import unittest

from transformers.testing_utils import is_torch_available, require_torch, tooslow

from ...generation.test_utils import torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    CpmAntConfig,
				    CpmAntForCausalLM,
				    CpmAntModel,
				    CpmAntTokenizer,
				)





@require_torch
class 					__snake_case    :




					def __init__(  self       ,			__UpperCamelCase       ,			__UpperCamelCase=2       ,			__UpperCamelCase=8       ,			__UpperCamelCase=True       ,			__UpperCamelCase=False       ,			__UpperCamelCase=False       ,			__UpperCamelCase=False       ,			__UpperCamelCase=False       ,			__UpperCamelCase=99       ,			__UpperCamelCase=32       ,			__UpperCamelCase=3       ,			__UpperCamelCase=4       ,			__UpperCamelCase=37       ,			__UpperCamelCase=32       ,			__UpperCamelCase=128       ,			__UpperCamelCase=8       ,			__UpperCamelCase=8       ,			__UpperCamelCase=8       ,			__UpperCamelCase=1.0       ,			__UpperCamelCase=True       ,			)	->    int:



									'''simple docstring'''







									snake_case__   :      Any   				= parent
									snake_case__   :      str   				= batch_size
									snake_case__   :      List[Any]   				= seq_length
									snake_case__   :      Union[str, Any]   				= is_training
									snake_case__   :      int   				= use_token_type_ids
									snake_case__   :      Dict   				= use_input_mask
									snake_case__   :      str   				= use_labels
									snake_case__   :      Optional[int]   				= use_mc_token_ids
									snake_case__   :      Any   				= vocab_size
									snake_case__   :      int   				= hidden_size
									snake_case__   :      str   				= num_hidden_layers
									snake_case__   :      Tuple   				= num_attention_heads
									snake_case__   :      Union[str, Any]   				= intermediate_size
									snake_case__   :      Any   				= num_buckets
									snake_case__   :      int   				= max_distance
									snake_case__   :      List[str]   				= prompt_length
									snake_case__   :      str   				= prompt_types
									snake_case__   :      int   				= segment_types
									snake_case__   :      Any   				= init_std
									snake_case__   :      Dict   				= return_dict




					def      __a     (  self						)	->    Tuple:



									'''simple docstring'''







									snake_case__   :      Tuple   				= {}
									snake_case__   :      int   				= ids_tensor([self.batch_size, self.seq_length]       ,			self.vocab_size						).type(torch.intaa						)
									snake_case__   :      Optional[Any]   				= False

									snake_case__   :      List[str]   				= self.get_config()

									return (config, input_ids)




					def      __a     (  self						)	->    Dict:



									'''simple docstring'''







									return CpmAntConfig(
									    vocab_size=self.vocab_size       ,			hidden_size=self.hidden_size       ,			num_hidden_layers=self.num_hidden_layers       ,			num_attention_heads=self.num_attention_heads       ,			dim_ff=self.intermediate_size       ,			position_bias_num_buckets=self.num_buckets       ,			position_bias_max_distance=self.max_distance       ,			prompt_types=self.prompt_types       ,			prompt_length=self.prompt_length       ,			segment_types=self.segment_types       ,			use_cache=UpperCAmelCase_       ,			init_std=self.init_std       ,			return_dict=self.return_dict       ,			)




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			*__UpperCamelCase						)	->    Tuple:



									'''simple docstring'''







									snake_case__   :      int   				= CpmAntModel(config=UpperCAmelCase_						)
									model.to(UpperCAmelCase_						)
									model.eval()

									snake_case__   :      Dict   				= model(**UpperCAmelCase_						).last_hidden_state

									self.parent.assertEqual(hidden_states.shape       ,			(self.batch_size, self.seq_length, config.hidden_size)						)




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			*__UpperCamelCase						)	->    Optional[int]:



									'''simple docstring'''







									snake_case__   :      int   				= CpmAntForCausalLM(UpperCAmelCase_						)
									model.to(UpperCAmelCase_						)
									snake_case__   :      List[str]   				= input_ids['input_ids'].to(UpperCAmelCase_						)
									model.eval()

									snake_case__   :      Dict   				= model(**UpperCAmelCase_						)
									self.parent.assertEqual(
									    model_output.logits.shape       ,			(self.batch_size, self.seq_length, config.vocab_size + config.prompt_types * config.prompt_length)       ,			)




					def      __a     (  self						)	->    Any:



