





'''simple docstring'''

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  _UpperCAmelCase ( unittest.TestCase    ):

     """simple docstring"""

     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

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


     @require_multi_gpu
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       print(f'''Found {torch.cuda.device_count()} devices.''' )
       a_      :	List[str]      =							['''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 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       print(f'''Found {torch.cuda.device_count()} devices.''' )
       a_      :	List[Any]      =							['''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 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							['''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 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       print(f'''Found {torch.cuda.device_count()} devices, using 2 devices only''' )
       a_      :	List[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(UpperCAmelCase__							,		env=os.environ.copy() )


if __name__ == "__main__":
    __snake_case:  Dict								=			Accelerator()
    __snake_case:  Optional[Any]								=			(accelerator.state.process_index + 2, 10)
    __snake_case:  Dict								=			torch.randint(0, 10, shape).to(accelerator.device)

    __snake_case:  Optional[Any]								=			''

    __snake_case:  Tuple								=			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)."

    __snake_case:  str								=			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."
    __snake_case:  List[Any]								=			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)




'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__snake_case:  Dict								=			{"configuration_encoder_decoder": ["EncoderDecoderConfig"]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case:  List[str]								=			["EncoderDecoderModel"]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case:  Any								=			["TFEncoderDecoderModel"]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case:  List[str]								=			["FlaxEncoderDecoderModel"]

if TYPE_CHECKING:
    from .configuration_encoder_decoder import EncoderDecoderConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_encoder_decoder import EncoderDecoderModel

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
    import sys

    __snake_case:  Tuple								=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


