
"""simple docstring"""

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, 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
					_A            = 'platform'

					import jax
					import jax.numpy as jnp

					from transformers import AutoTokenizer
					from transformers.models.mbart.modeling_flax_mbart import (
					    FlaxMBartForConditionalGeneration,
					    FlaxMBartForQuestionAnswering,
					    FlaxMBartForSequenceClassification,
					    FlaxMBartModel,
					    shift_tokens_right,
					)
def 		UpperCAmelCase					(		a_,						a_,						a_=None,						a_=None,						a_=None,						a_=None,						a_=None,						a_=None,						):





		'''simple docstring'''


		if attention_mask is None:
				lowerCamelCase     :			Dict		=    np.where(input_ids != config.pad_token_id,						1,						0			)
		if decoder_attention_mask is None:
				lowerCamelCase     :			Dict		=    np.where(decoder_input_ids != config.pad_token_id,						1,						0			)
		if head_mask is None:
				lowerCamelCase     :			List[Any]		=    np.ones((config.encoder_layers, config.encoder_attention_heads)			)
		if decoder_head_mask is None:
				lowerCamelCase     :			Optional[Any]		=    np.ones((config.decoder_layers, config.decoder_attention_heads)			)
		if cross_attn_head_mask is None:
				lowerCamelCase     :			Any		=    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": decoder_attention_mask,
		}






class        _lowercase   (				unittest.TestCase		):




	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_=2		, UpperCAmelCase_=0.02		, )		->							Union[str, Any]:
			lowerCamelCase     :			Any		=    parent
			lowerCamelCase     :			Dict		=    batch_size
			lowerCamelCase     :			str		=    seq_length
			lowerCamelCase     :			Dict		=    is_training
			lowerCamelCase     :			List[Any]		=    use_labels
			lowerCamelCase     :			Dict		=    vocab_size
			lowerCamelCase     :			List[str]		=    hidden_size
			lowerCamelCase     :			Tuple		=    num_hidden_layers
			lowerCamelCase     :			Union[str, Any]		=    num_attention_heads
			lowerCamelCase     :			int		=    intermediate_size
			lowerCamelCase     :			str		=    hidden_act
			lowerCamelCase     :			str		=    hidden_dropout_prob
			lowerCamelCase     :			str		=    attention_probs_dropout_prob
			lowerCamelCase     :			List[str]		=    max_position_embeddings
			lowerCamelCase     :			int		=    eos_token_id
			lowerCamelCase     :			Optional[int]		=    pad_token_id
			lowerCamelCase     :			Dict		=    bos_token_id
			lowerCamelCase     :			List[Any]		=    decoder_start_token_id
			lowerCamelCase     :			Dict		=    initializer_range




	def 			_UpperCamelCase    ( self					)		->							List[str]:
			lowerCamelCase     :			List[Any]		=    np.clip(ids_tensor([self.batch_size, self.seq_length - 1]		, self.vocab_size					)		, 3		, self.vocab_size					)
			lowerCamelCase     :			List[Any]		=    np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)		, dtype=np.intaa					))		, -1					)

			lowerCamelCase     :			int		=    shift_tokens_right(UpperCAmelCase_		, 1					)

			lowerCamelCase     :			Dict		=    MBartConfig(
			    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		, decoder_start_token_id=self.decoder_start_token_id		, initializer_range=self.initializer_range		, use_cache=UpperCAmelCase_		, )
			lowerCamelCase     :			List[str]		=    prepare_mbart_inputs_dict(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)
			return config, inputs_dict




	def 			_UpperCamelCase    ( self					)		->							Any:
			lowerCamelCase	,    lowerCamelCase     :			str		=    self.prepare_config_and_inputs()
			return config, inputs_dict




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)		->							Any:
			lowerCamelCase     :			List[str]		=    20
			lowerCamelCase     :			Optional[int]		=    model_class_name(UpperCAmelCase_					)

			lowerCamelCase     :			Dict		=    model.encode(inputs_dict['input_ids']					)

			lowerCamelCase	,    lowerCamelCase     :			List[str]		=    (
			    inputs_dict['decoder_input_ids'],
			    inputs_dict['decoder_attention_mask'],
			)

			lowerCamelCase     :			str		=    model.init_cache(decoder_input_ids.shape[0]		, UpperCAmelCase_		, UpperCAmelCase_					)
			lowerCamelCase     :			Dict		=    jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		, dtype='i4'					)

