






import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

lowerCAmelCase__							= logging.get_logger(__name__)





def 							lowercase__		(				lowerCamelCase,	lowerCamelCase = True					):
				_SCREAMING_SNAKE_CASE      :		int							=						{}
				for name in model.attn_processors.keys():
								_SCREAMING_SNAKE_CASE      :		Dict							=						name.endswith('attn2.processor'					) and not (
								    name.startswith('transformer_in'					) or 'temp_attentions' in name.split('.'					)
								)
								_SCREAMING_SNAKE_CASE      :		Optional[int]							=						model.config.cross_attention_dim if has_cross_attention else None
								if name.startswith('mid_block'					):
												_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						model.config.block_out_channels[-1]
								elif name.startswith('up_blocks'					):
												_SCREAMING_SNAKE_CASE      :		str							=						int(name[len('up_blocks.'					)]					)
												_SCREAMING_SNAKE_CASE      :		str							=						list(reversed(model.config.block_out_channels					)					)[block_id]
								elif name.startswith('down_blocks'					):
												_SCREAMING_SNAKE_CASE      :		List[Any]							=						int(name[len('down_blocks.'					)]					)
												_SCREAMING_SNAKE_CASE      :		List[str]							=						model.config.block_out_channels[block_id]
								elif name.startswith('transformer_in'					):
												# Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
												_SCREAMING_SNAKE_CASE      :		int							=						8 * model.config.attention_head_dim

								_SCREAMING_SNAKE_CASE      :		Tuple							=						LoRAAttnProcessor(hidden_size=lowercase__,	cross_attention_dim=lowercase__					)
								_SCREAMING_SNAKE_CASE      :		Any							=						lora_attn_procs[name].to(model.device					)

								if mock_weights:
												# add 1 to weights to mock trained weights
												with torch.no_grad():
																lora_attn_procs[name].to_q_lora.up.weight += 1
																lora_attn_procs[name].to_k_lora.up.weight += 1
																lora_attn_procs[name].to_v_lora.up.weight += 1
																lora_attn_procs[name].to_out_lora.up.weight += 1

				return lora_attn_procs






@skip_mps
class _lowerCAmelCase	(						__UpperCAmelCase      ,     __UpperCAmelCase      ,     unittest.TestCase					):
						SCREAMING_SNAKE_CASE_:				List[Any]   				=							UNetaDConditionModel
						SCREAMING_SNAKE_CASE_:				int   				=							"""sample"""







						@property
						def  A			(						self       )		->       Optional[Any]:
										_SCREAMING_SNAKE_CASE      :		Dict							=						4
										_SCREAMING_SNAKE_CASE      :		List[str]							=						4
										_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						4
										_SCREAMING_SNAKE_CASE      :		int							=						(3_2, 3_2)

										_SCREAMING_SNAKE_CASE      :		Optional[int]							=						floats_tensor((batch_size, num_channels, num_frames) + sizes       ).to(lowerCamelCase__       )
										_SCREAMING_SNAKE_CASE      :		Any							=						torch.tensor([1_0]       ).to(lowerCamelCase__       )
										_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						floats_tensor((batch_size, 4, 3_2)       ).to(lowerCamelCase__       )

										return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}







						@property
						def  A			(						self       )		->       Any:
										return (4, 4, 3_2, 3_2)







						@property
						def  A			(						self       )		->       Union[str, Any]:
										return (4, 4, 3_2, 3_2)







						def  A			(						self       )		->       Optional[int]:
										_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						{
										    'block_out_channels': (3_2, 6_4),
										    'down_block_types': (
										        'CrossAttnDownBlock3D',
										        'DownBlock3D',
										    ),
										    'up_block_types': ('UpBlock3D', 'CrossAttnUpBlock3D'),
										    'cross_attention_dim': 3_2,
										    'attention_head_dim': 8,
										    'out_channels': 4,
										    'in_channels': 4,
										    'layers_per_block': 1,
										    'sample_size': 3_2,
										}
										_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						self.dummy_input
										return init_dict, inputs_dict







