



"""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 			_lowerCAmelCase     (      self				:       Union[str, Any]    )						->	Optional[int]:





					"""simple docstring"""


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





	@require_multi_gpu
	def 			_lowerCAmelCase     (      self				:       Tuple    )						->	List[str]:





					"""simple docstring"""


					print(f"""Found {torch.cuda.device_count()} devices."""    )
					SCREAMING_SNAKE_CASE						:							Dict							=     ['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 			_lowerCAmelCase     (      self				:       List[Any]    )						->	Dict:





					"""simple docstring"""


					print(f"""Found {torch.cuda.device_count()} devices."""    )
					SCREAMING_SNAKE_CASE						:							str							=     ['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 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	Optional[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							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 			_lowerCAmelCase     (      self				:       Any    )						->	Any:





					"""simple docstring"""


					print(f"""Found {torch.cuda.device_count()} devices, using 2 devices only"""    )
					SCREAMING_SNAKE_CASE						:							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__":
							A_						:    Union[str, Any]				=      Accelerator()
							A_						:    Union[str, Any]				=      (accelerator.state.process_index + 2, 10)
							A_						:    List[str]				=      torch.randint(0, 10, shape).to(accelerator.device)

							A_						:    Tuple				=      ''

							A_						:    Optional[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)."

							A_						:    Optional[Any]				=      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_						:    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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


A_						:    Optional[int]				=      {
    'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LlamaConfig'],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:    int				=      ['LlamaTokenizer']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:    List[str]				=      ['LlamaTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:    List[str]				=      [
							    'LlamaForCausalLM',
							    'LlamaModel',
							    'LlamaPreTrainedModel',
							    'LlamaForSequenceClassification',
							]


if TYPE_CHECKING:
							from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_llama import LlamaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_llama_fast import LlamaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
							import sys

							A_						:    str				=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





