



'''simple docstring'''






from __future__ import annotations

import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available

from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester


if is_tf_available():
     from transformers import (
         TFBertModel,
         TFCLIPVisionModel,
         TFDeiTModel,
         TFRobertaModel,
         TFVisionTextDualEncoderModel,
         TFViTModel,
         VisionTextDualEncoderConfig,
     )

if is_vision_available():
     from PIL import Image

     from transformers import VisionTextDualEncoderProcessor
def 				lowerCamelCase__	(						_A    ):
  if isinstance(_A			,      collections.abc.Iterable    ):
    return x
  return (x, x)

@require_tf
class 							a__:







    def       lowercase_	(				self			:  Any						,      __snake_case			:  Dict						,      __snake_case			:  List[str]     ):
      pass







    def       lowercase_	(				self			:  Optional[int]     ):
      pass







    def       lowercase_	(				self			:  Any     ):
      pass







    def       lowercase_	(				self			:  str						,      __snake_case			:  Optional[int]						,      __snake_case			:  List[str]						,      __snake_case			:  Any						,      __snake_case			:  Optional[Any]						,      __snake_case			:  List[str]=None						,      **__snake_case			:  Optional[Any]     ):
      a						: int					       =					VisionTextDualEncoderConfig.from_vision_text_configs(__snake_case						,      __snake_case     )

      a						: Optional[int]					       =					TFVisionTextDualEncoderModel(__snake_case     )

      a						: str					       =					model(input_ids=__snake_case						,      pixel_values=__snake_case						,      attention_mask=__snake_case     )

      self.assertEqual(output['text_embeds'].shape						,      (input_ids.shape[0], config.projection_dim)     )
      self.assertEqual(output['image_embeds'].shape						,      (pixel_values.shape[0], config.projection_dim)     )







    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  Any						,      __snake_case			:  Tuple						,      __snake_case			:  List[str]						,      __snake_case			:  List[str]						,      __snake_case			:  Optional[Any]=None						,      **__snake_case			:  Dict     ):
      a       ,     a						: Optional[int]					       =					self.get_vision_text_model(__snake_case						,      __snake_case     )
      a						: int					       =					TFVisionTextDualEncoderModel(vision_model=__snake_case						,      text_model=__snake_case     )

      a						: Tuple					       =					model(input_ids=__snake_case						,      pixel_values=__snake_case						,      attention_mask=__snake_case     )

      self.assertEqual(output['text_embeds'].shape						,      (input_ids.shape[0], model.config.projection_dim)     )
      self.assertEqual(output['image_embeds'].shape						,      (pixel_values.shape[0], model.config.projection_dim)     )







    def       lowercase_	(				self			:  Union[str, Any]						,      __snake_case			:  int						,      __snake_case			:  Tuple						,      __snake_case			:  Optional[Any]						,      __snake_case			:  Tuple						,      __snake_case			:  Any=None						,      **__snake_case			:  List[str]     ):
      a       ,     a						: Any					       =					self.get_vision_text_model(__snake_case						,      __snake_case     )
      a						: Tuple					       =					{'vision_model': vision_model, 'text_model': text_model}
      a						: str					       =					TFVisionTextDualEncoderModel.from_vision_text_pretrained(**__snake_case     )

      a						: Optional[int]					       =					model(input_ids=__snake_case						,      pixel_values=__snake_case						,      attention_mask=__snake_case     )

      self.assertEqual(output['text_embeds'].shape						,      (input_ids.shape[0], model.config.projection_dim)     )
      self.assertEqual(output['image_embeds'].shape						,      (pixel_values.shape[0], model.config.projection_dim)     )