									'''simple docstring'''







									snake_case__				,    snake_case__   :      List[Any]   				= self.prepare_config_and_inputs()
									return config, inputs_dict





@require_torch
class 					__snake_case    (     snake_case__       ,snake_case__       ,unittest.TestCase      ):
					__lowerCamelCase			   =  (CpmAntModel, CpmAntForCausalLM) if is_torch_available() else ()
					__lowerCamelCase			   =  (
					    {"""feature-extraction""": CpmAntModel, """text-generation""": CpmAntForCausalLM} if is_torch_available() else {}
					)

					__lowerCamelCase			   =  False
					__lowerCamelCase			   =  False
					__lowerCamelCase			   =  False
					__lowerCamelCase			   =  False
					__lowerCamelCase			   =  False




					def      __a     (  self						)	->    List[Any]:



									'''simple docstring'''







									snake_case__   :      Optional[Any]   				= CpmAntModelTester(self						)
									snake_case__   :      List[Any]   				= ConfigTester(self       ,			config_class=UpperCAmelCase_						)




					def      __a     (  self						)	->    List[Any]:



									'''simple docstring'''







									self.config_tester.create_and_test_config_common_properties()
									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()




					def      __a     (  self						)	->    List[Any]:



									'''simple docstring'''







									unittest.skip('CPMAnt doesn\'t support input_embeds.'						)(self.test_inputs_embeds						)




					def      __a     (  self						)	->    List[Any]:



									'''simple docstring'''







									unittest.skip(
									    'CPMAnt doesn\'t support retain grad in hidden_states or attentions, because prompt management will peel off the output.hidden_states from graph.\\n                 So is attentions. We strongly recommand you use loss to tune model.'						)(self.test_retain_grad_hidden_states_attentions						)




					def      __a     (  self						)	->    int:



									'''simple docstring'''







									snake_case__				,    snake_case__   :      Optional[int]   				= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_cpmant_model(UpperCAmelCase_       ,			UpperCAmelCase_						)




					def      __a     (  self						)	->    Optional[int]:



									'''simple docstring'''







									snake_case__				,    snake_case__   :      Tuple   				= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_lm_head_model(UpperCAmelCase_       ,			UpperCAmelCase_						)





@require_torch
class 					__snake_case    (     unittest.TestCase      ):




					@tooslow
					def      __a     (  self						)	->    List[str]:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= '今天天气真好！'
									snake_case__   :      List[Any]   				= 'openbmb/cpm-ant-10b'
									snake_case__   :      List[str]   				= CpmAntModel.from_pretrained(UpperCAmelCase_						)
									snake_case__   :      str   				= CpmAntTokenizer.from_pretrained(UpperCAmelCase_						)
									snake_case__   :      int   				= tokenizer(UpperCAmelCase_       ,			return_tensors='pt'						)
									snake_case__   :      str   				= model(**UpperCAmelCase_						).last_hidden_state

									snake_case__   :      Dict   				= torch.tensor(
									    [[[6.1_7_0_8, 5.9_2_4_4, 1.0_8_3_5], [6.5_2_0_7, 6.2_8_9_3, -1_1.3_3_2_4], [-1.0_1_0_7, -0.0_5_7_6, -5.9_5_7_7]]]       ,			)
									self.assertTrue(torch.allclose(hidden_states[:, :3, :3]       ,			UpperCAmelCase_       ,			atol=1E-2						)						)





@require_torch
class 					__snake_case    (     unittest.TestCase      ):




					@tooslow
					def      __a     (  self						)	->    Optional[Any]:



									'''simple docstring'''







									snake_case__   :      List[Any]   				= '今天天气真好！'
									snake_case__   :      List[str]   				= 'openbmb/cpm-ant-10b'
									snake_case__   :      Any   				= CpmAntForCausalLM.from_pretrained(UpperCAmelCase_						)
									snake_case__   :      Union[str, Any]   				= CpmAntTokenizer.from_pretrained(UpperCAmelCase_						)
									snake_case__   :      Dict   				= tokenizer(UpperCAmelCase_       ,			return_tensors='pt'						)
									snake_case__   :      Optional[int]   				= model(**UpperCAmelCase_						).logits

									snake_case__   :      Any   				= torch.tensor(
									    [[[-6.4_2_6_7, -6.4_0_8_3, -6.3_9_5_8], [-5.8_8_0_2, -5.9_4_4_7, -5.7_8_1_1], [-5.3_8_9_6, -5.4_8_2_0, -5.4_2_9_5]]]       ,			)
									self.assertTrue(torch.allclose(hidden_states[:, :3, :3]       ,			UpperCAmelCase_       ,			atol=1E-2						)						)




