




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
		import os

		# The slow tests are often failing with OOM error on GPU
		# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
		# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
		UpperCAmelCase_    ="""platform"""

		import jax
		import jax.numpy as jnp

		from transformers import MarianTokenizer
		from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right



def    UpperCAmelCase							(  _snake_case    ,			_snake_case    ,			_snake_case=None    ,			_snake_case=None    ,			_snake_case=None    ,			_snake_case=None    ,			_snake_case=None    ,			_snake_case=None    ,			):
							if attention_mask is None:
														lowerCAmelCase =		np.where(input_ids != config.pad_token_id    ,			1    ,			0				)
							if decoder_attention_mask is None:
														lowerCAmelCase =		np.where(decoder_input_ids != config.pad_token_id    ,			1    ,			0				)
							if head_mask is None:
														lowerCAmelCase =		np.ones((config.encoder_layers, config.encoder_attention_heads)				)
							if decoder_head_mask is None:
														lowerCAmelCase =		np.ones((config.decoder_layers, config.decoder_attention_heads)				)
							if cross_attn_head_mask is None:
														lowerCAmelCase =		np.ones((config.decoder_layers, config.decoder_attention_heads)				)
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": attention_mask,
							}


class 				__UpperCamelCase      :





				'''simple docstring'''



				def __init__(			self							,			UpperCAmelCase_							,			UpperCAmelCase_=13							,			UpperCAmelCase_=7							,			UpperCAmelCase_=True							,			UpperCAmelCase_=False							,			UpperCAmelCase_=99							,			UpperCAmelCase_=16							,			UpperCAmelCase_=2							,			UpperCAmelCase_=4							,			UpperCAmelCase_=4							,			UpperCAmelCase_="gelu"							,			UpperCAmelCase_=0.1							,			UpperCAmelCase_=0.1							,			UpperCAmelCase_=32							,			UpperCAmelCase_=2							,			UpperCAmelCase_=1							,			UpperCAmelCase_=0							,			UpperCAmelCase_=0.02							,			):
											lowerCAmelCase =		parent
											lowerCAmelCase =		batch_size
											lowerCAmelCase =		seq_length
											lowerCAmelCase =		is_training
											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 =		eos_token_id
											lowerCAmelCase =		pad_token_id
											lowerCAmelCase =		bos_token_id
											lowerCAmelCase =		initializer_range



				def    __snake_case		(			self    ):
											lowerCAmelCase =		np.clip(ids_tensor([self.batch_size, self.seq_length - 1]							,			self.vocab_size    )							,			3							,			self.vocab_size    )
											lowerCAmelCase =		np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)							,			dtype=np.intaa    ))							,			-1    )

											lowerCAmelCase =		shift_tokens_right(UpperCAmelCase_							,			1							,			2    )

											lowerCAmelCase =		MarianConfig(
											    vocab_size=self.vocab_size							,			d_model=self.hidden_size							,			encoder_layers=self.num_hidden_layers							,			decoder_layers=self.num_hidden_layers							,			encoder_attention_heads=self.num_attention_heads							,			decoder_attention_heads=self.num_attention_heads							,			encoder_ffn_dim=self.intermediate_size							,			decoder_ffn_dim=self.intermediate_size							,			dropout=self.hidden_dropout_prob							,			attention_dropout=self.attention_probs_dropout_prob							,			max_position_embeddings=self.max_position_embeddings							,			eos_token_id=self.eos_token_id							,			bos_token_id=self.bos_token_id							,			pad_token_id=self.pad_token_id							,			initializer_range=self.initializer_range							,			use_cache=UpperCAmelCase_							,			)
											lowerCAmelCase =		prepare_marian_inputs_dict(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    )
											return config, inputs_dict



				def    __snake_case		(			self    ):
											lowerCAmelCase ,  lowerCAmelCase =		self.prepare_config_and_inputs()
											return config, inputs_dict



