
"""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 				lowerCAmelCase_				(      unittest.TestCase    ):



						'''simple docstring'''


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Optional[int]:
							A						   =   inspect.getfile(accelerate.test_utils      )
							A						   =   os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ['scripts', 'test_script.py']      )
							A						   =   os.path.sep.join(
							    mod_file.split(os.path.sep      )[:-1] + ['scripts', 'test_distributed_data_loop.py']      )
							A						   =   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]      )     ->   Optional[Any]:
							print(F'Found {torch.cuda.device_count()} devices.'      )
							A						   =   ['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 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   Tuple:
							print(F'Found {torch.cuda.device_count()} devices.'      )
							A						   =   ['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 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   Dict:
							A						   =   ['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 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   str:
							print(F'Found {torch.cuda.device_count()} devices, using 2 devices only'      )
							A						   =   ['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__":
		_lowercase							   =				Accelerator()
		_lowercase							   =				(accelerator.state.process_index + 2, 10)
		_lowercase							   =				torch.randint(0, 10, shape).to(accelerator.device)

		_lowercase							   =				''''''

		_lowercase							   =				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)."

		_lowercase							   =				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."
		_lowercase							   =				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_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_lowercase							   =				{
    '''configuration_perceiver''': ['''PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PerceiverConfig''', '''PerceiverOnnxConfig'''],
    '''tokenization_perceiver''': ['''PerceiverTokenizer'''],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				['''PerceiverFeatureExtractor''']
		_lowercase							   =				['''PerceiverImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				[
		    '''PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''PerceiverForImageClassificationConvProcessing''',
		    '''PerceiverForImageClassificationFourier''',
		    '''PerceiverForImageClassificationLearned''',
		    '''PerceiverForMaskedLM''',
		    '''PerceiverForMultimodalAutoencoding''',
		    '''PerceiverForOpticalFlow''',
		    '''PerceiverForSequenceClassification''',
		    '''PerceiverLayer''',
		    '''PerceiverModel''',
		    '''PerceiverPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
		from .tokenization_perceiver import PerceiverTokenizer

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_perceiver import PerceiverFeatureExtractor
				from .image_processing_perceiver import PerceiverImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_perceiver import (
				    PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PerceiverForImageClassificationConvProcessing,
				    PerceiverForImageClassificationFourier,
				    PerceiverForImageClassificationLearned,
				    PerceiverForMaskedLM,
				    PerceiverForMultimodalAutoencoding,
				    PerceiverForOpticalFlow,
				    PerceiverForSequenceClassification,
				    PerceiverLayer,
				    PerceiverModel,
				    PerceiverPreTrainedModel,
				)

else:
		import sys

		_lowercase							   =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)