



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      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		int			       =    0
					A__				:		bool			       =    False
					A__				:		float			       =    3.0







class      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






					def     _a							(     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=_snake_case					).to_kwargs()				,					{'a': 2, 'b': True}					)
								self.assertDictEqual(MockClass(a=2				,					c=2.25					).to_kwargs()				,					{'a': 2, 'c': 2.25}					)



					@require_cuda
					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								A__       =    GradScalerKwargs(init_scale=10_24				,					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				,					20_00					)
								self.assertEqual(scaler._enabled				,					_snake_case					)



					@require_multi_gpu
					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								A__       =    ['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__					)]
								execute_subprocess_async(_snake_case				,					env=os.environ.copy()					)


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__       =      DistributedDataParallelKwargs(bucket_cap_mb=1_5, find_unused_parameters=True)
							SCREAMING_SNAKE_CASE__       =      Accelerator(kwargs_handlers=[ddp_scaler])
							SCREAMING_SNAKE_CASE__       =      torch.nn.Linear(1_0_0, 2_0_0)
							SCREAMING_SNAKE_CASE__       =      accelerator.prepare(model)

							# Check the values changed in kwargs
							SCREAMING_SNAKE_CASE__       =      ''''''
							SCREAMING_SNAKE_CASE__       =      model.bucket_bytes_cap // (1_0_2_4 * 1_0_2_4)
							if observed_bucket_cap_map != 1_5:
														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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__       =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       =      {
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		List[str]			       =    "roberta"



					def __init__(     self					:       List[str]				,					_snake_case					:       Union[str, Any]=5_02_65				,					_snake_case					:       List[Any]=7_68				,					_snake_case					:       List[str]=12				,					_snake_case					:       List[str]=12				,					_snake_case					:       Any=30_72				,					_snake_case					:       Union[str, Any]="gelu"				,					_snake_case					:       int=0.1				,					_snake_case					:       Union[str, Any]=0.1				,					_snake_case					:       Tuple=5_12				,					_snake_case					:       Union[str, Any]=2				,					_snake_case					:       Any=0.02				,					_snake_case					:       Any=1E-12				,					_snake_case					:       List[Any]=1				,					_snake_case					:       int=0				,					_snake_case					:       Any=2				,					_snake_case					:       Optional[Any]="absolute"				,					_snake_case					:       int=True				,					_snake_case					:       Any=None				,					**_snake_case					:       Any				,					):






								"""simple docstring"""


								super().__init__(pad_token_id=_snake_case				,					bos_token_id=_snake_case				,					eos_token_id=_snake_case				,					**_snake_case					)

								A__       =    vocab_size
								A__       =    hidden_size
								A__       =    num_hidden_layers
								A__       =    num_attention_heads
								A__       =    hidden_act
								A__       =    intermediate_size
								A__       =    hidden_dropout_prob
								A__       =    attention_probs_dropout_prob
								A__       =    max_position_embeddings
								A__       =    type_vocab_size
								A__       =    initializer_range
								A__       =    layer_norm_eps
								A__       =    position_embedding_type
								A__       =    use_cache
								A__       =    classifier_dropout







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					@property
					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								if self.task == "multiple-choice":
											A__       =    {0: 'batch', 1: 'choice', 2: 'sequence'}
								else:
											A__       =    {0: 'batch', 1: 'sequence'}
								return OrderedDict(
								    [
								        ('input_ids', dynamic_axis),
								        ('attention_mask', dynamic_axis),
								    ]					)




