

import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY



@is_pipeline_test
class        _UpperCAmelCase (   unittest.TestCase			):
  '''simple docstring'''







  SCREAMING_SNAKE_CASE							:				Tuple					      =				MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
  SCREAMING_SNAKE_CASE							:				List[Any]					      =				TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING







  def 	UpperCamelCase       ( self	: str		,						UpperCamelCase__	: Any		,						UpperCamelCase__	: Optional[int]		,						UpperCamelCase__	: int ):
        if isinstance(model.config		,						UpperCamelCase__ ):
              A ,	A    =				list(tokenizer.lang_code_to_id.keys() )[:2]
              A    =				TranslationPipeline(model=UpperCamelCase__		,						tokenizer=UpperCamelCase__		,						src_lang=UpperCamelCase__		,						tgt_lang=UpperCamelCase__ )
        else:
              A    =				TranslationPipeline(model=UpperCamelCase__		,						tokenizer=UpperCamelCase__ )
        return translator, ["Some string", "Some other text"]







  def 	UpperCamelCase       ( self	: str		,						UpperCamelCase__	: List[str]		,						UpperCamelCase__	: Tuple ):
        A    =				translator('Some string' )
        self.assertEqual(UpperCamelCase__		,						[{'translation_text': ANY(UpperCamelCase__ )}] )

        A    =				translator(['Some string'] )
        self.assertEqual(UpperCamelCase__		,						[{'translation_text': ANY(UpperCamelCase__ )}] )

        A    =				translator(['Some string', 'other string'] )
        self.assertEqual(UpperCamelCase__		,						[{'translation_text': ANY(UpperCamelCase__ )}, {'translation_text': ANY(UpperCamelCase__ )}] )







  @require_torch
  def 	UpperCamelCase       ( self	: Optional[int] ):
        A    =				pipeline('translation_en_to_ro'		,						model='patrickvonplaten/t5-tiny-random'		,						framework='pt' )
        A    =				translator('This is a test string'		,						max_length=20 )
        self.assertEqual(
            UpperCamelCase__		,						[
                {
                    'translation_text': (
                        'Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'
                        ' Beide Beide'
                    )
                }
            ]		,						)







  @require_tf
  def 	UpperCamelCase       ( self	: Dict ):
        A    =				pipeline('translation_en_to_ro'		,						model='patrickvonplaten/t5-tiny-random'		,						framework='tf' )
        A    =				translator('This is a test string'		,						max_length=20 )
        self.assertEqual(
            UpperCamelCase__		,						[
                {
                    'translation_text': (
                        'Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'
                        ' Beide Beide'
                    )
                }
            ]		,						)







  @require_torch
  def 	UpperCamelCase       ( self	: str ):
        A    =				pipeline('translation_en_to_de'		,						model='patrickvonplaten/t5-tiny-random'		,						framework='pt' )
        A    =				translator('This is a test string'		,						max_length=20 )
        self.assertEqual(
            UpperCamelCase__		,						[
                {
                    'translation_text': (
                        'monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'
                        ' urine urine urine urine urine urine urine'
                    )
                }
            ]		,						)







  @require_tf
  def 	UpperCamelCase       ( self	: int ):
        A    =				pipeline('translation_en_to_de'		,						model='patrickvonplaten/t5-tiny-random'		,						framework='tf' )
        A    =				translator('This is a test string'		,						max_length=20 )
        self.assertEqual(
            UpperCamelCase__		,						[
                {
                    'translation_text': (
                        'monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'
                        ' urine urine urine urine urine urine urine'
                    )
                }
            ]		,						)



class        _UpperCAmelCase (   unittest.TestCase			):
  '''simple docstring'''







  @require_torch
  @slow
  def 	UpperCamelCase       ( self	: Union[str, Any] ):
        # We don't provide a default for this pair
        with self.assertRaises(UpperCamelCase__ ):
              pipeline(task='translation_cn_to_ar' )

        # but we do for this one
        A    =				pipeline(task='translation_en_to_de' )
        self.assertEqual(translator._preprocess_params['src_lang']		,						'en' )
        self.assertEqual(translator._preprocess_params['tgt_lang']		,						'de' )







  @require_torch
  @slow
  def 	UpperCamelCase       ( self	: Any ):
        A    =				MBartForConditionalGeneration.from_pretrained('facebook/mbart-large-50-many-to-many-mmt' )
        A    =				MBartaaTokenizerFast.from_pretrained('facebook/mbart-large-50-many-to-many-mmt' )

        A    =				pipeline(task='translation'		,						model=UpperCamelCase__		,						tokenizer=UpperCamelCase__ )
        # Missing src_lang, tgt_lang
        with self.assertRaises(UpperCamelCase__ ):
              translator('This is a test' )

        A    =				translator('This is a test'		,						src_lang='en_XX'		,						tgt_lang='ar_AR' )
        self.assertEqual(UpperCamelCase__		,						[{'translation_text': 'هذا إختبار'}] )

        A    =				translator('This is a test'		,						src_lang='en_XX'		,						tgt_lang='hi_IN' )
        self.assertEqual(UpperCamelCase__		,						[{'translation_text': 'यह एक परीक्षण है'}] )

        # src_lang, tgt_lang can be defined at pipeline call time
        A    =				pipeline(task='translation'		,						model=UpperCamelCase__		,						tokenizer=UpperCamelCase__		,						src_lang='en_XX'		,						tgt_lang='ar_AR' )
        A    =				translator('This is a test' )
        self.assertEqual(UpperCamelCase__		,						[{'translation_text': 'هذا إختبار'}] )







