





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   __lowerCAmelCase			(					unittest.TestCase  ):





   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





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





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





      print(F"""Found {torch.cuda.device_count()} devices."""      )
      a__	:						Optional[Any]      		=  ["torchrun", F"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
      with patch_environment(omp_num_threads=1      ):
         execute_subprocess_async(snake_case					,			env=os.environ.copy()      )





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





      print(F"""Found {torch.cuda.device_count()} devices."""      )
      a__	:						Dict      		=  ["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(snake_case					,			env=os.environ.copy()      )





   @require_multi_gpu
   def 	_snake_case ( self      )				->  Optional[int]:
      """simple docstring"""





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





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





      print(F"""Found {torch.cuda.device_count()} devices, using 2 devices only"""      )
      a__	:						str      		=  ["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(snake_case					,			env=os.environ.copy()      )


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__       :			int			      =						Accelerator()
     SCREAMING_SNAKE_CASE__       :			Union[str, Any]			      =						(accelerator.state.process_index + 2, 1_0)
     SCREAMING_SNAKE_CASE__       :			Dict			      =						torch.randint(0, 1_0, shape).to(accelerator.device)

     SCREAMING_SNAKE_CASE__       :			List[Any]			      =						""""""

     SCREAMING_SNAKE_CASE__       :			List[Any]			      =						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)."

     SCREAMING_SNAKE_CASE__       :			Tuple			      =						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."
     SCREAMING_SNAKE_CASE__       :			Tuple			      =						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)








# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE__       :			int			      =						{
    """configuration_cpmant""": ["""CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CpmAntConfig"""],
    """tokenization_cpmant""": ["""CpmAntTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			List[str]			      =						[
      """CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """CpmAntForCausalLM""",
      """CpmAntModel""",
      """CpmAntPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
  from .tokenization_cpmant import CpmAntTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_cpmant import (
        CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
        CpmAntForCausalLM,
        CpmAntModel,
        CpmAntPreTrainedModel,
    )


else:
  import sys

  SCREAMING_SNAKE_CASE__       :			Union[str, Any]			      =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