				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											lowerCAmelCase =		20
											lowerCAmelCase =		model_class_name(UpperCAmelCase_    )

											lowerCAmelCase =		model.encode(inputs_dict['''input_ids''']    )

											lowerCAmelCase ,  lowerCAmelCase =		(
											    inputs_dict['''decoder_input_ids'''],
											    inputs_dict['''decoder_attention_mask'''],
											)

											lowerCAmelCase =		model.init_cache(decoder_input_ids.shape[0]							,			UpperCAmelCase_							,			UpperCAmelCase_    )
											lowerCAmelCase =		jnp.ones((decoder_input_ids.shape[0], max_decoder_length)							,			dtype='''i4'''    )

											lowerCAmelCase =		jnp.broadcast_to(
											    jnp.arange(decoder_input_ids.shape[-1] - 1    )[None, :]							,			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,			)
											lowerCAmelCase =		model.decode(
											    decoder_input_ids[:, :-1]							,			UpperCAmelCase_							,			decoder_attention_mask=UpperCAmelCase_							,			past_key_values=UpperCAmelCase_							,			decoder_position_ids=UpperCAmelCase_							,			)

											lowerCAmelCase =		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,			dtype='''i4'''    )
											lowerCAmelCase =		model.decode(
											    decoder_input_ids[:, -1:]							,			UpperCAmelCase_							,			decoder_attention_mask=UpperCAmelCase_							,			past_key_values=outputs_cache.past_key_values							,			decoder_position_ids=UpperCAmelCase_							,			)

											lowerCAmelCase =		model.decode(UpperCAmelCase_							,			UpperCAmelCase_    )

											lowerCAmelCase =		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])    )    )
											self.parent.assertTrue(diff < 1E-3							,			msg=F"""Max diff is {diff}"""    )






				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											lowerCAmelCase =		20
											lowerCAmelCase =		model_class_name(UpperCAmelCase_    )

											lowerCAmelCase =		model.encode(inputs_dict['''input_ids''']    )

											lowerCAmelCase ,  lowerCAmelCase =		(
											    inputs_dict['''decoder_input_ids'''],
											    inputs_dict['''decoder_attention_mask'''],
											)

											lowerCAmelCase =		jnp.concatenate(
											    [
											        decoder_attention_mask,
											        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])    ),
											    ]							,			axis=-1							,			)

											lowerCAmelCase =		model.init_cache(decoder_input_ids.shape[0]							,			UpperCAmelCase_							,			UpperCAmelCase_    )
											lowerCAmelCase =		jnp.broadcast_to(
											    jnp.arange(decoder_input_ids.shape[-1] - 1    )[None, :]							,			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,			)

											lowerCAmelCase =		model.decode(
											    decoder_input_ids[:, :-1]							,			UpperCAmelCase_							,			decoder_attention_mask=UpperCAmelCase_							,			past_key_values=UpperCAmelCase_							,			decoder_position_ids=UpperCAmelCase_							,			)
											lowerCAmelCase =		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,			dtype='''i4'''    )
											lowerCAmelCase =		model.decode(
											    decoder_input_ids[:, -1:]							,			UpperCAmelCase_							,			past_key_values=outputs_cache.past_key_values							,			decoder_attention_mask=UpperCAmelCase_							,			decoder_position_ids=UpperCAmelCase_							,			)

											lowerCAmelCase =		model.decode(UpperCAmelCase_							,			UpperCAmelCase_							,			decoder_attention_mask=UpperCAmelCase_    )

											lowerCAmelCase =		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])    )    )
											self.parent.assertTrue(diff < 1E-3							,			msg=F"""Max diff is {diff}"""    )




@require_flax
class 				__UpperCamelCase      (			__UpperCAmelCase  ,       unittest.TestCase  ,       __UpperCAmelCase	):





				'''simple docstring'''



