


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

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




class 	a__       :





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_     ) ->							Tuple:






														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__      =			deepcopy(__UpperCamelCase     )
														elif os.path.exists(__UpperCamelCase     ):
																					with io.open(__UpperCamelCase      ,					'''r'''      ,					encoding='''utf-8'''     ) as f:
																												lowerCAmelCase__      =			json.load(__UpperCamelCase     )
														else:
																					try:
																												lowerCAmelCase__      =			baseaa.urlsafe_baadecode(__UpperCamelCase     ).decode('''utf-8'''     )
																												lowerCAmelCase__      =			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__      =			config

														self.set_stage_and_offload()







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[int]:
														lowerCAmelCase__      =			self.get_value('''zero_optimization.stage'''      ,					-1     )

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







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							str:
														lowerCAmelCase__      =			self.config

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

														return config, ds_key







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_=None     ) ->							Optional[Any]:
														lowerCAmelCase__     ,      lowerCAmelCase__      =			self.find_config_node(__UpperCamelCase     )
														if config is None:
																					return default
														return config.get(__UpperCamelCase      ,					__UpperCamelCase     )







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_=False     ) ->							List[Any]:
														lowerCAmelCase__      =			self.config

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







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							str:
														lowerCAmelCase__      =			self.get_value(__UpperCamelCase     )
														return False if value is None else not bool(__UpperCamelCase     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[Any]:
														return self._stage == 2







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Dict:
														return self._stage == 3







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[str]:
														return self._offload




class 	a__       :





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_     ) ->							Optional[int]:
														lowerCAmelCase__      =			engine







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					**lowerCamelCase_     ) ->							str:
														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 	a__       (							_UpperCAmelCase						):





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_     ) ->							Union[str, Any]:
														super().__init__(__UpperCamelCase      ,					device_placement=__UpperCamelCase      ,					scaler=__UpperCamelCase     )
														lowerCAmelCase__      =			hasattr(self.optimizer      ,					'''overflow'''     )







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_=None     ) ->							Optional[int]:
														pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







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







							@property
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Union[str, Any]:






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




class 	a__       (							_UpperCAmelCase						):





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							List[Any]:
														super().__init__(__UpperCamelCase      ,					__UpperCamelCase     )







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




class 	a__       :





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_      ,					lowerCamelCase_=0.001      ,					lowerCamelCase_=0      ,					**lowerCamelCase_     ) ->							Any:
														lowerCAmelCase__      =			params
														lowerCAmelCase__      =			lr
														lowerCAmelCase__      =			weight_decay
														lowerCAmelCase__      =			kwargs




class 	a__       :





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_      ,					lowerCamelCase_=None      ,					lowerCamelCase_=0      ,					**lowerCamelCase_     ) ->							int:
														lowerCAmelCase__      =			optimizer
														lowerCAmelCase__      =			total_num_steps
														lowerCAmelCase__      =			warmup_num_steps
														lowerCAmelCase__      =			kwargs



'''simple docstring'''






import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


__UpperCAmelCase					       =							logging.get_logger(__name__)

__UpperCAmelCase					       =							'''T5Config'''


def       _snake_case     (  A				,					A				,					A )		->					jnp.ndarray:
							lowerCAmelCase__      =			jnp.zeros_like(A )
							lowerCAmelCase__      =			shifted_input_ids.at[:, 1:].set(input_ids[:, :-1] )
							lowerCAmelCase__      =			shifted_input_ids.at[:, 0].set(A )

							lowerCAmelCase__      =			jnp.where(shifted_input_ids == -100				,					A				,					A )
							return shifted_input_ids




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     int					=   "mt5"
							lowercase__    :     Dict					=   MTaConfig




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     int					=   "mt5"
							lowercase__    :     Any					=   MTaConfig




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     Union[str, Any]					=   "mt5"
							lowercase__    :     Tuple					=   MTaConfig