





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)








import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
     from sagemaker import Session, TrainingJobAnalytics
     from sagemaker.huggingface import HuggingFace



@pytest.mark.skipif(
    literal_eval(os.getenv('''TEST_SAGEMAKER'''  ,						'''False'''     )     ) is not True  ,						reason='''Skipping test because should only be run when releasing minor transformers version'''  ,						)
@pytest.mark.usefixtures('''sm_env'''     )
@parameterized_class(
    [
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.p3.16xlarge''',
            '''results''': {'''train_runtime''': 6_5_0, '''eval_accuracy''': 0.7, '''eval_loss''': 0.6},
        },
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_ddp.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.p3.16xlarge''',
            '''results''': {'''train_runtime''': 6_0_0, '''eval_accuracy''': 0.7, '''eval_loss''': 0.6},
        },
        {
            '''framework''': '''tensorflow''',
            '''script''': '''run_tf_dist.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.p3.16xlarge''',
            '''results''': {'''train_runtime''': 6_0_0, '''eval_accuracy''': 0.6, '''eval_loss''': 0.7},
        },
    ]     )
class 							_UpperCamelCase					(	unittest.TestCase     ):

       def lowercase (		self: Union[str, Any]		)       -> List[str]:




              """simple docstring"""


              if self.framework == "pytorch":
                     subprocess.run(
                         f'''cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'''.split()					,   encoding="utf-8"					,   check=_SCREAMING_SNAKE_CASE					,   )
              assert hasattr(self					,   "env"		)

       def lowercase (		self: List[str]					,   _SCREAMING_SNAKE_CASE: Dict		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  f'''{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'''
              # distributed data settings
              UpperCamelCase_          =  {"smdistributed": {"dataparallel": {"enabled": True}}} if self.script != "run_ddp.py" else None

              # creates estimator
              return HuggingFace(
                  entry_point=self.script					,   source_dir=self.env.test_path					,   role=self.env.role					,   image_uri=self.env.image_uri					,   base_job_name=_SCREAMING_SNAKE_CASE					,   instance_count=_SCREAMING_SNAKE_CASE					,   instance_type=self.instance_type					,   debugger_hook_config=_SCREAMING_SNAKE_CASE					,   hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path}					,   metric_definitions=self.env.metric_definitions					,   distribution=_SCREAMING_SNAKE_CASE					,   py_version="py36"					,   )

       def lowercase (		self: List[str]					,   _SCREAMING_SNAKE_CASE: Any		)       -> List[Any]:




              """simple docstring"""


              TrainingJobAnalytics(_SCREAMING_SNAKE_CASE		).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''		)



       @parameterized.expand([(2,)]		)
       def lowercase (		self: str					,   _SCREAMING_SNAKE_CASE: Tuple		)       -> Optional[Any]:




              """simple docstring"""


              UpperCamelCase_          =  self.create_estimator(_SCREAMING_SNAKE_CASE		)

              # run training
              estimator.fit()

              # result dataframe
              UpperCamelCase_          =  TrainingJobAnalytics(estimator.latest_training_job.name		).dataframe()

              # extract kpis
              UpperCamelCase_          =  list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]		)
              UpperCamelCase_          =  list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]		)
              # get train time from SageMaker job, this includes starting, preprocessing, stopping
              UpperCamelCase_          =  (
                  Session().describe_training_job(estimator.latest_training_job.name		).get("TrainingTimeInSeconds"					,   999999		)
              )

              # assert kpis
              assert train_runtime <= self.results["train_runtime"]
              assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy		)
              assert all(t <= self.results["eval_loss"] for t in eval_loss		)

              # dump tests result into json file to share in PR
              with open(f'''{estimator.latest_training_job.name}.json'''					,   "w"		) as outfile:
                     json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}					,   _SCREAMING_SNAKE_CASE		)

