





'''simple docstring'''


import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
       import torch

@require_torch
@require_optimum
@slow
class    lowerCAmelCase_		(		unittest.TestCase		):




 def 							_snake_case   (	self		)    ->							int:
     _lowerCAmelCase				=      "hf-internal-testing/tiny-random-t5"
     _lowerCAmelCase				=      AutoTokenizer.from_pretrained(_lowerCAmelCase		)
     _lowerCAmelCase				=      AutoModelForSeqaSeqLM.from_pretrained(_lowerCAmelCase		)

     _lowerCAmelCase				=      tokenizer("This is me"			,     return_tensors="pt"		)

     _lowerCAmelCase				=      model.to_bettertransformer()

     self.assertTrue(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()		)		)

     _lowerCAmelCase				=      model.generate(**_lowerCAmelCase		)

     _lowerCAmelCase				=      model.reverse_bettertransformer()

     self.assertFalse(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()		)		)

     with tempfile.TemporaryDirectory() as tmpdirname:
         model.save_pretrained(_lowerCAmelCase		)

         _lowerCAmelCase				=      AutoModelForSeqaSeqLM.from_pretrained(_lowerCAmelCase		)

         self.assertFalse(
             any("BetterTransformer" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()		)		)

         _lowerCAmelCase				=      model_reloaded.generate(**_lowerCAmelCase		)
         self.assertTrue(torch.allclose(_lowerCAmelCase			,     _lowerCAmelCase		)		)





 def 							_snake_case   (	self		)    ->							int:
     _lowerCAmelCase				=      "hf-internal-testing/tiny-random-t5"
     _lowerCAmelCase				=      AutoModelForSeqaSeqLM.from_pretrained(_lowerCAmelCase		)

     _lowerCAmelCase				=      model.to_bettertransformer()

     with tempfile.TemporaryDirectory() as tmpdirname:
         with self.assertRaises(_lowerCAmelCase		):
             model.save_pretrained(_lowerCAmelCase		)

         _lowerCAmelCase				=      model.reverse_bettertransformer()
         model.save_pretrained(_lowerCAmelCase		)









'''simple docstring'''


import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
       from transformers import TvltImageProcessor

if is_speech_available():
       from transformers import TvltFeatureExtractor

from transformers import TvltProcessor

@require_torch
class    lowerCAmelCase_		(		unittest.TestCase		):




 def 							_snake_case   (	self		)    ->							Dict:
     _lowerCAmelCase				=      "ZinengTang/tvlt-base"
     _lowerCAmelCase				=      tempfile.mkdtemp()




 def 							_snake_case   (	self			,     **_lowerCAmelCase		)    ->							Any:
     return TvltImageProcessor.from_pretrained(self.checkpoint			,     **_lowerCAmelCase		)




 def 							_snake_case   (	self			,     **_lowerCAmelCase		)    ->							str:
     return TvltFeatureExtractor.from_pretrained(self.checkpoint			,     **_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							Optional[Any]:
     shutil.rmtree(self.tmpdirname		)




 def 							_snake_case   (	self		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      self.get_image_processor()
     _lowerCAmelCase				=      self.get_feature_extractor()

     _lowerCAmelCase				=      TvltProcessor(image_processor=_lowerCAmelCase			,     feature_extractor=_lowerCAmelCase		)
     processor.save_pretrained(self.tmpdirname		)
     _lowerCAmelCase				=      TvltProcessor.from_pretrained(self.tmpdirname		)

     self.assertIsInstance(processor.feature_extractor			,     _lowerCAmelCase		)
     self.assertIsInstance(processor.image_processor			,     _lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							Any:
     _lowerCAmelCase				=      self.get_image_processor()
     _lowerCAmelCase				=      self.get_feature_extractor()

     _lowerCAmelCase				=      TvltProcessor(image_processor=_lowerCAmelCase			,     feature_extractor=_lowerCAmelCase		)

     _lowerCAmelCase				=      np.ones([12000]		)

     _lowerCAmelCase				=      feature_extractor(_lowerCAmelCase			,     return_tensors="np"		)
     _lowerCAmelCase				=      processor(audio=_lowerCAmelCase			,     return_tensors="np"		)

     for key in audio_dict.keys():
         self.assertAlmostEqual(audio_dict[key].sum()			,     input_processor[key].sum()			,     delta=1E-2		)




 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      self.get_image_processor()
     _lowerCAmelCase				=      self.get_feature_extractor()

     _lowerCAmelCase				=      TvltProcessor(image_processor=_lowerCAmelCase			,     feature_extractor=_lowerCAmelCase		)

     _lowerCAmelCase				=      np.ones([3, 224, 224]		)

     _lowerCAmelCase				=      image_processor(_lowerCAmelCase			,     return_tensors="np"		)
     _lowerCAmelCase				=      processor(images=_lowerCAmelCase			,     return_tensors="np"		)

     for key in image_dict.keys():
         self.assertAlmostEqual(image_dict[key].sum()			,     input_processor[key].sum()			,     delta=1E-2		)




 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      self.get_image_processor()
     _lowerCAmelCase				=      self.get_feature_extractor()

     _lowerCAmelCase				=      TvltProcessor(image_processor=_lowerCAmelCase			,     feature_extractor=_lowerCAmelCase		)

     _lowerCAmelCase				=      np.ones([12000]		)
     _lowerCAmelCase				=      np.ones([3, 224, 224]		)

     _lowerCAmelCase				=      processor(audio=_lowerCAmelCase			,     images=_lowerCAmelCase		)

     self.assertListEqual(list(inputs.keys()		)			,     ["audio_values", "audio_mask", "pixel_values", "pixel_mask"]		)

     # test if it raises when no input is passed
     with pytest.raises(_lowerCAmelCase		):
         processor()





 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      self.get_image_processor()
     _lowerCAmelCase				=      self.get_feature_extractor()

     _lowerCAmelCase				=      TvltProcessor(image_processor=_lowerCAmelCase			,     feature_extractor=_lowerCAmelCase		)

     self.assertListEqual(
         processor.model_input_names			,     image_processor.model_input_names + feature_extractor.model_input_names			,     msg="`processor` and `image_processor`+`feature_extractor` model input names do not match"			,     )


