





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": 16_00, "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": 16_00, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ] )
class  __A(						unittest.TestCase ):

     def 	_UpperCamelCase     (      self      ):





            """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=A__,       )
            assert hasattr(self,       '''env'''      )

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						{
                """enabled""": True,
                """processes_per_host""": 8,
            }
            _UpperCamelCase              =						{
                """enabled""": True,
                """parameters""": {
                    """microbatches""": 4,
                    """placement_strategy""": """spread""",
                    """pipeline""": """interleaved""",
                    """optimize""": """speed""",
                    """partitions""": 4,
                    """ddp""": True,
                },
            }

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

            _UpperCamelCase              =						"""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=A__,       instance_type=self.instance_type,       debugger_hook_config=A__,       hyperparameters={
                    **self.env.hyperparameters,
                    '''model_name_or_path''': self.model_name_or_path,
                    '''max_steps''': 500,
                },       metric_definitions=self.env.metric_definitions,       distribution=A__,       py_version='''py36''',       )

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            TrainingJobAnalytics(A__      ).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''      )



     @parameterized.expand([(1,)]      )
     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						self.create_estimator(A__      )

            # 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''',       99_9999      )
            )

            # 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},       A__      )













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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_big_bird import BigBirdTokenizer
else:
       lowercase    : Union[str, Any]    	=  None

lowercase    : Union[str, Any]    	=  logging.get_logger(__name__)
lowercase    : Tuple    	=  {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowercase    : Any    	=  {
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    },
    """tokenizer_file""": {
        """google/bigbird-roberta-base""": (
            """https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"""
        ),
    },
}

lowercase    : Tuple    	=  {
    """google/bigbird-roberta-base""": 4096,
    """google/bigbird-roberta-large""": 4096,
    """google/bigbird-base-trivia-itc""": 4096,
}


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

class  __A(						__UpperCAmelCase ):
     __A	  =       VOCAB_FILES_NAMES
     __A	  =       PRETRAINED_VOCAB_FILES_MAP
     __A	  =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __A	  =       BigBirdTokenizer
     __A	  =       ["input_ids", "attention_mask"]
     __A	  =       []

     def __init__(      self,       A=None,       A=None,       A="<unk>",       A="<s>",       A="</s>",       A="<pad>",       A="[SEP]",       A="[MASK]",       A="[CLS]",       **A,       ):





            """simple docstring"""

            _UpperCamelCase              =						AddedToken(A,       lstrip=A,       rstrip=A      ) if isinstance(A,       A      ) else bos_token
            _UpperCamelCase              =						AddedToken(A,       lstrip=A,       rstrip=A      ) if isinstance(A,       A      ) else eos_token
            _UpperCamelCase              =						AddedToken(A,       lstrip=A,       rstrip=A      ) if isinstance(A,       A      ) else unk_token
            _UpperCamelCase              =						AddedToken(A,       lstrip=A,       rstrip=A      ) if isinstance(A,       A      ) else pad_token
            _UpperCamelCase              =						AddedToken(A,       lstrip=A,       rstrip=A      ) if isinstance(A,       A      ) else cls_token
            _UpperCamelCase              =						AddedToken(A,       lstrip=A,       rstrip=A      ) if isinstance(A,       A      ) else sep_token

            # Mask token behave like a normal word, i.e. include the space before it
            _UpperCamelCase              =						AddedToken(A,       lstrip=A,       rstrip=A      ) if isinstance(A,       A      ) else mask_token

            super().__init__(
                A,       tokenizer_file=A,       bos_token=A,       eos_token=A,       unk_token=A,       sep_token=A,       pad_token=A,       cls_token=A,       mask_token=A,       **A,       )

            _UpperCamelCase              =						vocab_file
            _UpperCamelCase              =						False if not self.vocab_file else True

     def 	_UpperCamelCase     (      self,       A,       A = None      ):





            """simple docstring"""

            _UpperCamelCase              =						[self.sep_token_id]
            _UpperCamelCase              =						[self.cls_token_id]
            if token_ids_a is None:
                   return cls + token_ids_a + sep
            return cls + token_ids_a + sep + token_ids_a + sep

     def 	_UpperCamelCase     (      self,       A,       A = None,       A = False      ):





            """simple docstring"""

            if already_has_special_tokens:
                   if token_ids_a is not None:
                          raise ValueError(
                              '''You should not supply a second sequence if the provided sequence of '''
                              '''ids is already formatted with special tokens for the model.'''      )
                   return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

            if token_ids_a is None:
                   return [1] + ([0] * len(A      )) + [1]
            return [1] + ([0] * len(A      )) + [1] + ([0] * len(A      )) + [1]

     def 	_UpperCamelCase     (      self,       A,       A = None      ):





            """simple docstring"""

            _UpperCamelCase              =						[self.sep_token_id]
            _UpperCamelCase              =						[self.cls_token_id]

            if token_ids_a is None:
                   return len(cls + token_ids_a + sep      ) * [0]
            return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]



     def 	_UpperCamelCase     (      self,       A,       A = None      ):





            """simple docstring"""

            if not self.can_save_slow_tokenizer:
                   raise ValueError(
                       '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                       '''tokenizer.'''      )

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

            if os.path.abspath(self.vocab_file      ) != os.path.abspath(A      ):
                   copyfile(self.vocab_file,       A      )

            return (out_vocab_file,)






