
'''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 			UpperCamelCase__	(     unittest.TestCase):
 UpperCAmelCase__     :     Any    					=       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
 UpperCAmelCase__     :     Any    					=       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING






 def 						lowercase_    (      self	:str						,					_A	:List[Any]						,					_A	:Optional[int]						,					_A	:Tuple      )					->      int:







       '''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 						lowercase_    (      self	:Any						,					_A	:Union[str, Any]						,					_A	:Dict      )					->      str:







       '''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 						lowercase_    (      self	:Union[str, Any]      )					->      Optional[int]:







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







       '''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 						lowercase_    (      self	:Tuple      )					->      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 			UpperCamelCase__	(     unittest.TestCase):






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







       '''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 						lowercase_    (      self	:str      )					->      Optional[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 						lowercase_    (      self	:List[Any]      )					->      Any:







       '''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 						lowercase_    (      self	:Any      )					->      Any:







       '''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 						lowercase_    (      self	:int      )					->      Optional[Any]:







       '''simple docstring'''



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











"""simple docstring"""







import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
							import jax
							import jax.numpy as jnp

							from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor






class   UpperCAmelCase__		(   unittest.TestCase  ):



		def __init__(			self	:     Dict   ,				snake_case	:     Dict   ,				snake_case	:     Optional[Any]=3   ,				snake_case	:     Union[str, Any]=32   ,				snake_case	:     int=3   ,				snake_case	:     Any=10   ,				snake_case	:     int=[10, 20, 30, 40]   ,				snake_case	:     List[Any]=[1, 1, 2, 1]   ,				snake_case	:     List[str]=True   ,				snake_case	:     Any=True   ,				snake_case	:     str="relu"   ,				snake_case	:     List[str]=3   ,				snake_case	:     List[str]=None   ,				)      ->    int:






									'''simple docstring'''

									A    =       parent
									A    =       batch_size
									A    =       image_size
									A    =       num_channels
									A    =       embeddings_size
									A    =       hidden_sizes
									A    =       depths
									A    =       is_training
									A    =       use_labels
									A    =       hidden_act
									A    =       num_labels
									A    =       scope
									A    =       len(snake_case					)



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






									'''simple docstring'''

									A    =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

									A    =       self.get_config()

									return config, pixel_values



		def        A_		(			self	:     int					)      ->    List[Any]:






									'''simple docstring'''

									return ResNetConfig(
									    num_channels=self.num_channels   ,				embeddings_size=self.embeddings_size   ,				hidden_sizes=self.hidden_sizes   ,				depths=self.depths   ,				hidden_act=self.hidden_act   ,				num_labels=self.num_labels   ,				image_size=self.image_size   ,				)



		def        A_		(			self	:     Optional[Any]   ,				snake_case	:     str   ,				snake_case	:     Any					)      ->    int:






									'''simple docstring'''

									A    =       FlaxResNetModel(config=snake_case					)
									A    =       model(snake_case					)

									# Output shape (b, c, h, w)
									self.parent.assertEqual(
									    result.last_hidden_state.shape   ,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)   ,				)



		def        A_		(			self	:     Dict   ,				snake_case	:     Tuple   ,				snake_case	:     Optional[Any]					)      ->    Tuple:






									'''simple docstring'''

									A    =       self.num_labels
									A    =       FlaxResNetForImageClassification(config=snake_case					)
									A    =       model(snake_case					)
									self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_labels)					)



		def        A_		(			self	:     List[str]					)      ->    Optional[Any]:






									'''simple docstring'''

									A    =       self.prepare_config_and_inputs()
									A   ,	A    =       config_and_inputs
									A    =       {'pixel_values': pixel_values}
									return config, inputs_dict






@require_flax
class   UpperCAmelCase__		(   UpperCamelCase ,unittest.TestCase  ):
		lowerCAmelCase_							:						List[str]							=  (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

		lowerCAmelCase_							:						Tuple							=  False
		lowerCAmelCase_							:						List[str]							=  False
		lowerCAmelCase_							:						List[str]							=  False



		def        A_		(			self	:     Optional[Any]					)      ->    None:






