

'''simple docstring'''
import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


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


lowerCamelCase :      Optional[int]					 =     {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

# See all BART models at https://huggingface.co/models?filter=bart
lowerCamelCase :      Any					 =     {
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json",
    },
}

lowerCamelCase :      Any					 =     {
    "facebook/bart-base": 1_0_2_4,
    "facebook/bart-large": 1_0_2_4,
    "facebook/bart-large-mnli": 1_0_2_4,
    "facebook/bart-large-cnn": 1_0_2_4,
    "facebook/bart-large-xsum": 1_0_2_4,
    "yjernite/bart_eli5": 1_0_2_4,
}







class 							A__    (       A__  ):
  A__		 =				VOCAB_FILES_NAMES
  A__		 =				PRETRAINED_VOCAB_FILES_MAP
  A__		 =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  A__		 =				['input_ids', 'attention_mask']
  A__		 =				BartTokenizer





  def __init__(		self      :	List[Any]							,					_a      :	Any=None							,					_a      :	str=None							,					_a      :	Any=None							,					_a      :	Dict="replace"							,					_a      :	List[Any]="<s>"							,					_a      :	List[Any]="</s>"							,					_a      :	Optional[Any]="</s>"							,					_a      :	str="<s>"							,					_a      :	Optional[Any]="<unk>"							,					_a      :	Any="<pad>"							,					_a      :	List[str]="<mask>"							,					_a      :	Union[str, Any]=False							,					_a      :	List[Any]=True							,					**_a      :	Tuple							,					)	->    List[str]:





       '''simple docstring'''






       super().__init__(
           _a							,					_a							,					tokenizer_file=_a							,					errors=_a							,					bos_token=_a							,					eos_token=_a							,					sep_token=_a							,					cls_token=_a							,					unk_token=_a							,					pad_token=_a							,					mask_token=_a							,					add_prefix_space=_a							,					trim_offsets=_a							,					**_a							,					)

       _SCREAMING_SNAKE_CASE							    =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
       if pre_tok_state.get('add_prefix_space'							,					_a ) != add_prefix_space:
            _SCREAMING_SNAKE_CASE							    =getattr(_a							,					pre_tok_state.pop('type' ) )
            _SCREAMING_SNAKE_CASE							    =add_prefix_space
            _SCREAMING_SNAKE_CASE							    =pre_tok_class(**_a )

       _SCREAMING_SNAKE_CASE							    =add_prefix_space

       # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
       _SCREAMING_SNAKE_CASE							    ='post_processor'
       _SCREAMING_SNAKE_CASE							    =getattr(self.backend_tokenizer							,					_a							,					_a )
       if tokenizer_component_instance:
            _SCREAMING_SNAKE_CASE							    =json.loads(tokenizer_component_instance.__getstate__() )

            # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
            if "sep" in state:
                 _SCREAMING_SNAKE_CASE							    =tuple(state['sep'] )
            if "cls" in state:
                 _SCREAMING_SNAKE_CASE							    =tuple(state['cls'] )

            _SCREAMING_SNAKE_CASE							    =False

            if state.get('add_prefix_space'							,					_a ) != add_prefix_space:
                 _SCREAMING_SNAKE_CASE							    =add_prefix_space
                 _SCREAMING_SNAKE_CASE							    =True

            if state.get('trim_offsets'							,					_a ) != trim_offsets:
                 _SCREAMING_SNAKE_CASE							    =trim_offsets
                 _SCREAMING_SNAKE_CASE							    =True

            if changes_to_apply:
                 _SCREAMING_SNAKE_CASE							    =getattr(_a							,					state.pop('type' ) )
                 _SCREAMING_SNAKE_CASE							    =component_class(**_a )
                 setattr(self.backend_tokenizer							,					_a							,					_a )





  @property
  def 						A  (		self      :	Tuple )	->    str:





       '''simple docstring'''






       if self._mask_token is None:
            if self.verbose:
                 logger.error('Using mask_token, but it is not set yet.' )
            return None
       return str(self._mask_token )





  @mask_token.setter
  def 						A  (		self      :	List[str]							,					_a      :	Union[str, Any] )	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =AddedToken(_a							,					lstrip=_a							,					rstrip=_a ) if isinstance(_a							,					_a ) else value
       _SCREAMING_SNAKE_CASE							    =value





  def 						A  (		self      :	Dict							,					*_a      :	int							,					**_a      :	str )	->    BatchEncoding:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =kwargs.get('is_split_into_words'							,					_a )

       if is_split_into_words and not self.add_prefix_space:
            raise ValueError(
                f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
                'to use it with pretokenized inputs.' )

       return super()._batch_encode_plus(*_a							,					**_a )





  def 						A  (		self      :	Optional[int]							,					*_a      :	Any							,					**_a      :	str )	->    BatchEncoding:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =kwargs.get('is_split_into_words'							,					_a )

       if is_split_into_words and not self.add_prefix_space:
            raise ValueError(
                f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
                'to use it with pretokenized inputs.' )

       return super()._encode_plus(*_a							,					**_a )





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self._tokenizer.model.save(_a							,					name=_a )
       return tuple(_a )





  def 						A  (		self      :	Tuple							,					_a      :	str							,					_a      :	int=None )	->    int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =[self.bos_token_id] + token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
            return output

       return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





  def 						A  (		self      :	Dict							,					_a      :	List[int]							,					_a      :	Optional[List[int]] = None )	->    List[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =[self.sep_token_id]
       _SCREAMING_SNAKE_CASE							    =[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 + sep + token_ids_a + sep ) * [0]




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": 650, "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": 600, "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": 600, "eval_accuracy": 0.6, "eval_loss": 0.7},
        },
    ])
class      lowerCAmelCase__       (				unittest.TestCase):




     '''simple docstring'''


     def  _lowerCamelCase (						self)			->				str:
            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=__lowerCamelCase ,						)
            assert hasattr(self ,						"env")


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Tuple:
            _A      :       Dict				    = F"{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"
            # distributed data settings
            _A      :       Optional[Any]				    = {"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=__lowerCamelCase ,						instance_count=__lowerCamelCase ,						instance_type=self.instance_type ,						debugger_hook_config=__lowerCamelCase ,						hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path} ,						metric_definitions=self.env.metric_definitions ,						distribution=__lowerCamelCase ,						py_version="py36" ,						)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Optional[Any]:
            TrainingJobAnalytics(__lowerCamelCase).export_csv(F"{self.env.test_path}/{job_name}_metrics.csv")


     @parameterized.expand([(2,)])
     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Any:
            # create estimator
            _A      :       Union[str, Any]				    = self.create_estimator(__lowerCamelCase)

            # run training
            estimator.fit()

            # result dataframe
            _A      :       Optional[Any]				    = TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

            # extract kpis
            _A      :       List[Any]				    = list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"])
            _A      :       Dict				    = list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"])
            # get train time from SageMaker job, this includes starting, preprocessing, stopping
            _A      :       Optional[Any]				    = (
                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} ,						__lowerCamelCase)