						@unittest.skipIf(
						    torch_device != 'cuda' or not is_xformers_available()      ,							reason='XFormers attention is only available with CUDA and `xformers` installed'      ,							)
						def  A			(						self       )		->       str:
										_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		List[str]							=						self.prepare_init_args_and_inputs_for_common()
										_SCREAMING_SNAKE_CASE      :		Optional[int]							=						self.model_class(**lowerCamelCase__       )

										model.enable_xformers_memory_efficient_attention()

										assert (
										    model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
										    == "XFormersAttnProcessor"
										), "xformers is not enabled"







						def  A			(						self       )		->       Dict:
										_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		Optional[int]							=						self.prepare_init_args_and_inputs_for_common()

										_SCREAMING_SNAKE_CASE      :		Tuple							=						3_2

										_SCREAMING_SNAKE_CASE      :		List[str]							=						self.model_class(**lowerCamelCase__       )
										model.to(lowerCamelCase__       )
										model.eval()

										with torch.no_grad():
														_SCREAMING_SNAKE_CASE      :		Any							=						model(**lowerCamelCase__       )

														if isinstance(lowerCamelCase__      ,							lowerCamelCase__       ):
																		_SCREAMING_SNAKE_CASE      :		Dict							=						output.sample

										self.assertIsNotNone(lowerCamelCase__       )
										_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						inputs_dict['sample'].shape
										self.assertEqual(output.shape      ,							lowerCamelCase__      ,							'Input and output shapes do not match'       )







						def  A			(						self       )		->       List[Any]:
										_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		Optional[int]							=						self.prepare_init_args_and_inputs_for_common()
										_SCREAMING_SNAKE_CASE      :		Dict							=						self.model_class(**lowerCamelCase__       )
										model.to(lowerCamelCase__       )
										model.eval()

										with torch.no_grad():
														# Warmup pass when using mps (see #372)
														if torch_device == "mps" and isinstance(lowerCamelCase__      ,							lowerCamelCase__       ):
																		model(**self.dummy_input       )

														_SCREAMING_SNAKE_CASE      :		str							=						model(**lowerCamelCase__       )
														if isinstance(lowerCamelCase__      ,							lowerCamelCase__       ):
																		_SCREAMING_SNAKE_CASE      :		Optional[int]							=						first.sample

														_SCREAMING_SNAKE_CASE      :		int							=						model(**lowerCamelCase__       )
														if isinstance(lowerCamelCase__      ,							lowerCamelCase__       ):
																		_SCREAMING_SNAKE_CASE      :		Optional[int]							=						second.sample

										_SCREAMING_SNAKE_CASE      :		Dict							=						first.cpu().numpy()
										_SCREAMING_SNAKE_CASE      :		List[Any]							=						second.cpu().numpy()
										_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						out_a[~np.isnan(lowerCamelCase__       )]
										_SCREAMING_SNAKE_CASE      :		Optional[int]							=						out_a[~np.isnan(lowerCamelCase__       )]
										_SCREAMING_SNAKE_CASE      :		List[Any]							=						np.amax(np.abs(out_a - out_a       )       )
										self.assertLessEqual(lowerCamelCase__      ,							1e-5       )







						def  A			(						self       )		->       Any:
										_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		int							=						self.prepare_init_args_and_inputs_for_common()

										_SCREAMING_SNAKE_CASE      :		Optional[int]							=						8

										_SCREAMING_SNAKE_CASE      :		Optional[int]							=						self.model_class(**lowerCamelCase__       )
										model.to(lowerCamelCase__       )
										model.eval()

										model.set_attention_slice('auto'       )
										with torch.no_grad():
														_SCREAMING_SNAKE_CASE      :		Dict							=						model(**lowerCamelCase__       )
										assert output is not None

										model.set_attention_slice('max'       )
										with torch.no_grad():
														_SCREAMING_SNAKE_CASE      :		Optional[int]							=						model(**lowerCamelCase__       )
										assert output is not None

										model.set_attention_slice(2       )
										with torch.no_grad():
														_SCREAMING_SNAKE_CASE      :		List[Any]							=						model(**lowerCamelCase__       )
										assert output is not None







						def  A			(						self       )		->       Union[str, Any]:
										_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		List[Any]							=						self.prepare_init_args_and_inputs_for_common()

										_SCREAMING_SNAKE_CASE      :		Tuple							=						8

										_SCREAMING_SNAKE_CASE      :		List[str]							=						self.model_class(**lowerCamelCase__       )
										model.to(lowerCamelCase__       )

										with torch.no_grad():
														_SCREAMING_SNAKE_CASE      :		List[Any]							=						model(**lowerCamelCase__       ).sample

