"""simple docstring"""




import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu

class    a_   (  unittest.TestCase      ):




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




					@property
					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]  ):
							lowerCAmelCase__   = 1
							lowerCAmelCase__   = 3
							lowerCAmelCase__   = (32, 32)

							lowerCAmelCase__   = floats_tensor((batch_size, num_channels) + sizes	,    rng=random.Random(0  )  ).to(snake_case__  )
							return image




					@property
					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]  ):
							torch.manual_seed(0  )
							lowerCAmelCase__   = UNetaDConditionModel(
							    block_out_channels=(32, 64)	,    layers_per_block=2	,    sample_size=32	,    in_channels=4	,    out_channels=4	,    down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")	,    up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")	,    cross_attention_dim=32	,    )
							return model




					@property
					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]  ):
							torch.manual_seed(0  )
							lowerCAmelCase__   = AutoencoderKL(
							    block_out_channels=[32, 64]	,    in_channels=3	,    out_channels=3	,    down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]	,    up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]	,    latent_channels=4	,    )
							return model




					@property
					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							torch.manual_seed(0  )
							lowerCAmelCase__   = 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(snake_case__  )




					@property
					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							def extract(*snake_case__    :  List[str]	,    **snake_case__    :  Any  ):

									class    a_   :




														def __init__(						self    :  Tuple  ):
																lowerCAmelCase__   = torch.ones([0]  )




														def     _SCREAMING_SNAKE_CASE    (						self    :  Any	,    snake_case__    :  int  ):
																self.pixel_values.to(snake_case__  )
																return self
									return Out()

							return extract




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]  ):
							lowerCAmelCase__   = """cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase__   = self.dummy_cond_unet
							lowerCAmelCase__   = DDIMScheduler(
							    beta_start=0.0_0085	,    beta_end=0.012	,    beta_schedule="""scaled_linear"""	,    clip_sample=snake_case__	,    set_alpha_to_one=snake_case__	,    )

							lowerCAmelCase__   = self.dummy_vae
							lowerCAmelCase__   = self.dummy_text_encoder
							lowerCAmelCase__   = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  )

							# make sure here that pndm scheduler skips prk
							lowerCAmelCase__   = StableDiffusionPipeline(
							    unet=snake_case__	,    scheduler=snake_case__	,    vae=snake_case__	,    text_encoder=snake_case__	,    tokenizer=snake_case__	,    safety_checker=snake_case__	,    feature_extractor=self.dummy_extractor	,    )
							lowerCAmelCase__   = sd_pipe.to(snake_case__  )
							sd_pipe.set_progress_bar_config(disable=snake_case__  )

							lowerCAmelCase__   = """A painting of a squirrel eating a burger"""

							lowerCAmelCase__   = torch.Generator(device=snake_case__  ).manual_seed(0  )
							lowerCAmelCase__   = sd_pipe([prompt]	,    generator=snake_case__	,    guidance_scale=6.0	,    num_inference_steps=2	,    output_type="""np"""  )
							lowerCAmelCase__   = output.images

							lowerCAmelCase__   = torch.Generator(device=snake_case__  ).manual_seed(0  )
							lowerCAmelCase__   = sd_pipe(
							    [prompt]	,    generator=snake_case__	,    guidance_scale=6.0	,    num_inference_steps=2	,    output_type="""np"""	,    return_dict=snake_case__	,    )[0]

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

							assert image.shape == (1, 64, 64, 3)
							lowerCAmelCase__   = np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864]  )

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




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]  ):
							lowerCAmelCase__   = """cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase__   = self.dummy_cond_unet
							lowerCAmelCase__   = PNDMScheduler(skip_prk_steps=snake_case__  )
							lowerCAmelCase__   = self.dummy_vae
							lowerCAmelCase__   = self.dummy_text_encoder
							lowerCAmelCase__   = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  )

