


'''simple docstring'''




import unittest

from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
						import jax

						from transformers.models.big_bird.modeling_flax_big_bird import (
						    FlaxBigBirdForCausalLM,
						    FlaxBigBirdForMaskedLM,
						    FlaxBigBirdForMultipleChoice,
						    FlaxBigBirdForPreTraining,
						    FlaxBigBirdForQuestionAnswering,
						    FlaxBigBirdForSequenceClassification,
						    FlaxBigBirdForTokenClassification,
						    FlaxBigBirdModel,
						)



class  lowerCAmelCase				(				unittest.TestCase							):



	def __init__(					self ,					snake_case__ ,					snake_case__=2 ,					snake_case__=56 ,					snake_case__=True ,					snake_case__=True ,					snake_case__=True ,					snake_case__=True ,					snake_case__=99 ,					snake_case__=32 ,					snake_case__=2 ,					snake_case__=2 ,					snake_case__=7 ,					snake_case__="gelu_new" ,					snake_case__=0.1 ,					snake_case__=0.1 ,					snake_case__=512 ,					snake_case__=16 ,					snake_case__=2 ,					snake_case__=0.0_2 ,					snake_case__=4 ,					snake_case__="block_sparse" ,					snake_case__=True ,					snake_case__=False ,					snake_case__=2 ,					snake_case__=3 ,					):
		lowerCAmelCase					:    Optional[int]      							= parent
		lowerCAmelCase					:    Optional[Any]      							= batch_size
		lowerCAmelCase					:    Any      							= seq_length
		lowerCAmelCase					:    str      							= is_training
		lowerCAmelCase					:    List[Any]      							= use_attention_mask
		lowerCAmelCase					:    List[Any]      							= use_token_type_ids
		lowerCAmelCase					:    Optional[Any]      							= use_labels
		lowerCAmelCase					:    Any      							= vocab_size
		lowerCAmelCase					:    Tuple      							= hidden_size
		lowerCAmelCase					:    Dict      							= num_hidden_layers
		lowerCAmelCase					:    int      							= num_attention_heads
		lowerCAmelCase					:    Optional[Any]      							= intermediate_size
		lowerCAmelCase					:    Any      							= hidden_act
		lowerCAmelCase					:    Optional[int]      							= hidden_dropout_prob
		lowerCAmelCase					:    Dict      							= attention_probs_dropout_prob
		lowerCAmelCase					:    str      							= max_position_embeddings
		lowerCAmelCase					:    List[Any]      							= type_vocab_size
		lowerCAmelCase					:    Tuple      							= type_sequence_label_size
		lowerCAmelCase					:    List[Any]      							= initializer_range
		lowerCAmelCase					:    int      							= num_choices

		lowerCAmelCase					:    Optional[int]      							= rescale_embeddings
		lowerCAmelCase					:    Dict      							= attention_type
		lowerCAmelCase					:    Any      							= use_bias
		lowerCAmelCase					:    Optional[Any]      							= block_size
		lowerCAmelCase					:    Dict      							= num_random_blocks



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    int      							= ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size    )

		lowerCAmelCase					:    Dict      							= None
		if self.use_attention_mask:
			lowerCAmelCase					:    str      							= random_attention_mask([self.batch_size, self.seq_length]    )

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

		lowerCAmelCase					:    List[Any]      							= BigBirdConfig(
		    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 ,					is_decoder=snake_case__ ,					initializer_range=self.initializer_range ,					attention_type=self.attention_type ,					block_size=self.block_size ,					num_random_blocks=self.num_random_blocks ,					use_bias=self.use_bias ,					rescale_embeddings=self.rescale_embeddings ,					)

		return config, input_ids, token_type_ids, attention_mask



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    int      							= self.prepare_config_and_inputs()
		lowerCAmelCase					:    int      							= config_and_inputs
		lowerCAmelCase					:    Union[str, Any]      							= {
		    'input_ids': input_ids,
		    'token_type_ids': token_type_ids,
		    'attention_mask': attention_mask,
		}
		return config, inputs_dict



@require_flax
class  lowerCAmelCase				(				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					int			      = (
	    (
	        FlaxBigBirdForCausalLM,
	        FlaxBigBirdModel,
	        FlaxBigBirdForPreTraining,
	        FlaxBigBirdForMaskedLM,
	        FlaxBigBirdForMultipleChoice,
	        FlaxBigBirdForQuestionAnswering,
	        FlaxBigBirdForSequenceClassification,
	        FlaxBigBirdForTokenClassification,
	    )
	    if is_flax_available()
	    else ()
	)

	_lowerCamelCase   :					Tuple			      = False
	_lowerCamelCase   :					str			      = False



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[int]      							= FlaxBigBirdModelTester(self    )



