


"""simple docstring"""




from typing import List

import numpy as np
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Tuple		 =						{key: len(lowerCamelCase__						) for key, value in gen_kwargs.items() if isinstance(lowerCamelCase__      ,	lowerCamelCase__						)}
 if len(set(lists_lengths.values()						)						) > 1:
  raise RuntimeError(
      (
          'Sharding is ambiguous for this dataset: '
          + 'we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'
          + '\n'.join(f'\t- key {key} has length {length}' for key, length in lists_lengths.items()						)
          + '\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '
          + 'and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'
      )						)
 __lowerCamelCase		:							Optional[Any]		 =						max(lists_lengths.values()      ,	default=0						)
 return max(1      ,	lowerCamelCase__						)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							str		 =						[]
 for group_idx in range(lowerCamelCase__						):
  __lowerCamelCase		:							List[Any]		 =						num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
  if num_shards_to_add == 0:
   break
  __lowerCamelCase		:							Union[str, Any]		 =						shards_indices_per_group[-1].stop if shards_indices_per_group else 0
  __lowerCamelCase		:							int		 =						range(lowerCamelCase__      ,	start + num_shards_to_add						)
  shards_indices_per_group.append(lowerCamelCase__						)
 return shards_indices_per_group
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Optional[int]		 =						_number_of_shards_in_gen_kwargs(lowerCamelCase__						)
 if num_shards == 1:
  return [dict(lowerCamelCase__						)]
 else:
  __lowerCamelCase		:							List[Any]		 =						_distribute_shards(num_shards=lowerCamelCase__      ,	max_num_jobs=lowerCamelCase__						)
  return [
      {
          key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
          if isinstance(lowerCamelCase__      ,	lowerCamelCase__						)
          else value
          for key, value in gen_kwargs.items()
      }
      for group_idx in range(len(lowerCamelCase__						)						)
  ]
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 return {
     key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
     if isinstance(gen_kwargs_list[0][key]      ,	lowerCamelCase__						)
     else gen_kwargs_list[0][key]
     for key in gen_kwargs_list[0]
 }



def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Union[str, Any]		 =						{len(lowerCamelCase__						) for value in gen_kwargs.values() if isinstance(lowerCamelCase__      ,	lowerCamelCase__						)}
 __lowerCamelCase		:							Tuple		 =						{}
 for size in list_sizes:
  __lowerCamelCase		:							str		 =						list(range(lowerCamelCase__						)						)
  rng.shuffle(indices_per_size[size]						)
 # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
 __lowerCamelCase		:							Optional[Any]		 =						dict(lowerCamelCase__						)
 for key, value in shuffled_kwargs.items():
  if isinstance(lowerCamelCase__      ,	lowerCamelCase__						):
   __lowerCamelCase		:							int		 =						[value[i] for i in indices_per_size[len(lowerCamelCase__						)]]
 return shuffled_kwargs





from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
       import torch

if is_torch_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm


lowercase_	   =     logging.get_logger(__name__)


@dataclass
class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       [
          """no_inference""",
          """no_cuda""",
          """no_tpu""",
          """no_speed""",
          """no_memory""",
          """no_env_print""",
          """no_multi_process""",
      ]




      def __init__(    self:	Any						,				**a:	Optional[Any]    ):
       for deprecated_arg in self.deprecated_args:
        if deprecated_arg in kwargs:
         __lowerCamelCase		:							str		 =						deprecated_arg[3:]
         setattr(self						,				a						,				not kwargs.pop(a    )    )
         logger.warning(
             F'{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'
             F' {positive_arg}={kwargs[positive_arg]}'    )

       __lowerCamelCase		:							str		 =						kwargs.pop('torchscript'						,				self.torchscript    )
       __lowerCamelCase		:							int		 =						kwargs.pop('torch_xla_tpu_print_metrics'						,				self.torch_xla_tpu_print_metrics    )
       __lowerCamelCase		:							Dict		 =						kwargs.pop('fp16_opt_level'						,				self.fpaa_opt_level    )
       super().__init__(**a    )

      __snake_case 	=       field(default=__UpperCamelCase ,				metadata={"""help""": """Trace the models using torchscript"""}   )
      __snake_case 	=       field(default=__UpperCamelCase ,				metadata={"""help""": """Print Xla/PyTorch tpu metrics"""}   )
      __snake_case 	=       field(
          default="""O1""" ,				metadata={
              """help""": (
                  """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. """
                  """See details at https://nvidia.github.io/apex/amp.html"""
              )
          } ,				)




      @cached_property
      def 					_snake_case     (    self:	Dict    ):
       requires_backends(self						,				['torch']    )
       logger.info('PyTorch: setting up devices'    )
       if not self.cuda:
        __lowerCamelCase		:							Dict		 =						torch.device('cpu'    )
        __lowerCamelCase		:							str		 =						0
       elif is_torch_tpu_available():
        __lowerCamelCase		:							Optional[int]		 =						xm.xla_device()
        __lowerCamelCase		:							Dict		 =						0
       else:
        __lowerCamelCase		:							Any		 =						torch.device('cuda' if torch.cuda.is_available() else 'cpu'    )
        __lowerCamelCase		:							Union[str, Any]		 =						torch.cuda.device_count()
       return device, n_gpu




      @property
      def 					_snake_case     (    self:	Optional[Any]    ):
       return is_torch_tpu_available() and self.tpu




      @property
      def 					_snake_case     (    self:	Union[str, Any]    ):
       requires_backends(self						,				['torch']    )
       # TODO(PVP): currently only single GPU is supported
       return torch.cuda.current_device()




      @property
      def 					_snake_case     (    self:	int    ):
       requires_backends(self						,				['torch']    )
       return self._setup_devices[0]




      @property
      def 					_snake_case     (    self:	Union[str, Any]    ):
       requires_backends(self						,				['torch']    )
       return self._setup_devices[1]




      @property
      def 					_snake_case     (    self:	List[Any]    ):
       return self.n_gpu > 0

