
"""simple docstring"""


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

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







class 				lowerCAmelCase_				:



      '''simple docstring'''


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

       A						   =   config

       self.set_stage_and_offload()


      def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Dict:
       # 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.
       A						   =   self.get_value('zero_optimization.stage'  ,-1      )

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


      def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict  ,A_     :      List[Any]      )     ->   List[str]:
       A						   =   self.config

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

       return config, ds_key


      def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple  ,A_     :      Union[str, Any]  ,A_     :      List[str]=None      )     ->   Optional[int]:
       A   ,      A						   =   self.find_config_node(A_      )
       if config is None:
        return default
       return config.get(A_  ,A_      )


      def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      List[Any]  ,A_     :      Any=False      )     ->   Dict:
       A						   =   self.config

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


      def 						_SCREAMING_SNAKE_CASE      ( self     :      str  ,A_     :      int      )     ->   Union[str, Any]:
       A						   =   self.get_value(A_      )
       return False if value is None else not bool(A_      )


      def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   str:
       return self._stage == 2


      def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Dict:
       return self._stage == 3




      def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Optional[int]:
       return self._offload







class 				lowerCAmelCase_				:



      '''simple docstring'''


      def __init__( self     :      Dict  ,A_     :      str      )     ->   Union[str, Any]:
       A						   =   engine




      def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict  ,A_     :      Union[str, Any]  ,**A_     :      int      )     ->   Tuple:
       # runs backpropagation and handles mixed precision
       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 				lowerCAmelCase_				(      _lowercase    ):



      '''simple docstring'''


      def __init__( self     :      List[Any]  ,A_     :      str      )     ->   List[Any]:
       super().__init__(A_  ,device_placement=A_  ,scaler=A_      )
       A						   =   hasattr(self.optimizer  ,'overflow'      )


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


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




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







class 				lowerCAmelCase_				(      _lowercase    ):



      '''simple docstring'''


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




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







class 				lowerCAmelCase_				:



      '''simple docstring'''


      def __init__( self     :      str  ,A_     :      str  ,A_     :      List[str]=0.0_01  ,A_     :      Tuple=0  ,**A_     :      Tuple      )     ->   str:
       A						   =   params
       A						   =   lr
       A						   =   weight_decay
       A						   =   kwargs







class 				lowerCAmelCase_				:



      '''simple docstring'''


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

"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_lowercase							   =				{
    '''configuration_perceiver''': ['''PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PerceiverConfig''', '''PerceiverOnnxConfig'''],
    '''tokenization_perceiver''': ['''PerceiverTokenizer'''],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				['''PerceiverFeatureExtractor''']
		_lowercase							   =				['''PerceiverImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				[
		    '''PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''PerceiverForImageClassificationConvProcessing''',
		    '''PerceiverForImageClassificationFourier''',
		    '''PerceiverForImageClassificationLearned''',
		    '''PerceiverForMaskedLM''',
		    '''PerceiverForMultimodalAutoencoding''',
		    '''PerceiverForOpticalFlow''',
		    '''PerceiverForSequenceClassification''',
		    '''PerceiverLayer''',
		    '''PerceiverModel''',
		    '''PerceiverPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
		from .tokenization_perceiver import PerceiverTokenizer

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_perceiver import PerceiverFeatureExtractor
				from .image_processing_perceiver import PerceiverImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_perceiver import (
				    PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PerceiverForImageClassificationConvProcessing,
				    PerceiverForImageClassificationFourier,
				    PerceiverForImageClassificationLearned,
				    PerceiverForMaskedLM,
				    PerceiverForMultimodalAutoencoding,
				    PerceiverForOpticalFlow,
				    PerceiverForSequenceClassification,
				    PerceiverLayer,
				    PerceiverModel,
				    PerceiverPreTrainedModel,
				)

else:
		import sys

		_lowercase							   =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)