

'''simple docstring'''
from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy







class 							A__					( A__						):
  A__					=       'MarkupLMFeatureExtractor'
  A__					=       ('MarkupLMTokenizer', 'MarkupLMTokenizerFast')
  A__					=       True





  def __call__(	self   :			Dict							,      _a   :			str=None							,      _a   :			Tuple=None							,      _a   :			List[str]=None							,      _a   :			List[str]=None							,      _a   :			int=None							,      _a   :			bool = True							,      _a   :			Union[bool, str, PaddingStrategy] = False							,      _a   :			Union[bool, str, TruncationStrategy] = None							,      _a   :			Optional[int] = None							,      _a   :			int = 0							,      _a   :			Optional[int] = None							,      _a   :			Optional[bool] = None							,      _a   :			Optional[bool] = None							,      _a   :			bool = False							,      _a   :			bool = False							,      _a   :			bool = False							,      _a   :			bool = False							,      _a   :			bool = True							,      _a   :			Optional[Union[str, TensorType]] = None							,      **_a   :			Optional[Any]							,      )			->					BatchEncoding:





       '''simple docstring'''






       if self.parse_html:
            if html_strings is None:
                 raise ValueError('Make sure to pass HTML strings in case `parse_html` is set to `True`'				)

            if nodes is not None or xpaths is not None or node_labels is not None:
                 raise ValueError(
                     'Please don\'t pass nodes, xpaths nor node labels in case `parse_html` is set to `True`'				)

            _SCREAMING_SNAKE_CASE												=self.feature_extractor(_a				)
            _SCREAMING_SNAKE_CASE												=features['nodes']
            _SCREAMING_SNAKE_CASE												=features['xpaths']
       else:
            if html_strings is not None:
                 raise ValueError('You have passed HTML strings but `parse_html` is set to `False`.'				)
            if nodes is None or xpaths is None:
                 raise ValueError('Make sure to pass nodes and xpaths in case `parse_html` is set to `False`'				)

        # # second, apply the tokenizer
       if questions is not None and self.parse_html:
            if isinstance(_a							,      _a				):
                 _SCREAMING_SNAKE_CASE												=[questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

       _SCREAMING_SNAKE_CASE												=self.tokenizer(
           text=questions if questions is not None else nodes							,      text_pair=nodes if questions is not None else None							,      xpaths=_a							,      node_labels=_a							,      add_special_tokens=_a							,      padding=_a							,      truncation=_a							,      max_length=_a							,      stride=_a							,      pad_to_multiple_of=_a							,      return_token_type_ids=_a							,      return_attention_mask=_a							,      return_overflowing_tokens=_a							,      return_special_tokens_mask=_a							,      return_offsets_mapping=_a							,      return_length=_a							,      verbose=_a							,      return_tensors=_a							,      **_a							,      )

       return encoded_inputs





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





       '''simple docstring'''






       return self.tokenizer.batch_decode(*_a							,      **_a				)





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





       '''simple docstring'''






       return self.tokenizer.decode(*_a							,      **_a				)





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.tokenizer.model_input_names
       return tokenizer_input_names



'''simple docstring'''
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 							A__					( unittest.TestCase						):
  A__					=       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
  A__					=       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING





  def 		A						(	self   :			Optional[Any]							,      _a   :			Tuple							,      _a   :			Union[str, Any]							,      _a   :			str				)			->					List[Any]:





       '''simple docstring'''






       if isinstance(model.config							,      _a				):
            _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=list(tokenizer.lang_code_to_id.keys()				)[:2]
            _SCREAMING_SNAKE_CASE												=TranslationPipeline(model=_a							,      tokenizer=_a							,      src_lang=_a							,      tgt_lang=_a				)
       else:
            _SCREAMING_SNAKE_CASE												=TranslationPipeline(model=_a							,      tokenizer=_a				)
       return translator, ["Some string", "Some other text"]





  def 		A						(	self   :			Union[str, Any]							,      _a   :			List[Any]							,      _a   :			Optional[Any]				)			->					List[Any]:





       '''simple docstring'''






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

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

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





  @require_torch
  def 		A						(	self   :			Dict				)			->					Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('translation_en_to_ro'							,      model='patrickvonplaten/t5-tiny-random'							,      framework='pt'				)
       _SCREAMING_SNAKE_CASE												=translator('This is a test string'							,      max_length=20				)
       self.assertEqual(
           _a							,      [
               {
                   'translation_text': (
                       'Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'
                       ' Beide Beide'
                   )
               }
           ]							,      )





  @require_tf
  def 		A						(	self   :			Tuple				)			->					Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('translation_en_to_ro'							,      model='patrickvonplaten/t5-tiny-random'							,      framework='tf'				)
       _SCREAMING_SNAKE_CASE												=translator('This is a test string'							,      max_length=20				)
       self.assertEqual(
           _a							,      [
               {
                   'translation_text': (
                       'Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'
                       ' Beide Beide'
                   )
               }
           ]							,      )





  @require_torch
  def 		A						(	self   :			Any				)			->					Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('translation_en_to_de'							,      model='patrickvonplaten/t5-tiny-random'							,      framework='pt'				)
       _SCREAMING_SNAKE_CASE												=translator('This is a test string'							,      max_length=20				)
       self.assertEqual(
           _a							,      [
               {
                   'translation_text': (
                       'monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'
                       ' urine urine urine urine urine urine urine'
                   )
               }
           ]							,      )





  @require_tf
  def 		A						(	self   :			Dict				)			->					Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('translation_en_to_de'							,      model='patrickvonplaten/t5-tiny-random'							,      framework='tf'				)
       _SCREAMING_SNAKE_CASE												=translator('This is a test string'							,      max_length=20				)
       self.assertEqual(
           _a							,      [
               {
                   'translation_text': (
                       'monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'
                       ' urine urine urine urine urine urine urine'
                   )
               }
           ]							,      )







class 							A__					( unittest.TestCase						):





  @require_torch
  @slow
  def 		A						(	self   :			int				)			->					Optional[Any]:





       '''simple docstring'''






       with self.assertRaises(_a				):
            pipeline(task='translation_cn_to_ar'				)

       # but we do for this one
       _SCREAMING_SNAKE_CASE												=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 		A						(	self   :			Optional[Any]				)			->					List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=MBartForConditionalGeneration.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'				)
       _SCREAMING_SNAKE_CASE												=MBartaaTokenizerFast.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'				)

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

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

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

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





  @require_torch
  def 		A						(	self   :			int				)			->					int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												='patrickvonplaten/t5-tiny-random'
       _SCREAMING_SNAKE_CASE												=pipeline(task='translation_cn_to_ar'							,      model=_a				)
       self.assertEqual(translator._preprocess_params['src_lang']							,      'cn'				)
       self.assertEqual(translator._preprocess_params['tgt_lang']							,      'ar'				)





  @require_torch
  def 		A						(	self   :			str				)			->					List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												='patrickvonplaten/t5-tiny-random'
       with pytest.warns(_a							,      match=r'.*translation_en_to_de.*'				):
            _SCREAMING_SNAKE_CASE												=pipeline(task='translation'							,      model=_a				)
       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 		A						(	self   :			Any				)			->					Dict:





       '''simple docstring'''






       with self.assertRaises(_a				):
            pipeline(task='translation'				)