							# make sure here that pndm scheduler skips prk
							lowerCAmelCase__   = StableDiffusionPipeline(
							    unet=snake_case__	,    scheduler=snake_case__	,    vae=snake_case__	,    text_encoder=snake_case__	,    tokenizer=snake_case__	,    safety_checker=snake_case__	,    feature_extractor=self.dummy_extractor	,    )
							lowerCAmelCase__   = sd_pipe.to(snake_case__  )
							sd_pipe.set_progress_bar_config(disable=snake_case__  )

							lowerCAmelCase__   = """A painting of a squirrel eating a burger"""
							lowerCAmelCase__   = torch.Generator(device=snake_case__  ).manual_seed(0  )
							lowerCAmelCase__   = sd_pipe([prompt]	,    generator=snake_case__	,    guidance_scale=6.0	,    num_inference_steps=2	,    output_type="""np"""  )

							lowerCAmelCase__   = output.images

							lowerCAmelCase__   = torch.Generator(device=snake_case__  ).manual_seed(0  )
							lowerCAmelCase__   = sd_pipe(
							    [prompt]	,    generator=snake_case__	,    guidance_scale=6.0	,    num_inference_steps=2	,    output_type="""np"""	,    return_dict=snake_case__	,    )[0]

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

							assert image.shape == (1, 64, 64, 3)
							lowerCAmelCase__   = np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993]  )

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




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							lowerCAmelCase__   = StableDiffusionPipeline.from_pretrained(
							    """hf-internal-testing/tiny-stable-diffusion-lms-pipe"""	,    safety_checker=snake_case__  )
							assert isinstance(snake_case__	,    snake_case__  )
							assert isinstance(pipe.scheduler	,    snake_case__  )
							assert pipe.safety_checker is None

							lowerCAmelCase__   = pipe("""example prompt"""	,    num_inference_steps=2  ).images[0]
							assert image is not None

							# check that there's no error when saving a pipeline with one of the models being None
							with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(snake_case__  )
									lowerCAmelCase__   = StableDiffusionPipeline.from_pretrained(snake_case__  )

							# sanity check that the pipeline still works
							assert pipe.safety_checker is None
							lowerCAmelCase__   = pipe("""example prompt"""	,    num_inference_steps=2  ).images[0]
							assert image is not None




					@unittest.skipIf(torch_device != """cuda"""	,    """This test requires a GPU"""  )
					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]  ):
							lowerCAmelCase__   = self.dummy_cond_unet
							lowerCAmelCase__   = PNDMScheduler(skip_prk_steps=snake_case__  )
							lowerCAmelCase__   = self.dummy_vae
							lowerCAmelCase__   = self.dummy_text_encoder
							lowerCAmelCase__   = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  )

							# put models in fp16
							lowerCAmelCase__   = unet.half()
							lowerCAmelCase__   = vae.half()
							lowerCAmelCase__   = bert.half()

							# make sure here that pndm scheduler skips prk
							lowerCAmelCase__   = StableDiffusionPipeline(
							    unet=snake_case__	,    scheduler=snake_case__	,    vae=snake_case__	,    text_encoder=snake_case__	,    tokenizer=snake_case__	,    safety_checker=snake_case__	,    feature_extractor=self.dummy_extractor	,    )
							lowerCAmelCase__   = sd_pipe.to(snake_case__  )
							sd_pipe.set_progress_bar_config(disable=snake_case__  )

							lowerCAmelCase__   = """A painting of a squirrel eating a burger"""
							lowerCAmelCase__   = sd_pipe([prompt]	,    num_inference_steps=2	,    output_type="""np"""  ).images

							assert image.shape == (1, 64, 64, 3)

@nightly
@require_torch_gpu
class    a_   (  unittest.TestCase      ):




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]  ):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]  ):
							lowerCAmelCase__   = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""	,    safety_checker=snake_case__  )
							lowerCAmelCase__   = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config  )
							lowerCAmelCase__   = sd_pipe.to(snake_case__  )
							sd_pipe.set_progress_bar_config(disable=snake_case__  )

							lowerCAmelCase__   = (
							    """portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"""
							    """ coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"""
							    """ anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"""
							    """ children from bahnhof zoo, detailed """
							)
							lowerCAmelCase__   = 4003660346
							lowerCAmelCase__   = 7

							# without safety guidance (sld_guidance_scale = 0)
							lowerCAmelCase__   = torch.manual_seed(snake_case__  )
							lowerCAmelCase__   = sd_pipe(
							    [prompt]	,    generator=snake_case__	,    guidance_scale=snake_case__	,    num_inference_steps=50	,    output_type="""np"""	,    width=512	,    height=512	,    sld_guidance_scale=0	,    )

							lowerCAmelCase__   = output.images
							lowerCAmelCase__   = image[0, -3:, -3:, -1]
							lowerCAmelCase__   = [0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]

							assert image.shape == (1, 512, 512, 3)

