'''simple docstring'''

import re
import string

import numpy as np

import datasets


_A			:      Optional[Any]					      =				'''
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
'''

_A			:      List[str]					      =				'''
Args:
    predictions: List of predicted texts.
    references: List of reference texts.
    regexes_to_ignore: List, defaults to None. Regex expressions of characters to
        ignore when calculating the exact matches. Note: these regexes are removed
        from the input data before the changes based on the options below (e.g. ignore_case,
        ignore_punctuation, ignore_numbers) are applied.
    ignore_case: Boolean, defaults to False. If true, turns everything
        to lowercase so that capitalization differences are ignored.
    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
Returns:
    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.
Examples:
    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results["exact_match"], 1))
    25.0

    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results["exact_match"], 1))
    50.0


    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results["exact_match"], 1))
    75.0

    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)
    >>> print(round(results["exact_match"], 1))
    100.0

    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]
    >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results["exact_match"], 1))
    33.3

'''

_A			:      str					      =				'''
'''







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


 '''simple docstring'''






 def a					(      self   :	Tuple							)  ->    str:
  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"""							),
          }							)      ,       reference_urls=[]      ,       )





 def a					(      self   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	str      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	int=None      ,       SCREAMING_SNAKE_CASE__   :	int=False      ,       SCREAMING_SNAKE_CASE__   :	List[str]=False      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=False      ,       )  ->    int:
  if regexes_to_ignore is not None:
   for s in regexes_to_ignore:
    __lowerCAmelCase											=						np.array([re.sub(SCREAMING_SNAKE_CASE__      ,       """"""      ,       SCREAMING_SNAKE_CASE__							) for x in predictions]							)
    __lowerCAmelCase											=						np.array([re.sub(SCREAMING_SNAKE_CASE__      ,       """"""      ,       SCREAMING_SNAKE_CASE__							) for x in references]							)
  else:
   __lowerCAmelCase											=						np.asarray(SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase											=						np.asarray(SCREAMING_SNAKE_CASE__							)

  if ignore_case:
   __lowerCAmelCase											=						np.char.lower(SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase											=						np.char.lower(SCREAMING_SNAKE_CASE__							)

  if ignore_punctuation:
   __lowerCAmelCase											=						string.punctuation.maketrans(""""""      ,       """"""      ,       string.punctuation							)
   __lowerCAmelCase											=						np.char.translate(SCREAMING_SNAKE_CASE__      ,       table=SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase											=						np.char.translate(SCREAMING_SNAKE_CASE__      ,       table=SCREAMING_SNAKE_CASE__							)

  if ignore_numbers:
   __lowerCAmelCase											=						string.digits.maketrans(""""""      ,       """"""      ,       string.digits							)
   __lowerCAmelCase											=						np.char.translate(SCREAMING_SNAKE_CASE__      ,       table=SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase											=						np.char.translate(SCREAMING_SNAKE_CASE__      ,       table=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						predictions == references

  return {"exact_match": np.mean(SCREAMING_SNAKE_CASE__							) * 1_00}







'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 				_lowercase     (				unittest.TestCase			):


 '''simple docstring'''






 @property
 def a					(      self   :	Any							)  ->    int:
  torch.manual_seed(0							)
  __lowerCAmelCase											=						UNetaDModel(
      block_out_channels=(32, 64)      ,       layers_per_block=2      ,       sample_size=32      ,       in_channels=3      ,       out_channels=3      ,       down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")      ,       up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")      ,       )
  return model





 def a					(      self   :	Dict							)  ->    int:
  __lowerCAmelCase											=						self.dummy_uncond_unet
  __lowerCAmelCase											=						PNDMScheduler()

  __lowerCAmelCase											=						PNDMPipeline(unet=SCREAMING_SNAKE_CASE__      ,       scheduler=SCREAMING_SNAKE_CASE__							)
  pndm.to(SCREAMING_SNAKE_CASE__							)
  pndm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						torch.manual_seed(0							)
  __lowerCAmelCase											=						pndm(generator=SCREAMING_SNAKE_CASE__      ,       num_inference_steps=20      ,       output_type="""numpy"""							).images

  __lowerCAmelCase											=						torch.manual_seed(0							)
  __lowerCAmelCase											=						pndm(generator=SCREAMING_SNAKE_CASE__      ,       num_inference_steps=20      ,       output_type="""numpy"""      ,       return_dict=SCREAMING_SNAKE_CASE__							)[0]

  __lowerCAmelCase											=						image[0, -3:, -3:, -1]
  __lowerCAmelCase											=						image_from_tuple[0, -3:, -3:, -1]

  assert image.shape == (1, 32, 32, 3)
  __lowerCAmelCase											=						np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

  assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
  assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2







@slow
@require_torch
class 				_lowercase     (				unittest.TestCase			):


 '''simple docstring'''






 def a					(      self   :	Any							)  ->    Any:
  __lowerCAmelCase											=						"""google/ddpm-cifar10-32"""

  __lowerCAmelCase											=						UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						PNDMScheduler()

  __lowerCAmelCase											=						PNDMPipeline(unet=SCREAMING_SNAKE_CASE__      ,       scheduler=SCREAMING_SNAKE_CASE__							)
  pndm.to(SCREAMING_SNAKE_CASE__							)
  pndm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						torch.manual_seed(0							)
  __lowerCAmelCase											=						pndm(generator=SCREAMING_SNAKE_CASE__      ,       output_type="""numpy"""							).images

  __lowerCAmelCase											=						image[0, -3:, -3:, -1]

  assert image.shape == (1, 32, 32, 3)
  __lowerCAmelCase											=						np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5]							)

  assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2






