code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  from sklearn.metrics import mean_absolute_error
  
  import datasets
  
  
  _lowerCamelCase							:		str										=						"\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"
  
  _lowerCamelCase							:		List[str]										=						"\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n"
  
  
  _lowerCamelCase							:		List[Any]										=						"\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n                 \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n                 \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\nReturns:\n    mae : mean absolute error.\n        If multioutput is \"raw_values\", then mean absolute error is returned for each output separately. If multioutput is \"uniform_average\" or an ndarray of weights, then the weighted average of all output errors is returned.\n        MAE output is non-negative floating point. The best value is 0.0.\nExamples:\n\n    >>> mae_metric = datasets.load_metric(\"mae\")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mae': 0.5}\n\n    If you're using multi-dimensional lists, then set the config as follows :\n\n    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mae': 0.75}\n    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')\n    >>> print(results)\n    {'mae': array([0.5, 1. ])}\n"
  
  
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION							)
  class   lowerCamelCase_							( datasets.Metric							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							return datasets.MetricInfo(
  							    description=_DESCRIPTION,						citation=_CITATION,						inputs_description=_KWARGS_DESCRIPTION,						features=datasets.Features(self._get_feature_types()				),						reference_urls=[
  							        "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html"
  							    ],						)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  
  
  							if self.config_name == "multilist":
  									return {
  									    "predictions": datasets.Sequence(datasets.Value("float"				)				),
  									    "references": datasets.Sequence(datasets.Value("float"				)				),
  									}
  							else:
  									return {
  									    "predictions": datasets.Value("float"				),
  									    "references": datasets.Value("float"				),
  									}
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase=None,						__UpperCamelCase="uniform_average"				):
  							_snake_case			 =							mean_absolute_error(__UpperCamelCase,						__UpperCamelCase,						sample_weight=__UpperCamelCase,						multioutput=__UpperCamelCase				)
  
  							return {"mae": mae_score}
  
  
  
  
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import json
  import os
  import unittest
  
  from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
      VOCAB_FILES_NAMES,
      GPTNeoXJapaneseTokenizer,
  )
  from transformers.testing_utils import require_tokenizers, slow
  
  from ...test_tokenization_common import TokenizerTesterMixin
  
  
  
  
  
  @require_tokenizers
  class   lowerCamelCase_							( UpperCAmelCase_, unittest.TestCase							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					lowercase__					=				GPTNeoXJapaneseTokenizer
  					lowercase__					=				False
  					lowercase__					=				{"""do_clean_text""": False, """add_prefix_space""": False}
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							super().setUp()
  
  							_snake_case			 = [
  							    "こん",
  							    "こんに",
  							    "にちは",
  							    "ばんは",
  							    "世界,㔺界",
  							    "、",
  							    "。",
  							    "<BR>",
  							    "<SP>",
  							    "<TAB>",
  							    "<URL>",
  							    "<EMAIL>",
  							    "<TEL>",
  							    "<DATE>",
  							    "<PRICE>",
  							    "<BLOCK>",
  							    "<KIGOU>",
  							    "<U2000U2BFF>",
  							    "<|emoji1|>",
  							    "<unk>",
  							    "<|startoftext|>",
  							    "<|endoftext|>",
  							]
  							_snake_case			 = {"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}}  # 😀
  							_snake_case			 = {"unk_token": "<unk>"}
  
  							_snake_case			 = os.path.join(self.tmpdirname,						VOCAB_FILES_NAMES["vocab_file"]				)
  							_snake_case			 = os.path.join(self.tmpdirname,						VOCAB_FILES_NAMES["emoji_file"]				)
  							with open(self.vocab_file,						"w",						encoding="utf-8"				) as vocab_writer:
  									vocab_writer.write("".join([x + "\n" for x in vocab_tokens]				)				)
  							with open(self.emoji_file,						"w"				) as emoji_writer:
  									emoji_writer.write(json.dumps(__UpperCamelCase				)				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						**__UpperCamelCase				):
  							kwargs.update(self.special_tokens_map				)
  							return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname,						**__UpperCamelCase				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  							_snake_case			 = "こんにちは、世界。 \nこんばんは、㔺界。😀"
  							_snake_case			 = "こんにちは、世界。 \nこんばんは、世界。😀"
  							return input_text, output_text
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  							_snake_case ,					_snake_case			 = self.get_input_output_texts(__UpperCamelCase				)
  							_snake_case			 = tokenizer.encode(__UpperCamelCase,						add_special_tokens=__UpperCamelCase				)
  							_snake_case			 = tokenizer.decode(__UpperCamelCase,						clean_up_tokenization_spaces=__UpperCamelCase				)
  							return text, ids
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							pass  # TODO add if relevant
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							pass  # TODO add if relevant
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							pass  # TODO add if relevant
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = self.get_tokenizer()
  
  							# Testing tokenization
  							_snake_case			 = "こんにちは、世界。　こんばんは、㔺界。"
  							_snake_case			 = ["こん", "にちは", "、", "世界", "。", "<SP>", "こん", "ばんは", "、", "㔺界", "。"]
  							_snake_case			 = tokenizer.tokenize(__UpperCamelCase				)
  							self.assertListEqual(__UpperCamelCase,						__UpperCamelCase				)
  
  							# Testing conversion to ids without special tokens
  							_snake_case			 = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
  							_snake_case			 = tokenizer.convert_tokens_to_ids(__UpperCamelCase				)
  							self.assertListEqual(__UpperCamelCase,						__UpperCamelCase				)
  
  							# Testing conversion to ids with special tokens
  							_snake_case			 = tokens + [tokenizer.unk_token]
  							_snake_case			 = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
  							_snake_case			 = tokenizer.convert_tokens_to_ids(__UpperCamelCase				)
  							self.assertListEqual(__UpperCamelCase,						__UpperCamelCase				)
  
  
  
  
  
  
  					@slow
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = self.tokenizer_class.from_pretrained("abeja/gpt-neox-japanese-2.7b"				)
  
  							_snake_case			 = tokenizer.encode("ありがとう。",						add_special_tokens=__UpperCamelCase				)
  							_snake_case			 = tokenizer.encode("どういたしまして。",						add_special_tokens=__UpperCamelCase				)
  
  							_snake_case			 = tokenizer.build_inputs_with_special_tokens(__UpperCamelCase				)
  							_snake_case			 = tokenizer.build_inputs_with_special_tokens(__UpperCamelCase,						__UpperCamelCase				)
  
  							assert encoded_sentence == ids_a
  							assert encoded_pair == ids_a + ids_a
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							# Intentionally convert some words to accommodate character fluctuations unique to Japanese
  							pass
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							# tokenizer has no padding token
  							pass
  
  
  
  
  
  