import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available






def  snake_case  (       *snake_case__			:Optional[Any])						->   Any:
       if not isinstance(_A   ,	_A):
              _A        =  list(_A)
       for i in range(len(_A)):
              _A        =  None
       gc.collect()
       if is_xpu_available():
              torch.xpu.empty_cache()
       elif is_npu_available():
              torch.npu.empty_cache()
       else:
              torch.cuda.empty_cache()
       return objects






def  snake_case  (       snake_case__			:Any)						->   Tuple:
       _A        =  [
           """CUDA out of memory.""",  # CUDA OOM
           """cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""",  # CUDNN SNAFU
           """DefaultCPUAllocator: can\'t allocate memory""",  # CPU OOM
       ]
       if isinstance(_A   ,	_A) and len(exception.args) == 1:
              return any(err in exception.args[0] for err in _statements)
       return False







def  snake_case  (       snake_case__			:str = None   ,	snake_case__			:Optional[Any] = 128)						->   Tuple:
       if function is None:
              return functools.partial(_A   ,	starting_batch_size=_A)

       _A        =  starting_batch_size

       def decorator(*snake_case__			:Any   ,	**snake_case__			:str):
              nonlocal batch_size
              gc.collect()
              if is_xpu_available():
                     torch.xpu.empty_cache()
              elif is_npu_available():
                     torch.npu.empty_cache()
              else:
                     torch.cuda.empty_cache()
              _A        =  list(inspect.signature(_A).parameters.keys())
              # Guard against user error
              if len(_A) < (len(_A) + 1):
                     _A        =  """, """.join([F'''{arg}={value}''' for arg, value in zip(params[1:]   ,	args[1:])])
                     raise TypeError(
                         F'''Batch size was passed into `{function.__name__}` as the first argument when called.'''
                         F'''Remove this as the decorator already does so: `{function.__name__}({arg_str})`''')
              while True:
                     if batch_size == 0:
                            raise RuntimeError("""No executable batch size found, reached zero.""")
                     try:
                            return function(_A   ,	*_A   ,	**_A)
                     except Exception as e:
                            if should_reduce_batch_size(_A):
                                   gc.collect()
                                   if is_xpu_available():
                                          torch.xpu.empty_cache()
                                   elif is_npu_available():
                                          torch.npu.empty_cache()
                                   else:
                                          torch.cuda.empty_cache()
                                   batch_size //= 2
                            else:
                                   raise

       return decorator










import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


_A     =			'''scheduler_config.json'''


class  A		(  __UpperCAmelCase     ):
		__snake_case									=   1
		__snake_case									=   2
		__snake_case									=   3
		__snake_case									=   4
		__snake_case									=   5
		__snake_case									=   6
		__snake_case									=   7
		__snake_case									=   8
		__snake_case									=   9
		__snake_case									=   10
		__snake_case									=   11
		__snake_case									=   12
		__snake_case									=   13
		__snake_case									=   14







@dataclass
class  A		(  __UpperCAmelCase     ):
		__snake_case									=   42







class  A		:
		__snake_case									=   SCHEDULER_CONFIG_NAME
		__snake_case									=   []
		__snake_case									=   True



		@classmethod
		def        SCREAMING_SNAKE_CASE__			(     cls,	UpperCamelCase__ = None,	UpperCamelCase__ = None,	UpperCamelCase__=False,	**UpperCamelCase__,	):







								"""simple docstring"""



								lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_      			=  cls.load_config(
								    pretrained_model_name_or_path=UpperCamelCase__,	subfolder=UpperCamelCase__,	return_unused_kwargs=UpperCamelCase__,	return_commit_hash=UpperCamelCase__,	**UpperCamelCase__,	)
								return cls.from_config(UpperCamelCase__,	return_unused_kwargs=UpperCamelCase__,	**UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__ = False,	**UpperCamelCase__   ):







								"""simple docstring"""



								self.save_config(save_directory=UpperCamelCase__,	push_to_hub=UpperCamelCase__,	**UpperCamelCase__   )



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return self._get_compatibles()







		@classmethod
		def        SCREAMING_SNAKE_CASE__			(     cls   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  list(set([cls.__name__] + cls._compatibles   )   )
								lowerCAmelCase_      			=  importlib.import_module(__name__.split('''.'''   )[0]   )
								lowerCAmelCase_      			=  [
								    getattr(UpperCamelCase__,	UpperCamelCase__   ) for c in compatible_classes_str if hasattr(UpperCamelCase__,	UpperCamelCase__   )
								]
								return compatible_classes




