
'''simple docstring'''



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       ,							A__      )    ->			List[str]:
          if isinstance(lowercase__       ,							lowercase__      ):
             # 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
             _SCREAMING_SNAKE_CASE          =				deepcopy(lowercase__      )
          elif os.path.exists(lowercase__      ):
             with io.open(lowercase__       ,							"""r"""       ,							encoding="""utf-8"""      ) as f:
                _SCREAMING_SNAKE_CASE          =				json.load(lowercase__      )
          else:
             try:
                _SCREAMING_SNAKE_CASE          =				baseaa.urlsafe_baadecode(lowercase__      ).decode("""utf-8"""      )
                _SCREAMING_SNAKE_CASE          =				json.loads(lowercase__      )
             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}"      )

          _SCREAMING_SNAKE_CASE          =				config

          self.set_stage_and_offload()






       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          # 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.
          _SCREAMING_SNAKE_CASE          =				self.get_value("""zero_optimization.stage"""       ,							-1      )

          # offload
          _SCREAMING_SNAKE_CASE          =				False
          if self.is_zeroa() or self.is_zeroa():
             _SCREAMING_SNAKE_CASE          =				set(["""cpu""", """nvme"""]      )
             _SCREAMING_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:
                _SCREAMING_SNAKE_CASE          =				True






       def        UpperCamelCase				(							self       ,							A__      )    ->			Optional[int]:
          _SCREAMING_SNAKE_CASE          =				self.config

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

          return config, ds_key






       def        UpperCamelCase				(							self       ,							A__       ,							A__=None      )    ->			List[Any]:
          _SCREAMING_SNAKE_CASE          =				self.find_config_node(lowercase__      )
          if config is None:
             return default
          return config.get(lowercase__       ,							lowercase__      )






       def        UpperCamelCase				(							self       ,							A__       ,							A__=False      )    ->			Any:
          _SCREAMING_SNAKE_CASE          =				self.config

          # find the config node of interest if it exists
          _SCREAMING_SNAKE_CASE          =				ds_key_long.split("""."""      )
          for node in nodes:
             _SCREAMING_SNAKE_CASE          =				config
             _SCREAMING_SNAKE_CASE          =				config.get(lowercase__      )
             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(lowercase__      )






       def        UpperCamelCase				(							self       ,							A__      )    ->			str:
          _SCREAMING_SNAKE_CASE          =				self.get_value(lowercase__      )
          return False if value is None else bool(lowercase__      )






       def        UpperCamelCase				(							self       ,							A__      )    ->			Optional[Any]:
          _SCREAMING_SNAKE_CASE          =				self.get_value(lowercase__      )
          return False if value is None else not bool(lowercase__      )






       def        UpperCamelCase				(							self      )    ->			int:
          return self._stage == 2






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






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






class    _a				:





       """simple docstring"""




       def __init__(							self       ,							A__      )    ->			List[Any]:
          _SCREAMING_SNAKE_CASE          =				engine






       def        UpperCamelCase				(							self       ,							A__       ,							**A__      )    ->			List[str]:
          # runs backpropagation and handles mixed precision
          self.engine.backward(lowercase__       ,							**lowercase__      )

          # 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       ,							A__      )    ->			Union[str, Any]:
          super().__init__(lowercase__       ,							device_placement=lowercase__       ,							scaler=lowercase__      )
          _SCREAMING_SNAKE_CASE          =				hasattr(self.optimizer       ,							"""overflow"""      )






       def        UpperCamelCase				(							self       ,							A__=None      )    ->			Dict:
          pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






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






       @property
       def        UpperCamelCase				(							self      )    ->			Optional[Any]:
          if self.__has_overflow__:
             return self.optimizer.overflow
          return False






class    _a				(_UpperCAmelCase):





       """simple docstring"""




       def __init__(							self       ,							A__       ,							A__      )    ->			Dict:
          super().__init__(lowercase__       ,							lowercase__      )






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






class    _a				:





       """simple docstring"""




       def __init__(							self       ,							A__       ,							A__=0.001       ,							A__=0       ,							**A__      )    ->			Optional[int]:
          _SCREAMING_SNAKE_CASE          =				params
          _SCREAMING_SNAKE_CASE          =				lr
          _SCREAMING_SNAKE_CASE          =				weight_decay
          _SCREAMING_SNAKE_CASE          =				kwargs






class    _a				:





       """simple docstring"""




       def __init__(							self       ,							A__       ,							A__=None       ,							A__=0       ,							**A__      )    ->			Any:
          _SCREAMING_SNAKE_CASE          =				optimizer
          _SCREAMING_SNAKE_CASE          =				total_num_steps
          _SCREAMING_SNAKE_CASE          =				warmup_num_steps
          _SCREAMING_SNAKE_CASE          =				kwargs








'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase__      :		str       							=  {
    "configuration_canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConfig"],
    "tokenization_canine": ["CanineTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase__      :		List[Any]       							=  [
         "CANINE_PRETRAINED_MODEL_ARCHIVE_LIST",
         "CanineForMultipleChoice",
         "CanineForQuestionAnswering",
         "CanineForSequenceClassification",
         "CanineForTokenClassification",
         "CanineLayer",
         "CanineModel",
         "CaninePreTrainedModel",
         "load_tf_weights_in_canine",
     ]


if TYPE_CHECKING:
     from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
     from .tokenization_canine import CanineTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_canine import (
              CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
              CanineForMultipleChoice,
              CanineForQuestionAnswering,
              CanineForSequenceClassification,
              CanineForTokenClassification,
              CanineLayer,
              CanineModel,
              CaninePreTrainedModel,
              load_tf_weights_in_canine,
          )


else:
     import sys

     UpperCamelCase__      :		str       							=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