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

							# without safety guidance (strong configuration)
							lowerCAmelCase__   = torch.manual_seed(snake_case__  )
							lowerCAmelCase__   = sd_pipe(
							    [prompt]	,    generator=snake_case__	,    guidance_scale=snake_case__	,    num_inference_steps=50	,    output_type="""np"""	,    width=512	,    height=512	,    sld_guidance_scale=2000	,    sld_warmup_steps=7	,    sld_threshold=0.025	,    sld_momentum_scale=0.5	,    sld_mom_beta=0.7	,    )

							lowerCAmelCase__   = output.images
							lowerCAmelCase__   = image[0, -3:, -3:, -1]
							lowerCAmelCase__   = [0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]

							assert image.shape == (1, 512, 512, 3)

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




					def     _SCREAMING_SNAKE_CASE    (						self    :  int  ):
							lowerCAmelCase__   = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""	,    safety_checker=snake_case__  )
							lowerCAmelCase__   = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config  )
							lowerCAmelCase__   = sd_pipe.to(snake_case__  )
							sd_pipe.set_progress_bar_config(disable=snake_case__  )

							lowerCAmelCase__   = """padme amidala taking a bath artwork, safe for work, no nudity"""
							lowerCAmelCase__   = 2734971755
							lowerCAmelCase__   = 7

							lowerCAmelCase__   = torch.manual_seed(snake_case__  )
							lowerCAmelCase__   = sd_pipe(
							    [prompt]	,    generator=snake_case__	,    guidance_scale=snake_case__	,    num_inference_steps=50	,    output_type="""np"""	,    width=512	,    height=512	,    sld_guidance_scale=0	,    )

							lowerCAmelCase__   = output.images
							lowerCAmelCase__   = image[0, -3:, -3:, -1]
							lowerCAmelCase__   = [0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]

							assert image.shape == (1, 512, 512, 3)

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

							lowerCAmelCase__   = torch.manual_seed(snake_case__  )
							lowerCAmelCase__   = sd_pipe(
							    [prompt]	,    generator=snake_case__	,    guidance_scale=snake_case__	,    num_inference_steps=50	,    output_type="""np"""	,    width=512	,    height=512	,    sld_guidance_scale=2000	,    sld_warmup_steps=7	,    sld_threshold=0.025	,    sld_momentum_scale=0.5	,    sld_mom_beta=0.7	,    )

							lowerCAmelCase__   = output.images
							lowerCAmelCase__   = image[0, -3:, -3:, -1]
							lowerCAmelCase__   = [0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]

							assert image.shape == (1, 512, 512, 3)

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




					def     _SCREAMING_SNAKE_CASE    (						self    :  str  ):
							lowerCAmelCase__   = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""  )
							lowerCAmelCase__   = sd_pipe.to(snake_case__  )
							sd_pipe.set_progress_bar_config(disable=snake_case__  )

							lowerCAmelCase__   = (
							    """the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."""
							    """ leyendecker"""
							)
							lowerCAmelCase__   = 1044355234
							lowerCAmelCase__   = 12

							lowerCAmelCase__   = torch.manual_seed(snake_case__  )
							lowerCAmelCase__   = sd_pipe(
							    [prompt]	,    generator=snake_case__	,    guidance_scale=snake_case__	,    num_inference_steps=50	,    output_type="""np"""	,    width=512	,    height=512	,    sld_guidance_scale=0	,    )

							lowerCAmelCase__   = output.images
							lowerCAmelCase__   = image[0, -3:, -3:, -1]
							lowerCAmelCase__   = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]  )

							assert image.shape == (1, 512, 512, 3)

							assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-7

							lowerCAmelCase__   = torch.manual_seed(snake_case__  )
							lowerCAmelCase__   = sd_pipe(
							    [prompt]	,    generator=snake_case__	,    guidance_scale=snake_case__	,    num_inference_steps=50	,    output_type="""np"""	,    width=512	,    height=512	,    sld_guidance_scale=2000	,    sld_warmup_steps=7	,    sld_threshold=0.025	,    sld_momentum_scale=0.5	,    sld_mom_beta=0.7	,    )

							lowerCAmelCase__   = output.images
							lowerCAmelCase__   = image[0, -3:, -3:, -1]
							lowerCAmelCase__   = np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561]  )
							assert image.shape == (1, 512, 512, 3)

