

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 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




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





   @require_multi_gpu
   def      _snake_case		(       self					:			str			):
       print(f"Found {torch.cuda.device_count()} devices."			)
       SCREAMING_SNAKE_CASE										=   ["torchrun", f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
       with patch_environment(omp_num_threads=1			):
           execute_subprocess_async(__lowerCamelCase     , env=os.environ.copy()			)





   @require_multi_gpu
   def      _snake_case		(       self					:			List[Any]			):
       print(f"Found {torch.cuda.device_count()} devices."			)
       SCREAMING_SNAKE_CASE										=   ["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(__lowerCamelCase     , env=os.environ.copy()			)





   @require_multi_gpu
   def      _snake_case		(       self					:			Optional[int]			):
       SCREAMING_SNAKE_CASE										=   ["torchrun", f"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__			)]
       with patch_environment(omp_num_threads=1			):
           execute_subprocess_async(__lowerCamelCase     , env=os.environ.copy()			)





   @require_multi_gpu
   def      _snake_case		(       self					:			Union[str, Any]			):
       print(f"Found {torch.cuda.device_count()} devices, using 2 devices only"			)
       SCREAMING_SNAKE_CASE										=   ["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(__lowerCamelCase     , env=os.environ.copy()			)


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

    __A			:					Optional[Any]						=			''

    __A			:					int						=			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)."

    __A			:					Optional[int]						=			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."
    __A			:					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)


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A			:					List[Any]						=			{
    'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
    'processing_git': ['GitProcessor'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __A			:					int						=			[
        'GIT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'GitForCausalLM',
        'GitModel',
        'GitPreTrainedModel',
        'GitVisionModel',
    ]

if TYPE_CHECKING:
    from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
    from .processing_git import GitProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_git import (
            GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            GitForCausalLM,
            GitModel,
            GitPreTrainedModel,
            GitVisionModel,
        )

else:
    import sys

    __A			:					List[Any]						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)