





'''simple docstring'''



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

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






class 							__lowercase    :







 '''simple docstring'''

 def __init__(self       ,_lowerCamelCase	)							->							Union[str, Any]:







    '''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
       __lowercase			  =							deepcopy(__A	)
    elif os.path.exists(__A	):
       with io.open(__A       ,'''r'''       ,encoding='''utf-8'''	) as f:
          __lowercase			  =							json.load(__A	)
    else:
       try:
          __lowercase			  =							baseaa.urlsafe_baadecode(__A	).decode('''utf-8'''	)
          __lowercase			  =							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}"	)

    __lowercase			  =							config

    self.set_stage_and_offload()


 def   _UpperCAmelCase       (self	)							->							Tuple:







    '''simple docstring'''





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

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


 def   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							Optional[Any]:







    '''simple docstring'''





    __lowercase			  =							self.config

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

    return config, ds_key


 def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase=None	)							->							Optional[Any]:







    '''simple docstring'''





    __lowercase			  =							self.find_config_node(__A	)
    if config is None:
       return default
    return config.get(__A       ,__A	)


 def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase=False	)							->							Optional[Any]:







    '''simple docstring'''





    __lowercase			  =							self.config

    # find the config node of interest if it exists
    __lowercase			  =							ds_key_long.split('''.'''	)
    for node in nodes:
       __lowercase			  =							config
       __lowercase			  =							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       ,_lowerCamelCase	)							->							Union[str, Any]:







    '''simple docstring'''





    __lowercase			  =							self.get_value(__A	)
    return False if value is None else bool(__A	)


 def   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							Optional[int]:







    '''simple docstring'''





    __lowercase			  =							self.get_value(__A	)
    return False if value is None else not bool(__A	)


 def   _UpperCAmelCase       (self	)							->							str:







    '''simple docstring'''





    return self._stage == 2


 def   _UpperCAmelCase       (self	)							->							Union[str, Any]:







    '''simple docstring'''





    return self._stage == 3


 def   _UpperCAmelCase       (self	)							->							Union[str, Any]:







    '''simple docstring'''





    return self._offload






class 							__lowercase    :







 '''simple docstring'''

 def __init__(self       ,_lowerCamelCase	)							->							Optional[int]:







    '''simple docstring'''





    __lowercase			  =							engine


 def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,**_lowerCamelCase	)							->							str:







    '''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 							__lowercase    ( A__  ):







 '''simple docstring'''

 def __init__(self       ,_lowerCamelCase	)							->							List[str]:







    '''simple docstring'''





    super().__init__(__A       ,device_placement=__A       ,scaler=__A	)
    __lowercase			  =							hasattr(self.optimizer       ,'''overflow'''	)


 def   _UpperCAmelCase       (self       ,_lowerCamelCase=None	)							->							Optional[Any]:







    '''simple docstring'''





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


 def   _UpperCAmelCase       (self	)							->							List[Any]:







    '''simple docstring'''





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


 @property
 def   _UpperCAmelCase       (self	)							->							int:







    '''simple docstring'''





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






class 							__lowercase    ( A__  ):







 '''simple docstring'''

 def __init__(self       ,_lowerCamelCase       ,_lowerCamelCase	)							->							Optional[int]:







    '''simple docstring'''





    super().__init__(__A       ,__A	)


 def   _UpperCAmelCase       (self	)							->							Any:







    '''simple docstring'''





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






class 							__lowercase    :







 '''simple docstring'''

 def __init__(self       ,_lowerCamelCase       ,_lowerCamelCase=0.0_0_1       ,_lowerCamelCase=0       ,**_lowerCamelCase	)							->							List[Any]:







    '''simple docstring'''





    __lowercase			  =							params
    __lowercase			  =							lr
    __lowercase			  =							weight_decay
    __lowercase			  =							kwargs






class 							__lowercase    :







 '''simple docstring'''

 def __init__(self       ,_lowerCamelCase       ,_lowerCamelCase=None       ,_lowerCamelCase=0       ,**_lowerCamelCase	)							->							List[str]:







    '''simple docstring'''





    __lowercase			  =							optimizer
    __lowercase			  =							total_num_steps
    __lowercase			  =							warmup_num_steps
    __lowercase			  =							kwargs












