




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	:    Optional[int]	      =			MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
     a	:    Optional[int]	      =			TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING







     def     lowerCAmelCase_       (			self						,	A						,	A						,	A				)						->					Any:



          '''simple docstring'''



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







     def     lowerCAmelCase_       (			self						,	A						,	A				)						->					Any:



          '''simple docstring'''



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

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

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







     @require_torch
     def     lowerCAmelCase_       (			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(
              A						,	[
                  {
                      "translation_text": (
                          "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
                          " Beide Beide"
                      )
                  }
              ]						,	)







     @require_tf
     def     lowerCAmelCase_       (			self				)						->					List[Any]:



          '''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(
              A						,	[
                  {
                      "translation_text": (
                          "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
                          " Beide Beide"
                      )
                  }
              ]						,	)







     @require_torch
     def     lowerCAmelCase_       (			self				)						->					Tuple:



          '''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(
              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     lowerCAmelCase_       (			self				)						->					str:



          '''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(
              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     lowerCAmelCase_       (			self				)						->					List[str]:



          '''simple docstring'''



          with self.assertRaises(A				):
               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     lowerCAmelCase_       (			self				)						->					int:



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

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

          a											=   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
          a											=   pipeline(task="translation"						,	model=A						,	tokenizer=A						,	src_lang="en_XX"						,	tgt_lang="ar_AR"				)
          a											=   translator("This is a test"				)
          self.assertEqual(A						,	[{"translation_text": "هذا إختبار"}]				)







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



          '''simple docstring'''



          a											=   "patrickvonplaten/t5-tiny-random"
          a											=   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     lowerCAmelCase_       (			self				)						->					Optional[int]:



          '''simple docstring'''



          a											=   "patrickvonplaten/t5-tiny-random"
          with pytest.warns(A						,	match=r".*translation_en_to_de.*"				):
               a											=   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     lowerCAmelCase_       (			self				)						->					Any:



          '''simple docstring'''



          with self.assertRaises(A				):
               pipeline(task="translation"				)







import unittest

from transformers.image_processing_utils import get_size_dict




class     a__ (      unittest.TestCase   ):







     def     lowerCAmelCase_       (			self				)						->					Optional[int]:



          '''simple docstring'''



          a											=   {"wrong_key": 224}
          with self.assertRaises(A				):
               get_size_dict(A				)

          a											=   {"height": 224}
          with self.assertRaises(A				):
               get_size_dict(A				)

          a											=   {"width": 224, "shortest_edge": 224}
          with self.assertRaises(A				):
               get_size_dict(A				)

          # Test a dict with the correct keys is returned as is
          a											=   {"height": 224, "width": 224}
          a											=   get_size_dict(A				)
          self.assertEqual(A						,	A				)

          a											=   {"shortest_edge": 224}
          a											=   get_size_dict(A				)
          self.assertEqual(A						,	{"shortest_edge": 224}				)

          a											=   {"longest_edge": 224, "shortest_edge": 224}
          a											=   get_size_dict(A				)
          self.assertEqual(A						,	{"longest_edge": 224, "shortest_edge": 224}				)

          # Test a single int value which  represents (size, size)
          a											=   get_size_dict(224				)
          self.assertEqual(A						,	{"height": 224, "width": 224}				)

          # Test a single int value which represents the shortest edge
          a											=   get_size_dict(224						,	default_to_square=A				)
          self.assertEqual(A						,	{"shortest_edge": 224}				)

          # Test a tuple of ints which represents (height, width)
          a											=   get_size_dict((150, 200)				)
          self.assertEqual(A						,	{"height": 150, "width": 200}				)

          # Test a tuple of ints which represents (width, height)
          a											=   get_size_dict((150, 200)						,	height_width_order=A				)
          self.assertEqual(A						,	{"height": 200, "width": 150}				)

          # Test an int representing the shortest edge and max_size which represents the longest edge
          a											=   get_size_dict(224						,	max_size=256						,	default_to_square=A				)
          self.assertEqual(A						,	{"shortest_edge": 224, "longest_edge": 256}				)

          # Test int with default_to_square=True and max_size fails
          with self.assertRaises(A				):
               get_size_dict(224						,	max_size=256						,	default_to_square=A				)

