






import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler







class 	_UpperCAmelCase  :




      """simple docstring"""



      def __init__(   self    ,	_lowerCAmelCase		):



          '''simple docstring'''






          if isinstance(__UpperCamelCase    ,	__UpperCamelCase		):
              # Don't modify user's data should they want to reuse it (e.g. in tests), because once we
              # modified it, it will not be accepted here again, since `auto` values would have been overridden
              lowerCAmelCase__   :str      							=				deepcopy(__UpperCamelCase		)
          elif os.path.exists(__UpperCamelCase		):
              with io.open(__UpperCamelCase    ,	"r"    ,	encoding="utf-8"		) as f:
                  lowerCAmelCase__   :Any      							=				json.load(__UpperCamelCase		)
          else:
              try:
                  lowerCAmelCase__   :Union[str, Any]      							=				baseaa.urlsafe_baadecode(__UpperCamelCase		).decode("utf-8"		)
                  lowerCAmelCase__   :int      							=				json.loads(__UpperCamelCase		)
              except (UnicodeDecodeError, AttributeError, ValueError):
                  raise ValueError(
                      f'''Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}'''		)

          lowerCAmelCase__   :Optional[int]      							=				config

          self.set_stage_and_offload()
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Tuple      							=				self.get_value("zero_optimization.stage"    ,	-1		)

          # offload
          lowerCAmelCase__   :Dict      							=				False
          if self.is_zeroa() or self.is_zeroa():
              lowerCAmelCase__   :Any      							=				set(["cpu", "nvme"]		)
              lowerCAmelCase__   :Optional[Any]      							=				set(
                  [
                      self.get_value("zero_optimization.offload_optimizer.device"		),
                      self.get_value("zero_optimization.offload_param.device"		),
                  ]		)
              if len(offload_devices & offload_devices_valid		) > 0:
                  lowerCAmelCase__   :Tuple      							=				True
      def 			snake_case_      (   self    ,	_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :Any      							=				self.config

          # find the config node of interest if it exists
          lowerCAmelCase__   :List[Any]      							=				ds_key_long.split("."		)
          lowerCAmelCase__   :Optional[Any]      							=				nodes.pop()
          for node in nodes:
              lowerCAmelCase__   :Optional[Any]      							=				config.get(__UpperCamelCase		)
              if config is None:
                  return None, ds_key

          return config, ds_key
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase=None		):



          '''simple docstring'''
          lowerCAmelCase__							,lowerCAmelCase__   :Optional[Any]      							=				self.find_config_node(__UpperCamelCase		)
          if config is None:
              return default
          return config.get(__UpperCamelCase    ,	__UpperCamelCase		)
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase=False		):



          '''simple docstring'''
          lowerCAmelCase__   :List[str]      							=				self.config

          # find the config node of interest if it exists
          lowerCAmelCase__   :List[Any]      							=				ds_key_long.split("."		)
          for node in nodes:
              lowerCAmelCase__   :int      							=				config
              lowerCAmelCase__   :Tuple      							=				config.get(__UpperCamelCase		)
              if config is None:
                  if must_exist:
                      raise ValueError(f'''Can\'t find {ds_key_long} entry in the config: {self.config}'''		)
                  else:
                      return

        # if found remove it
          if parent_config is not None:
              parent_config.pop(__UpperCamelCase		)
      def 			snake_case_      (   self    ,	_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :Dict      							=				self.get_value(__UpperCamelCase		)
          return False if value is None else bool(__UpperCamelCase		)
      def 			snake_case_      (   self    ,	_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :Any      							=				self.get_value(__UpperCamelCase		)
          return False if value is None else not bool(__UpperCamelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return self._stage == 2
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return self._stage == 3



      def 			snake_case_      (   self		):



          '''simple docstring'''
          return self._offload







class 	_UpperCAmelCase  :




      """simple docstring"""



      def __init__(   self    ,	_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :Union[str, Any]      							=				engine