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





"""simple docstring"""




import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__lowerCAmelCase     :				Optional[Any]    				=    "\n@inproceedings{xu-etal-2016-optimizing,\n    title = {Optimizing Statistical Machine Translation for Text Simplification},\n    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n    journal = {Transactions of the Association for Computational Linguistics},\n    volume = {4},\n    year={2016},\n    url = {https://www.aclweb.org/anthology/Q16-1029},\n    pages = {401--415\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     :				str    				=    "\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n"


__lowerCAmelCase     :				int    				=    "\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\n    sacrebleu: sacrebleu score\n    exact: exact score\n\nExamples:\n    >>> sources=[\"About 95 species are currently accepted .\"]\n    >>> predictions=[\"About 95 you now get in .\"]\n    >>> references=[[\"About 95 species are currently known .\"]]\n    >>> wiki_split = datasets.load_metric(\"wiki_split\")\n    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}\n"







def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		def remove_articles(lowerCamelCase__  ):
				lowerCAmelCase__   = re.compile(r"""\b(a|an|the)\b"""      ,	re.UNICODE  )
				return re.sub(lowerCamelCase__      ,	""" """      ,	lowerCamelCase__  )

		def white_space_fix(lowerCamelCase__  ):
				return " ".join(text.split()  )

		def remove_punc(lowerCamelCase__  ):
				lowerCAmelCase__   = set(string.punctuation  )
				return "".join(ch for ch in text if ch not in exclude  )

		def lower(lowerCamelCase__  ):
				return text.lower()

		return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase__  )  )  )  )







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		return int(normalize_answer(lowerCamelCase__  ) == normalize_answer(lowerCamelCase__  )  )







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = [any(compute_exact(lowerCamelCase__      ,	lowerCamelCase__  ) for ref in refs  ) for pred, refs in zip(lowerCamelCase__      ,	lowerCamelCase__  )]
		return (sum(lowerCamelCase__  ) / len(lowerCamelCase__  )) * 100







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = [rgram for rgrams in rgramslist for rgram in rgrams]
		lowerCAmelCase__   = Counter(lowerCamelCase__  )

		lowerCAmelCase__   = Counter(lowerCamelCase__  )
		lowerCAmelCase__   = Counter()
		for sgram, scount in sgramcounter.items():
				lowerCAmelCase__   = scount * numref

		lowerCAmelCase__   = Counter(lowerCamelCase__  )
		lowerCAmelCase__   = Counter()
		for cgram, ccount in cgramcounter.items():
				lowerCAmelCase__   = ccount * numref

		# KEEP
		lowerCAmelCase__   = sgramcounter_rep & cgramcounter_rep
		lowerCAmelCase__   = keepgramcounter_rep & rgramcounter
		lowerCAmelCase__   = sgramcounter_rep & rgramcounter

		lowerCAmelCase__   = 0
		lowerCAmelCase__   = 0
		for keepgram in keepgramcountergood_rep:
				keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
				# Fix an alleged bug [2] in the keep score computation.
				# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
				keeptmpscorea += keepgramcountergood_rep[keepgram]
		# Define 0/0=1 instead of 0 to give higher scores for predictions that match
		#      a target exactly.
		lowerCAmelCase__   = 1
		lowerCAmelCase__   = 1
		if len(lowerCamelCase__  ) > 0:
				lowerCAmelCase__   = keeptmpscorea / len(lowerCamelCase__  )
		if len(lowerCamelCase__  ) > 0:
				# Fix an alleged bug [2] in the keep score computation.
				# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
				lowerCAmelCase__   = keeptmpscorea / sum(keepgramcounterall_rep.values()  )
		lowerCAmelCase__   = 0
		if keepscore_precision > 0 or keepscore_recall > 0:
				lowerCAmelCase__   = 2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

		# DELETION
		lowerCAmelCase__   = sgramcounter_rep - cgramcounter_rep
		lowerCAmelCase__   = delgramcounter_rep - rgramcounter
		lowerCAmelCase__   = sgramcounter_rep - rgramcounter
		lowerCAmelCase__   = 0
		lowerCAmelCase__   = 0
		for delgram in delgramcountergood_rep:
				deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
				deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
		# Define 0/0=1 instead of 0 to give higher scores for predictions that match
		# a target exactly.
		lowerCAmelCase__   = 1
		if len(lowerCamelCase__  ) > 0:
				lowerCAmelCase__   = deltmpscorea / len(lowerCamelCase__  )

