





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     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):



 def 				UpperCamelCase			(							self ):
        A__											=						'''ZinengTang/tvlt-base'''
        A__											=						tempfile.mkdtemp()



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        return TvltImageProcessor.from_pretrained(self.checkpoint,**__lowercase )



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        return TvltFeatureExtractor.from_pretrained(self.checkpoint,**__lowercase )



 def 				UpperCamelCase			(							self ):
        shutil.rmtree(self.tmpdirname )



 def 				UpperCamelCase			(							self ):
        A__											=						self.get_image_processor()
        A__											=						self.get_feature_extractor()

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

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



 def 				UpperCamelCase			(							self ):
        A__											=						self.get_image_processor()
        A__											=						self.get_feature_extractor()

        A__											=						TvltProcessor(image_processor=__lowercase,feature_extractor=__lowercase )

        A__											=						np.ones([1_2000] )

        A__											=						feature_extractor(__lowercase,return_tensors='''np''' )
        A__											=						processor(audio=__lowercase,return_tensors='''np''' )

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



 def 				UpperCamelCase			(							self ):
        A__											=						self.get_image_processor()
        A__											=						self.get_feature_extractor()

        A__											=						TvltProcessor(image_processor=__lowercase,feature_extractor=__lowercase )

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

        A__											=						image_processor(__lowercase,return_tensors='''np''' )
        A__											=						processor(images=__lowercase,return_tensors='''np''' )

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



 def 				UpperCamelCase			(							self ):
        A__											=						self.get_image_processor()
        A__											=						self.get_feature_extractor()

        A__											=						TvltProcessor(image_processor=__lowercase,feature_extractor=__lowercase )

        A__											=						np.ones([1_2000] )
        A__											=						np.ones([3, 224, 224] )

        A__											=						processor(audio=__lowercase,images=__lowercase )

        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(__lowercase ):
               processor()




 def 				UpperCamelCase			(							self ):
        A__											=						self.get_image_processor()
        A__											=						self.get_feature_extractor()

        A__											=						TvltProcessor(image_processor=__lowercase,feature_extractor=__lowercase )

        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''',)






from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowercase							 =					logging.get_logger(__name__)


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Optional[Any]						=      ["""pixel_values"""]







    def __init__(	self , __lowercase = True , __lowercase = 32 , __lowercase=PILImageResampling.BILINEAR , __lowercase = True , **__lowercase , )       ->	None:
        __UpperCamelCase     :Optional[int]			  =		do_resize
        __UpperCamelCase     :Any			  =		do_rescale
        __UpperCamelCase     :str			  =		size_divisor
        __UpperCamelCase     :Dict			  =		resample
        super().__init__(**__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase , __lowercase = None , **__lowercase)       ->	np.ndarray:
        __UpperCamelCase  ,       __UpperCamelCase     :int			  =		get_image_size(__lowercase)
        # Rounds the height and width down to the closest multiple of size_divisor
        __UpperCamelCase     :List[Any]			  =		height // size_divisor * size_divisor
        __UpperCamelCase     :List[str]			  =		width // size_divisor * size_divisor
        __UpperCamelCase     :str			  =		resize(__lowercase , (new_h, new_w) , resample=__lowercase , data_format=__lowercase , **__lowercase)
        return image







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase = None , **__lowercase)       ->	np.ndarray:
        return rescale(image=__lowercase , scale=__lowercase , data_format=__lowercase , **__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = None , __lowercase = None , __lowercase=None , __lowercase = None , __lowercase = None , __lowercase = ChannelDimension.FIRST , **__lowercase , )       ->	BatchFeature:
        __UpperCamelCase     :Union[str, Any]			  =		do_resize if do_resize is not None else self.do_resize
        __UpperCamelCase     :Tuple			  =		do_rescale if do_rescale is not None else self.do_rescale
        __UpperCamelCase     :List[str]			  =		size_divisor if size_divisor is not None else self.size_divisor
        __UpperCamelCase     :List[Any]			  =		resample if resample is not None else self.resample

        if do_resize and size_divisor is None:
            raise ValueError('''size_divisor is required for resizing''')

        __UpperCamelCase     :List[Any]			  =		make_list_of_images(__lowercase)

        if not valid_images(__lowercase):
            raise ValueError('''Invalid image(s)''')

        # All transformations expect numpy arrays.
        __UpperCamelCase     :Optional[Any]			  =		[to_numpy_array(__lowercase) for img in images]

        if do_resize:
            __UpperCamelCase     :List[str]			  =		[self.resize(__lowercase , size_divisor=__lowercase , resample=__lowercase) for image in images]

        if do_rescale:
            __UpperCamelCase     :Dict			  =		[self.rescale(__lowercase , scale=1 / 255) for image in images]

        __UpperCamelCase     :str			  =		[to_channel_dimension_format(__lowercase , __lowercase) for image in images]

        __UpperCamelCase     :int			  =		{'''pixel_values''': images}
        return BatchFeature(data=__lowercase , tensor_type=__lowercase)