  @require_torch
  def 	UpperCamelCase       ( self	: Optional[int] ):
        A    =				'patrickvonplaten/t5-tiny-random'
        A    =				pipeline(task='translation_cn_to_ar'		,						model=UpperCamelCase__ )
        self.assertEqual(translator._preprocess_params['src_lang']		,						'cn' )
        self.assertEqual(translator._preprocess_params['tgt_lang']		,						'ar' )







  @require_torch
  def 	UpperCamelCase       ( self	: List[str] ):
        A    =				'patrickvonplaten/t5-tiny-random'
        with pytest.warns(UpperCamelCase__		,						match=R'.*translation_en_to_de.*' ):
              A    =				pipeline(task='translation'		,						model=UpperCamelCase__ )
        self.assertEqual(translator.task		,						'translation_en_to_de' )
        self.assertEqual(translator._preprocess_params['src_lang']		,						'en' )
        self.assertEqual(translator._preprocess_params['tgt_lang']		,						'de' )







  @require_torch
  def 	UpperCamelCase       ( self	: List[Any] ):
        with self.assertRaises(UpperCamelCase__ ):
              pipeline(task='translation' )






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


_UpperCAmelCase    	=			logging.getLogger(__name__)



@dataclass
class        _UpperCAmelCase :
  '''simple docstring'''







  SCREAMING_SNAKE_CASE							:				str					      =				field(
      metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}			)
  SCREAMING_SNAKE_CASE							:				Optional[str]					      =				field(
      default=__lowercase     ,				metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}			)
  SCREAMING_SNAKE_CASE							:				Optional[str]					      =				field(
      default=__lowercase     ,				metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}			)
  SCREAMING_SNAKE_CASE							:				bool					      =				field(default=__lowercase     ,				metadata={'''help''': '''Set this flag to use fast tokenization.'''}			)
  # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
  # or just modify its tokenizer_config.json.
  SCREAMING_SNAKE_CASE							:				Optional[str]					      =				field(
      default=__lowercase     ,				metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}     ,				)


def 			__UpperCamelCase    ()     ->  Tuple:
      # See all possible arguments in src/transformers/training_args.py
      # or by passing the --help flag to this script.
      # We now keep distinct sets of args, for a cleaner separation of concerns.

      A    =				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )

      if len(sys.argv     ) == 2 and sys.argv[1].endswith('.json'     ):
            # If we pass only one argument to the script and it's the path to a json file,
            # let's parse it to get our arguments.
            A ,	A ,	A    =				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
      else:
            A ,	A ,	A    =				parser.parse_args_into_dataclasses()

      if (
          os.path.exists(training_args.output_dir     )
          and os.listdir(training_args.output_dir     )
          and training_args.do_train
          and not training_args.overwrite_output_dir
      ):
            raise ValueError(
                f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
                ' --overwrite_output_dir to overcome.'     )

      # Setup logging
      logging.basicConfig(
          format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',    datefmt='%m/%d/%Y %H:%M:%S',    level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,    )
      logger.warning(
          'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s',    training_args.local_rank,    training_args.device,    training_args.n_gpu,    bool(training_args.local_rank != -1     ),    training_args.fpaa,    )
      # Set the verbosity to info of the Transformers logger (on main process only):
      if is_main_process(training_args.local_rank     ):
            transformers.utils.logging.set_verbosity_info()
            transformers.utils.logging.enable_default_handler()
            transformers.utils.logging.enable_explicit_format()
      logger.info('Training/evaluation parameters %s',    lowerCAmelCase     )

      # Prepare Question-Answering task
      # Load pretrained model and tokenizer
      #
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.

      A    =				AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path,    cache_dir=model_args.cache_dir,    )
      A    =				AutoTokenizer.from_pretrained(
          model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,    cache_dir=model_args.cache_dir,    use_fast=lowerCAmelCase,    )
      A    =				AutoModelForQuestionAnswering.from_pretrained(
          model_args.model_name_or_path,    from_tf=bool('.ckpt' in model_args.model_name_or_path     ),    config=lowerCAmelCase,    cache_dir=model_args.cache_dir,    )

      # Get datasets
      A    =				hasattr(model.config,    'lang2id'     )
      A    =				(
          SquadDataset(
              lowerCAmelCase,    tokenizer=lowerCAmelCase,    is_language_sensitive=lowerCAmelCase,    cache_dir=model_args.cache_dir     )
          if training_args.do_train
          else None
      )
      A    =				(
          SquadDataset(
              lowerCAmelCase,    tokenizer=lowerCAmelCase,    mode='dev',    is_language_sensitive=lowerCAmelCase,    cache_dir=model_args.cache_dir,    )
          if training_args.do_eval
          else None
      )

      # Data collator
      A    =				DataCollatorWithPadding(lowerCAmelCase,    pad_to_multiple_of=8     ) if training_args.fpaa else None

      # Initialize our Trainer
      A    =				Trainer(
          model=lowerCAmelCase,    args=lowerCAmelCase,    train_dataset=lowerCAmelCase,    eval_dataset=lowerCAmelCase,    data_collator=lowerCAmelCase,    )

      # Training
      if training_args.do_train:
            trainer.train(
                model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path     ) else None     )
            trainer.save_model()
            # For convenience, we also re-save the tokenizer to the same directory,
            # so that you can share your model easily on huggingface.co/models =)
            if trainer.is_world_master():
                  tokenizer.save_pretrained(training_args.output_dir     )


def 			__UpperCamelCase    (lowerCAmelCase					:  Tuple     )     ->  List[Any]:
      # For xla_spawn (TPUs)
      main()


if __name__ == "__main__":
  main()



