




"""simple docstring"""




from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers.models.layoutlm.modeling_tf_layoutlm import (
					    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TFLayoutLMForMaskedLM,
					    TFLayoutLMForQuestionAnswering,
					    TFLayoutLMForSequenceClassification,
					    TFLayoutLMForTokenClassification,
					    TFLayoutLMModel,
					)






class 					__magic_name__							:






				'''simple docstring'''





				def __init__( self	,				_a	,				_a=13	,				_a=7	,				_a=True	,				_a=True	,				_a=True	,				_a=True	,				_a=99	,				_a=32	,				_a=2	,				_a=4	,				_a=37	,				_a="gelu"	,				_a=0.1	,				_a=0.1	,				_a=512	,				_a=16	,				_a=2	,				_a=0.02	,				_a=3	,				_a=4	,				_a=None	,				_a=1_000	,				):





							"""simple docstring"""
							lowerCamelCase       =      parent
							lowerCamelCase       =      batch_size
							lowerCamelCase       =      seq_length
							lowerCamelCase       =      is_training
							lowerCamelCase       =      use_input_mask
							lowerCamelCase       =      use_token_type_ids
							lowerCamelCase       =      use_labels
							lowerCamelCase       =      vocab_size
							lowerCamelCase       =      hidden_size
							lowerCamelCase       =      num_hidden_layers
							lowerCamelCase       =      num_attention_heads
							lowerCamelCase       =      intermediate_size
							lowerCamelCase       =      hidden_act
							lowerCamelCase       =      hidden_dropout_prob
							lowerCamelCase       =      attention_probs_dropout_prob
							lowerCamelCase       =      max_position_embeddings
							lowerCamelCase       =      type_vocab_size
							lowerCamelCase       =      type_sequence_label_size
							lowerCamelCase       =      initializer_range
							lowerCamelCase       =      num_labels
							lowerCamelCase       =      num_choices
							lowerCamelCase       =      scope
							lowerCamelCase       =      range_bbox





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      ids_tensor([self.batch_size, self.seq_length]	,				self.vocab_size				)

							# convert bbox to numpy since TF does not support item assignment
							lowerCamelCase       =      ids_tensor([self.batch_size, self.seq_length, 4]	,				self.range_bbox				).numpy()
							# Ensure that bbox is legal
							for i in range(bbox.shape[0]				):
										for j in range(bbox.shape[1]				):
													if bbox[i, j, 3] < bbox[i, j, 1]:
																lowerCamelCase       =      bbox[i, j, 3]
																lowerCamelCase       =      bbox[i, j, 1]
																lowerCamelCase       =      t
													if bbox[i, j, 2] < bbox[i, j, 0]:
																lowerCamelCase       =      bbox[i, j, 2]
																lowerCamelCase       =      bbox[i, j, 0]
																lowerCamelCase       =      t
							lowerCamelCase       =      tf.convert_to_tensor(_a				)

							lowerCamelCase       =      None
							if self.use_input_mask:
										lowerCamelCase       =      random_attention_mask([self.batch_size, self.seq_length]				)

							lowerCamelCase       =      None
							if self.use_token_type_ids:
										lowerCamelCase       =      ids_tensor([self.batch_size, self.seq_length]	,				self.type_vocab_size				)

							lowerCamelCase       =      None
							lowerCamelCase       =      None
							lowerCamelCase       =      None
							if self.use_labels:
										lowerCamelCase       =      ids_tensor([self.batch_size]	,				self.type_sequence_label_size				)
										lowerCamelCase       =      ids_tensor([self.batch_size, self.seq_length]	,				self.num_labels				)
										lowerCamelCase       =      ids_tensor([self.batch_size]	,				self.num_choices				)

							lowerCamelCase       =      LayoutLMConfig(
							    vocab_size=self.vocab_size	,				hidden_size=self.hidden_size	,				num_hidden_layers=self.num_hidden_layers	,				num_attention_heads=self.num_attention_heads	,				intermediate_size=self.intermediate_size	,				hidden_act=self.hidden_act	,				hidden_dropout_prob=self.hidden_dropout_prob	,				attention_probs_dropout_prob=self.attention_probs_dropout_prob	,				max_position_embeddings=self.max_position_embeddings	,				type_vocab_size=self.type_vocab_size	,				initializer_range=self.initializer_range	,				)

							return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





				def      _lowerCAmelCase       ( self	,				_a	,				_a	,				_a	,				_a	,				_a	,				_a	,				_a	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      TFLayoutLMModel(config=_a				)