										_SCREAMING_SNAKE_CASE      :		Tuple							=						create_lora_layers(lowerCamelCase__       )

										# make sure we can set a list of attention processors
										model.set_attn_processor(lowerCamelCase__       )
										model.to(lowerCamelCase__       )

										# test that attn processors can be set to itself
										model.set_attn_processor(model.attn_processors       )

										with torch.no_grad():
														_SCREAMING_SNAKE_CASE      :		List[str]							=						model(**lowerCamelCase__      ,							cross_attention_kwargs={'scale': 0.0}       ).sample
														_SCREAMING_SNAKE_CASE      :		Dict							=						model(**lowerCamelCase__      ,							cross_attention_kwargs={'scale': 0.5}       ).sample
														_SCREAMING_SNAKE_CASE      :		Optional[int]							=						model(**lowerCamelCase__      ,							cross_attention_kwargs={'scale': 0.5}       ).sample

										assert (samplea - samplea).abs().max() < 3e-3
										assert (samplea - samplea).abs().max() < 3e-3

										# sample 2 and sample 3 should be different
										assert (samplea - samplea).abs().max() > 3e-3







						def  A			(						self       )		->       Optional[int]:
										_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						self.prepare_init_args_and_inputs_for_common()

										_SCREAMING_SNAKE_CASE      :		Any							=						8

										torch.manual_seed(0       )
										_SCREAMING_SNAKE_CASE      :		str							=						self.model_class(**lowerCamelCase__       )
										model.to(lowerCamelCase__       )

										with torch.no_grad():
														_SCREAMING_SNAKE_CASE      :		Tuple							=						model(**lowerCamelCase__       ).sample

										_SCREAMING_SNAKE_CASE      :		Optional[int]							=						create_lora_layers(lowerCamelCase__       )
										model.set_attn_processor(lowerCamelCase__       )

										with torch.no_grad():
														_SCREAMING_SNAKE_CASE      :		List[str]							=						model(**lowerCamelCase__      ,							cross_attention_kwargs={'scale': 0.5}       ).sample

										with tempfile.TemporaryDirectory() as tmpdirname:
														model.save_attn_procs(lowerCamelCase__       )
														self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__      ,							'pytorch_lora_weights.bin'       )       )       )
														torch.manual_seed(0       )
														_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						self.model_class(**lowerCamelCase__       )
														new_model.to(lowerCamelCase__       )
														new_model.load_attn_procs(lowerCamelCase__       )

										with torch.no_grad():
														_SCREAMING_SNAKE_CASE      :		str							=						new_model(**lowerCamelCase__      ,							cross_attention_kwargs={'scale': 0.5}       ).sample

										assert (sample - new_sample).abs().max() < 1e-3

										# LoRA and no LoRA should NOT be the same
										assert (sample - old_sample).abs().max() > 1e-4







						def  A			(						self       )		->       Any:
										_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		List[Any]							=						self.prepare_init_args_and_inputs_for_common()

										_SCREAMING_SNAKE_CASE      :		Any							=						8

										torch.manual_seed(0       )
										_SCREAMING_SNAKE_CASE      :		List[str]							=						self.model_class(**lowerCamelCase__       )
										model.to(lowerCamelCase__       )

										with torch.no_grad():
														_SCREAMING_SNAKE_CASE      :		Any							=						model(**lowerCamelCase__       ).sample

										_SCREAMING_SNAKE_CASE      :		List[Any]							=						create_lora_layers(lowerCamelCase__       )
										model.set_attn_processor(lowerCamelCase__       )

										with torch.no_grad():
														_SCREAMING_SNAKE_CASE      :		Optional[int]							=						model(**lowerCamelCase__      ,							cross_attention_kwargs={'scale': 0.5}       ).sample

										with tempfile.TemporaryDirectory() as tmpdirname:
														model.save_attn_procs(lowerCamelCase__      ,							safe_serialization=lowerCamelCase__       )
														self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__      ,							'pytorch_lora_weights.safetensors'       )       )       )
														torch.manual_seed(0       )
														_SCREAMING_SNAKE_CASE      :		List[Any]							=						self.model_class(**lowerCamelCase__       )
														new_model.to(lowerCamelCase__       )
														new_model.load_attn_procs(lowerCamelCase__       )

										with torch.no_grad():
														_SCREAMING_SNAKE_CASE      :		Optional[int]							=						new_model(**lowerCamelCase__      ,							cross_attention_kwargs={'scale': 0.5}       ).sample

										assert (sample - new_sample).abs().max() < 3e-3

										# LoRA and no LoRA should NOT be the same
										assert (sample - old_sample).abs().max() > 1e-4







						def  A			(						self       )		->       Dict:
										_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		Any							=						self.prepare_init_args_and_inputs_for_common()

