





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 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     """ZinengTang/tvlt-base"""
														UpperCamelCase__			       =     tempfile.mkdtemp()






							def 			_lowerCamelCase						(  self					,	**__lowerCAmelCase					):
														return TvltImageProcessor.from_pretrained(self.checkpoint					,	**__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					,	**__lowerCAmelCase					):
														return TvltFeatureExtractor.from_pretrained(self.checkpoint					,	**__lowerCAmelCase					)






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






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.get_image_processor()
														UpperCamelCase__			       =     self.get_feature_extractor()

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

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






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.get_image_processor()
														UpperCamelCase__			       =     self.get_feature_extractor()

														UpperCamelCase__			       =     TvltProcessor(image_processor=__lowerCAmelCase					,	feature_extractor=__lowerCAmelCase					)

														UpperCamelCase__			       =     np.ones([12000]					)

														UpperCamelCase__			       =     feature_extractor(__lowerCAmelCase					,	return_tensors="""np"""					)
														UpperCamelCase__			       =     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 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.get_image_processor()
														UpperCamelCase__			       =     self.get_feature_extractor()

														UpperCamelCase__			       =     TvltProcessor(image_processor=__lowerCAmelCase					,	feature_extractor=__lowerCAmelCase					)

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

														UpperCamelCase__			       =     image_processor(__lowerCAmelCase					,	return_tensors="""np"""					)
														UpperCamelCase__			       =     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 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.get_image_processor()
														UpperCamelCase__			       =     self.get_feature_extractor()

														UpperCamelCase__			       =     TvltProcessor(image_processor=__lowerCAmelCase					,	feature_extractor=__lowerCAmelCase					)

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

														UpperCamelCase__			       =     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 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.get_image_processor()
														UpperCamelCase__			       =     self.get_feature_extractor()

														UpperCamelCase__			       =     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"""					,	)











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


UpperCamelCase__     =	logging.get_logger(__name__)

UpperCamelCase__     =	{
    "microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}

class     __SCREAMING_SNAKE_CASE   (       _a    ):
							snake_case       : int         =    """cvt"""






							def __init__(  self					,	__lowerCAmelCase=3					,	__lowerCAmelCase=[7, 3, 3]					,	__lowerCAmelCase=[4, 2, 2]					,	__lowerCAmelCase=[2, 1, 1]					,	__lowerCAmelCase=[64, 192, 384]					,	__lowerCAmelCase=[1, 3, 6]					,	__lowerCAmelCase=[1, 2, 10]					,	__lowerCAmelCase=[4.0, 4.0, 4.0]					,	__lowerCAmelCase=[0.0, 0.0, 0.0]					,	__lowerCAmelCase=[0.0, 0.0, 0.0]					,	__lowerCAmelCase=[0.0, 0.0, 0.1]					,	__lowerCAmelCase=[True, True, True]					,	__lowerCAmelCase=[False, False, True]					,	__lowerCAmelCase=["dw_bn", "dw_bn", "dw_bn"]					,	__lowerCAmelCase=[3, 3, 3]					,	__lowerCAmelCase=[1, 1, 1]					,	__lowerCAmelCase=[2, 2, 2]					,	__lowerCAmelCase=[1, 1, 1]					,	__lowerCAmelCase=[1, 1, 1]					,	__lowerCAmelCase=0.02					,	__lowerCAmelCase=1E-12					,	**__lowerCAmelCase					,	):
														super().__init__(**__lowerCAmelCase					)
														UpperCamelCase__			       =     num_channels
														UpperCamelCase__			       =     patch_sizes
														UpperCamelCase__			       =     patch_stride
														UpperCamelCase__			       =     patch_padding
														UpperCamelCase__			       =     embed_dim
														UpperCamelCase__			       =     num_heads
														UpperCamelCase__			       =     depth
														UpperCamelCase__			       =     mlp_ratio
														UpperCamelCase__			       =     attention_drop_rate
														UpperCamelCase__			       =     drop_rate
														UpperCamelCase__			       =     drop_path_rate
														UpperCamelCase__			       =     qkv_bias
														UpperCamelCase__			       =     cls_token
														UpperCamelCase__			       =     qkv_projection_method
														UpperCamelCase__			       =     kernel_qkv
														UpperCamelCase__			       =     padding_kv
														UpperCamelCase__			       =     stride_kv
														UpperCamelCase__			       =     padding_q
														UpperCamelCase__			       =     stride_q
														UpperCamelCase__			       =     initializer_range
														UpperCamelCase__			       =     layer_norm_eps




