"""simple docstring"""




# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()

class    a_   (  __UpperCamelCase			,		__UpperCamelCase			,		__UpperCamelCase			,		unittest.TestCase      ):
					UpperCamelCase_			:						int      =     StableDiffusionControlNetImgaImgPipeline
					UpperCamelCase_			:						int      =     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
					UpperCamelCase_			:						Union[str, Any]      =     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
					UpperCamelCase_			:						Optional[Any]      =     IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"control_image"}      )
					UpperCamelCase_			:						Optional[Any]      =     IMAGE_TO_IMAGE_IMAGE_PARAMS




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							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	,    )
							torch.manual_seed(0  )
							lowerCAmelCase__   = ControlNetModel(
							    block_out_channels=(32, 64)	,    layers_per_block=2	,    in_channels=4	,    down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")	,    cross_attention_dim=32	,    conditioning_embedding_out_channels=(16, 32)	,    )
							torch.manual_seed(0  )
							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__	,    )
							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	,    )
							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	,    )
							lowerCAmelCase__   = CLIPTextModel(snake_case__  )
							lowerCAmelCase__   = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  )

							lowerCAmelCase__   = {
							    """unet""": unet,
							    """controlnet""": controlnet,
							    """scheduler""": scheduler,
							    """vae""": vae,
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """safety_checker""": None,
							    """feature_extractor""": None,
							}
							return components




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]	,    snake_case__    :  Optional[Any]	,    snake_case__    :  List[str]=0  ):
							if str(snake_case__  ).startswith("""mps"""  ):
									lowerCAmelCase__   = torch.manual_seed(snake_case__  )
							else:
									lowerCAmelCase__   = torch.Generator(device=snake_case__  ).manual_seed(snake_case__  )

							lowerCAmelCase__   = 2
							lowerCAmelCase__   = randn_tensor(
							    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)	,    generator=snake_case__	,    device=torch.device(snake_case__  )	,    )
							lowerCAmelCase__   = floats_tensor(control_image.shape	,    rng=random.Random(snake_case__  )  ).to(snake_case__  )
							lowerCAmelCase__   = image.cpu().permute(0	,    2	,    3	,    1  )[0]
							lowerCAmelCase__   = Image.fromarray(np.uinta(snake_case__  )  ).convert("""RGB"""  ).resize((64, 64)  )
							lowerCAmelCase__   = {
							    """prompt""": """A painting of a squirrel eating a burger""",
							    """generator""": generator,
							    """num_inference_steps""": 2,
							    """guidance_scale""": 6.0,
							    """output_type""": """numpy""",
							    """image""": image,
							    """control_image""": control_image,
							}

							return inputs




					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]  ):
							return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3  )




					@unittest.skipIf(
					    torch_device != """cuda""" or not is_xformers_available()	,    reason="""XFormers attention is only available with CUDA and `xformers` installed"""	,    )
					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]  ):
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple  ):
							self._test_inference_batch_single_identical(expected_max_diff=2E-3  )

class    a_   (  __UpperCamelCase			,		__UpperCamelCase			,		unittest.TestCase      ):
					UpperCamelCase_			:						Any      =     StableDiffusionControlNetImgaImgPipeline
					UpperCamelCase_			:						str      =     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
					UpperCamelCase_			:						Union[str, Any]      =     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
					UpperCamelCase_			:						Optional[int]      =     frozenset([]      )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess




					def     _SCREAMING_SNAKE_CASE    (						self    :  str  ):
							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	,    )
							torch.manual_seed(0  )

							def init_weights(snake_case__    :  List[Any]  ):
									if isinstance(snake_case__	,    torch.nn.Convad  ):
											torch.nn.init.normal(m.weight  )
											m.bias.data.fill_(1.0  )

							lowerCAmelCase__   = ControlNetModel(
							    block_out_channels=(32, 64)	,    layers_per_block=2	,    in_channels=4	,    down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")	,    cross_attention_dim=32	,    conditioning_embedding_out_channels=(16, 32)	,    )
							controlneta.controlnet_down_blocks.apply(snake_case__  )

							torch.manual_seed(0  )
							lowerCAmelCase__   = ControlNetModel(
							    block_out_channels=(32, 64)	,    layers_per_block=2	,    in_channels=4	,    down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")	,    cross_attention_dim=32	,    conditioning_embedding_out_channels=(16, 32)	,    )
							controlneta.controlnet_down_blocks.apply(snake_case__  )

							torch.manual_seed(0  )
							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__	,    )
							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	,    )
							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	,    )
							lowerCAmelCase__   = CLIPTextModel(snake_case__  )
							lowerCAmelCase__   = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  )

							lowerCAmelCase__   = MultiControlNetModel([controlneta, controlneta]  )

							lowerCAmelCase__   = {
							    """unet""": unet,
							    """controlnet""": controlnet,
							    """scheduler""": scheduler,
							    """vae""": vae,
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """safety_checker""": None,
							    """feature_extractor""": None,
							}
							return components