										_SCREAMING_SNAKE_CASE      :		str							=						8

										torch.manual_seed(0       )
										_SCREAMING_SNAKE_CASE      :		Tuple							=						self.model_class(**lowerCamelCase__       )
										model.to(lowerCamelCase__       )

										_SCREAMING_SNAKE_CASE      :		List[str]							=						create_lora_layers(lowerCamelCase__      ,							mock_weights=lowerCamelCase__       )
										model.set_attn_processor(lowerCamelCase__       )
										# Saving as torch, properly reloads with directly filename
										with tempfile.TemporaryDirectory() as tmpdirname:
														model.save_attn_procs(lowerCamelCase__       )
														self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__      ,							'pytorch_lora_weights.bin'       )       )       )
														torch.manual_seed(0       )
														_SCREAMING_SNAKE_CASE      :		Dict							=						self.model_class(**lowerCamelCase__       )
														new_model.to(lowerCamelCase__       )
														new_model.load_attn_procs(lowerCamelCase__      ,							weight_name='pytorch_lora_weights.bin'       )







						def  A			(						self       )		->       List[Any]:
										_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		int							=						self.prepare_init_args_and_inputs_for_common()

										_SCREAMING_SNAKE_CASE      :		List[Any]							=						8

										torch.manual_seed(0       )
										_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						self.model_class(**lowerCamelCase__       )
										model.to(lowerCamelCase__       )

										_SCREAMING_SNAKE_CASE      :		Tuple							=						create_lora_layers(lowerCamelCase__      ,							mock_weights=lowerCamelCase__       )
										model.set_attn_processor(lowerCamelCase__       )
										# Saving as torch, properly reloads with directly filename
										with tempfile.TemporaryDirectory() as tmpdirname:
														model.save_attn_procs(lowerCamelCase__       )
														self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__      ,							'pytorch_lora_weights.bin'       )       )       )
														torch.manual_seed(0       )
														_SCREAMING_SNAKE_CASE      :		Tuple							=						self.model_class(**lowerCamelCase__       )
														new_model.to(lowerCamelCase__       )
														with self.assertRaises(lowerCamelCase__       ) as e:
																		new_model.load_attn_procs(lowerCamelCase__      ,							use_safetensors=lowerCamelCase__       )
														self.assertIn('Error no file named pytorch_lora_weights.safetensors'      ,							str(e.exception       )       )







						def  A			(						self       )		->       Any:
										_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		List[Any]							=						self.prepare_init_args_and_inputs_for_common()

										_SCREAMING_SNAKE_CASE      :		Optional[int]							=						8

										torch.manual_seed(0       )
										_SCREAMING_SNAKE_CASE      :		Dict							=						self.model_class(**lowerCamelCase__       )
										model.to(lowerCamelCase__       )

										with torch.no_grad():
														_SCREAMING_SNAKE_CASE      :		int							=						model(**lowerCamelCase__       ).sample

										_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						create_lora_layers(lowerCamelCase__       )
										model.set_attn_processor(lowerCamelCase__       )

										with torch.no_grad():
														_SCREAMING_SNAKE_CASE      :		Tuple							=						model(**lowerCamelCase__      ,							cross_attention_kwargs={'scale': 0.0}       ).sample

										model.set_attn_processor(AttnProcessor()       )

										with torch.no_grad():
														_SCREAMING_SNAKE_CASE      :		List[Any]							=						model(**lowerCamelCase__       ).sample

										assert (sample - new_sample).abs().max() < 1e-4
										assert (sample - old_sample).abs().max() < 3e-3