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

			lowerCamelCase     :			Optional[int]		=    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		, dtype='i4'					)
			lowerCamelCase     :			Union[str, Any]		=    model.decode(
			    decoder_input_ids[:, -1:]		, UpperCAmelCase_		, decoder_attention_mask=UpperCAmelCase_		, past_key_values=outputs_cache.past_key_values		, decoder_position_ids=UpperCAmelCase_		, )

			lowerCamelCase     :			Optional[Any]		=    model.decode(UpperCAmelCase_		, UpperCAmelCase_					)

			lowerCamelCase     :			Tuple		=    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 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)		->							Union[str, Any]:
			lowerCamelCase     :			int		=    20
			lowerCamelCase     :			Tuple		=    model_class_name(UpperCAmelCase_					)

			lowerCamelCase     :			Optional[Any]		=    model.encode(inputs_dict['input_ids']					)

			lowerCamelCase	,    lowerCamelCase     :			Dict		=    (
			    inputs_dict['decoder_input_ids'],
			    inputs_dict['decoder_attention_mask'],
			)

			lowerCamelCase     :			Optional[Any]		=    jnp.concatenate(
			    [
			        decoder_attention_mask,
			        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])					),
			    ]		, axis=-1		, )

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

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

			lowerCamelCase     :			List[Any]		=    model.decode(UpperCAmelCase_		, UpperCAmelCase_		, decoder_attention_mask=UpperCAmelCase_					)

			lowerCamelCase     :			Tuple		=    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        _lowercase   (				unittest.TestCase		):
	lowercase_        =	9_9




	def 			_UpperCamelCase    ( self					)		->							Optional[Any]:
			lowerCamelCase     :			Optional[int]		=    np.array(
			    [
			        [71, 82, 18, 33, 46, 91, 2],
			        [68, 34, 26, 58, 30, 82, 2],
			        [5, 97, 17, 39, 94, 40, 2],
			        [76, 83, 94, 25, 70, 78, 2],
			        [87, 59, 41, 35, 48, 66, 2],
			        [55, 13, 16, 58, 5, 2, 1],  # note padding
			        [64, 27, 31, 51, 12, 75, 2],
			        [52, 64, 86, 17, 83, 39, 2],
			        [48, 61, 9, 24, 71, 82, 2],
			        [26, 1, 60, 48, 22, 13, 2],
			        [21, 5, 62, 28, 14, 76, 2],
			        [45, 98, 37, 86, 59, 48, 2],
			        [70, 70, 50, 9, 28, 0, 2],
			    ]		, dtype=np.intaa		, )

			lowerCamelCase     :			List[Any]		=    input_ids.shape[0]
			lowerCamelCase     :			Optional[Any]		=    MBartConfig(
			    vocab_size=self.vocab_size		, d_model=24		, encoder_layers=2		, decoder_layers=2		, encoder_attention_heads=2		, decoder_attention_heads=2		, encoder_ffn_dim=32		, decoder_ffn_dim=32		, max_position_embeddings=48		, eos_token_id=2		, pad_token_id=1		, bos_token_id=0		, )
			return config, input_ids, batch_size




	def 			_UpperCamelCase    ( self					)		->							Union[str, Any]:
			lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase     :			List[Any]		=    self._get_config_and_data()
			lowerCamelCase     :			str		=    FlaxMBartForSequenceClassification(UpperCAmelCase_					)
			lowerCamelCase     :			Tuple		=    model(input_ids=UpperCAmelCase_		, decoder_input_ids=UpperCAmelCase_					)
			lowerCamelCase     :			Optional[int]		=    (batch_size, config.num_labels)
			self.assertEqual(outputs['logits'].shape		, UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							Any:
			lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase     :			str		=    self._get_config_and_data()
			lowerCamelCase     :			Any		=    FlaxMBartForQuestionAnswering(UpperCAmelCase_					)
			lowerCamelCase     :			Any		=    model(input_ids=UpperCAmelCase_					)

