
from sklearn.metrics import mean_absolute_error

import datasets


lowercase__							:							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"

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


lowercase__							:							Dict													=     "\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     SCREAMING_SNAKE_CASE__      (							datasets.Metric      ):

							"""simple docstring"""






							def       A__						(					self     )->				Optional[Any]:





													'''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       A__						(					self     )->				int:





													'''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       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=None       ,		SCREAMING_SNAKE_CASE_="uniform_average"     )->				Any:





													'''simple docstring'''






													__UpperCamelCase       					=     mean_absolute_error(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		sample_weight=SCREAMING_SNAKE_CASE_       ,		multioutput=SCREAMING_SNAKE_CASE_     )

													return {"mae": mae_score}






import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available




def 		A_	(							snake_case							:				Union[str, Any]	, snake_case							:				str	, snake_case							:				Dict	, snake_case							:				Optional[Any]=None	) ->      Optional[Any]:



						'''simple docstring'''

						__UpperCamelCase       					=     None
						# Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
						if str(weight.dtype	) == "torch.bfloat16":
												# Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
												__UpperCamelCase       					=     weight.view(torch.intaa	)
												__UpperCamelCase       					=     '''bfloat16'''
						__UpperCamelCase       					=     weight.cpu().numpy()
						__UpperCamelCase       					=     os.path.join(snake_case	, f"{weight_name}.dat"	)
						if index is not None:
												if dtype is None:
																		__UpperCamelCase       					=     str(array.dtype	)
												__UpperCamelCase       					=     {'''dtype''': dtype, '''shape''': list(array.shape	)}
						if array.ndim == 0:
												__UpperCamelCase       					=     array[None]
						__UpperCamelCase       					=     np.memmap(snake_case	, dtype=array.dtype	, mode='''w+'''	, shape=array.shape	)
						__UpperCamelCase       					=     array[:]
						file_array.flush()
						return index




def 		A_	(							snake_case							:				Tuple	, snake_case							:				Union[str, Any]	) ->      Dict:



						'''simple docstring'''

						__UpperCamelCase       					=     tuple(weight_info['''shape''']	)
						if shape == ():
												# NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
												__UpperCamelCase       					=     (1,)

						__UpperCamelCase       					=     weight_info['''dtype''']
						if dtype == "bfloat16":
												# NumPy does not support bfloat16 so this was saved as a int16
												__UpperCamelCase       					=     '''int16'''

						__UpperCamelCase       					=     np.memmap(snake_case	, dtype=snake_case	, shape=snake_case	, mode='''r'''	)

						if len(weight_info['''shape''']	) == 0:
												__UpperCamelCase       					=     weight[0]
						__UpperCamelCase       					=     torch.tensor(snake_case	)
						if weight_info["dtype"] == "bfloat16":
												__UpperCamelCase       					=     weight.view(torch.bfloataa	)

						return weight




def 		A_	(							snake_case							:				Dict	, snake_case							:				int	) ->      str:



						'''simple docstring'''

						if index is None or len(snake_case	) == 0:
												# Nothing to save
												return

						__UpperCamelCase       					=     os.path.join(snake_case	, '''index.json'''	)
						if os.path.isfile(snake_case	):
												with open(snake_case	, '''r'''	, encoding='''utf-8'''	) as f:
																		__UpperCamelCase       					=     json.load(snake_case	)
						else:
												__UpperCamelCase       					=     {}
						current_index.update(snake_case	)

						with open(snake_case	, '''w'''	, encoding='''utf-8'''	) as f:
												json.dump(snake_case	, snake_case	, indent=2	)




def 		A_	(							snake_case							:				Union[str, os.PathLike]	, snake_case							:				Dict[str, torch.Tensor]	) ->      Dict:



						'''simple docstring'''

						os.makedirs(snake_case	, exist_ok=snake_case	)
						__UpperCamelCase       					=     {}
						for name, parameter in state_dict.items():
												__UpperCamelCase       					=     offload_weight(snake_case	, snake_case	, snake_case	, index=snake_case	)

						# Update index
						save_offload_index(snake_case	, snake_case	)






class     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							def __init__(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )->				str:





													'''simple docstring'''






													__UpperCamelCase       					=     dataset
													__UpperCamelCase       					=     prefix



							def __getitem__(					self       ,		SCREAMING_SNAKE_CASE_     )->				Union[str, Any]:





													'''simple docstring'''






													return self.dataset[F"{self.prefix}{key}"]



							def __iter__(					self     )->				Union[str, Any]:





													'''simple docstring'''






													return iter([key for key in self.dataset if key.startswith(self.prefix     )]     )



							def __len__(					self     )->				str:





													'''simple docstring'''






													return len(self.dataset     )






class     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							def __init__(					self       ,		SCREAMING_SNAKE_CASE_ = None       ,		SCREAMING_SNAKE_CASE_ = None       ,		SCREAMING_SNAKE_CASE_ = None       ,		SCREAMING_SNAKE_CASE_=None       ,		)->				Tuple:





													'''simple docstring'''






													if state_dict is None and save_folder is None:
																			raise ValueError('''Need either a `state_dict` or a `save_folder` containing offloaded weights.'''     )

													__UpperCamelCase       					=     {} if state_dict is None else state_dict
													__UpperCamelCase       					=     save_folder
													if index is None and save_folder is not None:
																			with open(os.path.join(SCREAMING_SNAKE_CASE_       ,		'''index.json'''     )     ) as f:
																									__UpperCamelCase       					=     json.load(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     {} if index is None else index
													__UpperCamelCase       					=     list(self.state_dict.keys()     )
													self.all_keys.extend([key for key in self.index if key not in self.all_keys]     )
													__UpperCamelCase       					=     device



							def __getitem__(					self       ,		SCREAMING_SNAKE_CASE_     )->				Tuple:





													'''simple docstring'''






													if key in self.state_dict:
																			return self.state_dict[key]
													__UpperCamelCase       					=     self.index[key]
													if weight_info.get('''safetensors_file'''     ) is not None:
																			if not is_safetensors_available():
																									raise ImportError('''These offloaded weights require the use of safetensors: `pip install safetensors`.'''     )

																			from safetensors import safe_open

																			__UpperCamelCase       					=     '''cpu''' if self.device is None else self.device
																			with safe_open(weight_info['''safetensors_file''']       ,		framework='''pt'''       ,		device=SCREAMING_SNAKE_CASE_     ) as f:
																									__UpperCamelCase       					=     f.get_tensor(weight_info.get('''weight_name'''       ,		SCREAMING_SNAKE_CASE_     )     )

																			if "dtype" in weight_info:
																									return tensor.to(getattr(SCREAMING_SNAKE_CASE_       ,		weight_info['''dtype''']     )     )
																			else:
																									return tensor

													__UpperCamelCase       					=     os.path.join(self.save_folder       ,		F"{key}.dat"     )
													return load_offloaded_weight(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )



							def __iter__(					self     )->				Dict:





													'''simple docstring'''






													return iter(self.all_keys     )



							def __len__(					self     )->				Dict:





													'''simple docstring'''






													return len(self.all_keys     )





def 		A_	(							snake_case							:				Dict[str, torch.Tensor]	, snake_case							:				List[str]	) ->      Tuple:



						'''simple docstring'''

						__UpperCamelCase       					=     {}
						for module_name in submodule_names:
												# We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
												# submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
												result.update(
												    {
												        key: param
												        for key, param in state_dict.items()
												        if key == module_name or key.startswith(module_name + '''.'''	)
												    }	)
						return result