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  str						,      __snake_case			:  Union[str, Any]						,      __snake_case			:  Optional[int]						,      __snake_case			:  Union[str, Any]						,      __snake_case			:  Optional[int]=None						,      **__snake_case			:  Optional[Any]     ):
      a       ,     a						: str					       =					self.get_vision_text_model(__snake_case						,      __snake_case     )
      a						: List[Any]					       =					TFVisionTextDualEncoderModel(vision_model=__snake_case						,      text_model=__snake_case     )

      a						: str					       =					model(input_ids=__snake_case						,      pixel_values=__snake_case						,      attention_mask=__snake_case     )
      a						: List[str]					       =					output[0].numpy()

      with tempfile.TemporaryDirectory() as tmpdirname:
        model.save_pretrained(__snake_case     )
        a						: Optional[Any]					       =					TFVisionTextDualEncoderModel.from_pretrained(__snake_case     )

        a						: int					       =					model(input_ids=__snake_case						,      pixel_values=__snake_case						,      attention_mask=__snake_case     )
        a						: List[str]					       =					after_output[0].numpy()
        a						: Optional[int]					       =					np.amax(np.abs(out_a - out_a     )     )
        self.assertLessEqual(__snake_case						,      1e-5     )







    def       lowercase_	(				self			:  Tuple						,      __snake_case			:  Optional[Any]						,      __snake_case			:  Dict						,      __snake_case			:  Optional[int]						,      __snake_case			:  Optional[Any]						,      __snake_case			:  Tuple=None						,      **__snake_case			:  Union[str, Any]     ):
      a       ,     a						: Tuple					       =					self.get_vision_text_model(__snake_case						,      __snake_case     )
      a						: List[Any]					       =					TFVisionTextDualEncoderModel(vision_model=__snake_case						,      text_model=__snake_case     )

      a						: Any					       =					model(
          input_ids=__snake_case						,      pixel_values=__snake_case						,      attention_mask=__snake_case						,      output_attentions=__snake_case     )

      a						: Dict					       =					output.vision_model_output.attentions
      self.assertEqual(len(__snake_case     )						,      vision_config.num_hidden_layers     )

      # in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
      a						: List[Any]					       =					to_atuple(vision_model.config.image_size     )
      a						: Tuple					       =					to_atuple(vision_model.config.patch_size     )
      a						: Optional[int]					       =					(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
      a						: Any					       =					num_patches + 1
      self.assertEqual(vision_attentions[0].shape[-3:]						,      (vision_config.num_attention_heads, seq_len, seq_len)     )

      a						: Any					       =					output.text_model_output.attentions
      self.assertEqual(len(__snake_case     )						,      text_config.num_hidden_layers     )

      self.assertEqual(
          text_attentions[0].shape[-3:]						,      (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])						,      )







    def       lowercase_	(				self			:  List[Any]						,      __snake_case			:  np.ndarray						,      __snake_case			:  np.ndarray						,      __snake_case			:  float     ):
      a						: Optional[int]					       =					np.abs((a - b)     ).max()
      self.assertLessEqual(__snake_case						,      __snake_case						,      F"""Difference between torch and flax is {diff} (>= {tol})."""     )







    def       lowercase_	(				self			:  Tuple     ):
      a						: Dict					       =					self.prepare_config_and_inputs()
      self.check_vision_text_dual_encoder_model(**__snake_case     )







    def       lowercase_	(				self			:  Optional[Any]     ):
      a						: List[Any]					       =					self.prepare_config_and_inputs()
      self.check_model_from_pretrained_configs(**__snake_case     )







    def       lowercase_	(				self			:  List[Any]     ):
      a						: Optional[Any]					       =					self.prepare_config_and_inputs()
      self.check_vision_text_dual_encoder_from_pretrained(**__snake_case     )







    def       lowercase_	(				self			:  Any     ):
      a						: Optional[Any]					       =					self.prepare_config_and_inputs()
      self.check_save_load(**__snake_case     )







    def       lowercase_	(				self			:  List[Any]     ):
      a						: Optional[int]					       =					self.prepare_config_and_inputs()
      self.check_vision_text_output_attention(**__snake_case     )







    @slow
    def       lowercase_	(				self			:  List[str]     ):
      a       ,     a						: List[str]					       =					self.get_pretrained_model_and_inputs()

      a						: List[str]					       =					model_a(**__snake_case     )
      a						: Optional[Any]					       =					outputs[0].numpy()

      with tempfile.TemporaryDirectory() as tmp_dirname:
        model_a.save_pretrained(__snake_case     )
        a						: List[str]					       =					TFVisionTextDualEncoderModel.from_pretrained(__snake_case     )

        a						: Any					       =					model_a(**__snake_case     )
        a						: str					       =					after_outputs[0].numpy()
        a						: List[Any]					       =					np.amax(np.abs(out_a - out_a     )     )
        self.assertLessEqual(__snake_case						,      1e-5     )

