


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase__							:						Dict							     =   logging.get_logger(__name__)
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							Any	   =       'encoder-decoder'
     __lowercase				:							Optional[Any]	   =       True

     def __init__(					self:Dict     ,		**_a:Optional[Any]   ):
       super().__init__(**_a   )
       assert (
           "encoder" in kwargs and "decoder" in kwargs
       ), "Config has to be initialized with encoder and decoder config"
       snake_case__						= kwargs.pop('''encoder'''   )
       snake_case__						= encoder_config.pop('''model_type'''   )
       snake_case__						= kwargs.pop('''decoder'''   )
       snake_case__						= decoder_config.pop('''model_type'''   )

       from ..auto.configuration_auto import AutoConfig

       snake_case__						= AutoConfig.for_model(_a     ,		**_a   )
       snake_case__						= AutoConfig.for_model(_a     ,		**_a   )
       snake_case__						= True

     @classmethod
     def 	SCREAMING_SNAKE_CASE__						(					cls:List[Any]     ,		_a:PretrainedConfig     ,		_a:PretrainedConfig     ,		**_a:int   ):
       logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''   )
       snake_case__						= True
       snake_case__						= True

       return cls(encoder=encoder_config.to_dict()     ,		decoder=decoder_config.to_dict()     ,		**_a   )







     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       snake_case__						= copy.deepcopy(self.__dict__   )
       snake_case__						= self.encoder.to_dict()
       snake_case__						= self.decoder.to_dict()
       snake_case__						= self.__class__.model_type
       return output





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 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       snake_case__						= '''ZinengTang/tvlt-base'''
       snake_case__						= tempfile.mkdtemp()

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		**_a:List[Any]   ):
       return TvltImageProcessor.from_pretrained(self.checkpoint     ,		**_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]     ,		**_a:Tuple   ):
       return TvltFeatureExtractor.from_pretrained(self.checkpoint     ,		**_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       shutil.rmtree(self.tmpdirname   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       snake_case__						= self.get_image_processor()
       snake_case__						= self.get_feature_extractor()

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

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

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       snake_case__						= self.get_image_processor()
       snake_case__						= self.get_feature_extractor()

       snake_case__						= TvltProcessor(image_processor=_a     ,		feature_extractor=_a   )

       snake_case__						= np.ones([1_20_00]   )

       snake_case__						= feature_extractor(_a     ,		return_tensors='''np'''   )
       snake_case__						= processor(audio=_a     ,		return_tensors='''np'''   )

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

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       snake_case__						= self.get_image_processor()
       snake_case__						= self.get_feature_extractor()

       snake_case__						= TvltProcessor(image_processor=_a     ,		feature_extractor=_a   )

       snake_case__						= np.ones([3, 2_24, 2_24]   )

       snake_case__						= image_processor(_a     ,		return_tensors='''np'''   )
       snake_case__						= processor(images=_a     ,		return_tensors='''np'''   )

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

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       snake_case__						= self.get_image_processor()
       snake_case__						= self.get_feature_extractor()

       snake_case__						= TvltProcessor(image_processor=_a     ,		feature_extractor=_a   )

       snake_case__						= np.ones([1_20_00]   )
       snake_case__						= np.ones([3, 2_24, 2_24]   )

       snake_case__						= processor(audio=_a     ,		images=_a   )

       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(_a   ):
         processor()







     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       snake_case__						= self.get_image_processor()
       snake_case__						= self.get_feature_extractor()

       snake_case__						= TvltProcessor(image_processor=_a     ,		feature_extractor=_a   )

       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'''     ,		)