				__a				:			str					    =True
				__a				:			Union[str, Any]					    =(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
				__a				:			str					    =(FlaxMarianMTModel,) if is_flax_available() else ()



				def    __snake_case		(			self    ):
											lowerCAmelCase =		FlaxMarianModelTester(self    )



				def    __snake_case		(			self    ):
											lowerCAmelCase ,  lowerCAmelCase =		self.model_tester.prepare_config_and_inputs()
											for model_class in self.all_model_classes:
																		self.model_tester.check_use_cache_forward(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    )



				def    __snake_case		(			self    ):
											lowerCAmelCase ,  lowerCAmelCase =		self.model_tester.prepare_config_and_inputs()
											for model_class in self.all_model_classes:
																		self.model_tester.check_use_cache_forward_with_attn_mask(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    )



				def    __snake_case		(			self    ):
											lowerCAmelCase ,  lowerCAmelCase =		self.model_tester.prepare_config_and_inputs_for_common()

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

																									@jax.jit
																									def encode_jitted(UpperCAmelCase_							,			UpperCAmelCase_=None							,			**UpperCAmelCase_    ):
																																return model.encode(input_ids=UpperCAmelCase_							,			attention_mask=UpperCAmelCase_    )

																									with self.subTest('''JIT Enabled'''    ):
																																lowerCAmelCase =		encode_jitted(**UpperCAmelCase_    ).to_tuple()

																									with self.subTest('''JIT Disabled'''    ):
																																with jax.disable_jit():
																																							lowerCAmelCase =		encode_jitted(**UpperCAmelCase_    ).to_tuple()

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



				def    __snake_case		(			self    ):
											lowerCAmelCase ,  lowerCAmelCase =		self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__    ):
																									lowerCAmelCase =		model_class(UpperCAmelCase_    )
																									lowerCAmelCase =		model.encode(inputs_dict['''input_ids''']							,			inputs_dict['''attention_mask''']    )

																									lowerCAmelCase =		{
																									    '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
																									    '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
																									    '''encoder_outputs''': encoder_outputs,
																									}

																									@jax.jit
																									def decode_jitted(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
																																return model.decode(
																																    decoder_input_ids=UpperCAmelCase_							,			decoder_attention_mask=UpperCAmelCase_							,			encoder_outputs=UpperCAmelCase_							,			)

																									with self.subTest('''JIT Enabled'''    ):
																																lowerCAmelCase =		decode_jitted(**UpperCAmelCase_    ).to_tuple()

																									with self.subTest('''JIT Disabled'''    ):
																																with jax.disable_jit():
																																							lowerCAmelCase =		decode_jitted(**UpperCAmelCase_    ).to_tuple()

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






				@slow
				def    __snake_case		(			self    ):
											for model_class_name in self.all_model_classes:
																		lowerCAmelCase =		model_class_name.from_pretrained('''Helsinki-NLP/opus-mt-en-de'''    )
																		# FlaxMarianForSequenceClassification expects eos token in input_ids
																		lowerCAmelCase =		np.ones((1, 1)    ) * model.config.eos_token_id
																		lowerCAmelCase =		model(UpperCAmelCase_    )
																		self.assertIsNotNone(UpperCAmelCase_    )




@require_flax
@require_sentencepiece
@require_tokenizers
class 				__UpperCamelCase      (			unittest.TestCase	):





				'''simple docstring'''



				__a				:			Tuple					    =None
				__a				:			Union[str, Any]					    =None



				@classmethod
				def    __snake_case		(			cls    ):
											lowerCAmelCase =		F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
											return cls



				@cached_property
				def    __snake_case		(			self    ):
											return MarianTokenizer.from_pretrained(self.model_name    )



				@property
				def    __snake_case		(			self    ):
											return self.tokenizer.eos_token_id