@require_tf
class 							a__( lowerCamelCase__		,			unittest.TestCase     ):







    def       lowercase_	(				self			:  Tuple     ):
      a						: Any					       =					TFVisionTextDualEncoderModel.from_vision_text_pretrained(
          'hf-internal-testing/tiny-random-vit'						,      'hf-internal-testing/tiny-random-bert'     )
      a						: Tuple					       =					13
      a						: Union[str, Any]					       =					floats_tensor(
          [
              batch_size,
              model.vision_model.config.num_channels,
              model.vision_model.config.image_size,
              model.vision_model.config.image_size,
          ]     )
      a						: Tuple					       =					ids_tensor([batch_size, 4]						,      model.text_model.config.vocab_size     )
      a						: Optional[Any]					       =					random_attention_mask([batch_size, 4]     )
      a						: Optional[Any]					       =					{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

      return model, inputs







    def       lowercase_	(				self			:  str						,      __snake_case			:  Optional[Any]						,      __snake_case			:  str     ):
      a						: str					       =					TFViTModel(__snake_case						,      name='vision_model'     )
      a						: Any					       =					TFBertModel(__snake_case						,      name='text_model'     )
      return vision_model, text_model







    def       lowercase_	(				self			:  Dict     ):
      a						: Tuple					       =					TFViTModelTester(self     )
      a						: List[str]					       =					TFBertModelTester(self     )
      a						: List[Any]					       =					vit_model_tester.prepare_config_and_inputs()
      a						: List[str]					       =					bert_model_tester.prepare_config_and_inputs()

      a       ,     a       ,     a						: Optional[int]					       =					vision_config_and_inputs

      (
          (
          a
      )       ,     (
          a
      )       ,     (
          a
      )       ,     (
          a
      )       ,     (
          a
      )       ,     (
          a
      )       ,     (
          a
      )       ,     
      )						: Any					       =					text_config_and_inputs

      return {
          "text_config": text_config,
          "vision_config": vision_config,
          "pixel_values": pixel_values,
          "attention_mask": input_mask,
          "input_ids": input_ids,
          "text_token_type_ids": token_type_ids,
          "text_sequence_labels": sequence_labels,
          "text_token_labels": token_labels,
          "text_choice_labels": choice_labels,
      }

@require_tf
class 							a__( lowerCamelCase__		,			unittest.TestCase     ):







    def       lowercase_	(				self			:  Dict     ):
      # DeiT repo doesn't have TF weights, but we don't actually use the weights at all so let's
      # just reinitialize it.
      a						: Tuple					       =					TFVisionTextDualEncoderModel.from_vision_text_pretrained(
          'Rocketknight1/tiny-random-deit-tf'						,      'hf-internal-testing/tiny-random-roberta'     )
      a						: Union[str, Any]					       =					13
      a						: Dict					       =					floats_tensor(
          [
              batch_size,
              model.vision_model.config.num_channels,
              model.vision_model.config.image_size,
              model.vision_model.config.image_size,
          ]     )
      a						: Dict					       =					ids_tensor([batch_size, 4]						,      model.text_model.config.vocab_size     )
      a						: List[Any]					       =					random_attention_mask([batch_size, 4]     )
      a						: Optional[Any]					       =					{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

      return model, inputs







    def       lowercase_	(				self			:  str						,      __snake_case			:  Dict						,      __snake_case			:  Optional[Any]						,      __snake_case			:  List[str]						,      __snake_case			:  Optional[Any]						,      __snake_case			:  Optional[int]=None						,      **__snake_case			:  Tuple     ):
      a       ,     a						: List[str]					       =					self.get_vision_text_model(__snake_case						,      __snake_case     )
      a						: List[str]					       =					TFVisionTextDualEncoderModel(vision_model=__snake_case						,      text_model=__snake_case     )

      a						: List[str]					       =					model(
          input_ids=__snake_case						,      pixel_values=__snake_case						,      attention_mask=__snake_case						,      output_attentions=__snake_case     )

      a						: Union[str, Any]					       =					output.vision_model_output.attentions
      self.assertEqual(len(__snake_case     )						,      vision_config.num_hidden_layers     )

