




'''simple docstring'''





from sklearn.metrics import mean_absolute_error

import datasets


a		:	Union[str, Any]       =      '''\
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
'''

a		:	List[str]       =      '''\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
'''


a		:	Optional[Any]       =      '''
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

                 "raw_values" : Returns a full set of errors in case of multioutput input.

                 "uniform_average" : Errors of all outputs are averaged with uniform weight.

Returns:
    mae : mean absolute error.
        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.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric("mae")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mae\': 0.5}

    If you\'re using multi-dimensional lists, then set the config as follows :

    >>> mae_metric = datasets.load_metric("mae", "multilist")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mae\': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
    >>> print(results)
    {\'mae\': array([0.5, 1. ])}
'''




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



  def     a_							(			self ):







      '''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 ):







      '''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		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase=None		,     __lowerCAmelCase="uniform_average" ):







      '''simple docstring'''


      _a				:							int							       =      mean_absolute_error(__lowerCAmelCase		,     __lowerCAmelCase		,     sample_weight=__lowerCAmelCase		,     multioutput=__lowerCAmelCase )

      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   _lowerCAmelCase(						__A ,							__A ,							__A ,							__A=None      ):
  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(__A ,							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(__A ,							dtype=array.dtype ,							mode="w+" ,							shape=array.shape      )
  UpperCAmelCase          =	array[:]
  file_array.flush()
  return index
def   _lowerCAmelCase(						__A ,							__A      ):
  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(__A ,							dtype=__A ,							shape=__A ,							mode="r"      )

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

  return weight
def   _lowerCAmelCase(						__A ,							__A      ):
  if index is None or len(__A      ) == 0:
    # Nothing to save
    return

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

  with open(__A ,							"w" ,							encoding="utf-8"      ) as f:
    json.dump(__A ,							__A ,							indent=2      )
def   _lowerCAmelCase(						__A ,							__A      ):
  os.makedirs(__A ,							exist_ok=__A      )
  UpperCAmelCase          =	{}
  for name, parameter in state_dict.items():
    UpperCAmelCase          =	offload_weight(__A ,							__A ,							__A ,							index=__A      )

  # Update index
  save_offload_index(__A ,							__A      )






class     __magic_name__     (							_snake_case     ):


 def __init__(    self		:			Tuple      ,    lowerCAmelCase__		:			Mapping      ,    lowerCAmelCase__		:			str     )     ->   Tuple:
   UpperCAmelCase          =	dataset
   UpperCAmelCase          =	prefix


 def __getitem__(    self		:			Dict      ,    lowerCAmelCase__		:			Any     )     ->   Optional[int]:
   return self.dataset[f"{self.prefix}{key}"]


 def __iter__(    self		:			Optional[int]     )     ->   Any:
   return iter([key for key in self.dataset if key.startswith(self.prefix     )]     )


 def __len__(    self		:			Union[str, Any]     )     ->   int:
   return len(self.dataset     )






class     __magic_name__     (							_snake_case     ):


 def __init__(    self		:			Optional[int]      ,    lowerCAmelCase__		:			Dict[str, torch.Tensor] = None      ,    lowerCAmelCase__		:			Optional[Union[str, os.PathLike]] = None      ,    lowerCAmelCase__		:			Mapping = None      ,    lowerCAmelCase__		:			Dict=None      ,    )     ->   Dict:
   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(lowerCAmelCase__      ,    "index.json"     )     ) as f:
       UpperCAmelCase          =	json.load(lowerCAmelCase__     )
   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		:			str      ,    lowerCAmelCase__		:			str     )     ->   Dict:
   # State dict gets priority
   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=lowerCAmelCase__     ) as f:
       UpperCAmelCase          =	f.get_tensor(weight_info.get("weight_name"      ,    lowerCAmelCase__     )     )

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

   UpperCAmelCase          =	os.path.join(self.save_folder      ,    f"{key}.dat"     )
   return load_offloaded_weight(lowerCAmelCase__      ,    lowerCAmelCase__     )


 def __iter__(    self		:			str     )     ->   Dict:
   return iter(self.all_keys     )


 def __len__(    self		:			Optional[Any]     )     ->   List[Any]:
   return len(self.all_keys     )

def   _lowerCAmelCase(						__A ,							__A      ):
  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



