





"""simple docstring"""





# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


lowercase__				:Dict				   =       subprocess.check_output('git merge-base main HEAD'.split()).decode('utf-8')
lowercase__				:Dict				   =       (
    subprocess.check_output(f"""git diff --diff-filter=d --name-only {fork_point_sha}""".split()).decode('utf-8').split()
)

lowercase__				:Optional[Any]				   =       '|'.join(sys.argv[1:])
lowercase__				:List[str]				   =       re.compile(Rf"""^({joined_dirs}).*?\.py$""")

lowercase__				:Tuple				   =       [x for x in modified_files if regex.match(x)]
print(' '.join(relevant_modified_files), end='')






"""simple docstring"""





from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
  import tensorflow as tf


lowercase__				:Optional[int]				   =       logging.get_logger(__name__)




@dataclass
class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      _A     :       Tuple         =       [
          'no_inference',
          'no_cuda',
          'no_tpu',
          'no_speed',
          'no_memory',
          'no_env_print',
          'no_multi_process',
      ]

      def __init__(							self       :      Dict						,				**__lowercase       :      Dict ):


        '''simple docstring'''




        for deprecated_arg in self.deprecated_args:
          if deprecated_arg in kwargs:
            __UpperCAmelCase    :						Optional[Any]       =						deprecated_arg[3:]
            __UpperCAmelCase    :						List[str]       =						not kwargs.pop(__lowercase )
            logger.warning(
                f'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
                f''' {positive_arg}={kwargs[positive_arg]}''' )
        __UpperCAmelCase    :						int       =						kwargs.pop('''tpu_name'''						,				self.tpu_name )
        __UpperCAmelCase    :						Any       =						kwargs.pop('''device_idx'''						,				self.device_idx )
        __UpperCAmelCase    :						Tuple       =						kwargs.pop('''eager_mode'''						,				self.eager_mode )
        __UpperCAmelCase    :						Optional[int]       =						kwargs.pop('''use_xla'''						,				self.use_xla )
        super().__init__(**__lowercase )

      _A     :       str         =       field(
          default=__UpperCAmelCase      ,  metadata={'help': 'Name of TPU'}      ,  )
      _A     :       int         =       field(
          default=0      ,  metadata={'help': 'CPU / GPU device index. Defaults to 0.'}      ,  )
      _A     :       bool         =       field(default=__UpperCAmelCase      ,  metadata={'help': 'Benchmark models in eager model.'} )
      _A     :       bool         =       field(
          default=__UpperCAmelCase      ,  metadata={
              'help': 'Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'
          }      ,  )

      @cached_property
      def       A_					(							self       :      int ):


        '''simple docstring'''




        requires_backends(self						,				['''tf'''] )
        __UpperCAmelCase    :						Any       =						None
        if self.tpu:
          try:
            if self.tpu_name:
              __UpperCAmelCase    :						List[str]       =						tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name )
            else:
              __UpperCAmelCase    :						Any       =						tf.distribute.cluster_resolver.TPUClusterResolver()
          except ValueError:
            __UpperCAmelCase    :						int       =						None
        return tpu

      @cached_property
      def       A_					(							self       :      Any ):


        '''simple docstring'''




        requires_backends(self						,				['''tf'''] )
        if self.is_tpu:
          tf.config.experimental_connect_to_cluster(self._setup_tpu )
          tf.tpu.experimental.initialize_tpu_system(self._setup_tpu )

          __UpperCAmelCase    :						Any       =						tf.distribute.TPUStrategy(self._setup_tpu )
        else:
          # currently no multi gpu is allowed
          if self.is_gpu:
            # TODO: Currently only single GPU is supported
            tf.config.set_visible_devices(self.gpu_list[self.device_idx]						,				'''GPU''' )
            __UpperCAmelCase    :						Any       =						tf.distribute.OneDeviceStrategy(device=f'''/gpu:{self.device_idx}''' )
          else:
            tf.config.set_visible_devices([]						,				'''GPU''' )  # disable GPU
            __UpperCAmelCase    :						str       =						tf.distribute.OneDeviceStrategy(device=f'''/cpu:{self.device_idx}''' )

        return strategy

      @property
      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        requires_backends(self						,				['''tf'''] )
        return self._setup_tpu is not None

      @property
      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




        requires_backends(self						,				['''tf'''] )
        return self._setup_strategy

      @property
      def       A_					(							self       :      Any ):


        '''simple docstring'''




        requires_backends(self						,				['''tf'''] )
        return tf.config.list_physical_devices('''GPU''' )

      @property
      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        requires_backends(self						,				['''tf'''] )
        if self.cuda:
          return len(self.gpu_list )
        return 0







      @property
      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        return self.n_gpu > 0