
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 ):





     """simple docstring"""







     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







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






     @require_multi_gpu
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[int]:




           """simple docstring"""







           print(F'''Found {torch.cuda.device_count()} devices.'''  )
           UpperCamelCase__    :   Union[str, Any]					=				['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
           with patch_environment(omp_num_threads=1  ):
                 execute_subprocess_async(__SCREAMING_SNAKE_CASE						,						env=os.environ.copy()  )






     @require_multi_gpu
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           print(F'''Found {torch.cuda.device_count()} devices.'''  )
           UpperCamelCase__    :   Optional[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(__SCREAMING_SNAKE_CASE						,						env=os.environ.copy()  )






     @require_multi_gpu
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Tuple:




           """simple docstring"""







           UpperCamelCase__    :   int					=				['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__  )]
           with patch_environment(omp_num_threads=1  ):
                 execute_subprocess_async(__SCREAMING_SNAKE_CASE						,						env=os.environ.copy()  )






     @require_multi_gpu
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Union[str, Any]:




           """simple docstring"""







           print(F'''Found {torch.cuda.device_count()} devices, using 2 devices only'''  )
           UpperCamelCase__    :   Optional[Any]					=				['''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(__SCREAMING_SNAKE_CASE						,						env=os.environ.copy()  )


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

    lowerCamelCase      =""

    lowerCamelCase      =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)."

    lowerCamelCase      =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."
    lowerCamelCase      =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)




from typing import TYPE_CHECKING

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


lowerCamelCase      ={
    "configuration_mobilebert": [
        "MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileBertConfig",
        "MobileBertOnnxConfig",
    ],
    "tokenization_mobilebert": ["MobileBertTokenizer"],
}

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

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "MobileBertForMaskedLM",
        "MobileBertForMultipleChoice",
        "MobileBertForNextSentencePrediction",
        "MobileBertForPreTraining",
        "MobileBertForQuestionAnswering",
        "MobileBertForSequenceClassification",
        "MobileBertForTokenClassification",
        "MobileBertLayer",
        "MobileBertModel",
        "MobileBertPreTrainedModel",
        "load_tf_weights_in_mobilebert",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFMobileBertForMaskedLM",
        "TFMobileBertForMultipleChoice",
        "TFMobileBertForNextSentencePrediction",
        "TFMobileBertForPreTraining",
        "TFMobileBertForQuestionAnswering",
        "TFMobileBertForSequenceClassification",
        "TFMobileBertForTokenClassification",
        "TFMobileBertMainLayer",
        "TFMobileBertModel",
        "TFMobileBertPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_mobilebert import (
        MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        MobileBertConfig,
        MobileBertOnnxConfig,
    )
    from .tokenization_mobilebert import MobileBertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mobilebert_fast import MobileBertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mobilebert import (
            MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            MobileBertForMaskedLM,
            MobileBertForMultipleChoice,
            MobileBertForNextSentencePrediction,
            MobileBertForPreTraining,
            MobileBertForQuestionAnswering,
            MobileBertForSequenceClassification,
            MobileBertForTokenClassification,
            MobileBertLayer,
            MobileBertModel,
            MobileBertPreTrainedModel,
            load_tf_weights_in_mobilebert,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mobilebert import (
            TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFMobileBertForMaskedLM,
            TFMobileBertForMultipleChoice,
            TFMobileBertForNextSentencePrediction,
            TFMobileBertForPreTraining,
            TFMobileBertForQuestionAnswering,
            TFMobileBertForSequenceClassification,
            TFMobileBertForTokenClassification,
            TFMobileBertMainLayer,
            TFMobileBertModel,
            TFMobileBertPreTrainedModel,
        )

else:
    import sys

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