      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	**_lowerCAmelCase		):



          '''simple docstring'''
          self.engine.backward(__UpperCamelCase    ,	**__UpperCamelCase		)

          # Deepspeed's `engine.step` performs the following operations:
          # - gradient accumulation check
          # - gradient clipping
          # - optimizer step
          # - zero grad
          # - checking overflow
          # - lr_scheduler step (only if engine.lr_scheduler is not None)
          self.engine.step()
          # and this plugin overrides the above calls with no-ops when Accelerate runs under
          # Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
          # training loop that works transparently under many training regimes.







class 	_UpperCAmelCase  (  _UpperCAmelCase		):




      """simple docstring"""



      def __init__(   self    ,	_lowerCAmelCase		):



          '''simple docstring'''
          super().__init__(__UpperCamelCase    ,	device_placement=__UpperCamelCase    ,	scaler=__UpperCamelCase		)
          lowerCAmelCase__   :Dict      							=				hasattr(self.optimizer    ,	"overflow"		)
      def 			snake_case_      (   self    ,	_lowerCAmelCase=None		):



          '''simple docstring'''
          pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
      def 			snake_case_      (   self		):



          '''simple docstring'''
          pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed



      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''






          if self.__has_overflow__:
              return self.optimizer.overflow
          return False







class 	_UpperCAmelCase  (  _UpperCAmelCase		):




      """simple docstring"""



      def __init__(   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase		):



          '''simple docstring'''
          super().__init__(__UpperCamelCase    ,	__UpperCamelCase		)



      def 			snake_case_      (   self		):



          '''simple docstring'''
          pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







class 	_UpperCAmelCase  :




      """simple docstring"""



      def __init__(   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase=0.001    ,	_lowerCAmelCase=0    ,	**_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :Any      							=				params
          lowerCAmelCase__   :Dict      							=				lr
          lowerCAmelCase__   :int      							=				weight_decay
          lowerCAmelCase__   :int      							=				kwargs







class 	_UpperCAmelCase  :




      """simple docstring"""



      def __init__(   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase=None    ,	_lowerCAmelCase=0    ,	**_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :Tuple      							=				optimizer
          lowerCAmelCase__   :Optional[Any]      							=				total_num_steps
          lowerCAmelCase__   :int      							=				warmup_num_steps
          lowerCAmelCase__   :str      							=				kwargs











from __future__ import annotations

import numpy as np


def 				snake_case__							(							UpperCAmelCase							:  np.ndarray       ):
    lowerCAmelCase__							,lowerCAmelCase__   :List[str]      							=				np.shape(UpperCAmelCase       )
    if rows != columns:
        lowerCAmelCase__   :Tuple      							=				(
            "'table' has to be of square shaped array but got a "
            F'''{rows}x{columns} array:\n{table}'''
        )
        raise ValueError(UpperCAmelCase       )

    lowerCAmelCase__   :Optional[Any]      							=				np.zeros((rows, columns)       )
    lowerCAmelCase__   :Tuple      							=				np.zeros((rows, columns)       )
    for i in range(UpperCAmelCase       ):
        for j in range(UpperCAmelCase       ):
            lowerCAmelCase__   :Any      							=				sum(lower[i][k] * upper[k][j] for k in range(UpperCAmelCase       )       )
            if upper[j][j] == 0:
                raise ArithmeticError("No LU decomposition exists"       )
            lowerCAmelCase__   :Any      							=				(table[i][j] - total) / upper[j][j]
        lowerCAmelCase__   :Tuple      							=				1
        for j in range(UpperCAmelCase    ,			UpperCAmelCase       ):
            lowerCAmelCase__   :Optional[Any]      							=				sum(lower[i][k] * upper[k][j] for k in range(UpperCAmelCase       )       )
            lowerCAmelCase__   :Dict      							=				table[i][j] - total
    return lower, upper


if __name__ == "__main__":
 import doctest

 doctest.testmod()