'''simple docstring'''



import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


_SCREAMING_SNAKE_CASE   =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   =      {
    '''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/resolve/main/config.json''',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			Union[str, Any]     =    "dpt"


	def __init__(self       ,_lowerCamelCase=768       ,_lowerCamelCase=12       ,_lowerCamelCase=12       ,_lowerCamelCase=3072       ,_lowerCamelCase="gelu"       ,_lowerCamelCase=0.0       ,_lowerCamelCase=0.0       ,_lowerCamelCase=0.0_2       ,_lowerCamelCase=1E-1_2       ,_lowerCamelCase=384       ,_lowerCamelCase=16       ,_lowerCamelCase=3       ,_lowerCamelCase=False       ,_lowerCamelCase=True       ,_lowerCamelCase=[2, 5, 8, 11]       ,_lowerCamelCase="project"       ,_lowerCamelCase=[4, 2, 1, 0.5]       ,_lowerCamelCase=[96, 192, 384, 768]       ,_lowerCamelCase=256       ,_lowerCamelCase=-1       ,_lowerCamelCase=False       ,_lowerCamelCase=True       ,_lowerCamelCase=0.4       ,_lowerCamelCase=255       ,_lowerCamelCase=0.1       ,_lowerCamelCase=[1, 1024, 24, 24]       ,_lowerCamelCase=[0, 1]       ,_lowerCamelCase=None       ,**_lowerCamelCase       ,)							->							Tuple:







				'''simple docstring'''





				super().__init__(**_lowerCamelCase	)

				__lowercase			  =							hidden_size
				__lowercase			  =							is_hybrid

				if self.is_hybrid:
							if backbone_config is None:
										logger.info('''Initializing the config with a `BiT` backbone.'''	)
										__lowercase			  =							{
										    '''global_padding''': '''same''',
										    '''layer_type''': '''bottleneck''',
										    '''depths''': [3, 4, 9],
										    '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
										    '''embedding_dynamic_padding''': True,
										}
										__lowercase			  =							BitConfig(**_lowerCamelCase	)
							elif isinstance(_lowerCamelCase       ,_lowerCamelCase	):
										logger.info('''Initializing the config with a `BiT` backbone.'''	)
										__lowercase			  =							BitConfig(**_lowerCamelCase	)
							elif isinstance(_lowerCamelCase       ,_lowerCamelCase	):
										__lowercase			  =							backbone_config
							else:
										raise ValueError(
										    f"backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}."	)

							__lowercase			  =							backbone_featmap_shape
							__lowercase			  =							neck_ignore_stages

							if readout_type != "project":
										raise ValueError('''Readout type must be \'project\' when using `DPT-hybrid` mode.'''	)
				else:
							__lowercase			  =							None
							__lowercase			  =							None
							__lowercase			  =							[]

				__lowercase			  =							num_hidden_layers
				__lowercase			  =							num_attention_heads
				__lowercase			  =							intermediate_size
				__lowercase			  =							hidden_act
				__lowercase			  =							hidden_dropout_prob
				__lowercase			  =							attention_probs_dropout_prob
				__lowercase			  =							initializer_range
				__lowercase			  =							layer_norm_eps
				__lowercase			  =							image_size
				__lowercase			  =							patch_size
				__lowercase			  =							num_channels
				__lowercase			  =							qkv_bias
				__lowercase			  =							backbone_out_indices
				if readout_type not in ["ignore", "add", "project"]:
							raise ValueError('''Readout_type must be one of [\'ignore\', \'add\', \'project\']'''	)
				__lowercase			  =							readout_type
				__lowercase			  =							reassemble_factors
				__lowercase			  =							neck_hidden_sizes
				__lowercase			  =							fusion_hidden_size
				__lowercase			  =							head_in_index
				__lowercase			  =							use_batch_norm_in_fusion_residual
				# auxiliary head attributes (semantic segmentation)
				__lowercase			  =							use_auxiliary_head
				__lowercase			  =							auxiliary_loss_weight
				__lowercase			  =							semantic_loss_ignore_index
				__lowercase			  =							semantic_classifier_dropout


	def   _UpperCAmelCase       (self	)							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase			  =							copy.deepcopy(self.__dict__	)

				if output["backbone_config"] is not None:
							__lowercase			  =							self.backbone_config.to_dict()

				__lowercase			  =							self.__class__.model_type
				return output





