.sqrt(-dt  ) * noise

											return x, x_mean






							def __len__(    self  ):
											return self.config.num_train_timesteps








'''simple docstring'''


import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
UpperCAmelCase_		:   Union[str, Any]							   =		pytest.mark.integration


UpperCAmelCase_		:   List[Any]							   =		{'comet'}
UpperCAmelCase_		:   int							   =		importlib.util.find_spec('fairseq') is not None

UpperCAmelCase_		:   Optional[Any]							   =		{'code_eval'}
UpperCAmelCase_		:   Optional[int]							   =		os.name == 'nt'

UpperCAmelCase_		:   Dict							   =		{'bertscore', 'frugalscore', 'perplexity'}
UpperCAmelCase_		:   Dict							   =		importlib.util.find_spec('transformers') is not None







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				@wraps(SCREAMING_SNAKE_CASE__     )
				def wrapper(self							,      SCREAMING_SNAKE_CASE__     ):
								if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
												self.skipTest("""\"test requires Fairseq\""""     )
								else:
												test_case(self							,      SCREAMING_SNAKE_CASE__     )

				return wrapper







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				@wraps(SCREAMING_SNAKE_CASE__     )
				def wrapper(self							,      SCREAMING_SNAKE_CASE__     ):
								if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
												self.skipTest("""\"test requires transformers\""""     )
								else:
												test_case(self							,      SCREAMING_SNAKE_CASE__     )

				return wrapper







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				@wraps(SCREAMING_SNAKE_CASE__     )
				def wrapper(self							,      SCREAMING_SNAKE_CASE__     ):
								if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
												self.skipTest("""\"test not supported on Windows\""""     )
								else:
												test_case(self							,      SCREAMING_SNAKE_CASE__     )

				return wrapper







def 			snake_case_   (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Dict     		=      [metric_dir.split(os.sep     )[-2] for metric_dir in glob.glob("""./metrics/*/"""     )]
				return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished





@parameterized.named_parameters(get_local_metric_names()					)
@for_all_test_methods(
    _snake_case     ,     _snake_case     ,     _snake_case					)
@local
class     lowercase__  (						parameterized.TestCase					):





							'''simple docstring'''


							A_							:      Optional[int]      	=  {}
							A_							:      Union[str, Any]      	=  None






							@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning"""  )
							@pytest.mark.filterwarnings("""ignore:load_metric is deprecated:FutureWarning"""  )
							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       str     		=      """[...]"""
											_SCREAMING_SNAKE_CASE :       Any     		=      importlib.import_module(
											    datasets.load.metric_module_factory(os.path.join("""metrics"""    ,					__snake_case  )  ).module_path  )
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      datasets.load.import_main_class(metric_module.__name__    ,					dataset=__snake_case  )
											# check parameters
											_SCREAMING_SNAKE_CASE :       Tuple     		=      inspect.signature(metric._compute  ).parameters
											self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()  )  )  # no **kwargs
											# run doctest
											with self.patch_intensive_calls(__snake_case    ,					metric_module.__name__  ):
															with self.use_local_metrics():
																			try:
																							_SCREAMING_SNAKE_CASE :       int     		=      doctest.testmod(__snake_case    ,					verbose=__snake_case    ,					raise_on_error=__snake_case  )
																			except doctest.UnexpectedException as e:
																							raise e.exc_info[1]  # raise the exception that doctest caught
											self.assertEqual(results.failed    ,					0  )
											self.assertGreater(results.attempted    ,					1  )






							@slow
							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      """[...]"""
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      importlib.import_module(
											    datasets.load.metric_module_factory(os.path.join("""metrics"""    ,					__snake_case  )  ).module_path  )
											# run doctest
											with self.use_local_metrics():
															_SCREAMING_SNAKE_CASE :       List[str]     		=      doctest.testmod(__snake_case    ,					verbose=__snake_case    ,					raise_on_error=__snake_case  )
											self.assertEqual(results.failed    ,					0  )
											self.assertGreater(results.attempted    ,					1  )






