

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

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





class 	snake_case_   :







			'''simple docstring'''

			def __init__(     self :				List[Any]							,   _UpperCamelCase :				Dict							)      ->Any:
									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
															snake_case_   =	deepcopy(_UpperCamelCase							)
									elif os.path.exists(_UpperCamelCase							):
															with io.open(_UpperCamelCase							,   '''r'''							,   encoding='''utf-8'''							) as f:
																					snake_case_   =	json.load(_UpperCamelCase							)
									else:
															try:
																					snake_case_   =	baseaa.urlsafe_baadecode(_UpperCamelCase							).decode('''utf-8'''							)
																					snake_case_   =	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}'''							)

									snake_case_   =	config

									self.set_stage_and_offload()







			def 				snake_case__(     self :				Optional[int]							)      ->Optional[Any]:
									# zero stage - this is done as early as possible, before model is created, to allow
									# ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
									# during ``zero.Init()`` which needs to know the dtype, and some other hparams.
									snake_case_   =	self.get_value('''zero_optimization.stage'''							,   -1							)

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







			def 				snake_case__(     self :				int							,   _UpperCamelCase :				List[Any]							)      ->List[Any]:
									snake_case_   =	self.config

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

									return config, ds_key







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				List[str]							,   _UpperCamelCase :				List[Any]=None							)      ->List[Any]:
									snake_case_,    snake_case_   =	self.find_config_node(_UpperCamelCase							)
									if config is None:
															return default
									return config.get(_UpperCamelCase							,   _UpperCamelCase							)







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				Tuple							,   _UpperCamelCase :				Tuple=False							)      ->List[Any]:
									snake_case_   =	self.config

									# find the config node of interest if it exists
									snake_case_   =	ds_key_long.split('''.'''							)
									for node in nodes:
															snake_case_   =	config
															snake_case_   =	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 :				int							,   _UpperCamelCase :				Union[str, Any]							)      ->Optional[Any]:
									snake_case_   =	self.get_value(_UpperCamelCase							)
									return False if value is None else bool(_UpperCamelCase							)







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				Dict							)      ->Optional[Any]:
									snake_case_   =	self.get_value(_UpperCamelCase							)
									return False if value is None else not bool(_UpperCamelCase							)







			def 				snake_case__(     self :				Optional[int]							)      ->Dict:
									return self._stage == 2







			def 				snake_case__(     self :				List[str]							)      ->List[str]:
									return self._stage == 3







			def 				snake_case__(     self :				Any							)      ->Any:
									return self._offload






class 	snake_case_   :







			'''simple docstring'''

			def __init__(     self :				Dict							,   _UpperCamelCase :				Tuple							)      ->Optional[int]:
									snake_case_   =	engine







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				Tuple							,   **_UpperCamelCase :				Dict							)      ->Tuple:
									# runs backpropagation and handles mixed precision
									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 	snake_case_   (		__A					):







			'''simple docstring'''

			def __init__(     self :				Tuple							,   _UpperCamelCase :				Any							)      ->Optional[Any]:
									super().__init__(_UpperCamelCase							,   device_placement=_UpperCamelCase							,   scaler=_UpperCamelCase							)
									snake_case_   =	hasattr(self.optimizer							,   '''overflow'''							)







			def 				snake_case__(     self :				Optional[Any]							,   _UpperCamelCase :				List[str]=None							)      ->List[str]:
									pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







			def 				snake_case__(     self :				Tuple							)      ->Any:
									pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







			@property
			def 				snake_case__(     self :				Dict							)      ->List[str]:
									if self.__has_overflow__:
															return self.optimizer.overflow
									return False






class 	snake_case_   (		__A					):







			'''simple docstring'''

			def __init__(     self :				Dict							,   _UpperCamelCase :				List[str]							,   _UpperCamelCase :				Any							)      ->Dict:
									super().__init__(_UpperCamelCase							,   _UpperCamelCase							)







			def 				snake_case__(     self :				Union[str, Any]							)      ->List[Any]:
									pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






class 	snake_case_   :







			'''simple docstring'''

			def __init__(     self :				Tuple							,   _UpperCamelCase :				str							,   _UpperCamelCase :				Any=0.001							,   _UpperCamelCase :				Optional[int]=0							,   **_UpperCamelCase :				List[str]							)      ->int:
									snake_case_   =	params
									snake_case_   =	lr
									snake_case_   =	weight_decay
									snake_case_   =	kwargs






class 	snake_case_   :







			'''simple docstring'''

			def __init__(     self :				int							,   _UpperCamelCase :				Tuple							,   _UpperCamelCase :				List[str]=None							,   _UpperCamelCase :				Optional[Any]=0							,   **_UpperCamelCase :				Dict							)      ->List[Any]:
									snake_case_   =	optimizer
									snake_case_   =	total_num_steps
									snake_case_   =	warmup_num_steps
									snake_case_   =	kwargs



from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
_snake_case             =     2_99_79_24_58

# Symbols
_snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case             =     symbols('''ct x y z''')






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  if velocity > c:
    raise ValueError("Speed must not exceed light speed 299,792,458 [m/s]!"	)
  elif velocity < 1:
    # Usually the speed should be much higher than 1 (c order of magnitude)
    raise ValueError("Speed must be greater than or equal to 1!"	)

  return velocity / c






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  return 1 / sqrt(1 - beta(SCREAMING_SNAKE_CASE_	) ** 2	)






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  return np.array(
      [
          [gamma(SCREAMING_SNAKE_CASE_	), -gamma(SCREAMING_SNAKE_CASE_	) * beta(SCREAMING_SNAKE_CASE_	), 0, 0],
          [-gamma(SCREAMING_SNAKE_CASE_	) * beta(SCREAMING_SNAKE_CASE_	), gamma(SCREAMING_SNAKE_CASE_	), 0, 0],
          [0, 0, 1, 0],
          [0, 0, 0, 1],
      ]	)






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_ = None	):


  '''simple docstring'''

  if event is None:
    lowerCamelCase :					Tuple    =				np.array([ct, x, y, z]	)  # Symbolic four vector
  else:
    event[0] *= c  # x0 is ct (speed of light * time)

  return transformation_matrix(SCREAMING_SNAKE_CASE_	) @ event


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   # Example of symbolic vector:
   _snake_case             =     transform(29_97_92_45)
   print('''Example of four vector: ''')
   print(f'''ct\' = {four_vector[0]}''')
   print(f'''x\' = {four_vector[1]}''')
   print(f'''y\' = {four_vector[2]}''')
   print(f'''z\' = {four_vector[3]}''')

   # Substitute symbols with numerical values
   _snake_case             =     {ct: c, x: 1, y: 1, z: 1}
   _snake_case             =     [four_vector[i].subs(sub_dict) for i in range(4)]

   print(f'''\n{numerical_vector}''')