							lowerCamelCase       =      model(_a	,				_a	,				attention_mask=_a	,				token_type_ids=_a				)
							lowerCamelCase       =      model(_a	,				_a	,				token_type_ids=_a				)
							lowerCamelCase       =      model(_a	,				_a				)
							self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, self.seq_length, self.hidden_size)				)
							self.parent.assertEqual(result.pooler_output.shape	,				(self.batch_size, self.hidden_size)				)





				def      _lowerCAmelCase       ( self	,				_a	,				_a	,				_a	,				_a	,				_a	,				_a	,				_a	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      TFLayoutLMForMaskedLM(config=_a				)

							lowerCamelCase       =      model(_a	,				_a	,				attention_mask=_a	,				token_type_ids=_a	,				labels=_a				)
							self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.seq_length, self.vocab_size)				)





				def      _lowerCAmelCase       ( self	,				_a	,				_a	,				_a	,				_a	,				_a	,				_a	,				_a	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      self.num_labels
							lowerCamelCase       =      TFLayoutLMForSequenceClassification(config=_a				)

							lowerCamelCase       =      model(_a	,				_a	,				attention_mask=_a	,				token_type_ids=_a				)
							self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.num_labels)				)





				def      _lowerCAmelCase       ( self	,				_a	,				_a	,				_a	,				_a	,				_a	,				_a	,				_a	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      self.num_labels
							lowerCamelCase       =      TFLayoutLMForTokenClassification(config=_a				)

							lowerCamelCase       =      model(_a	,				_a	,				attention_mask=_a	,				token_type_ids=_a	,				labels=_a				)
							self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.seq_length, self.num_labels)				)





				def      _lowerCAmelCase       ( self	,				_a	,				_a	,				_a	,				_a	,				_a	,				_a	,				_a	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      TFLayoutLMForQuestionAnswering(config=_a				)

							lowerCamelCase       =      model(_a	,				_a	,				attention_mask=_a	,				token_type_ids=_a				)
							self.parent.assertEqual(result.start_logits.shape	,				(self.batch_size, self.seq_length)				)
							self.parent.assertEqual(result.end_logits.shape	,				(self.batch_size, self.seq_length)				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.prepare_config_and_inputs()
							(
							    (
							    lowerCamelCase
							)   ,     (
							    lowerCamelCase
							)   ,     (
							    lowerCamelCase
							)   ,     (
							    lowerCamelCase
							)   ,     (
							    lowerCamelCase
							)   ,     (
							    lowerCamelCase
							)   ,     (
							    lowerCamelCase
							)   ,     (
							    lowerCamelCase
							)   ,     
							)       =      config_and_inputs
							lowerCamelCase       =      {
							    """input_ids""": input_ids,
							    """bbox""": bbox,
							    """token_type_ids""": token_type_ids,
							    """attention_mask""": input_mask,
							}
							return config, inputs_dict






@require_tf
class 					__magic_name__							( UpperCAmelCase__		,      UpperCAmelCase__		,      unittest.TestCase       ):






				'''simple docstring'''
				__UpperCamelCase    						=		(
				    (
				        TFLayoutLMModel,
				        TFLayoutLMForMaskedLM,
				        TFLayoutLMForTokenClassification,
				        TFLayoutLMForSequenceClassification,
				        TFLayoutLMForQuestionAnswering,
				    )
				    if is_tf_available()
				    else ()
				)
				__UpperCamelCase    						=		(
				    {
				        "feature-extraction": TFLayoutLMModel,
				        "fill-mask": TFLayoutLMForMaskedLM,
				        "text-classification": TFLayoutLMForSequenceClassification,
				        "token-classification": TFLayoutLMForTokenClassification,
				        "zero-shot": TFLayoutLMForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				__UpperCamelCase    						=		False
				__UpperCamelCase    						=		True
				__UpperCamelCase    						=		10





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      TFLayoutLMModelTester(self				)
							lowerCamelCase       =      ConfigTester(self	,				config_class=_a	,				hidden_size=37				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							self.config_tester.run_common_tests()





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_a				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*_a				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*_a				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*_a				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*_a				)





				@slow
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCamelCase       =      TFLayoutLMModel.from_pretrained(_a				)
										self.assertIsNotNone(_a				)





