





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

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

class  __A:

     def __init__(      self,       A      ):





            """simple docstring"""

            if isinstance(A,       A      ):
                   # 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
                   _UpperCamelCase              =						deepcopy(A      )
            elif os.path.exists(A      ):
                   with io.open(A,       '''r''',       encoding='''utf-8'''      ) as f:
                          _UpperCamelCase              =						json.load(A      )
            else:
                   try:
                          _UpperCamelCase              =						baseaa.urlsafe_baadecode(A      ).decode('''utf-8'''      )
                          _UpperCamelCase              =						json.loads(A      )
                   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}'''      )

            _UpperCamelCase              =						config

            self.set_stage_and_offload()

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_value('''zero_optimization.stage''',       -1      )

            # offload
            _UpperCamelCase              =						False
            if self.is_zeroa() or self.is_zeroa():
                   _UpperCamelCase              =						set(['''cpu''', '''nvme''']      )
                   _UpperCamelCase              =						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:
                          _UpperCamelCase              =						True

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						self.config

            # find the config node of interest if it exists
            _UpperCamelCase              =						ds_key_long.split('''.'''      )
            _UpperCamelCase              =						nodes.pop()
            for node in nodes:
                   _UpperCamelCase              =						config.get(A      )
                   if config is None:
                          return None, ds_key

            return config, ds_key

     def 	_UpperCamelCase     (      self,       A,       A=None      ):





            """simple docstring"""

            _UpperCamelCase       ,		_UpperCamelCase              =						self.find_config_node(A      )
            if config is None:
                   return default
            return config.get(A,       A      )

     def 	_UpperCamelCase     (      self,       A,       A=False      ):





            """simple docstring"""

            _UpperCamelCase              =						self.config

            # find the config node of interest if it exists
            _UpperCamelCase              =						ds_key_long.split('''.'''      )
            for node in nodes:
                   _UpperCamelCase              =						config
                   _UpperCamelCase              =						config.get(A      )
                   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(A      )

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_value(A      )
            return False if value is None else bool(A      )

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_value(A      )
            return False if value is None else not bool(A      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return self._stage == 2

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return self._stage == 3



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return self._offload






class  __A:

     def __init__(      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						engine



     def 	_UpperCamelCase     (      self,       A,       **A      ):





            """simple docstring"""

            self.engine.backward(A,       **A      )

            # 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  __A(						__UpperCAmelCase ):

     def __init__(      self,       A      ):





            """simple docstring"""

            super().__init__(A,       device_placement=A,       scaler=A      )
            _UpperCamelCase              =						hasattr(self.optimizer,       '''overflow'''      )

     def 	_UpperCamelCase     (      self,       A=None      ):





            """simple docstring"""

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

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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



     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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






class  __A(						__UpperCAmelCase ):

     def __init__(      self,       A,       A      ):





            """simple docstring"""

            super().__init__(A,       A      )



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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






class  __A:

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





            """simple docstring"""

            _UpperCamelCase              =						params
            _UpperCamelCase              =						lr
            _UpperCamelCase              =						weight_decay
            _UpperCamelCase              =						kwargs






class  __A:

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





            """simple docstring"""

            _UpperCamelCase              =						optimizer
            _UpperCamelCase              =						total_num_steps
            _UpperCamelCase              =						warmup_num_steps
            _UpperCamelCase              =						kwargs













from math import factorial


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase = 20					):
       _UpperCamelCase              =						2 * n  # middle entry of odd rows starting at row 3 is the solution for n = 1,
       # 2, 3,...
       _UpperCamelCase              =						n // 2

       return int(factorial(lowerCAmelCase					) / (factorial(lowerCAmelCase					) * factorial(n - k					))					)


if __name__ == "__main__":
       import sys

       if len(sys.argv) == 1:
              print(solution(20))
       else:
              try:
                     lowercase    : Dict    	=  int(sys.argv[1])
                     print(solution(n))
              except ValueError:
                     print("""Invalid entry - please enter a number.""")






