




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase	:		Optional[Any]							       =   logging.get_logger(__name__)


_lowerCamelCase	:		List[str]							       =   """▁"""

_lowerCamelCase	:		List[Any]							       =   {"""vocab_file""": """spiece.model"""}

_lowerCamelCase	:		List[str]							       =   {
    """vocab_file""": {
        """google/reformer-crime-and-punishment""": (
            """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"""
        )
    }
}

_lowerCamelCase	:		Optional[int]							       =   {
    """google/reformer-crime-and-punishment""": 524288,
}


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      VOCAB_FILES_NAMES
    UpperCAmelCase__							  =      PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__							  =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__							  =      ['''input_ids''', '''attention_mask''']


    def __init__(			self      :	Union[str, Any]					, UpperCAmelCase__      :	Dict					, UpperCAmelCase__      :	List[str]="</s>"					, UpperCAmelCase__      :	Optional[Any]="<unk>"					, UpperCAmelCase__      :	List[str]=[]					, UpperCAmelCase__      :	Optional[Dict[str, Any]] = None					, **UpperCAmelCase__      :	Dict					, )						->None:





           '''simple docstring'''

           A__       =    {} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               eos_token=UpperCAmelCase__					, unk_token=UpperCAmelCase__					, additional_special_tokens=UpperCAmelCase__					, sp_model_kwargs=self.sp_model_kwargs					, **UpperCAmelCase__					, )

           A__       =    vocab_file
           A__       =    spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(UpperCAmelCase__)


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	List[Any])						->List[str]:





           '''simple docstring'''

           return self.sp_model.get_piece_size()


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any])						->Dict[str, int]:





           '''simple docstring'''

           A__       =    {self.convert_ids_to_tokens(UpperCAmelCase__): i for i in range(self.vocab_size)}
           vocab.update(self.added_tokens_encoder)
           return vocab


    def __getstate__(			self      :	Any)						->Dict:





           '''simple docstring'''

           A__       =    self.__dict__.copy()
           A__       =    None
           return state


    def __setstate__(			self      :	int					, UpperCAmelCase__      :	List[str])						->Dict:





           '''simple docstring'''

           A__       =    d

           # for backward compatibility
           if not hasattr(self					, '''sp_model_kwargs'''):
                  A__       =    {}

           A__       =    spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(self.vocab_file)


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str]					, UpperCAmelCase__      :	str)						->List[str]:





           '''simple docstring'''

           return self.sp_model.encode(UpperCAmelCase__					, out_type=UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str]					, UpperCAmelCase__      :	Any)						->int:





           '''simple docstring'''

           return self.sp_model.piece_to_id(UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str]					, UpperCAmelCase__      :	List[str])						->Tuple:





           '''simple docstring'''

           if index < self.sp_model.get_piece_size():
                  A__       =    self.sp_model.IdToPiece(UpperCAmelCase__)
           return token


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str]					, UpperCAmelCase__      :	Dict)						->Tuple:





           '''simple docstring'''

           A__       =    []
           A__       =    ''''''
           for token in tokens:
                  # make sure that special tokens are not decoded using sentencepiece model
                  if token in self.all_special_tokens:
                         out_string += self.sp_model.decode(UpperCAmelCase__) + token
                         A__       =    []
                  else:
                         current_sub_tokens.append(UpperCAmelCase__)
           out_string += self.sp_model.decode(UpperCAmelCase__)
           return out_string.strip()





    def        SCREAMING_SNAKE_CASE							(			self      :	List[str]					, UpperCAmelCase__      :	str					, UpperCAmelCase__      :	Optional[str] = None)						->Tuple[str]:





           '''simple docstring'''

           if not os.path.isdir(UpperCAmelCase__):
                  logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
                  return
           A__       =    os.path.join(
               UpperCAmelCase__					, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

           if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCAmelCase__) and os.path.isfile(self.vocab_file):
                  copyfile(self.vocab_file					, UpperCAmelCase__)
           elif not os.path.isfile(self.vocab_file):
                  with open(UpperCAmelCase__					, '''wb''') as fi:
                         A__       =    self.sp_model.serialized_model_proto()
                         fi.write(UpperCAmelCase__)

           return (out_vocab_file,)





"""simple docstring"""





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_model_parallelism.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 1_6_0_0, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
        {
            "framework": "pytorch",
            "script": "run_glue.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 1_6_0_0, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]   )
class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 def 					_lowercase (			self							:			Optional[int]	):
    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=UpperCAmelCase__,					)
    assert hasattr(self,					"env"	)


 def 					_lowercase (			self							:			str,					UpperCAmelCase__							:			List[Any]	):
    # configuration for running training on smdistributed Model Parallel
    __lowercase								=						{
        "enabled": True,
        "processes_per_host": 8,
    }
    __lowercase								=						{
        "enabled": True,
        "parameters": {
            "microbatches": 4,
            "placement_strategy": "spread",
            "pipeline": "interleaved",
            "optimize": "speed",
            "partitions": 4,
            "ddp": True,
        },
    }

    __lowercase								=						{"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}

    __lowercase								=						"trainer" if self.script == "run_glue.py" else "smtrainer"
    # 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=F"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}""",					instance_count=UpperCAmelCase__,					instance_type=self.instance_type,					debugger_hook_config=UpperCAmelCase__,					hyperparameters={
            **self.env.hyperparameters,
            "model_name_or_path": self.model_name_or_path,
            "max_steps": 5_0_0,
        },					metric_definitions=self.env.metric_definitions,					distribution=UpperCAmelCase__,					py_version="py36",					)


 def 					_lowercase (			self							:			Tuple,					UpperCAmelCase__							:			int	):
    TrainingJobAnalytics(UpperCAmelCase__	).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""	)



 @parameterized.expand([(1,)]	)
 def 					_lowercase (			self							:			str,					UpperCAmelCase__							:			Union[str, Any]	):
    # create estimator
    __lowercase								=						self.create_estimator(UpperCAmelCase__	)

    # run training
    estimator.fit()

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

    # extract kpis
    __lowercase								=						list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]	)
    __lowercase								=						list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]	)
    # get train time from SageMaker job, this includes starting, preprocessing, stopping
    __lowercase								=						(
        Session().describe_training_job(estimator.latest_training_job.name	).get("TrainingTimeInSeconds",					9_9_9_9_9_9	)
    )

    # 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},					UpperCAmelCase__	)