						@unittest.skipIf(
						    torch_device != 'cuda' or not is_xformers_available()      ,							reason='XFormers attention is only available with CUDA and `xformers` installed'      ,							)
						def  A			(						self       )		->       Optional[int]:
										_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		List[Any]							=						self.prepare_init_args_and_inputs_for_common()

										_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						4

										torch.manual_seed(0       )
										_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						self.model_class(**lowerCamelCase__       )
										model.to(lowerCamelCase__       )
										_SCREAMING_SNAKE_CASE      :		Tuple							=						create_lora_layers(lowerCamelCase__       )
										model.set_attn_processor(lowerCamelCase__       )

										# default
										with torch.no_grad():
														_SCREAMING_SNAKE_CASE      :		Optional[int]							=						model(**lowerCamelCase__       ).sample

														model.enable_xformers_memory_efficient_attention()
														_SCREAMING_SNAKE_CASE      :		List[Any]							=						model(**lowerCamelCase__       ).sample

														model.disable_xformers_memory_efficient_attention()
														_SCREAMING_SNAKE_CASE      :		str							=						model(**lowerCamelCase__       ).sample

										assert (sample - on_sample).abs().max() < 1e-4
										assert (sample - off_sample).abs().max() < 1e-4







						def  A			(						self       )		->       List[Any]:
										_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		List[Any]							=						self.prepare_init_args_and_inputs_for_common()
										_SCREAMING_SNAKE_CASE      :		Tuple							=						3_2

										_SCREAMING_SNAKE_CASE      :		str							=						self.model_class(**lowerCamelCase__       )
										model.to(lowerCamelCase__       )
										model.eval()

										with torch.no_grad():
														_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						model(**lowerCamelCase__       )[0]

										model.enable_forward_chunking()
										with torch.no_grad():
														_SCREAMING_SNAKE_CASE      :		int							=						model(**lowerCamelCase__       )[0]

										self.assertEqual(output.shape      ,							output_a.shape      ,							'Shape doesn\'t match'       )
										assert np.abs(output.cpu() - output_a.cpu()       ).max() < 1e-2


# (todo: sayakpaul) implement SLOW tests.









"""simple docstring"""
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


lowerCAmelCase__							= '\\n@inproceedings{xu-etal-2016-optimizing,\ntitle = {Optimizing Statistical Machine Translation for Text Simplification},\nauthors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\njournal = {Transactions of the Association for Computational Linguistics},\nvolume = {4},\nyear={2016},\nurl = {https://www.aclweb.org/anthology/Q16-1029},\npages = {401--415},\n}\n'

lowerCAmelCase__							= '\\nSARI is a metric used for evaluating automatic text simplification systems.\nThe metric compares the predicted simplified sentences against the reference\nand the source sentences. It explicitly measures the goodness of words that are\nadded, deleted and kept by the system.\nSari = (F1_add + F1_keep + P_del) / 3\nwhere\nF1_add: n-gram F1 score for add operation\nF1_keep: n-gram F1 score for keep operation\nP_del: n-gram precision score for delete operation\nn = 4, as in the original paper.\n\nThis implementation is adapted from Tensorflow\'s tensor2tensor implementation [3].\nIt has two differences with the original GitHub [1] implementation:\n  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match\n      a target exactly.\n  (2) Fixes an alleged bug [2] in the keep score computation.\n[1] https://github.com/cocoxu/simplification/blob/master/SARI.py\n    (commit 0210f15)\n[2] https://github.com/cocoxu/simplification/issues/6\n[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py\n'


lowerCAmelCase__							= '\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences.\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\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 .","About 95 species are now accepted .","95 species are now accepted ."]]\n    >>> sari = datasets.load_metric("sari")\n    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {\'sari\': 26.953601953601954}\n'





def 							lowercase__		(				lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase					):
				_SCREAMING_SNAKE_CASE      :		int							=						[rgram for rgrams in rgramslist for rgram in rgrams]
				_SCREAMING_SNAKE_CASE      :		Any							=						Counter(lowerCamelCase					)

				_SCREAMING_SNAKE_CASE      :		List[Any]							=						Counter(lowerCamelCase					)
				_SCREAMING_SNAKE_CASE      :		int							=						Counter()
				for sgram, scount in sgramcounter.items():
								_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						scount * numref

