

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 		__magic_name__     (				unittest.TestCase				):

   '''simple docstring'''




   lowerCamelCase__					:      Tuple        =  MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
   lowerCamelCase__					:      Tuple        =  TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_,       lowercase_			)				->       Dict:






     """simple docstring"""






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



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_			)				->       Dict:






     """simple docstring"""






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

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

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



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






     """simple docstring"""






     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(
         lowercase_,       [
             {
                 '''translation_text''': (
                     '''Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'''
                     ''' Beide Beide'''
                 )
             }
         ],       )



   @require_tf
   def 					_UpperCAmelCase					(							self			)				->       int:






     """simple docstring"""






     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(
         lowercase_,       [
             {
                 '''translation_text''': (
                     '''Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'''
                     ''' Beide Beide'''
                 )
             }
         ],       )



   @require_torch
   def 					_UpperCAmelCase					(							self			)				->       int:






     """simple docstring"""






     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(
         lowercase_,       [
             {
                 '''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			)				->       Union[str, Any]:






     """simple docstring"""






     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(
         lowercase_,       [
             {
                 '''translation_text''': (
                     '''monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'''
                     ''' urine urine urine urine urine urine urine'''
                 )
             }
         ],       )







class 		__magic_name__     (				unittest.TestCase				):

   '''simple docstring'''




   @require_torch
   @slow
   def 					_UpperCAmelCase					(							self			)				->       Dict:






     """simple docstring"""






     with self.assertRaises(lowercase_			):
       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			)				->       List[str]:






     """simple docstring"""






     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=lowercase_,       tokenizer=lowercase_			)
     # Missing src_lang, tgt_lang
     with self.assertRaises(lowercase_			):
       translator('''This is a test'''			)

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

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

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



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






     """simple docstring"""






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



   @require_torch
   def 					_UpperCAmelCase					(							self			)				->       Any:






     """simple docstring"""






     a__    ='''patrickvonplaten/t5-tiny-random'''
     with pytest.warns(lowercase_,       match=R'''.*translation_en_to_de.*'''			):
       a__    =pipeline(task='''translation''',       model=lowercase_			)
     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			)				->       str:






     """simple docstring"""






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



import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor







class 		__magic_name__     (				unittest.TestCase				):

   '''simple docstring'''




   @property
   def 					_UpperCAmelCase					(							self			)				->       Tuple:






     """simple docstring"""






     a__    =1
     a__    =3
     a__    =8
     a__    =8

     a__    =torch.rand((batch_size, num_channels, height, width)			)

     return sample



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       int:






     """simple docstring"""






     if isinstance(image[0],       PIL.Image.Image			):
       return np.stack([np.array(lowercase_			) for i in image],       axis=0			)
     elif isinstance(lowercase_,       torch.Tensor			):
       return image.cpu().numpy().transpose(0,       2,       3,       1			)
     return image



   def 					_UpperCAmelCase					(							self			)				->       int:






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)

     a__    =self.dummy_sample
     a__    =self.to_np(lowercase_			)

     for output_type in ["pt", "np", "pil"]:
       a__    =image_processor.postprocess(
           image_processor.preprocess(lowercase_			),       output_type=lowercase_,       )
       a__    =self.to_np(lowercase_			)
       a__    =(input_np * 255).round() if output_type == '''pil''' else input_np
       assert (
           np.abs(in_np - out_np			).max() < 1E-6
       ), F"""decoded output does not match input for output_type {output_type}"""



   def 					_UpperCAmelCase					(							self			)				->       List[Any]:






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)
     a__    =self.dummy_sample.cpu().numpy().transpose(0,       2,       3,       1			)

     for output_type in ["pt", "np", "pil"]:
       a__    =image_processor.postprocess(image_processor.preprocess(lowercase_			),       output_type=lowercase_			)

       a__    =self.to_np(lowercase_			)
       a__    =(input_np * 255).round() if output_type == '''pil''' else input_np
       assert (
           np.abs(in_np - out_np			).max() < 1E-6
       ), F"""decoded output does not match input for output_type {output_type}"""



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






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)

     a__    =self.dummy_sample.cpu().numpy().transpose(0,       2,       3,       1			)
     a__    =image_processor.numpy_to_pil(lowercase_			)

     for output_type in ["pt", "np", "pil"]:
       a__    =image_processor.postprocess(image_processor.preprocess(lowercase_			),       output_type=lowercase_			)
       for i, o in zip(lowercase_,       lowercase_			):
         a__    =np.array(lowercase_			)
         a__    =self.to_np(lowercase_			) if output_type == '''pil''' else (self.to_np(lowercase_			) * 255).round()
         assert (
             np.abs(in_np - out_np			).max() < 1E-6
         ), F"""decoded output does not match input for output_type {output_type}"""



   def 					_UpperCAmelCase					(							self			)				->       Union[str, Any]:






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)

     a__    =self.dummy_sample
     a__    =input_pt_ad.squeeze(0			)

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )
     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     a__    =self.to_np(self.dummy_sample			)
     a__    =input_np_ad.squeeze(0			)

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )
     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     assert np.abs(out_pt_ad - out_pt_ad			).max() < 1E-6
     assert np.abs(out_np_ad - out_np_ad			).max() < 1E-6



   def 					_UpperCAmelCase					(							self			)				->       List[Any]:






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)

     a__    =self.dummy_sample
     a__    =list(lowercase_			)

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     a__    =self.to_np(self.dummy_sample			)
     list(lowercase_			)

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     assert np.abs(out_pt_ad - out_pt_list			).max() < 1E-6
     assert np.abs(out_np_ad - out_np_list			).max() < 1E-6