			self.assertEqual(outputs['start_logits'].shape		, input_ids.shape					)
			self.assertEqual(outputs['end_logits'].shape		, input_ids.shape					)




	def 			_UpperCamelCase    ( self					)		->							Optional[Any]:
			lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase     :			Optional[Any]		=    self._get_config_and_data()
			lowerCamelCase     :			List[str]		=    FlaxMBartForConditionalGeneration(UpperCAmelCase_					)
			lowerCamelCase     :			Optional[int]		=    lm_model(input_ids=UpperCAmelCase_					)
			lowerCamelCase     :			List[str]		=    (batch_size, input_ids.shape[1], config.vocab_size)
			self.assertEqual(outputs['logits'].shape		, UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							Optional[int]:
			lowerCamelCase     :			Optional[Any]		=    MBartConfig(
			    vocab_size=self.vocab_size		, d_model=14		, encoder_layers=2		, decoder_layers=2		, encoder_attention_heads=2		, decoder_attention_heads=2		, encoder_ffn_dim=8		, decoder_ffn_dim=8		, max_position_embeddings=48		, )
			lowerCamelCase     :			int		=    FlaxMBartForConditionalGeneration(UpperCAmelCase_					)
			lowerCamelCase     :			List[str]		=    np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]		, dtype=np.intaa					)
			lowerCamelCase     :			Any		=    np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]		, dtype=np.intaa					)
			lowerCamelCase     :			List[Any]		=    lm_model(input_ids=UpperCAmelCase_		, decoder_input_ids=UpperCAmelCase_					)
			lowerCamelCase     :			List[Any]		=    (*summary.shape, config.vocab_size)
			self.assertEqual(outputs['logits'].shape		, UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							Tuple:
			lowerCamelCase     :			Optional[int]		=    np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]		, dtype=np.intaa					)
			lowerCamelCase     :			List[Any]		=    shift_tokens_right(UpperCAmelCase_		, 1					)
			lowerCamelCase     :			str		=    np.equal(UpperCAmelCase_		, 1					).astype(np.floataa					).sum()
			lowerCamelCase     :			Any		=    np.equal(UpperCAmelCase_		, 1					).astype(np.floataa					).sum()
			self.assertEqual(shifted.shape		, input_ids.shape					)
			self.assertEqual(UpperCAmelCase_		, n_pad_before - 1					)
			self.assertTrue(np.equal(shifted[:, 0]		, 2					).all()					)






@require_flax
class        _lowercase   (				__UpperCAmelCase				,       unittest.TestCase				,       __UpperCAmelCase		):
	lowercase_        =	True
	lowercase_        =	(
	    (
	        FlaxMBartModel,
	        FlaxMBartForConditionalGeneration,
	        FlaxMBartForSequenceClassification,
	        FlaxMBartForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)
	lowercase_        =	(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()




	def 			_UpperCamelCase    ( self					)		->							Any:
			lowerCamelCase     :			Any		=    FlaxMBartModelTester(self					)




	def 			_UpperCamelCase    ( self					)		->							Optional[int]:
			lowerCamelCase	,    lowerCamelCase     :			int		=    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 			_UpperCamelCase    ( self					)		->							List[Any]:
			lowerCamelCase	,    lowerCamelCase     :			str		=    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 			_UpperCamelCase    ( self					)		->							List[Any]:
			lowerCamelCase	,    lowerCamelCase     :			Tuple		=    self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__					):
							lowerCamelCase     :			Union[str, Any]		=    self._prepare_for_class(UpperCAmelCase_		, UpperCAmelCase_					)
							lowerCamelCase     :			Dict		=    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     :			Dict		=    encode_jitted(**UpperCAmelCase_					).to_tuple()

							with self.subTest('JIT Disabled'					):
									with jax.disable_jit():
											lowerCamelCase     :			List[Any]		=    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 			_UpperCamelCase    ( self					)		->							str:
			lowerCamelCase	,    lowerCamelCase     :			Union[str, Any]		=    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		=    model_class(UpperCAmelCase_					)
							lowerCamelCase     :			Any		=    model.encode(inputs_dict['input_ids']		, inputs_dict['attention_mask']					)

							lowerCamelCase     :			Optional[int]		=    {
							    '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     :			int		=    decode_jitted(**UpperCAmelCase_					).to_tuple()

