


'''simple docstring'''

import json
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import yaml

from ...utils import ComputeEnvironment, DistributedType, SageMakerDistributedType
from ...utils.constants import SAGEMAKER_PYTHON_VERSION, SAGEMAKER_PYTORCH_VERSION, SAGEMAKER_TRANSFORMERS_VERSION


a_  :    Dict      =			os.path.expanduser(
    os.getenv("""HF_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """huggingface"""))
)
a_  :    Dict      =			os.path.join(hf_cache_home, """accelerate""")
a_  :    Optional[int]      =			os.path.join(cache_dir, """default_config.yaml""")
a_  :    int      =			os.path.join(cache_dir, """default_config.yaml""")

# For backward compatibility: the default config is the json one if it's the only existing file.
if os.path.isfile(default_yaml_config_file) or not os.path.isfile(default_json_config_file):
    a_  :    str      =			default_yaml_config_file
else:
    a_  :    str      =			default_json_config_file





def __snake_case (				UpperCAmelCase_						:						Optional[Any]		):
 if config_file is not None:
  if not os.path.isfile(UpperCAmelCase_		):
   raise FileNotFoundError(
       F'''The passed configuration file `{config_file}` does not exist. '''
       "Please pass an existing file to `accelerate launch`, or use the the default one "
       "created through `accelerate config` and run `accelerate launch` "
       "without the `--config_file` argument."		)
 else:
  lowerCamelCase_								=  default_config_file
 with open(UpperCAmelCase_ ,    "r" ,    encoding="utf-8"		) as f:
  if config_file.endswith(".json"		):
   if (
       json.load(UpperCAmelCase_		).get("compute_environment" ,    ComputeEnvironment.LOCAL_MACHINE		)
       == ComputeEnvironment.LOCAL_MACHINE
   ):
    lowerCamelCase_								=  ClusterConfig
   else:
    lowerCamelCase_								=  SageMakerConfig
   return config_class.from_json_file(json_file=UpperCAmelCase_		)
  else:
   if (
       yaml.safe_load(UpperCAmelCase_		).get("compute_environment" ,    ComputeEnvironment.LOCAL_MACHINE		)
       == ComputeEnvironment.LOCAL_MACHINE
   ):
    lowerCamelCase_								=  ClusterConfig
   else:
    lowerCamelCase_								=  SageMakerConfig
   return config_class.from_yaml_file(yaml_file=UpperCAmelCase_		)





@dataclass
class 					snake_case   :





  """simple docstring"""




  _lowerCamelCase		   =  42
  _lowerCamelCase		   =  42
  _lowerCamelCase		   =  42
  _lowerCamelCase		   =  42






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  self.__dict__
   # For serialization, it's best to convert Enums to strings (or their underlying value type).
   for key, value in result.items():
    if isinstance(UpperCamelCase      ,  UpperCamelCase					):
     lowerCamelCase_								=  value.value
    if isinstance(UpperCamelCase      ,  UpperCamelCase					) and not bool(UpperCamelCase					):
     lowerCamelCase_								=  None
   lowerCamelCase_								=  {k: v for k, v in result.items() if v is not None}
   return result






  @classmethod
  def   snake_case		(   cls      ,  UpperCamelCase=None					):







   """simple docstring"""
   lowerCamelCase_								=  default_json_config_file if json_file is None else json_file
   with open(UpperCamelCase      ,  "r"      ,  encoding="utf-8"					) as f:
    lowerCamelCase_								=  json.load(UpperCamelCase					)
   if "compute_environment" not in config_dict:
    lowerCamelCase_								=  ComputeEnvironment.LOCAL_MACHINE
   if "mixed_precision" not in config_dict:
    lowerCamelCase_								=  "fp16" if ("fp16" in config_dict and config_dict["fp16"]) else None
   if "fp16" in config_dict:  # Convert the config to the new format.
    del config_dict["fp16"]
   if "dynamo_backend" in config_dict:  # Convert the config to the new format.
    lowerCamelCase_								=  config_dict.pop("dynamo_backend"					)
    lowerCamelCase_								=  {} if dynamo_backend == "NO" else {"dynamo_backend": dynamo_backend}
   if "use_cpu" not in config_dict:
    lowerCamelCase_								=  False
   return cls(**UpperCamelCase					)






  def   snake_case		(   self      ,  UpperCamelCase					):







   """simple docstring"""
   with open(UpperCamelCase      ,  "w"      ,  encoding="utf-8"					) as f:
    lowerCamelCase_								=  json.dumps(self.to_dict()      ,  indent=2      ,  sort_keys=UpperCamelCase					) + "\n"
    f.write(UpperCamelCase					)






