






"""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       ( _A							):
					SCREAMING_SNAKE_CASE_		:				int							    =   0
					SCREAMING_SNAKE_CASE_		:				bool							    =   False
					SCREAMING_SNAKE_CASE_		:				float							    =   3.0





class 					_UpperCAmelCase       ( unittest.TestCase							):




					def   A  (				self     :						List[str]   )  ->  List[Any]:
							# If no defaults are changed, `to_kwargs` returns an empty dict.
							self.assertDictEqual(MockClass().to_kwargs()    ,						{}   )
							self.assertDictEqual(MockClass(a=2   ).to_kwargs()    ,						{'''a''': 2}   )
							self.assertDictEqual(MockClass(a=2    ,						b=A   ).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     :						Union[str, Any]   )  ->  Optional[int]:
							# If no defaults are changed, `to_kwargs` returns an empty dict.
							lowercase_ : Optional[Any]							       =   GradScalerKwargs(init_scale=10_24    ,						growth_factor=2   )
							AcceleratorState._reset_state()
							lowercase_ : Union[str, Any]							       =   Accelerator(mixed_precision='''fp16'''    ,						kwargs_handlers=[scaler_handler]   )
							print(accelerator.use_fpaa   )
							lowercase_ : Dict							       =   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    ,						A   )






					@require_multi_gpu
					def   A  (				self     :						Any   )  ->  Any:
							lowercase_ : Any							       =   ['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__   )]
							execute_subprocess_async(A    ,						env=os.environ.copy()   )


if __name__ == "__main__":
						__A		:       Dict  				=		DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
						__A		:       Tuple  				=		Accelerator(kwargs_handlers=[ddp_scaler])
						__A		:       Optional[Any]  				=		torch.nn.Linear(100, 200)
						__A		:       Tuple  				=		accelerator.prepare(model)

						# Check the values changed in kwargs
						__A		:       List[Any]  				=		''''''
						__A		:       Any  				=		model.bucket_bytes_cap // (1_024 * 1_024)
						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 collections import OrderedDict
from typing import Mapping

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


_a 						=      logging.get_logger(__name__)

_a 						=      {
    "camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
    "umberto-commoncrawl-cased-v1": (
        "https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
    ),
    "umberto-wikipedia-uncased-v1": (
        "https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
    ),
}






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						"""camembert"""





			def __init__(    self       ,  __lowerCAmelCase=3_0_5_2_2       ,  __lowerCAmelCase=7_6_8       ,  __lowerCAmelCase=1_2       ,  __lowerCAmelCase=1_2       ,  __lowerCAmelCase=3_0_7_2       ,  __lowerCAmelCase="gelu"       ,  __lowerCAmelCase=0.1       ,  __lowerCAmelCase=0.1       ,  __lowerCAmelCase=5_1_2       ,  __lowerCAmelCase=2       ,  __lowerCAmelCase=0.02       ,  __lowerCAmelCase=1E-12       ,  __lowerCAmelCase=1       ,  __lowerCAmelCase=0       ,  __lowerCAmelCase=2       ,  __lowerCAmelCase="absolute"       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=None       ,  **__lowerCAmelCase       ,  ):



				'''simple docstring'''







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

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






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







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