      # in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
      a						: Union[str, Any]					       =					to_atuple(vision_model.config.image_size     )
      a						: Optional[Any]					       =					to_atuple(vision_model.config.patch_size     )
      a						: Optional[int]					       =					(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
      a						: Optional[Any]					       =					num_patches + 2
      self.assertEqual(vision_attentions[0].shape[-3:]						,      (vision_config.num_attention_heads, seq_len, seq_len)     )

      a						: Optional[int]					       =					output.text_model_output.attentions
      self.assertEqual(len(__snake_case     )						,      text_config.num_hidden_layers     )

      self.assertEqual(
          text_attentions[0].shape[-3:]						,      (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])						,      )







    def       lowercase_	(				self			:  Union[str, Any]						,      __snake_case			:  Any						,      __snake_case			:  Dict     ):
      a						: Dict					       =					TFDeiTModel(__snake_case						,      name='vision_model'     )
      a						: Tuple					       =					TFRobertaModel(__snake_case						,      name='text_model'     )
      return vision_model, text_model







    def       lowercase_	(				self			:  Dict     ):
      a						: Optional[Any]					       =					TFDeiTModelTester(self     )
      a						: Dict					       =					TFRobertaModelTester(self     )
      a						: List[str]					       =					vit_model_tester.prepare_config_and_inputs()
      a						: Any					       =					bert_model_tester.prepare_config_and_inputs()

      a       ,     a       ,     a						: Tuple					       =					vision_config_and_inputs

      (
          (
          a
      )       ,     (
          a
      )       ,     (
          a
      )       ,     (
          a
      )       ,     (
          a
      )       ,     (
          a
      )       ,     (
          a
      )       ,     
      )						: Union[str, Any]					       =					text_config_and_inputs

      return {
          "text_config": text_config,
          "vision_config": vision_config,
          "pixel_values": pixel_values,
          "attention_mask": input_mask,
          "input_ids": input_ids,
          "text_token_type_ids": token_type_ids,
          "text_sequence_labels": sequence_labels,
          "text_token_labels": token_labels,
          "text_choice_labels": choice_labels,
      }

@require_tf
class 							a__( lowerCamelCase__		,			unittest.TestCase     ):







    def       lowercase_	(				self			:  List[Any]     ):
      a						: int					       =					TFVisionTextDualEncoderModel.from_vision_text_pretrained(
          'Rocketknight1/tiny-random-clip-tf'						,      'hf-internal-testing/tiny-random-bert'     )
      a						: List[Any]					       =					13
      a						: str					       =					floats_tensor(
          [
              batch_size,
              model.vision_model.config.num_channels,
              model.vision_model.config.image_size,
              model.vision_model.config.image_size,
          ]     )
      a						: Union[str, Any]					       =					ids_tensor([batch_size, 4]						,      model.text_model.config.vocab_size     )
      a						: Dict					       =					random_attention_mask([batch_size, 4]     )
      a						: int					       =					{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

      return model, inputs







    def       lowercase_	(				self			:  str						,      __snake_case			:  List[str]						,      __snake_case			:  Optional[Any]     ):
      a						: str					       =					TFCLIPVisionModel(__snake_case						,      name='vision_model'     )
      a						: int					       =					TFBertModel(__snake_case						,      name='text_model'     )
      return vision_model, text_model







    def       lowercase_	(				self			:  int     ):
      a						: Optional[Any]					       =					TFCLIPVisionModelTester(self     )
      a						: Union[str, Any]					       =					TFBertModelTester(self     )
      a						: Union[str, Any]					       =					clip_model_tester.prepare_config_and_inputs()
      a						: str					       =					bert_model_tester.prepare_config_and_inputs()

      a       ,     a						: Optional[Any]					       =					vision_config_and_inputs

      (
          (
          a
      )       ,     (
          a
      )       ,     (
          a
      )       ,     (
          a
      )       ,     (
          a
      )       ,     (
          a
      )       ,     (
          a
      )       ,     
      )						: str					       =					text_config_and_inputs

      return {
          "text_config": text_config,
          "vision_config": vision_config,
          "pixel_values": pixel_values,
          "attention_mask": input_mask,
          "input_ids": input_ids,
          "text_token_type_ids": token_type_ids,
          "text_sequence_labels": sequence_labels,
          "text_token_labels": token_labels,
          "text_choice_labels": choice_labels,
      }