				@cached_property
				def    __snake_case		(			self    ):
											lowerCAmelCase =		FlaxMarianMTModel.from_pretrained(self.model_name    )
											self.assertEqual(model.config.decoder_start_token_id							,			model.config.pad_token_id    )
											return model



				def    __snake_case		(			self							,			**UpperCAmelCase_    ):
											lowerCAmelCase =		self.translate_src_text(**UpperCAmelCase_    )
											self.assertListEqual(self.expected_text							,			UpperCAmelCase_    )






				def    __snake_case		(			self							,			**UpperCAmelCase_    ):
											lowerCAmelCase =		self.tokenizer(self.src_text							,			padding=UpperCAmelCase_							,			return_tensors='''np'''							,			**UpperCAmelCase_    )
											lowerCAmelCase =		self.model.generate(
											    model_inputs.input_ids							,			attention_mask=model_inputs.attention_mask							,			num_beams=2							,			max_length=1_28							,			).sequences
											lowerCAmelCase =		self.tokenizer.batch_decode(UpperCAmelCase_							,			skip_special_tokens=UpperCAmelCase_    )
											return generated_words




@require_flax
@require_sentencepiece
@require_tokenizers
class 				__UpperCamelCase      (			__UpperCAmelCase	):





				'''simple docstring'''



				__a				:			Any					    ="""en"""
				__a				:			str					    ="""fr"""
				__a				:			Dict					    =[
				    """I am a small frog.""",
				    """Now I can forget the 100 words of german that I know.""",
				]
				__a				:			List[str]					    =[
				    """Je suis une petite grenouille.""",
				    """Maintenant, je peux oublier les 100 mots d'allemand que je connais.""",
				]



				@slow
				def    __snake_case		(			self    ):
											self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class 				__UpperCamelCase      (			__UpperCAmelCase	):





				'''simple docstring'''



				__a				:			Union[str, Any]					    ="""fr"""
				__a				:			Optional[Any]					    ="""en"""
				__a				:			Optional[Any]					    =[
				    """Donnez moi le micro.""",
				    """Tom et Mary étaient assis à une table.""",  # Accents
				]
				__a				:			Tuple					    =[
				    """Give me the microphone.""",
				    """Tom and Mary were sitting at a table.""",
				]



				@slow
				def    __snake_case		(			self    ):
											self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class 				__UpperCamelCase      (			__UpperCAmelCase	):





				'''simple docstring'''



				__a				:			Any					    ="""mt"""
				__a				:			Any					    ="""en"""
				__a				:			List[Any]					    =["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
				__a				:			str					    =["""Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."""]



				@slow
				def    __snake_case		(			self    ):
											self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class 				__UpperCamelCase      (			__UpperCAmelCase	):





				'''simple docstring'''



				__a				:			str					    ="""en"""
				__a				:			Dict					    ="""de"""
				__a				:			Dict					    =[
				    """I am a small frog.""",
				    """Now I can forget the 100 words of german that I know.""",
				    """Tom asked his teacher for advice.""",
				    """That's how I would do it.""",
				    """Tom really admired Mary's courage.""",
				    """Turn around and close your eyes.""",
				]
				__a				:			str					    =[
				    """Ich bin ein kleiner Frosch.""",
				    """Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.""",
				    """Tom bat seinen Lehrer um Rat.""",
				    """So würde ich das machen.""",
				    """Tom bewunderte Marias Mut wirklich.""",
				    """Drehen Sie sich um und schließen Sie die Augen.""",
				]



				@slow
				def    __snake_case		(			self    ):
											self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class 				__UpperCamelCase      (			__UpperCAmelCase	):





				'''simple docstring'''



				__a				:			List[str]					    ="""en"""
				__a				:			Union[str, Any]					    ="""zh"""
				__a				:			Union[str, Any]					    =["""My name is Wolfgang and I live in Berlin"""]
				__a				:			List[str]					    =["""我叫沃尔夫冈 我住在柏林"""]



				@slow
				def    __snake_case		(			self    ):
											self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class 				__UpperCamelCase      (			__UpperCAmelCase	):





				'''simple docstring'''



				__a				:			Optional[Any]					    ="""ru"""
				__a				:			Tuple					    ="""fr"""
				__a				:			List[Any]					    =["""Он показал мне рукопись своей новой пьесы."""]
				__a				:			List[str]					    =["""Il m'a montré le manuscrit de sa nouvelle pièce."""]



