from typing import List

import numpy as np




def    lowerCAmelCase_ (     __A							)     ->				int:







      '''simple docstring'''


      UpperCAmelCase__								=  {key: len(__A							) for key, value in gen_kwargs.items() if isinstance(__A,  __A							)}
      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."
                )							)
      UpperCAmelCase__								=  max(lists_lengths.values(),  default=0							)
      return max(1,  __A							)




def    lowerCAmelCase_ (     __A,  __A							)     ->				List[range]:







      '''simple docstring'''


      UpperCAmelCase__								=  []
      for group_idx in range(__A							):
            UpperCAmelCase__								=  num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
            if num_shards_to_add == 0:
                  break
            UpperCAmelCase__								=  shards_indices_per_group[-1].stop if shards_indices_per_group else 0
            UpperCAmelCase__								=  range(__A,  start + num_shards_to_add							)
            shards_indices_per_group.append(__A							)
      return shards_indices_per_group




def    lowerCAmelCase_ (     __A,  __A							)     ->				List[dict]:







      '''simple docstring'''


      UpperCAmelCase__								=  _number_of_shards_in_gen_kwargs(__A							)
      if num_shards == 1:
            return [dict(__A							)]
      else:
            UpperCAmelCase__								=  _distribute_shards(num_shards=__A,  max_num_jobs=__A							)
            return [
                {
                    key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
                    if isinstance(__A,  __A							)
                    else value
                    for key, value in gen_kwargs.items()
                }
                for group_idx in range(len(__A							)							)
            ]




def    lowerCAmelCase_ (     __A							)     ->				dict:







      '''simple docstring'''


      return {
          key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
          if isinstance(gen_kwargs_list[0][key],  __A							)
          else gen_kwargs_list[0][key]
          for key in gen_kwargs_list[0]
      }







def    lowerCAmelCase_ (     __A,  __A							)     ->				dict:







      '''simple docstring'''


      UpperCAmelCase__								=  {len(__A							) for value in gen_kwargs.values() if isinstance(__A,  __A							)}
      UpperCAmelCase__								=  {}
      for size in list_sizes:
            UpperCAmelCase__								=  list(range(__A							)							)
            rng.shuffle(indices_per_size[size]							)
      # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
      UpperCAmelCase__								=  dict(__A							)
      for key, value in shuffled_kwargs.items():
            if isinstance(__A,  __A							):
                  UpperCAmelCase__								=  [value[i] for i in indices_per_size[len(__A							)]]
      return shuffled_kwargs




import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment





class      A							(				unittest.TestCase     ):


   def 			lowercase_      (self     :						Union[str, Any]		)						->				str:
         """simple docstring"""



         UpperCAmelCase__								=  inspect.getfile(accelerate.test_utils		)
         UpperCAmelCase__								=  os.path.sep.join(mod_file.split(os.path.sep		)[:-1] + ["scripts", "test_script.py"]		)
         UpperCAmelCase__								=  os.path.sep.join(
             mod_file.split(os.path.sep		)[:-1] + ["scripts", "test_distributed_data_loop.py"]		)
         UpperCAmelCase__								=  os.path.sep.join(mod_file.split(os.path.sep		)[:-1] + ["scripts", "test_ops.py"]		)


   @require_multi_gpu
   def 			lowercase_      (self     :						List[str]		)						->				Any:
         """simple docstring"""



         print(f"""Found {torch.cuda.device_count()} devices."""		)
         UpperCAmelCase__								=  ["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
         with patch_environment(omp_num_threads=1		):
               execute_subprocess_async(__UpperCAmelCase       ,  env=os.environ.copy()		)


   @require_multi_gpu
   def 			lowercase_      (self     :						str		)						->				str:
         """simple docstring"""



         print(f"""Found {torch.cuda.device_count()} devices."""		)
         UpperCAmelCase__								=  ["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", self.operation_file_path]
         print(f"""Command: {cmd}"""		)
         with patch_environment(omp_num_threads=1		):
               execute_subprocess_async(__UpperCAmelCase       ,  env=os.environ.copy()		)


   @require_multi_gpu
   def 			lowercase_      (self     :						Tuple		)						->				int:
         """simple docstring"""



         UpperCAmelCase__								=  ["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__		)]
         with patch_environment(omp_num_threads=1		):
               execute_subprocess_async(__UpperCAmelCase       ,  env=os.environ.copy()		)





   @require_multi_gpu
   def 			lowercase_      (self     :						Dict		)						->				str:
         """simple docstring"""



         print(f"""Found {torch.cuda.device_count()} devices, using 2 devices only"""		)
         UpperCAmelCase__								=  ["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", self.data_loop_file_path]
         with patch_environment(omp_num_threads=1       ,  cuda_visible_devices="0,1"		):
               execute_subprocess_async(__UpperCAmelCase       ,  env=os.environ.copy()		)


if __name__ == "__main__":
      UpperCamelCase__											=					Accelerator()
      UpperCamelCase__											=					(accelerator.state.process_index + 2, 1_0)
      UpperCamelCase__											=					torch.randint(0, 1_0, shape).to(accelerator.device)

      UpperCamelCase__											=					''

      UpperCamelCase__											=					accelerator.pad_across_processes(tensor)
      if tensora.shape[0] != accelerator.state.num_processes + 1:
            error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
      if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
            error_msg += "Tensors have different values."
      if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
            error_msg += "Padding was not done with the right value (0)."

      UpperCamelCase__											=					accelerator.pad_across_processes(tensor, pad_first=True)
      if tensora.shape[0] != accelerator.state.num_processes + 1:
            error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
      UpperCamelCase__											=					accelerator.state.num_processes - accelerator.state.process_index - 1
      if not torch.equal(tensora[index:], tensor):
            error_msg += "Tensors have different values."
      if not torch.all(tensora[:index] == 0):
            error_msg += "Padding was not done with the right value (0)."

      # Raise error at the end to make sure we don't stop at the first failure.
      if len(error_msg) > 0:
            raise ValueError(error_msg)