					@tooslow
					def      __a     (  self						)	->    Optional[int]:



									'''simple docstring'''







									snake_case__   :      str   				= 'openbmb/cpm-ant-10b'
									snake_case__   :      Any   				= CpmAntForCausalLM.from_pretrained(UpperCAmelCase_						)
									snake_case__   :      List[str]   				= CpmAntTokenizer.from_pretrained(UpperCAmelCase_						)
									snake_case__   :      Optional[Any]   				= '今天天气不错，'
									snake_case__   :      Optional[int]   				= '今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的'
									snake_case__   :      Dict   				= tokenizer(UpperCAmelCase_       ,			return_tensors='pt'						)
									snake_case__   :      List[Any]   				= model.generate(**UpperCAmelCase_						)
									snake_case__   :      Any   				= tokenizer.batch_decode(UpperCAmelCase_						)
									self.assertEqual(UpperCAmelCase_       ,			UpperCAmelCase_						)




					@tooslow
					def      __a     (  self						)	->    Union[str, Any]:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= 'openbmb/cpm-ant-10b'
									snake_case__   :      Dict   				= CpmAntForCausalLM.from_pretrained(UpperCAmelCase_						)
									snake_case__   :      List[str]   				= CpmAntTokenizer.from_pretrained(UpperCAmelCase_						)
									snake_case__   :      Optional[Any]   				= ['今天天气不错，', '新年快乐，万事如意！']
									snake_case__   :      str   				= [
									    '今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的',
									    '新年快乐，万事如意！在这辞旧迎新的美好时刻，我谨代表《农村新技术》杂志社全体同仁，向一直以来关心、支持《农村新技术》杂志发展的各级领导、各界朋友和广大读者致以最诚挚的',
									]
									snake_case__   :      Union[str, Any]   				= tokenizer(UpperCAmelCase_       ,			return_tensors='pt'       ,			padding=UpperCAmelCase_						)
									snake_case__   :      List[str]   				= model.generate(**UpperCAmelCase_						)
									snake_case__   :      List[Any]   				= tokenizer.batch_decode(UpperCAmelCase_						)
									self.assertEqual(UpperCAmelCase_       ,			UpperCAmelCase_						)






"""simple docstring"""
from typing import List

from packaging import version
from sklearn.metrics import fa_score

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse('3.8'):
				import importlib_metadata
else:
				import importlib.metadata as importlib_metadata


# TODO(Patrick/Anton)
__SCREAMING_SNAKE_CASE				:     str		=     '\\n'

__SCREAMING_SNAKE_CASE				:     Union[str, Any]		=     '\\n    XTREME-S is a benchmark to evaluate universal cross-lingual speech representations in many languages.\n    XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval.\n'

__SCREAMING_SNAKE_CASE				:     Any		=     '\nCompute XTREME-S evaluation metric associated to each XTREME-S dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    bleu_kwargs: optional dict of keywords to be passed when computing \'bleu\'.\n        Keywords include Dict can be one of \'smooth_method\', \'smooth_value\', \'force\', \'lowercase\',\n        \'tokenize\', \'use_effective_order\'.\n    wer_kwargs: optional dict of keywords to be passed when computing \'wer\' and \'cer\'.\n        Keywords include \'concatenate_texts\'.\nReturns: depending on the XTREME-S task, one or several of:\n    "accuracy": Accuracy - for \'fleurs-lang_id\', \'minds14\'\n    "f1": F1 score - for \'minds14\'\n    "wer": Word error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'\n    "cer": Character error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'\n    "bleu": BLEU score according to the `sacrebleu` metric - for \'covost2\'\nExamples:\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'mls\')  # \'mls\', \'voxpopuli\', \'fleurs-asr\' or \'babel\'\n    >>> references = ["it is sunny here", "paper and pen are essentials"]\n    >>> predictions = ["it\'s sunny", "paper pen are essential"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'wer\': 0.56, \'cer\': 0.27}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'covost2\')\n    >>> references = ["bonjour paris", "il est necessaire de faire du sport de temps en temp"]\n    >>> predictions = ["bonjour paris", "il est important de faire du sport souvent"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'bleu\': 31.65}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'fleurs-lang_id\')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'accuracy\': 0.6}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'minds14\')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'f1\': 0.58, \'accuracy\': 0.6}\n'

