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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import List

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase)    ->  Tuple:






					if name != "training_function" and name != "main":
										raise ValueError(f'''Incorrect function name passed: {name}, choose either \'main\' or \'training_function\'''')
					a						,   a											=   [], False
					for line in lines:
										if not found_start and f'''def {name}''' in line:
															a											=   True
															good_lines.append(UpperCamelCase_)
															continue
										if found_start:
															if name == "training_function" and "def main" in line:
																				return good_lines
															if name == "main" and "if __name__" in line:
																				return good_lines
															good_lines.append(UpperCamelCase_)

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase)    ->  List[str]:
					return [line for line in lines if not line.lstrip().startswith("#") and line != "\n"]





def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase = None)    ->  Any:






					with open(UpperCamelCase_	,      "r") as f:
										a											=   f.readlines()
					with open(os.path.abspath(os.path.join("examples"	,      "nlp_example.py"))	,      "r") as f:
										a											=   f.readlines()
					with open(UpperCamelCase_	,      "r") as f:
										a											=   f.readlines()
					if secondary_filename is not None:
										with open(UpperCamelCase_	,      "r") as f:
															a											=   f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
					if parser_only:
										a											=   clean_lines(get_function_contents_by_name(UpperCamelCase_	,      "main"))
										a											=   clean_lines(get_function_contents_by_name(UpperCamelCase_	,      "main"))
										a											=   clean_lines(get_function_contents_by_name(UpperCamelCase_	,      "main"))
										if secondary_filename is not None:
															a											=   clean_lines(get_function_contents_by_name(UpperCamelCase_	,      "main"))
					else:
										a											=   clean_lines(get_function_contents_by_name(UpperCamelCase_	,      "training_function"))
										a											=   clean_lines(get_function_contents_by_name(UpperCamelCase_	,      "training_function"))
										a											=   clean_lines(get_function_contents_by_name(UpperCamelCase_	,      "training_function"))
										if secondary_filename is not None:
															a											=   clean_lines(
															    get_function_contents_by_name(UpperCamelCase_	,      "training_function"))

					a											=   "train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n"

					# Specific code in our script that differs from the full version, aka what is new
					a											=   []
					a											=   []  # We keep track of the idxs just in case it's a repeated statement
					a											=   iter(UpperCamelCase_)
					for i in range(len(UpperCamelCase_) - 1):
										if i not in passed_idxs:
															a											=   next(UpperCamelCase_)
															if (line not in full_file_func) and (line.lstrip() != _dl_line):
																				if "TESTING_MOCKED_DATALOADERS" not in line:
																									new_feature_code.append(UpperCamelCase_)
																									passed_idxs.append(UpperCamelCase_)
																				else:
																									# Skip over the `config['num_epochs'] = 2` statement
																									a											=   next(UpperCamelCase_)

    # Extract out just the new parts from the full_file_training_func
					a											=   []
					a											=   []  # We keep track of the idxs just in case it's a repeated statement
					for i, line in enumerate(UpperCamelCase_):
										if i not in passed_idxs:
															if (line not in full_file_func) and (line.lstrip() != _dl_line):
																				if "TESTING_MOCKED_DATALOADERS" not in line:
																									new_full_example_parts.append(UpperCamelCase_)
																									passed_idxs.append(UpperCamelCase_)

    # Finally, get the overall diff
					a											=   [line for line in new_feature_code if line not in new_full_example_parts]
					if secondary_filename is not None:
										a											=   [line for line in full_file_contents if line not in secondary_file_func]
										a											=   [line for line in diff_from_example if line not in diff_from_two]

					return diff_from_example







from sklearn.metrics import mean_absolute_error

import datasets


lowercase__   :    int 					=      "\\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"

lowercase__   :    str 					=      "\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n"


lowercase__   :    Tuple 					=      "\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     a__ (      datasets.Metric   ):







     def     lowerCAmelCase_       (			self				)						->					Dict:



          '''simple docstring'''



          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     lowerCAmelCase_       (			self				)						->					Tuple:



          '''simple docstring'''



          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     lowerCAmelCase_       (			self						,	A						,	A						,	A=None						,	A="uniform_average"				)						->					List[Any]:



          '''simple docstring'''



          a											=   mean_absolute_error(A						,	A						,	sample_weight=A						,	multioutput=A				)

          return {"mae": mae_score}