							@contextmanager
							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case  ):
											if metric_name in self.INTENSIVE_CALLS_PATCHER:
															with self.INTENSIVE_CALLS_PATCHER[metric_name](__snake_case  ):
																			yield
											else:
															yield






							@contextmanager
							def UpperCAmelCase_	(    self  ):
											def load_local_metric(__snake_case    ,					*__snake_case    ,					**__snake_case  ):
															return load_metric(os.path.join("""metrics"""    ,					__snake_case  )    ,					*__snake_case    ,					**__snake_case  )

											with patch("""datasets.load_metric"""  ) as mock_load_metric:
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      load_local_metric
															yield






							@classmethod
							def UpperCAmelCase_	(    cls    ,					__snake_case  ):
											def wrapper(__snake_case  ):
															_SCREAMING_SNAKE_CASE :       Any     		=      contextmanager(__snake_case  )
															_SCREAMING_SNAKE_CASE :       int     		=      patcher
															return patcher

											return wrapper







@LocalMetricTest.register_intensive_calls_patcher("""bleurt"""     )
def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				import tensorflow.compat.va as tf
				from bleurt.score import Predictor

				tf.flags.DEFINE_string("""sv"""							,      """"""							,      """"""     )  # handle pytest cli flags





				class     lowercase__  (						_snake_case					):





											'''simple docstring'''


											def UpperCAmelCase_	(    self    ,					__snake_case  ):
															assert len(input_dict["""input_ids"""]  ) == 2
															return np.array([1.03, 1.04]  )

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
				with patch("""bleurt.score._create_predictor"""     ) as mock_create_predictor:
								_SCREAMING_SNAKE_CASE :       Any     		=      MockedPredictor()
								yield







@LocalMetricTest.register_intensive_calls_patcher("""bertscore"""     )
def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				import torch

				def bert_cos_score_idf(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      *SCREAMING_SNAKE_CASE__							,      **SCREAMING_SNAKE_CASE__     ):
								return torch.tensor([[1.0, 1.0, 1.0]] * len(SCREAMING_SNAKE_CASE__     )     )

				# mock get_model which is supposed to do download a bert model
				# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
				with patch("""bert_score.scorer.get_model"""     ), patch(
				    """bert_score.scorer.bert_cos_score_idf"""     ) as mock_bert_cos_score_idf:
								_SCREAMING_SNAKE_CASE :       Any     		=      bert_cos_score_idf
								yield







@LocalMetricTest.register_intensive_calls_patcher("""comet"""     )
def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				def load_from_checkpoint(SCREAMING_SNAKE_CASE__     ):





								class     lowercase__  :





															'''simple docstring'''


															def UpperCAmelCase_	(    self    ,					__snake_case    ,					*__snake_case    ,					**__snake_case  ):
																			assert len(__snake_case  ) == 2
																			_SCREAMING_SNAKE_CASE :       Dict     		=      [0.19, 0.92]
																			return scores, sum(__snake_case  ) / len(__snake_case  )



								return Model()

				# mock load_from_checkpoint which is supposed to do download a bert model
				# mock load_from_checkpoint which is supposed to do download a bert model
				with patch("""comet.download_model"""     ) as mock_download_model:
								_SCREAMING_SNAKE_CASE :       Any     		=      None
								with patch("""comet.load_from_checkpoint"""     ) as mock_load_from_checkpoint:
												_SCREAMING_SNAKE_CASE :       List[str]     		=      load_from_checkpoint
												yield







def 			snake_case_   (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      load_metric(os.path.join("""metrics"""							,      """seqeval"""     )     )
				_SCREAMING_SNAKE_CASE :       List[str]     		=      """ERROR"""
				_SCREAMING_SNAKE_CASE :       Tuple     		=      f"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"""
				with pytest.raises(SCREAMING_SNAKE_CASE__							,      match=re.escape(SCREAMING_SNAKE_CASE__     )     ):
								metric.compute(predictions=[]							,      references=[]							,      scheme=SCREAMING_SNAKE_CASE__     )