__SCREAMING_SNAKE_CASE				:     List[str]		=     ['fleurs-asr', 'mls', 'voxpopuli', 'babel', 'covost2', 'fleurs-lang_id', 'minds14']
__SCREAMING_SNAKE_CASE				:     List[str]		=     ''

try:
				from jiwer import transforms as tr

				__SCREAMING_SNAKE_CASE				:     Dict		=     True
except ImportError:
				__SCREAMING_SNAKE_CASE				:     Optional[Any]		=     False

if _jiwer_available and version.parse(importlib_metadata.version('jiwer')) < version.parse('2.3.0'):





				class   __A  (tr.AbstractTransform):





											'''simple docstring'''

											def __init__(					self				:   Optional[int]		,							UpperCAmelCase_				:   str = " "							)       ->str:



																		"""simple docstring"""



																		snake_case_     							=		sentence_delimiter



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



																		"""simple docstring"""



																		return list(UpperCAmelCase_							)



											def 		lowerCAmelCase							(					self				:   int		,							UpperCAmelCase_				:   List[str]							)       ->Any:



																		"""simple docstring"""



																		snake_case_     							=		[]
																		for sent_idx, sentence in enumerate(UpperCAmelCase_							):
																									chars.extend(self.process_string(UpperCAmelCase_							)							)
																									if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(UpperCAmelCase_							) - 1:
																																chars.append(self.sentence_delimiter							)
																		return chars


				__SCREAMING_SNAKE_CASE				:     str		=     tr.Compose(
				    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
				)
elif _jiwer_available:
				__SCREAMING_SNAKE_CASE				:     Optional[int]		=     tr.Compose(
				    [
				        tr.RemoveMultipleSpaces(),
				        tr.Strip(),
				        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
				        tr.ReduceToListOfListOfChars(),
				    ]
				)
else:
				__SCREAMING_SNAKE_CASE				:     Any		=     None







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					Any:
							return float((preds == labels).mean()					)







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					int:
							return {
							    "f1": float(fa_score(y_true=_SCREAMING_SNAKE_CASE					,			y_pred=_SCREAMING_SNAKE_CASE					,			average="""macro"""					)					),
							    "accuracy": simple_accuracy(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					),
							}







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE="exp"					,			_SCREAMING_SNAKE_CASE=None					,			_SCREAMING_SNAKE_CASE=False					,			_SCREAMING_SNAKE_CASE=False					,			_SCREAMING_SNAKE_CASE=None					,			_SCREAMING_SNAKE_CASE=False					,			)     ->					Optional[Any]:
							# xtreme-s can only have one label
							snake_case_     							=		[[label] for label in labels]
							snake_case_     							=		list(_SCREAMING_SNAKE_CASE					)
							try:
														import sacrebleu as scb
							except ImportError:
														raise ValueError(
														    """sacrebleu has to be installed in order to apply the bleu metric for covost2."""
														    """You can install it via `pip install sacrebleu`."""					)

							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\"`."""					)

							snake_case_     							=		len(labels[0]					)
							if any(len(_SCREAMING_SNAKE_CASE					) != references_per_prediction for refs in labels					):
														raise ValueError("""Sacrebleu requires the same number of references for each prediction"""					)
							snake_case_     							=		[[refs[i] for refs in labels] for i in range(_SCREAMING_SNAKE_CASE					)]
							snake_case_     							=		scb.corpus_bleu(
							    _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			smooth_method=_SCREAMING_SNAKE_CASE					,			smooth_value=_SCREAMING_SNAKE_CASE					,			force=_SCREAMING_SNAKE_CASE					,			lowercase=_SCREAMING_SNAKE_CASE					,			use_effective_order=_SCREAMING_SNAKE_CASE					,			**({"""tokenize""": tokenize} if tokenize else {})					,			)
							return {"bleu": output.score}







