

'''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				):






     '''simple docstring'''



     UpperCAmelCase_		:      int    						=    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
     UpperCAmelCase_		:      Tuple    						=    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING


     def   UpperCAmelCase_			(						self					:	List[str]	,     lowercase_					:	Union[str, Any]	,     lowercase_					:	List[str]	,     lowercase_					:	Optional[Any]			)		->      int:
      if isinstance(model.config	,     lowerCAmelCase__			):
       UpperCAmelCase   ,					UpperCAmelCase			:      Any		   =					list(tokenizer.lang_code_to_id.keys()			)[:2]
       UpperCAmelCase			:      int		   =					TranslationPipeline(model=lowerCAmelCase__	,     tokenizer=lowerCAmelCase__	,     src_lang=lowerCAmelCase__	,     tgt_lang=lowerCAmelCase__			)
      else:
       UpperCAmelCase			:      List[str]		   =					TranslationPipeline(model=lowerCAmelCase__	,     tokenizer=lowerCAmelCase__			)
      return translator, ["Some string", "Some other text"]


     def   UpperCAmelCase_			(						self					:	str	,     lowercase_					:	Tuple	,     lowercase_					:	List[Any]			)		->      Union[str, Any]:
      UpperCAmelCase			:      Any		   =					translator('Some string'			)
      self.assertEqual(lowerCAmelCase__	,     [{'translation_text': ANY(lowerCAmelCase__			)}]			)

      UpperCAmelCase			:      List[Any]		   =					translator(['Some string']			)
      self.assertEqual(lowerCAmelCase__	,     [{'translation_text': ANY(lowerCAmelCase__			)}]			)

      UpperCAmelCase			:      str		   =					translator(['Some string', 'other string']			)
      self.assertEqual(lowerCAmelCase__	,     [{'translation_text': ANY(lowerCAmelCase__			)}, {'translation_text': ANY(lowerCAmelCase__			)}]			)


     @require_torch
     def   UpperCAmelCase_			(						self					:	Optional[int]			)		->      List[Any]:
      UpperCAmelCase			:      List[Any]		   =					pipeline('translation_en_to_ro'	,     model='patrickvonplaten/t5-tiny-random'	,     framework='pt'			)
      UpperCAmelCase			:      Dict		   =					translator('This is a test string'	,     max_length=20			)
      self.assertEqual(
          lowerCAmelCase__	,     [
              {
                  'translation_text': (
                      'Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'
                      ' Beide Beide'
                  )
              }
          ]	,     )


     @require_tf
     def   UpperCAmelCase_			(						self					:	str			)		->      Tuple:
      UpperCAmelCase			:      Union[str, Any]		   =					pipeline('translation_en_to_ro'	,     model='patrickvonplaten/t5-tiny-random'	,     framework='tf'			)
      UpperCAmelCase			:      Optional[Any]		   =					translator('This is a test string'	,     max_length=20			)
      self.assertEqual(
          lowerCAmelCase__	,     [
              {
                  'translation_text': (
                      'Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'
                      ' Beide Beide'
                  )
              }
          ]	,     )


     @require_torch
     def   UpperCAmelCase_			(						self					:	Optional[int]			)		->      int:
      UpperCAmelCase			:      Any		   =					pipeline('translation_en_to_de'	,     model='patrickvonplaten/t5-tiny-random'	,     framework='pt'			)
      UpperCAmelCase			:      List[Any]		   =					translator('This is a test string'	,     max_length=20			)
      self.assertEqual(
          lowerCAmelCase__	,     [
              {
                  '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					:	List[str]			)		->      Optional[int]:
      UpperCAmelCase			:      str		   =					pipeline('translation_en_to_de'	,     model='patrickvonplaten/t5-tiny-random'	,     framework='tf'			)
      UpperCAmelCase			:      List[str]		   =					translator('This is a test string'	,     max_length=20			)
      self.assertEqual(
          lowerCAmelCase__	,     [
              {
                  '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				):






     '''simple docstring'''