				_SCREAMING_SNAKE_CASE      :		Dict							=						Counter(lowerCamelCase					)
				_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						Counter()
				for cgram, ccount in cgramcounter.items():
								_SCREAMING_SNAKE_CASE      :		Optional[int]							=						ccount * numref

				# KEEP
				_SCREAMING_SNAKE_CASE      :		List[str]							=						sgramcounter_rep & cgramcounter_rep
				_SCREAMING_SNAKE_CASE      :		str							=						keepgramcounter_rep & rgramcounter
				_SCREAMING_SNAKE_CASE      :		Tuple							=						sgramcounter_rep & rgramcounter

				_SCREAMING_SNAKE_CASE      :		Dict							=						0
				_SCREAMING_SNAKE_CASE      :		Any							=						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.
				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						1
				_SCREAMING_SNAKE_CASE      :		Dict							=						1
				if len(lowerCamelCase					) > 0:
								_SCREAMING_SNAKE_CASE      :		str							=						keeptmpscorea / len(lowerCamelCase					)
				if len(lowerCamelCase					) > 0:
								# Fix an alleged bug [2] in the keep score computation.
								# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
								_SCREAMING_SNAKE_CASE      :		List[str]							=						keeptmpscorea / sum(keepgramcounterall_rep.values()					)
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						0
				if keepscore_precision > 0 or keepscore_recall > 0:
								_SCREAMING_SNAKE_CASE      :		Dict							=						2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

				# DELETION
				_SCREAMING_SNAKE_CASE      :		int							=						sgramcounter_rep - cgramcounter_rep
				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						delgramcounter_rep - rgramcounter
				_SCREAMING_SNAKE_CASE      :		str							=						sgramcounter_rep - rgramcounter
				_SCREAMING_SNAKE_CASE      :		Tuple							=						0
				_SCREAMING_SNAKE_CASE      :		Optional[int]							=						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.
				_SCREAMING_SNAKE_CASE      :		str							=						1
				if len(lowerCamelCase					) > 0:
								_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						deltmpscorea / len(lowerCamelCase					)

				# ADDITION
				_SCREAMING_SNAKE_CASE      :		Any							=						set(lowerCamelCase					) - set(lowerCamelCase					)
				_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						set(lowerCamelCase					) & set(lowerCamelCase					)
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						set(lowerCamelCase					) - set(lowerCamelCase					)

				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						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.
				_SCREAMING_SNAKE_CASE      :		Tuple							=						1
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						1
				if len(lowerCamelCase					) > 0:
								_SCREAMING_SNAKE_CASE      :		str							=						addtmpscore / len(lowerCamelCase					)
				if len(lowerCamelCase					) > 0:
								_SCREAMING_SNAKE_CASE      :		str							=						addtmpscore / len(lowerCamelCase					)
				_SCREAMING_SNAKE_CASE      :		List[str]							=						0
				if addscore_precision > 0 or addscore_recall > 0:
								_SCREAMING_SNAKE_CASE      :		Dict							=						2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

				return (keepscore, delscore_precision, addscore)





def 							lowercase__		(				lowerCamelCase,	lowerCamelCase,	lowerCamelCase					):
				_SCREAMING_SNAKE_CASE      :		Tuple							=						len(lowerCamelCase					)

				_SCREAMING_SNAKE_CASE      :		Tuple							=						ssent.split(' '					)
				_SCREAMING_SNAKE_CASE      :		Tuple							=						csent.split(' '					)
				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						[]
				_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						[]
				_SCREAMING_SNAKE_CASE      :		List[str]							=						[]
				_SCREAMING_SNAKE_CASE      :		Tuple							=						[]
				_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						[]
				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						[]