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					Union[str, Any]:
							try:
														from jiwer import compute_measures
							except ImportError:
														raise ValueError(
														    f"""jiwer has to be installed in order to apply the wer metric for {config_name}."""
														    """You can install it via `pip install jiwer`."""					)

							if concatenate_texts:
														snake_case_     							=		compute_measures(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)["""wer"""]

														snake_case_     							=		compute_measures(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			truth_transform=_SCREAMING_SNAKE_CASE					,			hypothesis_transform=_SCREAMING_SNAKE_CASE					)["""wer"""]
														return {"wer": wer, "cer": cer}
							else:

														def compute_score(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE="wer"					):
																					snake_case_     							=		0
																					snake_case_     							=		0
																					for prediction, reference in zip(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					):
																												if score_type == "wer":
																																			snake_case_     							=		compute_measures(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)
																												elif score_type == "cer":
																																			snake_case_     							=		compute_measures(
																																			    _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			truth_transform=_SCREAMING_SNAKE_CASE					,			hypothesis_transform=_SCREAMING_SNAKE_CASE					)
																												incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																												total += measures["substitutions"] + measures["deletions"] + measures["hits"]
																					return incorrect / total

														return {"wer": compute_score(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			"""wer"""					), "cer": compute_score(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			"""cer"""					)}





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





							'''simple docstring'''

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



														"""simple docstring"""



														if self.config_name not in _CONFIG_NAMES:
																					raise KeyError(F"""You should supply a configuration name selected in {_CONFIG_NAMES}"""							)

														snake_case_     							=		"""int64""" if self.config_name in ["""fleurs-lang_id""", """minds14"""] else """string"""

														return datasets.MetricInfo(
														    description=_DESCRIPTION		,							citation=_CITATION		,							inputs_description=_KWARGS_DESCRIPTION		,							features=datasets.Features(
														        {"""predictions""": datasets.Value(UpperCAmelCase_							), """references""": datasets.Value(UpperCAmelCase_							)}							)		,							codebase_urls=[]		,							reference_urls=[]		,							format="""numpy"""		,							)



							def 		lowerCAmelCase							(					self				:   Optional[int]		,							UpperCAmelCase_				:   List[str]		,							UpperCAmelCase_				:   str		,							UpperCAmelCase_				:   List[str]=None		,							UpperCAmelCase_				:   List[Any]=None							)       ->List[str]:



														"""simple docstring"""



														snake_case_     							=		bleu_kwargs if bleu_kwargs is not None else {}
														snake_case_     							=		wer_kwargs if wer_kwargs is not None else {}

														if self.config_name == "fleurs-lang_id":
																					return {"accuracy": simple_accuracy(UpperCAmelCase_		,							UpperCAmelCase_							)}
														elif self.config_name == "minds14":
																					return fa_and_simple_accuracy(UpperCAmelCase_		,							UpperCAmelCase_							)
														elif self.config_name == "covost2":
																					snake_case_     							=		bleu_kwargs.pop("""smooth_method"""		,							"""exp"""							)
																					snake_case_     							=		bleu_kwargs.pop("""smooth_value"""		,							UpperCAmelCase_							)
																					snake_case_     							=		bleu_kwargs.pop("""force"""		,							UpperCAmelCase_							)
																					snake_case_     							=		bleu_kwargs.pop("""lowercase"""		,							UpperCAmelCase_							)
																					snake_case_     							=		bleu_kwargs.pop("""tokenize"""		,							UpperCAmelCase_							)
																					snake_case_     							=		bleu_kwargs.pop("""use_effective_order"""		,							UpperCAmelCase_							)
																					return bleu(
																					    preds=UpperCAmelCase_		,							labels=UpperCAmelCase_		,							smooth_method=UpperCAmelCase_		,							smooth_value=UpperCAmelCase_		,							force=UpperCAmelCase_		,							lowercase=UpperCAmelCase_		,							tokenize=UpperCAmelCase_		,							use_effective_order=UpperCAmelCase_		,							)
														elif self.config_name in ["fleurs-asr", "mls", "voxpopuli", "babel"]:
																					snake_case_     							=		wer_kwargs.pop("""concatenate_texts"""		,							UpperCAmelCase_							)
																					return wer_and_cer(UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_		,							self.config_name							)
														else:
																					raise KeyError(F"""You should supply a configuration name selected in {_CONFIG_NAMES}"""							)