  @classmethod
  def   snake_case		(   cls      ,  UpperCamelCase=None					):







   """simple docstring"""
   lowerCamelCase_								=  default_yaml_config_file if yaml_file is None else yaml_file
   with open(UpperCamelCase      ,  "r"      ,  encoding="utf-8"					) as f:
    lowerCamelCase_								=  yaml.safe_load(UpperCamelCase					)
   if "compute_environment" not in config_dict:
    lowerCamelCase_								=  ComputeEnvironment.LOCAL_MACHINE

   if "mixed_precision" not in config_dict:
    lowerCamelCase_								=  "fp16" if ("fp16" in config_dict and config_dict["fp16"]) else None
   if isinstance(config_dict["mixed_precision"]      ,  UpperCamelCase					) and not config_dict["mixed_precision"]:
    lowerCamelCase_								=  "no"
   if "fp16" in config_dict:  # Convert the config to the new format.
    del config_dict["fp16"]
   if "dynamo_backend" in config_dict:  # Convert the config to the new format.
    lowerCamelCase_								=  config_dict.pop("dynamo_backend"					)
    lowerCamelCase_								=  {} if dynamo_backend == "NO" else {"dynamo_backend": dynamo_backend}
   if "use_cpu" not in config_dict:
    lowerCamelCase_								=  False
   return cls(**UpperCamelCase					)






  def   snake_case		(   self      ,  UpperCamelCase					):







   """simple docstring"""
   with open(UpperCamelCase      ,  "w"      ,  encoding="utf-8"					) as f:
    yaml.safe_dump(self.to_dict()      ,  UpperCamelCase					)







  def   snake_case		(   self					):







   """simple docstring"""
   if isinstance(self.compute_environment      ,  UpperCamelCase					):
    lowerCamelCase_								=  ComputeEnvironment(self.compute_environment					)
   if isinstance(self.distributed_type      ,  UpperCamelCase					):
    if self.compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
     lowerCamelCase_								=  SageMakerDistributedType(self.distributed_type					)
    else:
     lowerCamelCase_								=  DistributedType(self.distributed_type					)
   if self.dynamo_config is None:
    lowerCamelCase_								=  {}





@dataclass
class 					snake_case   (   lowercase      ):





  """simple docstring"""




  _lowerCamelCase		   =  42
  _lowerCamelCase		   =  0
  _lowerCamelCase		   =  1
  _lowerCamelCase		   =  None
  _lowerCamelCase		   =  None
  _lowerCamelCase		   =  None
  _lowerCamelCase		   =  "static"
  _lowerCamelCase		   =  False
  _lowerCamelCase		   =  "main"

  # args for deepspeed_plugin
  _lowerCamelCase		   =  None
  # args for fsdp
  _lowerCamelCase		   =  None
  # args for megatron_lm
  _lowerCamelCase		   =  None
  # args for ipex
  _lowerCamelCase		   =  None
  # args for TPU
  _lowerCamelCase		   =  False

  # args for TPU pods
  _lowerCamelCase		   =  None
  _lowerCamelCase		   =  None
  _lowerCamelCase		   =  False
  _lowerCamelCase		   =  False
  _lowerCamelCase		   =  None
  _lowerCamelCase		   =  None
  _lowerCamelCase		   =  None
  _lowerCamelCase		   =  None

  # args for dynamo
  _lowerCamelCase		   =  None






  def   snake_case		(   self					):







   """simple docstring"""
   if self.deepspeed_config is None:
    lowerCamelCase_								=  {}
   if self.fsdp_config is None:
    lowerCamelCase_								=  {}
   if self.megatron_lm_config is None:
    lowerCamelCase_								=  {}
   if self.ipex_config is None:
    lowerCamelCase_								=  {}
   return super().__post_init__()





@dataclass
class 					snake_case   (   lowercase      ):





  """simple docstring"""




  _lowerCamelCase		   =  42
  _lowerCamelCase		   =  42
  _lowerCamelCase		   =  None
  _lowerCamelCase		   =  None
  _lowerCamelCase		   =  "us-east-1"
  _lowerCamelCase		   =  1
  _lowerCamelCase		   =  "all"
  _lowerCamelCase		   =  F"accelerate-sagemaker-{num_machines}"
  _lowerCamelCase		   =  SAGEMAKER_PYTORCH_VERSION
  _lowerCamelCase		   =  SAGEMAKER_TRANSFORMERS_VERSION
  _lowerCamelCase		   =  SAGEMAKER_PYTHON_VERSION
  _lowerCamelCase		   =  None
  _lowerCamelCase		   =  None
  _lowerCamelCase		   =  None
  _lowerCamelCase		   =  None