							with self.subTest('JIT Disabled'					):
									with jax.disable_jit():
											lowerCamelCase     :			Dict		=    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 			_UpperCamelCase    ( self					)		->							Union[str, Any]:
			for model_class_name in self.all_model_classes:
					lowerCamelCase     :			List[Any]		=    model_class_name.from_pretrained('facebook/mbart-large-cc25'		, from_pt=UpperCAmelCase_					)
					# FlaxMBartForSequenceClassification expects eos token in input_ids
					lowerCamelCase     :			str		=    np.ones((1, 1)					) * model.config.eos_token_id
					lowerCamelCase     :			int		=    model(UpperCAmelCase_					)
					self.assertIsNotNone(UpperCAmelCase_					)






@require_flax
@require_sentencepiece
@require_tokenizers
class        _lowercase   (				unittest.TestCase		):
	lowercase_        =	[
	    ' UN Chief Says There Is No Military Solution in Syria',
	]
	lowercase_        =	[
	    'Şeful ONU declară că nu există o soluţie militară în Siria',
	]
	lowercase_        =	'facebook/mbart-large-en-ro'




	@cached_property
	def 			_UpperCamelCase    ( self					)		->							Union[str, Any]:
			return AutoTokenizer.from_pretrained(self.model_name					)




	@cached_property
	def 			_UpperCamelCase    ( self					)		->							Optional[Any]:
			lowerCamelCase     :			Optional[int]		=    FlaxMBartForConditionalGeneration.from_pretrained(self.model_name		, from_pt=UpperCAmelCase_					)
			return model




	def 			_UpperCamelCase    ( self		, **UpperCAmelCase_					)		->							int:
			lowerCamelCase     :			List[str]		=    self.translate_src_text(**UpperCAmelCase_					)
			self.assertListEqual(self.expected_text		, UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self		, **UpperCAmelCase_					)		->							Dict:
			lowerCamelCase     :			Optional[int]		=    self.tokenizer(self.src_text		, **UpperCAmelCase_		, return_tensors='np'					)
			lowerCamelCase     :			List[Any]		=    self.model.generate(
			    model_inputs.input_ids		, attention_mask=model_inputs.attention_mask		, decoder_start_token_id=self.tokenizer.lang_code_to_id['ro_RO']		, early_stopping=UpperCAmelCase_		, num_beams=2		, ).sequences
			lowerCamelCase     :			str		=    self.tokenizer.batch_decode(UpperCAmelCase_		, skip_special_tokens=UpperCAmelCase_					)
			return generated_words




	@slow
	def 			_UpperCamelCase    ( self					)		->							Union[str, Any]:
			self._assert_generated_batch_equal_expected()












'''simple docstring'''



from sklearn.metrics import accuracy_score

import datasets


UpperCAmelCase_				:     List[Any]     			=						'''
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
'''


UpperCAmelCase_				:     List[Any]     			=						'''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {\'accuracy\': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {\'accuracy\': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
        >>> print(results)
        {\'accuracy\': 0.8778625954198473}
'''


UpperCAmelCase_				:     Union[str, Any]     			=						'''
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
'''





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


       def 					lowerCAmelCase					(      self     )					->     List[str]:



            '''simple docstring'''




            return datasets.MetricInfo(
                description=_DESCRIPTION			,     citation=_CITATION			,     inputs_description=_KWARGS_DESCRIPTION			,     features=datasets.Features(
                    {
                        '''predictions''': datasets.Sequence(datasets.Value('''int32'''     )     ),
                        '''references''': datasets.Sequence(datasets.Value('''int32'''     )     ),
                    }
                    if self.config_name == '''multilabel'''
                    else {
                        '''predictions''': datasets.Value('''int32'''     ),
                        '''references''': datasets.Value('''int32'''     ),
                    }     )			,     reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html''']			,     )




       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=True			,     __SCREAMING_SNAKE_CASE=None     )					->     Any:



            '''simple docstring'''




            return {
                "accuracy": float(
                    accuracy_score(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     normalize=__SCREAMING_SNAKE_CASE			,     sample_weight=__SCREAMING_SNAKE_CASE     )     )
            }