	@slow
	# copied from `test_modeling_flax_common` because it takes much longer than other models
	def 				lowercase     (					self    ):
		super().test_from_pretrained_save_pretrained()



	@slow
	# copied from `test_modeling_flax_common` because it takes much longer than other models
	def 				lowercase     (					self    ):
		super().test_from_pretrained_with_no_automatic_init()



	@slow
	# copied from `test_modeling_flax_common` because it takes much longer than other models
	def 				lowercase     (					self    ):
		super().test_no_automatic_init()



	@slow
	# copied from `test_modeling_flax_common` because it takes much longer than other models
	def 				lowercase     (					self    ):
		super().test_hidden_states_output()



	@slow
	def 				lowercase     (					self    ):
		for model_class_name in self.all_model_classes:
			lowerCAmelCase					:    Union[str, Any]      							= model_class_name.from_pretrained('google/bigbird-roberta-base'    )
			self.assertIsNotNone(snake_case__    )



	def 				lowercase     (					self    ):
		if self.test_attn_probs:
			super().test_attention_outputs()



	@slow
	# copied from `test_modeling_flax_common` because it takes much longer than other models
	def 				lowercase     (					self    ):
		lowerCAmelCase					:    int      							= self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			with self.subTest(model_class.__name__    ):
				lowerCAmelCase					:    int      							= self._prepare_for_class(snake_case__ ,					snake_case__    )
				lowerCAmelCase					:    int      							= model_class(snake_case__    )

				@jax.jit
				def model_jitted(snake_case__ ,					snake_case__=None ,					**snake_case__    ):
					return model(input_ids=snake_case__ ,					attention_mask=snake_case__ ,					**snake_case__    )

				with self.subTest('JIT Enabled'    ):
					lowerCAmelCase					:    Tuple      							= model_jitted(**snake_case__    ).to_tuple()

				with self.subTest('JIT Disabled'    ):
					with jax.disable_jit():
						lowerCAmelCase					:    List[str]      							= model_jitted(**snake_case__    ).to_tuple()

				self.assertEqual(len(snake_case__    ) ,					len(snake_case__    )    )
				for jitted_output, output in zip(snake_case__ ,					snake_case__    ):
					self.assertEqual(jitted_output.shape ,					output.shape    )



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__=1e-5 ,					snake_case__="outputs" ,					snake_case__=None    ):
		# `bigbird_block_sparse_attention` in `FlaxBigBird` returns `attention_probs = None`, while in PyTorch version,
		# an effort was done to return `attention_probs` (yet to be verified).
		if name.startswith('outputs.attentions'    ):
			return
		else:
			super().check_pt_flax_outputs(snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    )




'''simple docstring'''




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

import datasets


_lowerCAmelCase  :		Dict		     =	'\\n@inproceedings{popovic-2015-chrf,\n    title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",\n    author = "Popovi{\'c}, Maja",\n    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",\n    month = sep,\n    year = "2015",\n    address = "Lisbon, Portugal",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/W15-3049",\n    doi = "10.18653/v1/W15-3049",\n    pages = "392--395",\n}\n@inproceedings{popovic-2017-chrf,\n    title = "chr{F}++: words helping character n-grams",\n    author = "Popovi{\'c}, Maja",\n    booktitle = "Proceedings of the Second Conference on Machine Translation",\n    month = sep,\n    year = "2017",\n    address = "Copenhagen, Denmark",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/W17-4770",\n    doi = "10.18653/v1/W17-4770",\n    pages = "612--618",\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]		     =	'\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n'

_lowerCAmelCase  :		List[Any]		     =	'\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    \'score\' (float): The chrF (chrF++) score,\n    \'char_order\' (int): The character n-gram order,\n    \'word_order\' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    \'beta\' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> 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."]\n        >>> 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."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {\'score\': 84.64214891738334, \'char_order\': 6, \'word_order\': 0, \'beta\': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> 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."]\n        >>> 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."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {\'score\': 82.87263732906315, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> 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."]\n        >>> 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."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {\'score\': 92.12853119829202, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}\n'



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



	def 				lowercase     (					self    ):
		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 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ = CHRF.CHAR_ORDER ,					snake_case__ = CHRF.WORD_ORDER ,					snake_case__ = CHRF.BETA ,					snake_case__ = False ,					snake_case__ = False ,					snake_case__ = False ,					):
		lowerCAmelCase					:    List[str]      							= len(references[0]    )
		if any(len(snake_case__    ) != references_per_prediction for refs in references    ):
			raise ValueError('Sacrebleu requires the same number of references for each prediction'    )
		lowerCAmelCase					:    List[str]      							= [[refs[i] for refs in references] for i in range(snake_case__    )]

		lowerCAmelCase					:    Union[str, Any]      							= CHRF(snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    )
		lowerCAmelCase					:    Dict      							= sb_chrf.corpus_score(snake_case__ ,					snake_case__    )

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