				_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						[]
				_SCREAMING_SNAKE_CASE      :		int							=						[]
				_SCREAMING_SNAKE_CASE      :		int							=						[]
				_SCREAMING_SNAKE_CASE      :		int							=						[]
				for rsent in rsents:
								_SCREAMING_SNAKE_CASE      :		int							=						rsent.split(' '					)
								_SCREAMING_SNAKE_CASE      :		Optional[int]							=						[]
								_SCREAMING_SNAKE_CASE      :		str							=						[]
								_SCREAMING_SNAKE_CASE      :		List[str]							=						[]
								ragramslist.append(lowerCamelCase					)
								for i in range(0,	len(lowerCamelCase					) - 1					):
												if i < len(lowerCamelCase					) - 1:
																_SCREAMING_SNAKE_CASE      :		List[str]							=						ragrams[i] + ' ' + ragrams[i + 1]
																ragrams.append(lowerCamelCase					)
												if i < len(lowerCamelCase					) - 2:
																_SCREAMING_SNAKE_CASE      :		Any							=						ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2]
																ragrams.append(lowerCamelCase					)
												if i < len(lowerCamelCase					) - 3:
																_SCREAMING_SNAKE_CASE      :		List[Any]							=						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:
												_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						sagrams[i] + ' ' + sagrams[i + 1]
												sagrams.append(lowerCamelCase					)
								if i < len(lowerCamelCase					) - 2:
												_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
												sagrams.append(lowerCamelCase					)
								if i < len(lowerCamelCase					) - 3:
												_SCREAMING_SNAKE_CASE      :		Tuple							=						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:
												_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						cagrams[i] + ' ' + cagrams[i + 1]
												cagrams.append(lowerCamelCase					)
								if i < len(lowerCamelCase					) - 2:
												_SCREAMING_SNAKE_CASE      :		Optional[int]							=						cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
												cagrams.append(lowerCamelCase					)
								if i < len(lowerCamelCase					) - 3:
												_SCREAMING_SNAKE_CASE      :		Any							=						cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
												cagrams.append(lowerCamelCase					)

				((_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE))      :		Tuple							=						SARIngram(lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase					)
				((_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE))      :		int							=						SARIngram(lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase					)
				((_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE))      :		Optional[int]							=						SARIngram(lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase					)
				((_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE))      :		Union[str, Any]							=						SARIngram(lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase					)
				_SCREAMING_SNAKE_CASE      :		int							=						sum([keepascore, keepascore, keepascore, keepascore]					) / 4
				_SCREAMING_SNAKE_CASE      :		List[str]							=						sum([delascore, delascore, delascore, delascore]					) / 4
				_SCREAMING_SNAKE_CASE      :		List[str]							=						sum([addascore, addascore, addascore, addascore]					) / 4
				_SCREAMING_SNAKE_CASE      :		Dict							=						(avgkeepscore + avgdelscore + avgaddscore) / 3
				return finalscore





def 							lowercase__		(				lowerCamelCase,	lowerCamelCase = True,	lowerCamelCase = "13a",	lowerCamelCase = True					):
				# Normalization is requried for the ASSET dataset (one of the primary
				# datasets in sentence simplification) to allow using space
				# to split the sentence. Even though Wiki-Auto and TURK datasets,
				# do not require normalization, we do it for consistency.
				# Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
				# [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

				if lowercase:
								_SCREAMING_SNAKE_CASE      :		List[str]							=						sentence.lower()

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

				if not return_str:
								_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						normalized_sent.split()

				return normalized_sent






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







						def  A			(						self       )		->       Dict:
										return datasets.MetricInfo(
										    description=_DESCRIPTION      ,							citation=_CITATION      ,							inputs_description=_KWARGS_DESCRIPTION      ,							features=datasets.Features(
										        {
										            'sources': datasets.Value('string'      ,							id='sequence'       ),
										            'predictions': datasets.Value('string'      ,							id='sequence'       ),
										            'references': datasets.Sequence(datasets.Value('string'      ,							id='sequence'       )      ,							id='references'       ),
										        }       )      ,							codebase_urls=[
										        'https://github.com/cocoxu/simplification/blob/master/SARI.py',
										        'https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py',
										    ]      ,							reference_urls=['https://www.aclweb.org/anthology/Q16-1029.pdf']      ,							)







						def  A			(						self      ,							lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_       )		->       Optional[int]:
										if not (len(lowerCAmelCase_       ) == len(lowerCAmelCase_       ) == len(lowerCAmelCase_       )):
														raise ValueError('Sources length must match predictions and references lengths.'       )
										_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						0
										for src, pred, refs in zip(lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_       ):
														sari_score += SARIsent(normalize(lowerCAmelCase_       )      ,							normalize(lowerCAmelCase_       )      ,							[normalize(lowerCAmelCase_       ) for sent in refs]       )
										_SCREAMING_SNAKE_CASE      :		Optional[int]							=						sari_score / len(lowerCAmelCase_       )
										return {"sari": 1_0_0 * sari_score}