		# ADDITION
		lowerCAmelCase__   = set(lowerCamelCase__  ) - set(lowerCamelCase__  )
		lowerCAmelCase__   = set(lowerCamelCase__  ) & set(lowerCamelCase__  )
		lowerCAmelCase__   = set(lowerCamelCase__  ) - set(lowerCamelCase__  )

		lowerCAmelCase__   = 0
		for addgram in addgramcountergood:
				addtmpscore += 1

		# Define 0/0=1 instead of 0 to give higher scores for predictions that match
		# a target exactly.
		lowerCAmelCase__   = 1
		lowerCAmelCase__   = 1
		if len(lowerCamelCase__  ) > 0:
				lowerCAmelCase__   = addtmpscore / len(lowerCamelCase__  )
		if len(lowerCamelCase__  ) > 0:
				lowerCAmelCase__   = addtmpscore / len(lowerCamelCase__  )
		lowerCAmelCase__   = 0
		if addscore_precision > 0 or addscore_recall > 0:
				lowerCAmelCase__   = 2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

		return (keepscore, delscore_precision, addscore)







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = len(lowerCamelCase__  )

		lowerCAmelCase__   = ssent.split(""" """  )
		lowerCAmelCase__   = csent.split(""" """  )
		lowerCAmelCase__   = []
		lowerCAmelCase__   = []
		lowerCAmelCase__   = []
		lowerCAmelCase__   = []
		lowerCAmelCase__   = []
		lowerCAmelCase__   = []

		lowerCAmelCase__   = []
		lowerCAmelCase__   = []
		lowerCAmelCase__   = []
		lowerCAmelCase__   = []
		for rsent in rsents:
				lowerCAmelCase__   = rsent.split(""" """  )
				lowerCAmelCase__   = []
				lowerCAmelCase__   = []
				lowerCAmelCase__   = []
				ragramslist.append(lowerCamelCase__  )
				for i in range(0      ,	len(lowerCamelCase__  ) - 1  ):
						if i < len(lowerCamelCase__  ) - 1:
								lowerCAmelCase__   = ragrams[i] + """ """ + ragrams[i + 1]
								ragrams.append(lowerCamelCase__  )
						if i < len(lowerCamelCase__  ) - 2:
								lowerCAmelCase__   = ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2]
								ragrams.append(lowerCamelCase__  )
						if i < len(lowerCamelCase__  ) - 3:
								lowerCAmelCase__   = ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2] + """ """ + ragrams[i + 3]
								ragrams.append(lowerCamelCase__  )
				ragramslist.append(lowerCamelCase__  )
				ragramslist.append(lowerCamelCase__  )
				ragramslist.append(lowerCamelCase__  )

		for i in range(0      ,	len(lowerCamelCase__  ) - 1  ):
				if i < len(lowerCamelCase__  ) - 1:
						lowerCAmelCase__   = sagrams[i] + """ """ + sagrams[i + 1]
						sagrams.append(lowerCamelCase__  )
				if i < len(lowerCamelCase__  ) - 2:
						lowerCAmelCase__   = sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2]
						sagrams.append(lowerCamelCase__  )
				if i < len(lowerCamelCase__  ) - 3:
						lowerCAmelCase__   = sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2] + """ """ + sagrams[i + 3]
						sagrams.append(lowerCamelCase__  )

		for i in range(0      ,	len(lowerCamelCase__  ) - 1  ):
				if i < len(lowerCamelCase__  ) - 1:
						lowerCAmelCase__   = cagrams[i] + """ """ + cagrams[i + 1]
						cagrams.append(lowerCamelCase__  )
				if i < len(lowerCamelCase__  ) - 2:
						lowerCAmelCase__   = cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2]
						cagrams.append(lowerCamelCase__  )
				if i < len(lowerCamelCase__  ) - 3:
						lowerCAmelCase__   = cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2] + """ """ + cagrams[i + 3]
						cagrams.append(lowerCamelCase__  )

		((lowerCAmelCase__) ,       (lowerCAmelCase__) ,       (lowerCAmelCase__))   = SARIngram(lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  )
		((lowerCAmelCase__) ,       (lowerCAmelCase__) ,       (lowerCAmelCase__))   = SARIngram(lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  )
		((lowerCAmelCase__) ,       (lowerCAmelCase__) ,       (lowerCAmelCase__))   = SARIngram(lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  )
		((lowerCAmelCase__) ,       (lowerCAmelCase__) ,       (lowerCAmelCase__))   = SARIngram(lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  )
		lowerCAmelCase__   = sum([keepascore, keepascore, keepascore, keepascore]  ) / 4
		lowerCAmelCase__   = sum([delascore, delascore, delascore, delascore]  ) / 4
		lowerCAmelCase__   = sum([addascore, addascore, addascore, addascore]  ) / 4
		lowerCAmelCase__   = (avgkeepscore + avgdelscore + avgaddscore) / 3
		return finalscore







