
'''simple docstring'''
import json
import sys




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							Union[str, Any]:


		'''simple docstring'''


		with open(__snake_case ,       encoding='''utf-8'''			) as f:
				snake_case  :       int     			=						json.load(__snake_case			)

		snake_case  :       Optional[Any]     			=						['''<details>''', '''<summary>Show updated benchmarks!</summary>''', ''' ''']

		for benchmark_name in sorted(__snake_case			):
				snake_case  :       List[Any]     			=						results[benchmark_name]

				snake_case  :       Any     			=						benchmark_name.split('''/'''			)[-1]
				output_md.append(F'### Benchmark: {benchmark_file_name}'			)

				snake_case  :       Optional[Any]     			=						'''| metric |'''
				snake_case  :       Tuple     			=						'''|--------|'''
				snake_case  :       Optional[Any]     			=						'''| new / old (diff) |'''
				for metric_name in sorted(__snake_case			):
						snake_case  :       int     			=						benchmark_res[metric_name]
						snake_case  :       Any     			=						metric_vals['''new''']
						snake_case  :       str     			=						metric_vals.get('''old''' ,       __snake_case			)
						snake_case  :       List[Any]     			=						metric_vals.get('''diff''' ,       __snake_case			)

						snake_case  :       Union[str, Any]     			=						F' {new_val:f}' if isinstance(__snake_case ,       (int, float)			) else '''None'''

						if old_val is not None:
								val_str += F' / {old_val:f}' if isinstance(__snake_case ,       (int, float)			) else "None"
						if dif_val is not None:
								val_str += F' ({dif_val:f})' if isinstance(__snake_case ,       (int, float)			) else "None"

						title += " " + metric_name + " |"
						lines += "---|"
						value += val_str + " |"

				output_md += [title, lines, value, " "]

		output_md.append('''</details>'''			)

		with open(__snake_case ,       '''w''' ,       encoding='''utf-8'''			) as f:
				f.writelines('''\n'''.join(__snake_case			)			)


if __name__ == "__main__":
						lowercase__						       =      sys.argv[1]
						lowercase__						       =      sys.argv[2]

						format_json_to_md(input_json_file, output_md_file)



'''simple docstring'''

import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available



def snake_case_      (					*__snake_case  :							Optional[int])			->					int:
   if not isinstance(__snake_case	,   __snake_case):
      lowerCAmelCase_			=      list(__snake_case)
   for i in range(len(__snake_case)):
      lowerCAmelCase_			=      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  :							Exception)			->					bool:
   lowerCAmelCase_			=      [
       '''CUDA out of memory.''',  # CUDA OOM
       '''cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.''',  # CUDNN SNAFU
       '''DefaultCPUAllocator: can\'t allocate memory''',  # CPU OOM
   ]
   if isinstance(__snake_case	,   __snake_case) and len(exception.args) == 1:
      return any(err in exception.args[0] for err in _statements)
   return False






def snake_case_      (					__snake_case  :							callable = None	,   __snake_case  :							int = 128)			->					List[Any]:
   if function is None:
      return functools.partial(__snake_case	,   starting_batch_size=__snake_case)

   lowerCAmelCase_			=      starting_batch_size

   def decorator(*__snake_case  :							List[str]	,   **__snake_case  :							Union[str, Any]):
      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()
      lowerCAmelCase_			=      list(inspect.signature(__snake_case).parameters.keys())
      # Guard against user error
      if len(__snake_case) < (len(__snake_case) + 1):
         lowerCAmelCase_			=      ''', '''.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(__snake_case	,   *__snake_case	,   **__snake_case)
         except Exception as e:
            if should_reduce_batch_size(__snake_case):
               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





