



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						):







					def  _lowercase			(       self      :     Dict):
											A__	:							Dict						    = inspect.getfile(accelerate.test_utils)
											A__	:							Dict						    = os.path.sep.join(mod_file.split(os.path.sep)[:-1] + ["scripts", "test_script.py"])
											A__	:							Union[str, Any]						    = os.path.sep.join(
											    mod_file.split(os.path.sep)[:-1] + ["scripts", "test_distributed_data_loop.py"])
											A__	:							str						    = os.path.sep.join(mod_file.split(os.path.sep)[:-1] + ["scripts", "test_ops.py"])







					@require_multi_gpu
					def  _lowercase			(       self      :     Tuple):
											print(F'Found {torch.cuda.device_count()} devices.')
											A__	:							Any						    = ["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      :     Union[str, Any]):
											print(F'Found {torch.cuda.device_count()} devices.')
											A__	:							List[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(_A			,		env=os.environ.copy())







					@require_multi_gpu
					def  _lowercase			(       self      :     Union[str, Any]):
											A__	:							str						    = ["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      :     Union[str, Any]):
											print(F'Found {torch.cuda.device_count()} devices, using 2 devices only')
											A__	:							Dict						    = ["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__":
		snake_case    :						Union[str, Any]  =						Accelerator()
		snake_case    :						Any  =						(accelerator.state.process_index + 2, 1_0)
		snake_case    :						List[Any]  =						torch.randint(0, 1_0, shape).to(accelerator.device)

		snake_case    :						List[Any]  =						''

		snake_case    :						Dict  =						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)."

		snake_case    :						Dict  =						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."
		snake_case    :						Dict  =						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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case    :						str  =						{
    'configuration_albert': ['ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'AlbertConfig', 'AlbertOnnxConfig'],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case    :						Any  =						['AlbertTokenizer']

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

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case    :						List[str]  =						[
		    'ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'AlbertForMaskedLM',
		    'AlbertForMultipleChoice',
		    'AlbertForPreTraining',
		    'AlbertForQuestionAnswering',
		    'AlbertForSequenceClassification',
		    'AlbertForTokenClassification',
		    'AlbertModel',
		    'AlbertPreTrainedModel',
		    'load_tf_weights_in_albert',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case    :						Union[str, Any]  =						[
		    'TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFAlbertForMaskedLM',
		    'TFAlbertForMultipleChoice',
		    'TFAlbertForPreTraining',
		    'TFAlbertForQuestionAnswering',
		    'TFAlbertForSequenceClassification',
		    'TFAlbertForTokenClassification',
		    'TFAlbertMainLayer',
		    'TFAlbertModel',
		    'TFAlbertPreTrainedModel',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case    :						Any  =						[
		    'FlaxAlbertForMaskedLM',
		    'FlaxAlbertForMultipleChoice',
		    'FlaxAlbertForPreTraining',
		    'FlaxAlbertForQuestionAnswering',
		    'FlaxAlbertForSequenceClassification',
		    'FlaxAlbertForTokenClassification',
		    'FlaxAlbertModel',
		    'FlaxAlbertPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_albert import AlbertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_albert_fast import AlbertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_albert import (
				    ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    AlbertForMaskedLM,
				    AlbertForMultipleChoice,
				    AlbertForPreTraining,
				    AlbertForQuestionAnswering,
				    AlbertForSequenceClassification,
				    AlbertForTokenClassification,
				    AlbertModel,
				    AlbertPreTrainedModel,
				    load_tf_weights_in_albert,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_albert import (
				    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFAlbertForMaskedLM,
				    TFAlbertForMultipleChoice,
				    TFAlbertForPreTraining,
				    TFAlbertForQuestionAnswering,
				    TFAlbertForSequenceClassification,
				    TFAlbertForTokenClassification,
				    TFAlbertMainLayer,
				    TFAlbertModel,
				    TFAlbertPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_albert import (
				    FlaxAlbertForMaskedLM,
				    FlaxAlbertForMultipleChoice,
				    FlaxAlbertForPreTraining,
				    FlaxAlbertForQuestionAnswering,
				    FlaxAlbertForSequenceClassification,
				    FlaxAlbertForTokenClassification,
				    FlaxAlbertModel,
				    FlaxAlbertPreTrainedModel,
				)
else:
		import sys

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