'''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 			__A (					unittest.TestCase      ):



    def    _lowercase		(self       :       Optional[int]					):
         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       :       Any					):
         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(__a  ,     env=os.environ.copy()					)



    @require_multi_gpu
    def    _lowercase		(self       :       List[Any]					):
         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(__a  ,     env=os.environ.copy()					)



    @require_multi_gpu
    def    _lowercase		(self       :       Dict					):
         UpperCAmelCase_			       =  ["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__					)]
         with patch_environment(omp_num_threads=1					):
              execute_subprocess_async(__a  ,     env=os.environ.copy()					)






    @require_multi_gpu
    def    _lowercase		(self       :       Dict					):
         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(__a  ,     env=os.environ.copy()					)


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

    SCREAMING_SNAKE_CASE_:	Optional[Any]    =''

    SCREAMING_SNAKE_CASE_:	Union[str, 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_:	List[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."
    SCREAMING_SNAKE_CASE_:	List[str]    =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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_    						=       {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       ["""MBartTokenizer"""]

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       ["""MBartTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       [
							    """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MBartForCausalLM""",
							    """MBartForConditionalGeneration""",
							    """MBartForQuestionAnswering""",
							    """MBartForSequenceClassification""",
							    """MBartModel""",
							    """MBartPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       [
							    """TFMBartForConditionalGeneration""",
							    """TFMBartModel""",
							    """TFMBartPreTrainedModel""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       [
							    """FlaxMBartForConditionalGeneration""",
							    """FlaxMBartForQuestionAnswering""",
							    """FlaxMBartForSequenceClassification""",
							    """FlaxMBartModel""",
							    """FlaxMBartPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_mbart import MBartTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_mbart_fast import MBartTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mbart import (
														    MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MBartForCausalLM,
														    MBartForConditionalGeneration,
														    MBartForQuestionAnswering,
														    MBartForSequenceClassification,
														    MBartModel,
														    MBartPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_mbart import (
														    FlaxMBartForConditionalGeneration,
														    FlaxMBartForQuestionAnswering,
														    FlaxMBartForSequenceClassification,
														    FlaxMBartModel,
														    FlaxMBartPreTrainedModel,
														)

else:
							import sys

							UpperCamelCase_    						=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