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__ = True      ,	lowerCamelCase__ = "13a"      ,	lowerCamelCase__ = True  ):





		"""simple docstring"""




		if lowercase:
				lowerCAmelCase__   = sentence.lower()

		if tokenizer in ["13a", "intl"]:
				if version.parse(sacrebleu.__version__  ).major >= 2:
						lowerCAmelCase__   = sacrebleu.metrics.bleu._get_tokenizer(lowerCamelCase__  )()(lowerCamelCase__  )
				else:
						lowerCAmelCase__   = sacrebleu.TOKENIZERS[tokenizer]()(lowerCamelCase__  )
		elif tokenizer == "moses":
				lowerCAmelCase__   = sacremoses.MosesTokenizer().tokenize(lowerCamelCase__      ,	return_str=lowerCamelCase__      ,	escape=lowerCamelCase__  )
		elif tokenizer == "penn":
				lowerCAmelCase__   = sacremoses.MosesTokenizer().penn_tokenize(lowerCamelCase__      ,	return_str=lowerCamelCase__  )
		else:
				lowerCAmelCase__   = sentence

		if not return_str:
				lowerCAmelCase__   = normalized_sent.split()

		return normalized_sent







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		if not (len(lowerCamelCase__  ) == len(lowerCamelCase__  ) == len(lowerCamelCase__  )):
				raise ValueError("""Sources length must match predictions and references lengths."""  )
		lowerCAmelCase__   = 0
		for src, pred, refs in zip(lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):
				sari_score += SARIsent(normalize(lowerCamelCase__  )      ,	normalize(lowerCamelCase__  )      ,	[normalize(lowerCamelCase__  ) for sent in refs]  )
		lowerCAmelCase__   = sari_score / len(lowerCamelCase__  )
		return 100 * sari_score







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__="exp"      ,	lowerCamelCase__=None      ,	lowerCamelCase__=False      ,	lowerCamelCase__=False      ,	lowerCamelCase__=False      ,	):





		"""simple docstring"""




		lowerCAmelCase__   = len(references[0]  )
		if any(len(lowerCamelCase__  ) != references_per_prediction for refs in references  ):
				raise ValueError("""Sacrebleu requires the same number of references for each prediction"""  )
		lowerCAmelCase__   = [[refs[i] for refs in references] for i in range(lowerCamelCase__  )]
		lowerCAmelCase__   = sacrebleu.corpus_bleu(
		    lowerCamelCase__      ,	lowerCamelCase__      ,	smooth_method=lowerCamelCase__      ,	smooth_value=lowerCamelCase__      ,	force=lowerCamelCase__      ,	lowercase=lowerCamelCase__      ,	use_effective_order=lowerCamelCase__      ,	)
		return output.score






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




					def     _SCREAMING_SNAKE_CASE    (						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.Sequence(datasets.Value("""string"""	,    id="""sequence"""  )	,    id="""references"""  ),
							        }  )	,    codebase_urls=[
							        """https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py""",
							        """https://github.com/cocoxu/simplification/blob/master/SARI.py""",
							        """https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py""",
							        """https://github.com/mjpost/sacreBLEU""",
							    ]	,    reference_urls=[
							        """https://www.aclweb.org/anthology/Q16-1029.pdf""",
							        """https://github.com/mjpost/sacreBLEU""",
							        """https://en.wikipedia.org/wiki/BLEU""",
							        """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
							    ]	,    )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]	,    snake_case__    :  Dict	,    snake_case__    :  Union[str, Any]	,    snake_case__    :  Union[str, Any]  ):
							lowerCAmelCase__   = {}
							result.update({"""sari""": compute_sari(sources=snake_case__	,    predictions=snake_case__	,    references=snake_case__  )}  )
							result.update({"""sacrebleu""": compute_sacrebleu(predictions=snake_case__	,    references=snake_case__  )}  )
							result.update({"""exact""": compute_em(predictions=snake_case__	,    references=snake_case__  )}  )
							return result




