






'''simple docstring'''




import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler

@dataclass
class    _UpperCAmelCase			(     snake_case_						):




   """simple docstring"""

   snake_case    				=						0
   snake_case    				=						False
   snake_case    				=						3.0





class    _UpperCAmelCase			(     unittest.TestCase						):




   """simple docstring"""

   def 						lowerCAmelCase (		self :	Union[str, Any]		):





         '''simple docstring'''





         self.assertDictEqual(MockClass().to_kwargs()      ,      {}		)
         self.assertDictEqual(MockClass(a=2		).to_kwargs()      ,      {"a": 2}		)
         self.assertDictEqual(MockClass(a=2      ,      b=__UpperCAmelCase		).to_kwargs()      ,      {"a": 2, "b": True}		)
         self.assertDictEqual(MockClass(a=2      ,      c=2.25		).to_kwargs()      ,      {"a": 2, "c": 2.25}		)

   @require_cuda
   def 						lowerCAmelCase (		self :	int		):





         '''simple docstring'''





         _A			     =	GradScalerKwargs(init_scale=1024      ,      growth_factor=2		)
         AcceleratorState._reset_state()
         _A			     =	Accelerator(mixed_precision="fp16"      ,      kwargs_handlers=[scaler_handler]		)
         print(accelerator.use_fpaa		)
         _A			     =	accelerator.scaler

         # Check the kwargs have been applied
         self.assertEqual(scaler._init_scale      ,      1024.0		)
         self.assertEqual(scaler._growth_factor      ,      2.0		)

         # Check the other values are at the default
         self.assertEqual(scaler._backoff_factor      ,      0.5		)
         self.assertEqual(scaler._growth_interval      ,      2000		)
         self.assertEqual(scaler._enabled      ,      __UpperCAmelCase		)

   @require_multi_gpu
   def 						lowerCAmelCase (		self :	List[Any]		):





         '''simple docstring'''





         _A			     =	["torchrun", f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__		)]
         execute_subprocess_async(__UpperCAmelCase      ,      env=os.environ.copy()		)


if __name__ == "__main__":
    lowerCamelCase_												=					DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
    lowerCamelCase_												=					Accelerator(kwargs_handlers=[ddp_scaler])
    lowerCamelCase_												=					torch.nn.Linear(1_00, 2_00)
    lowerCamelCase_												=					accelerator.prepare(model)

    # Check the values changed in kwargs
    lowerCamelCase_												=					''''''
    lowerCamelCase_												=					model.bucket_bytes_cap // (10_24 * 10_24)
    if observed_bucket_cap_map != 15:
        error_msg += F"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
    if model.find_unused_parameters is not True:
        error_msg += F"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

    # Check the values of the defaults
    if model.dim != 0:
        error_msg += F"Default value not respected, should have `0` but found {model.dim}.\n"
    if model.broadcast_buffers is not True:
        error_msg += F"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
    if model.gradient_as_bucket_view is not False:
        error_msg += F"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

    # 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,
)


_A   =     {"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     ["XGLMTokenizer"]

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

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "XGLMForCausalLM",
			    "XGLMModel",
			    "XGLMPreTrainedModel",
			]


try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "FlaxXGLMForCausalLM",
			    "FlaxXGLMModel",
			    "FlaxXGLMPreTrainedModel",
			]


try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFXGLMForCausalLM",
			    "TFXGLMModel",
			    "TFXGLMPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xglm import XGLMTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xglm_fast import XGLMTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xglm import (
						    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXGLMForCausalLM,
						    TFXGLMModel,
						    TFXGLMPreTrainedModel,
						)


else:
			import sys

			_A   =     _LazyModule(__name__, globals()["__file__"], _import_structure)