				@unittest.skip("""Onnx compliancy broke with TF 2.10"""				)
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							pass




def 					a__				(       )		->      Tuple:
			# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
			# fmt: off
			lowerCamelCase       =      tf.convert_to_tensor([[1_01,10_19,10_14,10_16,10_37,1_28_49,47_47,10_04,1_42_46,22_78,54_39,45_24,50_02,29_30,21_93,29_30,43_41,32_08,10_05,10_55,21_71,28_48,1_13_00,35_31,1_02],[1_01,40_70,40_34,70_20,10_24,30_58,10_15,10_13,28_61,10_13,60_70,1_92_74,27_72,62_05,2_78_14,1_61_47,1_61_47,43_43,20_47,1_02_83,1_09_69,1_43_89,10_12,23_38,1_02]]       )  # noqa: E231
			lowerCamelCase       =      tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]       )  # noqa: E231
			lowerCamelCase       =      tf.convert_to_tensor([[[0,0,0,0],[4_23,2_37,4_40,2_51],[4_27,2_72,4_41,2_87],[4_19,1_15,4_37,1_29],[9_61,8_85,9_92,9_12],[2_56,38,3_30,58],[2_56,38,3_30,58],[3_36,42,3_53,57],[3_60,39,4_01,56],[3_60,39,4_01,56],[4_11,39,4_71,59],[4_79,41,5_28,59],[5_33,39,6_30,60],[67,1_13,1_34,1_31],[1_41,1_15,2_09,1_32],[68,1_49,1_33,1_66],[1_41,1_49,1_87,1_64],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[2_95,1_48,3_49,1_65],[4_41,1_49,4_92,1_66],[4_97,1_49,5_46,1_64],[64,2_01,1_25,2_18],[10_00,10_00,10_00,10_00]],[[0,0,0,0],[6_62,1_50,7_54,1_66],[6_65,1_99,7_42,2_11],[5_19,2_13,5_54,2_28],[5_19,2_13,5_54,2_28],[1_34,4_33,1_87,4_54],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[3_14,4_69,3_76,4_82],[5_04,6_84,5_82,7_06],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[6_10,7_49,6_52,7_65],[1_30,6_59,1_68,6_72],[1_76,6_57,2_37,6_72],[2_38,6_57,3_12,6_72],[4_43,6_53,6_28,6_72],[4_43,6_53,6_28,6_72],[7_16,3_01,8_25,3_17],[10_00,10_00,10_00,10_00]]]       )  # noqa: E231
			lowerCamelCase       =      tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]       )  # noqa: E231
			# these are sequence labels (i.e. at the token level)
			lowerCamelCase       =      tf.convert_to_tensor([[-1_00,10,10,10,9,1,-1_00,7,7,-1_00,7,7,4,2,5,2,8,8,-1_00,-1_00,5,0,3,2,-1_00],[-1_00,12,12,12,-1_00,12,10,-1_00,-1_00,-1_00,-1_00,10,12,9,-1_00,-1_00,-1_00,10,10,10,9,12,-1_00,10,-1_00]]       )  # noqa: E231
			# fmt: on

			return input_ids, attention_mask, bbox, token_type_ids, labels






@require_tf
class 					__magic_name__							( unittest.TestCase       ):






				'''simple docstring'''





				@slow
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      TFLayoutLMModel.from_pretrained("""microsoft/layoutlm-base-uncased"""				)

							lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase       =      prepare_layoutlm_batch_inputs()

							# forward pass
							lowerCamelCase       =      model(input_ids=_a	,				bbox=_a	,				attention_mask=_a	,				token_type_ids=_a				)

							# test the sequence output on [0, :3, :3]
							lowerCamelCase       =      tf.convert_to_tensor(
							    [[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]]	,				)

							self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]	,				_a	,				atol=1e-3				)				)

							# test the pooled output on [1, :3]
							lowerCamelCase       =      tf.convert_to_tensor([-0.6_580, -0.0_214, 0.8_552]				)

							self.assertTrue(np.allclose(outputs.pooler_output[1, :3]	,				_a	,				atol=1e-3				)				)





				@slow
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							# initialize model with randomly initialized sequence classification head
							lowerCamelCase       =      TFLayoutLMForSequenceClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""	,				num_labels=2				)

							lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase       =      prepare_layoutlm_batch_inputs()

							# forward pass
							lowerCamelCase       =      model(
							    input_ids=_a	,				bbox=_a	,				attention_mask=_a	,				token_type_ids=_a	,				labels=tf.convert_to_tensor([1, 1]				)	,				)

							# test whether we get a loss as a scalar
							lowerCamelCase       =      outputs.loss
							lowerCamelCase       =      (2,)
							self.assertEqual(loss.shape	,				_a				)

							# test the shape of the logits
							lowerCamelCase       =      outputs.logits
							lowerCamelCase       =      (2, 2)
							self.assertEqual(logits.shape	,				_a				)





				@slow
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							# initialize model with randomly initialized token classification head
							lowerCamelCase       =      TFLayoutLMForTokenClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""	,				num_labels=13				)

							lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase       =      prepare_layoutlm_batch_inputs()