@require_vision
@require_tf
class 							a__( unittest.TestCase     ):







    @slow
    def       lowercase_	(				self			:  List[str]     ):
      a						: List[str]					       =					TFVisionTextDualEncoderModel.from_pretrained(
          'clip-italian/clip-italian'						,      logit_scale_init_value=1.0						,      from_pt=__snake_case     )
      a						: Optional[int]					       =					VisionTextDualEncoderProcessor.from_pretrained('clip-italian/clip-italian'     )

      a						: List[str]					       =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
      a						: Optional[int]					       =					processor(
          text=['una foto di un gatto', 'una foto di un cane']						,      images=__snake_case						,      padding=__snake_case						,      return_tensors='np'     )

      a						: Union[str, Any]					       =					model(**__snake_case     )

      # verify the logits
      self.assertEqual(outputs.logits_per_image.shape						,      (inputs.pixel_values.shape[0], inputs.input_ids.shape[0])     )
      self.assertEqual(
          outputs.logits_per_text.shape						,      (inputs.input_ids.shape[0], inputs.pixel_values.shape[0])						,      )

      a						: Union[str, Any]					       =					np.array([[1.2284727, 0.3104122]]     )

      self.assertTrue(np.allclose(outputs.logits_per_image.numpy()						,      __snake_case						,      atol=1e-3     )     )




'''simple docstring'''






import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


lowerCAmelCase: Dict							=						'\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: Optional[Any]							=						'\\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: List[Any]							=						'\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 				lowerCamelCase__	(						_A    ):

  def remove_articles(_A    ):
    a						: Union[str, Any]					       =					re.compile(r'\b(a|an|the)\b'			,      re.UNICODE    )
    return re.sub(_A			,      ' '			,      _A    )

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

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

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

  return white_space_fix(remove_articles(remove_punc(lower(_A    )    )    )    )
def 				lowerCamelCase__	(						_A			,      _A    ):
  return int(normalize_answer(_A    ) == normalize_answer(_A    )    )
def 				lowerCamelCase__	(						_A			,      _A    ):
  a						: List[Any]					       =					[any(compute_exact(_A			,      _A    ) for ref in refs    ) for pred, refs in zip(_A			,      _A    )]
  return (sum(_A    ) / len(_A    )) * 100
def 				lowerCamelCase__	(						_A			,      _A			,      _A			,      _A    ):
  a						: List[Any]					       =					[rgram for rgrams in rgramslist for rgram in rgrams]
  a						: Any					       =					Counter(_A    )

  a						: Dict					       =					Counter(_A    )
  a						: Tuple					       =					Counter()
  for sgram, scount in sgramcounter.items():
    a						: List[str]					       =					scount * numref

  a						: Optional[int]					       =					Counter(_A    )
  a						: Optional[int]					       =					Counter()
  for cgram, ccount in cgramcounter.items():
    a						: List[str]					       =					ccount * numref

  # KEEP
  a						: Optional[Any]					       =					sgramcounter_rep & cgramcounter_rep
  a						: Union[str, Any]					       =					keepgramcounter_rep & rgramcounter
  a						: Any					       =					sgramcounter_rep & rgramcounter

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

  # DELETION
  a						: List[Any]					       =					sgramcounter_rep - cgramcounter_rep
  a						: Any					       =					delgramcounter_rep - rgramcounter
  a						: Union[str, Any]					       =					sgramcounter_rep - rgramcounter
  a						: Tuple					       =					0
  a						: str					       =					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.
  a						: str					       =					1
  if len(_A    ) > 0:
    a						: Tuple					       =					deltmpscorea / len(_A    )