'''simple docstring'''

import numpy as np





class 					snake_case   :





  """simple docstring"""




  def __init__(   self      ,  UpperCamelCase=5      ,  UpperCamelCase=5					):







   """simple docstring"""
   lowerCamelCase_								=  depth
   lowerCamelCase_								=  0
   lowerCamelCase_								=  None
   lowerCamelCase_								=  None
   lowerCamelCase_								=  min_leaf_size
   lowerCamelCase_								=  None






  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase					):







   """simple docstring"""
   if labels.ndim != 1:
    print("Error: Input labels must be one dimensional"					)

   return np.mean((labels - prediction) ** 2					)






  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase					):







   """simple docstring"""
   if x.ndim != 1:
    print("Error: Input data set must be one dimensional"					)
    return
   if len(UpperCamelCase					) != len(UpperCamelCase					):
    print("Error: X and y have different lengths"					)
    return
   if y.ndim != 1:
    print("Error: Data set labels must be one dimensional"					)
    return

   if len(UpperCamelCase					) < 2 * self.min_leaf_size:
    lowerCamelCase_								=  np.mean(UpperCamelCase					)
    return

   if self.depth == 1:
    lowerCamelCase_								=  np.mean(UpperCamelCase					)
    return

   lowerCamelCase_								=  0
   lowerCamelCase_								=  self.mean_squared_error(UpperCamelCase      ,  np.mean(UpperCamelCase					)					) * 2
   for i in range(len(UpperCamelCase					)					):
    if len(x[:i]					) < self.min_leaf_size:
     continue
    elif len(x[i:]					) < self.min_leaf_size:
     continue
    else:
     lowerCamelCase_								=  self.mean_squared_error(x[:i]      ,  np.mean(y[:i]					)					)
     lowerCamelCase_								=  self.mean_squared_error(x[i:]      ,  np.mean(y[i:]					)					)
     lowerCamelCase_								=  error_left + error_right
     if error < min_error:
      lowerCamelCase_								=  i
      lowerCamelCase_								=  error

   if best_split != 0:
    lowerCamelCase_								=  x[:best_split]
    lowerCamelCase_								=  y[:best_split]
    lowerCamelCase_								=  x[best_split:]
    lowerCamelCase_								=  y[best_split:]

    lowerCamelCase_								=  x[best_split]
    lowerCamelCase_								=  DecisionTree(
        depth=self.depth - 1      ,  min_leaf_size=self.min_leaf_size					)
    lowerCamelCase_								=  DecisionTree(
        depth=self.depth - 1      ,  min_leaf_size=self.min_leaf_size					)
    self.left.train(UpperCamelCase      ,  UpperCamelCase					)
    self.right.train(UpperCamelCase      ,  UpperCamelCase					)
   else:
    lowerCamelCase_								=  np.mean(UpperCamelCase					)

   return







  def   snake_case		(   self      ,  UpperCamelCase					):







   """simple docstring"""
   if self.prediction is not None:
    return self.prediction
   elif self.left or self.right is not None:
    if x >= self.decision_boundary:
     return self.right.predict(UpperCamelCase					)
    else:
     return self.left.predict(UpperCamelCase					)
   else:
    print("Error: Decision tree not yet trained"					)
    return None





class 					snake_case   :





  """simple docstring"""




  @staticmethod
  def   snake_case		(   UpperCamelCase      ,  UpperCamelCase					):







   """simple docstring"""
   lowerCamelCase_								=  float(0					)
   for label in labels:
    squared_error_sum += (label - prediction) ** 2

   return float(squared_error_sum / labels.size					)





def __snake_case (				):
 lowerCamelCase_								=  np.arange(-1.0 ,    1.0 ,    0.005		)
 lowerCamelCase_								=  np.sin(UpperCAmelCase_		)

 lowerCamelCase_								=  DecisionTree(depth=10 ,    min_leaf_size=10		)
 tree.train(UpperCAmelCase_ ,    UpperCAmelCase_		)

 lowerCamelCase_								=  (np.random.rand(10		) * 2) - 1
 lowerCamelCase_								=  np.array([tree.predict(UpperCAmelCase_		) for x in test_cases]		)
 lowerCamelCase_								=  np.mean((predictions - test_cases) ** 2		)

 print("Test values: " + str(UpperCAmelCase_		)		)
 print("Predictions: " + str(UpperCAmelCase_		)		)
 print("Average error: " + str(UpperCAmelCase_		)		)


if __name__ == "__main__":
     main()
     import doctest

     doctest.testmod(name="""mean_squarred_error""", verbose=True)



