'''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


snake_case__					:      List[Any]				      =  logging.get_logger(__name__)


snake_case__					:      List[str]				      =  {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all BART models at https://huggingface.co/models?filter=bart
snake_case__					:      str				      =  {
    '''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''',
    },
}

snake_case__					:      Optional[int]				      =  {
    '''facebook/bart-base''': 1024,
    '''facebook/bart-large''': 1024,
    '''facebook/bart-large-mnli''': 1024,
    '''facebook/bart-large-cnn''': 1024,
    '''facebook/bart-large-xsum''': 1024,
    '''yjernite/bart_eli5''': 1024,
}






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:Dict						   =		VOCAB_FILES_NAMES
  lowerCamelCase_		:Optional[int]						   =		PRETRAINED_VOCAB_FILES_MAP
  lowerCamelCase_		:Any						   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCamelCase_		:int						   =		['''input_ids''', '''attention_mask''']
  lowerCamelCase_		:List[Any]						   =		BartTokenizer


  def __init__(			self				,		snake_case_=None				,		snake_case_=None				,		snake_case_=None				,		snake_case_="replace"				,		snake_case_="<s>"				,		snake_case_="</s>"				,		snake_case_="</s>"				,		snake_case_="<s>"				,		snake_case_="<unk>"				,		snake_case_="<pad>"				,		snake_case_="<mask>"				,		snake_case_=False				,		snake_case_=True				,		**snake_case_				,		):




       '''simple docstring'''

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

       UpperCAmelCase_							:       List[Any]       =				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
       if pre_tok_state.get('add_prefix_space'				,		snake_case_						) != add_prefix_space:
            UpperCAmelCase_							:       Dict       =				getattr(snake_case_				,		pre_tok_state.pop('type'						)						)
            UpperCAmelCase_							:       Optional[Any]       =				add_prefix_space
            UpperCAmelCase_							:       List[str]       =				pre_tok_class(**snake_case_						)

       UpperCAmelCase_							:       List[str]       =				add_prefix_space

       # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
       UpperCAmelCase_							:       Any       =				'post_processor'
       UpperCAmelCase_							:       str       =				getattr(self.backend_tokenizer				,		snake_case_				,		snake_case_						)
       if tokenizer_component_instance:
            UpperCAmelCase_							:       List[Any]       =				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:
                 UpperCAmelCase_							:       Union[str, Any]       =				tuple(state['sep']						)
            if "cls" in state:
                 UpperCAmelCase_							:       int       =				tuple(state['cls']						)

            UpperCAmelCase_							:       Union[str, Any]       =				False

            if state.get('add_prefix_space'				,		snake_case_						) != add_prefix_space:
                 UpperCAmelCase_							:       Tuple       =				add_prefix_space
                 UpperCAmelCase_							:       List[str]       =				True

            if state.get('trim_offsets'				,		snake_case_						) != trim_offsets:
                 UpperCAmelCase_							:       Tuple       =				trim_offsets
                 UpperCAmelCase_							:       int       =				True

            if changes_to_apply:
                 UpperCAmelCase_							:       Any       =				getattr(snake_case_				,		state.pop('type'						)						)
                 UpperCAmelCase_							:       str       =				component_class(**snake_case_						)
                 setattr(self.backend_tokenizer				,		snake_case_				,		snake_case_						)


  @property
  def _UpperCamelCase					(			self						):




       '''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 _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				AddedToken(snake_case_				,		lstrip=snake_case_				,		rstrip=snake_case_						) if isinstance(snake_case_				,		snake_case_						) else value
       UpperCAmelCase_							:       Optional[int]       =				value


  def _UpperCamelCase					(			self				,		*snake_case_				,		**snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				kwargs.get('is_split_into_words'				,		snake_case_						)

       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(*snake_case_				,		**snake_case_						)


  def _UpperCamelCase					(			self				,		*snake_case_				,		**snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[Any]       =				kwargs.get('is_split_into_words'				,		snake_case_						)

       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(*snake_case_				,		**snake_case_						)


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




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				self._tokenizer.model.save(snake_case_				,		name=snake_case_						)
       return tuple(snake_case_						)


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




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				[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 _UpperCamelCase					(			self				,		snake_case_				,		snake_case_ = None						):




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				[self.sep_token_id]
       UpperCAmelCase_							:       str       =				[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]







'''simple docstring'''


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

import pytest
from parameterized import 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.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 650, '''eval_accuracy''': 0.6, '''eval_loss''': 0.9},
        },
        {
            '''framework''': '''tensorflow''',
            '''script''': '''run_tf.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 600, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
        },
    ]	)
class 					__SCREAMING_SNAKE_CASE						(	unittest.TestCase	):







  '''simple docstring'''






  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=snake_case_				,		)
       assert hasattr(self				,		'env'						)


  def _UpperCamelCase					(			self				,		snake_case_=1						):




       '''simple docstring'''

       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}-single'''				,		instance_count=snake_case_				,		instance_type=self.instance_type				,		debugger_hook_config=snake_case_				,		hyperparameters={**self.env.hyperparameters, 'model_name_or_path': self.model_name_or_path}				,		metric_definitions=self.env.metric_definitions				,		py_version='py36'				,		)


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

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






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				self.create_estimator()

       # run training
       estimator.fit()

       # result dataframe
       UpperCAmelCase_							:       int       =				TrainingJobAnalytics(estimator.latest_training_job.name						).dataframe()

       # extract kpis
       UpperCAmelCase_							:       Dict       =				list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']						)
       UpperCAmelCase_							:       List[str]       =				list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']						)
       # get train time from SageMaker job, this includes starting, preprocessing, stopping
       UpperCAmelCase_							:       int       =				(
           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}				,		snake_case_						)