  # ADDITION
  a						: Any					       =					set(_A    ) - set(_A    )
  a						: Optional[int]					       =					set(_A    ) & set(_A    )
  a						: Union[str, Any]					       =					set(_A    ) - set(_A    )

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

  return (keepscore, delscore_precision, addscore)
def 				lowerCamelCase__	(						_A			,      _A			,      _A    ):
  a						: List[str]					       =					len(_A    )

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

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

  for i in range(0			,      len(_A    ) - 1    ):
    if i < len(_A    ) - 1:
      a						: Tuple					       =					sagrams[i] + ' ' + sagrams[i + 1]
      sagrams.append(_A    )
    if i < len(_A    ) - 2:
      a						: Union[str, Any]					       =					sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
      sagrams.append(_A    )
    if i < len(_A    ) - 3:
      a						: List[str]					       =					sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
      sagrams.append(_A    )

  for i in range(0			,      len(_A    ) - 1    ):
    if i < len(_A    ) - 1:
      a						: Any					       =					cagrams[i] + ' ' + cagrams[i + 1]
      cagrams.append(_A    )
    if i < len(_A    ) - 2:
      a						: Optional[Any]					       =					cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
      cagrams.append(_A    )
    if i < len(_A    ) - 3:
      a						: Optional[Any]					       =					cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
      cagrams.append(_A    )

  ((a)       ,     (a)       ,     (a))						: int					       =					SARIngram(_A			,      _A			,      _A			,      _A    )
  ((a)       ,     (a)       ,     (a))						: Optional[int]					       =					SARIngram(_A			,      _A			,      _A			,      _A    )
  ((a)       ,     (a)       ,     (a))						: Union[str, Any]					       =					SARIngram(_A			,      _A			,      _A			,      _A    )
  ((a)       ,     (a)       ,     (a))						: int					       =					SARIngram(_A			,      _A			,      _A			,      _A    )
  a						: Dict					       =					sum([keepascore, keepascore, keepascore, keepascore]    ) / 4
  a						: Any					       =					sum([delascore, delascore, delascore, delascore]    ) / 4
  a						: Union[str, Any]					       =					sum([addascore, addascore, addascore, addascore]    ) / 4
  a						: str					       =					(avgkeepscore + avgdelscore + avgaddscore) / 3
  return finalscore
def 				lowerCamelCase__	(						_A			,      _A = True			,      _A = "13a"			,      _A = 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:
    a						: Dict					       =					sentence.lower()

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

  if not return_str:
    a						: Optional[Any]					       =					normalized_sent.split()

  return normalized_sent
def 				lowerCamelCase__	(						_A			,      _A			,      _A    ):
  if not (len(_A    ) == len(_A    ) == len(_A    )):
    raise ValueError('Sources length must match predictions and references lengths.'    )
  a						: Tuple					       =					0
  for src, pred, refs in zip(_A			,      _A			,      _A    ):
    sari_score += SARIsent(normalize(_A    )			,      normalize(_A    )			,      [normalize(_A    ) for sent in refs]    )
  a						: Tuple					       =					sari_score / len(_A    )
  return 100 * sari_score
def 				lowerCamelCase__	(						_A			,      _A			,      _A="exp"			,      _A=None			,      _A=False			,      _A=False			,      _A=False			,      ):
  a						: Optional[int]					       =					len(references[0]    )
  if any(len(_A    ) != references_per_prediction for refs in references    ):
    raise ValueError('Sacrebleu requires the same number of references for each prediction'    )
  a						: List[Any]					       =					[[refs[i] for refs in references] for i in range(_A    )]
  a						: Optional[Any]					       =					sacrebleu.corpus_bleu(
      _A			,      _A			,      smooth_method=_A			,      smooth_value=_A			,      force=_A			,      lowercase=_A			,      use_effective_order=_A			,      )
  return output.score

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







    def       lowercase_	(				self			:  Optional[int]     ):
      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       lowercase_	(				self			:  str						,      __snake_case			:  Optional[Any]						,      __snake_case			:  int						,      __snake_case			:  str     ):
      a						: int					       =					{}
      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