					def     _SCREAMING_SNAKE_CASE    (						self    :  str	,    snake_case__    :  Any	,    snake_case__    :  Any=0  ):
							if str(snake_case__  ).startswith("""mps"""  ):
									lowerCAmelCase__   = torch.manual_seed(snake_case__  )
							else:
									lowerCAmelCase__   = torch.Generator(device=snake_case__  ).manual_seed(snake_case__  )

							lowerCAmelCase__   = 2

							lowerCAmelCase__   = [
							    randn_tensor(
							        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)	,    generator=snake_case__	,    device=torch.device(snake_case__  )	,    ),
							    randn_tensor(
							        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)	,    generator=snake_case__	,    device=torch.device(snake_case__  )	,    ),
							]

							lowerCAmelCase__   = floats_tensor(control_image[0].shape	,    rng=random.Random(snake_case__  )  ).to(snake_case__  )
							lowerCAmelCase__   = image.cpu().permute(0	,    2	,    3	,    1  )[0]
							lowerCAmelCase__   = Image.fromarray(np.uinta(snake_case__  )  ).convert("""RGB"""  ).resize((64, 64)  )
							lowerCAmelCase__   = {
							    """prompt""": """A painting of a squirrel eating a burger""",
							    """generator""": generator,
							    """num_inference_steps""": 2,
							    """guidance_scale""": 6.0,
							    """output_type""": """numpy""",
							    """image""": image,
							    """control_image""": control_image,
							}

							return inputs




					def     _SCREAMING_SNAKE_CASE    (						self    :  str  ):
							lowerCAmelCase__   = self.get_dummy_components()
							lowerCAmelCase__   = self.pipeline_class(**snake_case__  )
							pipe.to(snake_case__  )

							lowerCAmelCase__   = 10.0
							lowerCAmelCase__   = 4

							lowerCAmelCase__   = self.get_dummy_inputs(snake_case__  )
							lowerCAmelCase__   = steps
							lowerCAmelCase__   = scale
							lowerCAmelCase__   = pipe(**snake_case__  )[0]

							lowerCAmelCase__   = self.get_dummy_inputs(snake_case__  )
							lowerCAmelCase__   = steps
							lowerCAmelCase__   = scale
							lowerCAmelCase__   = pipe(**snake_case__	,    control_guidance_start=0.1	,    control_guidance_end=0.2  )[0]

							lowerCAmelCase__   = self.get_dummy_inputs(snake_case__  )
							lowerCAmelCase__   = steps
							lowerCAmelCase__   = scale
							lowerCAmelCase__   = pipe(**snake_case__	,    control_guidance_start=[0.1, 0.3]	,    control_guidance_end=[0.2, 0.7]  )[0]

							lowerCAmelCase__   = self.get_dummy_inputs(snake_case__  )
							lowerCAmelCase__   = steps
							lowerCAmelCase__   = scale
							lowerCAmelCase__   = pipe(**snake_case__	,    control_guidance_start=0.4	,    control_guidance_end=[0.5, 0.8]  )[0]

							# make sure that all outputs are different
							assert np.sum(np.abs(output_a - output_a  )  ) > 1E-3
							assert np.sum(np.abs(output_a - output_a  )  ) > 1E-3
							assert np.sum(np.abs(output_a - output_a  )  ) > 1E-3




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3  )




					@unittest.skipIf(
					    torch_device != """cuda""" or not is_xformers_available()	,    reason="""XFormers attention is only available with CUDA and `xformers` installed"""	,    )
					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]  ):
							self._test_inference_batch_single_identical(expected_max_diff=2E-3  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple  ):
							lowerCAmelCase__   = self.get_dummy_components()
							lowerCAmelCase__   = self.pipeline_class(**snake_case__  )
							pipe.to(snake_case__  )
							pipe.set_progress_bar_config(disable=snake_case__  )
							with tempfile.TemporaryDirectory() as tmpdir:
									try:
											# save_pretrained is not implemented for Multi-ControlNet
											pipe.save_pretrained(snake_case__  )
									except NotImplementedError:
											pass

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




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							lowerCAmelCase__   = ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny"""  )

							lowerCAmelCase__   = StableDiffusionControlNetImgaImgPipeline.from_pretrained(
							    """runwayml/stable-diffusion-v1-5"""	,    safety_checker=snake_case__	,    controlnet=snake_case__  )
							pipe.enable_model_cpu_offload()
							pipe.set_progress_bar_config(disable=snake_case__  )

							lowerCAmelCase__   = torch.Generator(device="""cpu"""  ).manual_seed(0  )
							lowerCAmelCase__   = """evil space-punk bird"""
							lowerCAmelCase__   = load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"""  ).resize((512, 512)  )
							lowerCAmelCase__   = load_image(
							    """https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"""  ).resize((512, 512)  )

							lowerCAmelCase__   = pipe(
							    snake_case__	,    snake_case__	,    control_image=snake_case__	,    generator=snake_case__	,    output_type="""np"""	,    num_inference_steps=50	,    strength=0.6	,    )

							lowerCAmelCase__   = output.images[0]

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

							lowerCAmelCase__   = load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy"""  )

							assert np.abs(expected_image - image  ).max() < 9E-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