									'''simple docstring'''

									A    =       FlaxResNetModelTester(self					)
									A    =       ConfigTester(self   ,				config_class=snake_case   ,				has_text_modality=snake_case					)



		def        A_		(			self	:     List[str]					)      ->    Dict:






									'''simple docstring'''

									self.create_and_test_config_common_properties()
									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.create_and_test_config_with_num_labels()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()



		def        A_		(			self	:     Dict					)      ->    List[Any]:






									'''simple docstring'''

									return



		def        A_		(			self	:     str					)      ->    List[str]:






									'''simple docstring'''

									A    =       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*snake_case					)



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






									'''simple docstring'''

									A    =       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*snake_case					)



		@unittest.skip(reason='ResNet does not use inputs_embeds'					)
		def        A_		(			self	:     Tuple					)      ->    str:






									'''simple docstring'''

									pass



		@unittest.skip(reason='ResNet does not support input and output embeddings'					)
		def        A_		(			self	:     Tuple					)      ->    Any:






									'''simple docstring'''

									pass



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






									'''simple docstring'''

									A   ,	A    =       self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																A    =       model_class(snake_case					)
																A    =       inspect.signature(model.__call__					)
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																A    =       [*signature.parameters.keys()]

																A    =       ['pixel_values']
																self.assertListEqual(arg_names[:1]   ,				snake_case					)



		def        A_		(			self	:     Any					)      ->    Tuple:






									'''simple docstring'''

									def check_hidden_states_output(snake_case	:     Tuple   ,				snake_case	:     Union[str, Any]   ,				snake_case	:     Optional[int]					):
																A    =       model_class(snake_case					)

																A    =       model(**self._prepare_for_class(snake_case   ,				snake_case					)					)

																A    =       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																A    =       self.model_tester.num_stages
																self.assertEqual(len(snake_case					)   ,				expected_num_stages + 1					)



		@unittest.skip(reason='ResNet does not use feedforward chunking'					)
		def        A_		(			self	:     Optional[int]					)      ->    List[Any]:






									'''simple docstring'''

									pass



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






									'''simple docstring'''

									A   ,	A    =       self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__					):
																							A    =       self._prepare_for_class(snake_case   ,				snake_case					)
																							A    =       model_class(snake_case					)

																							@jax.jit
																							def model_jitted(snake_case	:     Any   ,				**snake_case	:     int					):
																														return model(pixel_values=snake_case   ,				**snake_case					)

																							with self.subTest('JIT Enabled'					):
																														A    =       model_jitted(**snake_case					).to_tuple()

																							with self.subTest('JIT Disabled'					):
																														with jax.disable_jit():
																																					A    =       model_jitted(**snake_case					).to_tuple()

																							self.assertEqual(len(snake_case					)   ,				len(snake_case					)					)
																							for jitted_output, output in zip(snake_case   ,				snake_case					):
																														self.assertEqual(jitted_output.shape   ,				output.shape					)



def 	lowerCAmelCase__      (  )			->				int:
							A    =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
							return image






@require_flax
class   UpperCAmelCase__		(   unittest.TestCase  ):



		@cached_property
		def        A_		(			self	:     Union[str, Any]					)      ->    int:






									'''simple docstring'''

									return AutoImageProcessor.from_pretrained('microsoft/resnet-50'					) if is_vision_available() else None



		@slow
		def        A_		(			self	:     Any					)      ->    Tuple:






									'''simple docstring'''

									A    =       FlaxResNetForImageClassification.from_pretrained('microsoft/resnet-50'					)

									A    =       self.default_image_processor
									A    =       prepare_img()
									A    =       image_processor(images=snake_case   ,				return_tensors='np'					)

									A    =       model(**snake_case					)

									# verify the logits
									A    =       (1, 1_000)
									self.assertEqual(outputs.logits.shape   ,				snake_case					)

									A    =       jnp.array([-11.1069, -9.7877, -8.3777]					)

									self.assertTrue(jnp.allclose(outputs.logits[0, :3]   ,				snake_case   ,				atol=1E-4					)					)



