
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder


# @@protoc_insertion_point(imports)

UpperCamelCase_ 						=						_symbol_database.Default()


UpperCamelCase_ 						=						_descriptor_pool.Default().AddSerializedFile(
    b"\n\x19sentencepiece_model.proto\x12\rsentencepiece\"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12\"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12\"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18  \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18\" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse\"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32\".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL\"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03"
)

UpperCamelCase_ 						=						globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, "sentencepiece_model_pb2", _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
					UpperCamelCase_ 						=						None
					UpperCamelCase_ 						=						B'H\003'
					# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
					# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
					# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
					# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
					# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
					UpperCamelCase_ 						=						4_5
					UpperCamelCase_ 						=						1_5_8_1
					UpperCamelCase_ 						=						1_5_1_7
					UpperCamelCase_ 						=						1_5_7_0
					UpperCamelCase_ 						=						1_5_8_4
					UpperCamelCase_ 						=						1_7_9_3
					UpperCamelCase_ 						=						1_7_9_5
					UpperCamelCase_ 						=						1_9_1_6
					UpperCamelCase_ 						=						1_8_6_4
					UpperCamelCase_ 						=						1_9_0_5
					UpperCamelCase_ 						=						1_9_1_9
					UpperCamelCase_ 						=						2_4_2_9
					UpperCamelCase_ 						=						2_2_0_8
					UpperCamelCase_ 						=						2_4_1_8
					UpperCamelCase_ 						=						2_3_2_3
					UpperCamelCase_ 						=						2_4_0_7
# @@protoc_insertion_point(module_scope)








'''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 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							0
				lowerCAmelCase_     			=							False
				lowerCAmelCase_     			=							3.0


class 			SCREAMING_SNAKE_CASE__      (     unittest.TestCase):







				def 						UpperCAmelCase_    (       self     )->		int:







											'''simple docstring'''






											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 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






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

											# Check the kwargs have been applied
											self.assertEqual(scaler._init_scale						,				1_024.0     )
											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						,				A_     )







				@require_multi_gpu
				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






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


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

						# Check the values changed in kwargs
						lowerCAmelCase					:    Dict           = ''
						lowerCAmelCase					:    Dict           = 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)




