



'''simple docstring'''

import re
import string

import numpy as np

import datasets


__lowerCAmelCase		     =  '''
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.
'''

__lowerCAmelCase		     =  '''
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

'''

__lowerCAmelCase		     =  '''
'''

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







							def 				__lowercase     (				self   :			List[Any]						):
									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 				__lowercase     (				self   :			Optional[Any]      ,_UpperCAmelCase   :			Union[str, Any]      ,_UpperCAmelCase   :			Dict      ,_UpperCAmelCase   :			Tuple=None      ,_UpperCAmelCase   :			Union[str, Any]=False      ,_UpperCAmelCase   :			Tuple=False      ,_UpperCAmelCase   :			List[Any]=False      ,):
									if regexes_to_ignore is not None:
											for s in regexes_to_ignore:
													_a	: List[str]								=				np.array([re.sub(_UpperCAmelCase      ,''      ,_UpperCAmelCase						) for x in predictions]						)
													_a	: Union[str, Any]								=				np.array([re.sub(_UpperCAmelCase      ,''      ,_UpperCAmelCase						) for x in references]						)
									else:
											_a	: Union[str, Any]								=				np.asarray(_UpperCAmelCase						)
											_a	: List[str]								=				np.asarray(_UpperCAmelCase						)

									if ignore_case:
											_a	: List[Any]								=				np.char.lower(_UpperCAmelCase						)
											_a	: str								=				np.char.lower(_UpperCAmelCase						)

									if ignore_punctuation:
											_a	: str								=				string.punctuation.maketrans(''      ,''      ,string.punctuation						)
											_a	: Tuple								=				np.char.translate(_UpperCAmelCase      ,table=_UpperCAmelCase						)
											_a	: int								=				np.char.translate(_UpperCAmelCase      ,table=_UpperCAmelCase						)

									if ignore_numbers:
											_a	: Union[str, Any]								=				string.digits.maketrans(''      ,''      ,string.digits						)
											_a	: Union[str, Any]								=				np.char.translate(_UpperCAmelCase      ,table=_UpperCAmelCase						)
											_a	: Union[str, Any]								=				np.char.translate(_UpperCAmelCase      ,table=_UpperCAmelCase						)

									_a	: Optional[int]								=				predictions == references

									return {"exact_match": np.mean(_UpperCAmelCase						) * 100}









'''simple docstring'''

import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class       __magic_name__		(       unittest.TestCase  ):







							@property
							def 				__lowercase     (				self   :			int						):
									torch.manual_seed(0						)
									_a	: int								=				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







							@property
							def 				__lowercase     (				self   :			Any						):
									torch.manual_seed(0						)
									_a	: int								=				VQModel(
									    block_out_channels=[32, 64]      ,in_channels=3      ,out_channels=3      ,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']      ,up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']      ,latent_channels=3      ,)
									return model







							@property
							def 				__lowercase     (				self   :			Union[str, Any]						):
									torch.manual_seed(0						)
									_a	: Dict								=				CLIPTextConfig(
									    bos_token_id=0      ,eos_token_id=2      ,hidden_size=32      ,intermediate_size=37      ,layer_norm_eps=1E-05      ,num_attention_heads=4      ,num_hidden_layers=5      ,pad_token_id=1      ,vocab_size=1000      ,)
									return CLIPTextModel(_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Dict						):
									_a	: Union[str, Any]								=				self.dummy_uncond_unet
									_a	: Union[str, Any]								=				DDIMScheduler()
									_a	: Union[str, Any]								=				self.dummy_vq_model

									_a	: List[Any]								=				LDMPipeline(unet=_UpperCAmelCase      ,vqvae=_UpperCAmelCase      ,scheduler=_UpperCAmelCase						)
									ldm.to(_UpperCAmelCase						)
									ldm.set_progress_bar_config(disable=_UpperCAmelCase						)

									_a	: Dict								=				torch.manual_seed(0						)
									_a	: int								=				ldm(generator=_UpperCAmelCase      ,num_inference_steps=2      ,output_type='numpy'						).images

									_a	: Tuple								=				torch.manual_seed(0						)
									_a	: List[Any]								=				ldm(generator=_UpperCAmelCase      ,num_inference_steps=2      ,output_type='numpy'      ,return_dict=_UpperCAmelCase						)[0]

									_a	: Any								=				image[0, -3:, -3:, -1]
									_a	: List[Any]								=				image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									_a	: Union[str, Any]								=				np.array([0.85_12, 0.8_18, 0.64_11, 0.68_08, 0.44_65, 0.56_18, 0.46, 0.62_31, 0.51_72]						)
									_a	: Any								=				1E-2 if torch_device != 'mps' else 3E-2

									assert np.abs(image_slice.flatten() - expected_slice						).max() < tolerance
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < tolerance

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







							def 				__lowercase     (				self   :			Optional[int]						):
									_a	: Dict								=				LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256'						)
									ldm.to(_UpperCAmelCase						)
									ldm.set_progress_bar_config(disable=_UpperCAmelCase						)

									_a	: str								=				torch.manual_seed(0						)
									_a	: Optional[Any]								=				ldm(generator=_UpperCAmelCase      ,num_inference_steps=5      ,output_type='numpy'						).images

									_a	: Optional[Any]								=				image[0, -3:, -3:, -1]

									assert image.shape == (1, 256, 256, 3)
									_a	: Union[str, Any]								=				np.array([0.43_99, 0.4_49_75, 0.4_68_25, 0.4_74, 0.43_59, 0.45_81, 0.4_50_95, 0.43_41, 0.44_47]						)
									_a	: List[str]								=				1E-2 if torch_device != 'mps' else 3E-2

									assert np.abs(image_slice.flatten() - expected_slice						).max() < tolerance