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

      # but we do for this one
      UpperCAmelCase			:      str		   =					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					:	List[Any]			)		->      Dict:
      UpperCAmelCase			:      int		   =					MBartForConditionalGeneration.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'			)
      UpperCAmelCase			:      int		   =					MBartaaTokenizerFast.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'			)

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

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

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

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


     @require_torch
     def   UpperCAmelCase_			(						self					:	str			)		->      Optional[Any]:
      UpperCAmelCase			:      int		   =					'patrickvonplaten/t5-tiny-random'
      UpperCAmelCase			:      Tuple		   =					pipeline(task='translation_cn_to_ar'	,     model=lowerCAmelCase__			)
      self.assertEqual(translator._preprocess_params['src_lang']	,     'cn'			)
      self.assertEqual(translator._preprocess_params['tgt_lang']	,     'ar'			)


     @require_torch
     def   UpperCAmelCase_			(						self					:	Dict			)		->      Optional[int]:
      UpperCAmelCase			:      List[str]		   =					'patrickvonplaten/t5-tiny-random'
      with pytest.warns(lowerCAmelCase__	,     match=R'.*translation_en_to_de.*'			):
       UpperCAmelCase			:      List[str]		   =					pipeline(task='translation'	,     model=lowerCAmelCase__			)
      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					:	Any			)		->      Any:
      with self.assertRaises(lowerCAmelCase__			):
       pipeline(task='translation'			)












import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard
class 	a_(		unittest.TestCase	):



     """simple docstring"""



     def     __UpperCamelCase						(		self					:	Optional[int])							->	List[str]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		{
          'model_details': {
              'Organization': 'testing',
              'Model date': 'today',
              'Model version': 'v2.1, Developed by Test Corp in 2019.',
              'Architecture': 'Convolutional Neural Network.',
          },
          'metrics': 'BLEU and ROUGE-1',
          'evaluation_data': {
              'Datasets': {'BLEU': 'My-great-dataset-v1', 'ROUGE-1': 'My-short-dataset-v2.1'},
              'Preprocessing': 'See details on https://arxiv.org/pdf/1810.03993.pdf',
          },
          'training_data': {
              'Dataset': 'English Wikipedia dump dated 2018-12-01',
              'Preprocessing': (
                  'Using SentencePiece vocabulary of size 52k tokens. See details on'
                  ' https://arxiv.org/pdf/1810.03993.pdf'
              ),
          },
          'quantitative_analyses': {'BLEU': 55.1, 'ROUGE-1': 7_6},
      }





     def     __UpperCamelCase						(		self					:	Optional[Any])							->	Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		ModelCard.from_dict(self.inputs_dict)
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'model_details'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'intended_use'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'factors'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'metrics'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'evaluation_data'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'training_data'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'quantitative_analyses'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'ethical_considerations'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'caveats_and_recommendations'))





     def     __UpperCamelCase						(		self					:	Any)							->	Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		ModelCard.from_dict(self.inputs_dict)
      SCREAMING_SNAKE_CASE				    =		json.loads(modelcard.to_json_string())
      for key, value in self.inputs_dict.items():
       self.assertEqual(obj[key]  ,  lowerCAmelCase__)





     def     __UpperCamelCase						(		self					:	int)							->	Optional[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		ModelCard.from_dict(self.inputs_dict)

      with tempfile.TemporaryDirectory() as tmpdirname:
       SCREAMING_SNAKE_CASE				    =		os.path.join(lowerCAmelCase__  ,  'modelcard.json')
       model_card_first.to_json_file(lowerCAmelCase__)
       SCREAMING_SNAKE_CASE				    =		ModelCard.from_json_file(lowerCAmelCase__)

      self.assertEqual(model_card_second.to_dict()  ,  model_card_first.to_dict())





     def     __UpperCamelCase						(		self					:	Optional[int])							->	Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		ModelCard.from_dict(self.inputs_dict)

      with tempfile.TemporaryDirectory() as tmpdirname:
       model_card_first.save_pretrained(lowerCAmelCase__)
       SCREAMING_SNAKE_CASE				    =		ModelCard.from_pretrained(lowerCAmelCase__)

      self.assertEqual(model_card_second.to_dict()  ,  model_card_first.to_dict())