				@slow
				def    __snake_case		(			self    ):
											self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class 				__UpperCamelCase      (			__UpperCAmelCase	):





				'''simple docstring'''



				__a				:			List[Any]					    ="""en"""
				__a				:			Optional[int]					    ="""ROMANCE"""
				__a				:			Tuple					    =[
				    """>>fr<< Don't spend so much time watching TV.""",
				    """>>pt<< Your message has been sent.""",
				    """>>es<< He's two years older than me.""",
				]
				__a				:			Union[str, Any]					    =[
				    """Ne passez pas autant de temps à regarder la télé.""",
				    """A sua mensagem foi enviada.""",
				    """Es dos años más viejo que yo.""",
				]



				@slow
				def    __snake_case		(			self    ):
											self._assert_generated_batch_equal_expected()









import sacrebleu as scb
from packaging import version

import datasets


UpperCAmelCase_    ="""\
@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\",
}
"""

UpperCAmelCase_    ="""\
SacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.
Inspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.
It also knows all the standard test sets and handles downloading, processing, and tokenization for you.

See the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.
"""

UpperCAmelCase_    ="""
Produces BLEU scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.
    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).
    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:
        - `'none'`: no smoothing
        - `'floor'`: increment zero counts
        - `'add-k'`: increment num/denom by k for n>1
        - `'exp'`: exponential decay
    smooth_value (`float`): The smoothing value. Only valid when `smooth_method='floor'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method='add-k'` (in which case `smooth_value` defaults to `1`).
    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `'zh'` for Chinese, `'ja-mecab'` for Japanese and `'13a'` (mteval) otherwise. Possible values are:
        - `'none'`: No tokenization.
        - `'zh'`: Chinese tokenization.
        - `'13a'`: mimics the `mteval-v13a` script from Moses.
        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses
        - `'char'`: Language-agnostic character-level tokenization.
        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).
    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.
    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.
    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.

Returns:
    'score': BLEU score,
    'counts': Counts,
    'totals': Totals,
    'precisions': Precisions,
    'bp': Brevity penalty,
    'sys_len': predictions length,
    'ref_len': reference length,

Examples:

    Example 1:
        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions, references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        100.0

    Example 2:
        >>> predictions = [\"hello there general kenobi\",
        ...                 \"on our way to ankh morpork\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"],
        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions,
        ...                             references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        39.8
"""


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





				'''simple docstring'''



				def    __snake_case		(			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'''							,			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''']							,			reference_urls=[
											        '''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    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_="exp"							,			UpperCAmelCase_=None							,			UpperCAmelCase_=False							,			UpperCAmelCase_=False							,			UpperCAmelCase_=None							,			UpperCAmelCase_=False							,			):
											lowerCAmelCase =		len(references[0]    )
											if any(len(UpperCAmelCase_    ) != 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(UpperCAmelCase_    )]
											lowerCAmelCase =		scb.corpus_bleu(
											    UpperCAmelCase_							,			UpperCAmelCase_							,			smooth_method=UpperCAmelCase_							,			smooth_value=UpperCAmelCase_							,			force=UpperCAmelCase_							,			lowercase=UpperCAmelCase_							,			use_effective_order=UpperCAmelCase_							,			**({'''tokenize''': tokenize} if tokenize else {})							,			)
											lowerCAmelCase =		{
											    '''score''': output.score,
											    '''counts''': output.counts,
											    '''totals''': output.totals,
											    '''precisions''': output.precisions,
											    '''bp''': output.bp,
											    '''sys_len''': output.sys_len,
											    '''ref_len''': output.ref_len,
											}
											return output_dict



