






import datasets


__UpperCAmelCase     							=						'\\n@InProceedings{conneau2018xnli,\n  author = \"Conneau, Alexis\n                 and Rinott, Ruty\n                 and Lample, Guillaume\n                 and Williams, Adina\n                 and Bowman, Samuel R.\n                 and Schwenk, Holger\n                 and Stoyanov, Veselin\",\n  title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",\n  booktitle = \"Proceedings of the 2018 Conference on Empirical Methods\n               in Natural Language Processing\",\n  year = \"2018\",\n  publisher = \"Association for Computational Linguistics\",\n  location = \"Brussels, Belgium\",\n}\n'

__UpperCAmelCase     							=						'\\nXNLI is a subset of a few thousand examples from MNLI which has been translated\ninto a 14 different languages (some low-ish resource). As with MNLI, the goal is\nto predict textual entailment (does sentence A imply/contradict/neither sentence\nB) and is a classification task (given two sentences, predict one of three\nlabels).\n'

__UpperCAmelCase     							=						'\nComputes XNLI score which is just simple accuracy.\nArgs:\n    predictions: Predicted labels.\n    references: Ground truth labels.\nReturns:\n    \'accuracy\': accuracy\nExamples:\n\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> xnli_metric = datasets.load_metric(\"xnli\")\n    >>> results = xnli_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0}\n'




def 					lowercase__				(					__snake_case   :							Union[str, Any]					,	__snake_case   :							Tuple  ):


					'''simple docstring'''


					return (preds == labels).mean()






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







							'''simple docstring'''



							def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
												return datasets.MetricInfo(
												    description=_DESCRIPTION    ,		citation=_CITATION    ,		inputs_description=_KWARGS_DESCRIPTION    ,		features=datasets.Features(
												        {
												            'predictions': datasets.Value('int64' if self.config_name != 'sts-b' else 'float32'       ),
												            'references': datasets.Value('int64' if self.config_name != 'sts-b' else 'float32'       ),
												        }       )    ,		codebase_urls=[]    ,		reference_urls=[]    ,		format='numpy'    ,		)



							def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							List[Any]:
												return {"accuracy": simple_accuracy(snake_case__    ,		snake_case__       )}














from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__UpperCAmelCase     							=						logging.get_logger(__name__)

__UpperCAmelCase     							=						{
    'google/umt5-small': 'https://huggingface.co/google/umt5-small/resolve/main/config.json',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   Union[str, Any]							=  '''umt5'''
		_snake_case   :   Union[str, Any]							=  ['''past_key_values''']



		def __init__(							self    ,		_UpperCamelCase=2_5_0_1_1_2    ,		_UpperCamelCase=5_1_2    ,		_UpperCamelCase=6_4    ,		_UpperCamelCase=1_0_2_4    ,		_UpperCamelCase=8    ,		_UpperCamelCase=None    ,		_UpperCamelCase=6    ,		_UpperCamelCase=3_2    ,		_UpperCamelCase=1_2_8    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=1E-6    ,		_UpperCamelCase=1.0    ,		_UpperCamelCase="gated-gelu"    ,		_UpperCamelCase=True    ,		_UpperCamelCase=True    ,		_UpperCamelCase="T5Tokenizer"    ,		_UpperCamelCase=True    ,		_UpperCamelCase=0    ,		_UpperCamelCase=1    ,		_UpperCamelCase=0    ,		**_UpperCamelCase    ,		)     ->							List[Any]:
							super().__init__(
							    is_encoder_decoder=_UpperCamelCase    ,		tokenizer_class=_UpperCamelCase    ,		tie_word_embeddings=_UpperCamelCase    ,		pad_token_id=_UpperCamelCase    ,		eos_token_id=_UpperCamelCase    ,		decoder_start_token_id=_UpperCamelCase    ,		**_UpperCamelCase    ,		)
							UpperCAmelCase_   :				Union[str, Any]						=						vocab_size
							UpperCAmelCase_   :				List[str]						=						d_model
							UpperCAmelCase_   :				Any						=						d_kv
							UpperCAmelCase_   :				Optional[int]						=						d_ff
							UpperCAmelCase_   :				List[Any]						=						num_layers
							UpperCAmelCase_   :				Optional[Any]						=						(
							    num_decoder_layers if num_decoder_layers is not None else self.num_layers
							)  # default = symmetry
							UpperCAmelCase_   :				Optional[int]						=						num_heads
							UpperCAmelCase_   :				Optional[int]						=						relative_attention_num_buckets
							UpperCAmelCase_   :				Dict						=						relative_attention_max_distance
							UpperCAmelCase_   :				Tuple						=						dropout_rate
							UpperCAmelCase_   :				Union[str, Any]						=						layer_norm_epsilon
							UpperCAmelCase_   :				Optional[int]						=						initializer_factor
							UpperCAmelCase_   :				List[str]						=						feed_forward_proj
							UpperCAmelCase_   :				Any						=						use_cache

							UpperCAmelCase_   :				List[Any]						=						self.feed_forward_proj.split('-'       )
							UpperCAmelCase_   :				List[Any]						=						act_info[-1]
							UpperCAmelCase_   :				Union[str, Any]						=						act_info[0] == 'gated'

							if len(_UpperCamelCase       ) > 1 and act_info[0] != "gated" or len(_UpperCamelCase       ) > 2:
												raise ValueError(
												    f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
												    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
												    '\'gated-gelu\' or \'relu\''       )

							if feed_forward_proj == "gated-gelu":
												UpperCAmelCase_   :				Optional[int]						=						'gelu_new'



		@property
		def  __UpperCAmelCase      (							self       )     ->							int:
							return self.d_model



		@property
		def  __UpperCAmelCase      (							self       )     ->							Any:
							return self.num_heads



		@property
		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							return self.num_layers





class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		@property
		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
		def  __UpperCAmelCase      (							self       )     ->							Mapping[str, Mapping[int, str]]:
							UpperCAmelCase_   :				str						=						{
							    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
							    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
							}
							if self.use_past:
												UpperCAmelCase_   :				Optional[int]						=						'past_encoder_sequence + sequence'
												UpperCAmelCase_   :				str						=						{0: 'batch'}
												UpperCAmelCase_   :				Optional[int]						=						{0: 'batch', 1: 'past_decoder_sequence + sequence'}
							else:
												UpperCAmelCase_   :				Optional[int]						=						{0: 'batch', 1: 'decoder_sequence'}
												UpperCAmelCase_   :				Union[str, Any]						=						{0: 'batch', 1: 'decoder_sequence'}

							if self.use_past:
												self.fill_with_past_key_values_(_UpperCamelCase    ,		direction='inputs'       )

							return common_inputs



		@property
		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
		def  __UpperCAmelCase      (							self       )     ->							int:
							return 1_3



		@property
		def  __UpperCAmelCase      (							self       )     ->							float:
							return 5E-4