							# forward pass
							lowerCamelCase       =      model(
							    input_ids=_a	,				bbox=_a	,				attention_mask=_a	,				token_type_ids=_a	,				labels=_a				)

							# test the shape of the logits
							lowerCamelCase       =      outputs.logits
							lowerCamelCase       =      tf.convert_to_tensor((2, 25, 13)				)
							self.assertEqual(logits.shape	,				_a				)





				@slow
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							# initialize model with randomly initialized token classification head
							lowerCamelCase       =      TFLayoutLMForQuestionAnswering.from_pretrained("""microsoft/layoutlm-base-uncased"""				)

							lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase       =      prepare_layoutlm_batch_inputs()

							# forward pass
							lowerCamelCase       =      model(input_ids=_a	,				bbox=_a	,				attention_mask=_a	,				token_type_ids=_a				)

							# test the shape of the logits
							lowerCamelCase       =      tf.convert_to_tensor((2, 25)				)
							self.assertEqual(outputs.start_logits.shape	,				_a				)
							self.assertEqual(outputs.end_logits.shape	,				_a				)









"""simple docstring"""




import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


lowerCAmelCase							:    List[Any]  							=							"""\
@inproceedings{popovic-2015-chrf,
    title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",
    author = \"Popovi{\'c}, Maja\",
    booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",
    month = sep,
    year = \"2015\",
    address = \"Lisbon, Portugal\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://aclanthology.org/W15-3049\",
    doi = \"10.18653/v1/W15-3049\",
    pages = \"392--395\",
}
@inproceedings{popovic-2017-chrf,
    title = \"chr{F}++: words helping character n-grams\",
    author = \"Popovi{\'c}, Maja\",
    booktitle = \"Proceedings of the Second Conference on Machine Translation\",
    month = sep,
    year = \"2017\",
    address = \"Copenhagen, Denmark\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://aclanthology.org/W17-4770\",
    doi = \"10.18653/v1/W17-4770\",
    pages = \"612--618\",
}
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

lowerCAmelCase							:    List[str]  							=							"""\
ChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,
and ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation
that is already present in sacrebleu.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.
"""

lowerCAmelCase							:    str  							=							"""
Produces ChrF(++) scores for hypotheses given reference translations.

Args:
    predictions (list of str): The predicted sentences.
    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.
    char_order (int): Character n-gram order. Defaults to `6`.
    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.
    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.
    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.
    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.
    eps_smoothing (bool): If `True`, applies epsilon smoothing similar
    to reference chrF++.py, NLTK and Moses implementations. If `False`,
    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.

Returns:
    'score' (float): The chrF (chrF++) score,
    'char_order' (int): The character n-gram order,
    'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,
    'beta' (int): Determine the importance of recall w.r.t precision

Examples:
    Example 1--a simple example of calculating chrF:
        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]
        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]
        >>> chrf = datasets.load_metric(\"chrf\")
        >>> results = chrf.compute(predictions=prediction, references=reference)
        >>> print(results)
        {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}

    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:
        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]
        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]
        >>> chrf = datasets.load_metric(\"chrf\")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2)
        >>> print(results)
        {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}

    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:
        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]
        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]
        >>> chrf = datasets.load_metric(\"chrf\")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2,
        ...                         lowercase=True)
        >>> print(results)
        {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}
"""






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






				'''simple docstring'''





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							if version.parse(scb.__version__				) < version.parse("""1.4.12"""				):
										raise ImportWarning(
										    """To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"""
										    """You can install it with `pip install \"sacrebleu>=1.4.12\"`."""				)
							return datasets.MetricInfo(
							    description=_DESCRIPTION	,				citation=_CITATION	,				homepage="""https://github.com/mjpost/sacreBLEU#chrf--chrf"""	,				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/mjpost/sacreBLEU#chrf--chrf"""]	,				reference_urls=[
							        """https://github.com/m-popovic/chrF""",
							    ]	,				)





				def      _lowerCAmelCase       ( self	,				_a	,				_a	,				_a = CHRF.CHAR_ORDER	,				_a = CHRF.WORD_ORDER	,				_a = CHRF.BETA	,				_a = False	,				_a = False	,				_a = False	,				):





							"""simple docstring"""
							lowerCamelCase       =      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"""				)
							lowerCamelCase       =      [[refs[i] for refs in references] for i in range(_a				)]

							lowerCamelCase       =      CHRF(_a	,				_a	,				_a	,				_a	,				_a	,				_a				)
							lowerCamelCase       =      sb_chrf.corpus_score(_a	,				_a				)

							return {
							    "score": output.score,
							    "char_order": output.char_order,
							    "word_order": output.word_order,
							    "beta": output.beta,
							}



