import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import MobileNetVaImageProcessor
class        a_				(		unittest.TestCase		):






	'''simple docstring'''






	def __init__(    self      :						Optional[Any]    ,					lowercase__      :						List[str]    ,					lowercase__      :						Tuple=7    ,					lowercase__      :						Union[str, Any]=3    ,					lowercase__      :						List[Any]=18    ,					lowercase__      :						Optional[Any]=30    ,					lowercase__      :						Tuple=400    ,					lowercase__      :						Union[str, Any]=True    ,					lowercase__      :						List[str]=None    ,					lowercase__      :						Optional[int]=True    ,					lowercase__      :						int=None    ,					):







								'''simple docstring'''



								lowerCAmelCase__  			=      size if size is not None else {'shortest_edge': 20}
								lowerCAmelCase__  			=      crop_size if crop_size is not None else {'height': 18, 'width': 18}
								lowerCAmelCase__  			=      parent
								lowerCAmelCase__  			=      batch_size
								lowerCAmelCase__  			=      num_channels
								lowerCAmelCase__  			=      image_size
								lowerCAmelCase__  			=      min_resolution
								lowerCAmelCase__  			=      max_resolution
								lowerCAmelCase__  			=      do_resize
								lowerCAmelCase__  			=      size
								lowerCAmelCase__  			=      do_center_crop
								lowerCAmelCase__  			=      crop_size




	def 	__snake_case		(    self      :						List[Any]):







								'''simple docstring'''



								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								}






@require_torch
@require_vision
class        a_				(		a_ ,  unittest.TestCase		):






	'''simple docstring'''






	UpperCAmelCase_							=      MobileNetVaImageProcessor if is_vision_available() else None




	def 	__snake_case		(    self      :						Tuple):







								'''simple docstring'''



								lowerCAmelCase__  			=      MobileNetVaImageProcessingTester(self)




	@property
	def 	__snake_case		(    self      :						List[str]):







								'''simple docstring'''



								return self.image_processor_tester.prepare_image_processor_dict()




	def 	__snake_case		(    self      :						Optional[Any]):







								'''simple docstring'''



								lowerCAmelCase__  			=      self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(lowercase__    ,					'do_resize'))
								self.assertTrue(hasattr(lowercase__    ,					'size'))
								self.assertTrue(hasattr(lowercase__    ,					'do_center_crop'))
								self.assertTrue(hasattr(lowercase__    ,					'center_crop'))




	def 	__snake_case		(    self      :						Any):







								'''simple docstring'''



								lowerCAmelCase__  			=      self.image_processing_class.from_dict(self.image_processor_dict)
								self.assertEqual(image_processor.size    ,					{'shortest_edge': 20})
								self.assertEqual(image_processor.crop_size    ,					{'height': 18, 'width': 18})

								lowerCAmelCase__  			=      self.image_processing_class.from_dict(self.image_processor_dict    ,					size=42    ,					crop_size=84)
								self.assertEqual(image_processor.size    ,					{'shortest_edge': 42})
								self.assertEqual(image_processor.crop_size    ,					{'height': 84, 'width': 84})




	def 	__snake_case		(    self      :						Optional[int]):







								'''simple docstring'''



								pass




	def 	__snake_case		(    self      :						Any):







								'''simple docstring'''



								lowerCAmelCase__  			=      self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								lowerCAmelCase__  			=      prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=lowercase__)
								for image in image_inputs:
															self.assertIsInstance(lowercase__    ,					Image.Image)

								# Test not batched input
								lowerCAmelCase__  			=      image_processing(image_inputs[0]    ,					return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape    ,					(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )    ,					)

								# Test batched
								lowerCAmelCase__  			=      image_processing(lowercase__    ,					return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape    ,					(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )    ,					)




	def 	__snake_case		(    self      :						Optional[Any]):







								'''simple docstring'''



								lowerCAmelCase__  			=      self.image_processing_class(**self.image_processor_dict)
								# create random numpy tensors
								lowerCAmelCase__  			=      prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=lowercase__    ,					numpify=lowercase__)
								for image in image_inputs:
															self.assertIsInstance(lowercase__    ,					np.ndarray)

								# Test not batched input
								lowerCAmelCase__  			=      image_processing(image_inputs[0]    ,					return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape    ,					(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )    ,					)

								# Test batched
								lowerCAmelCase__  			=      image_processing(lowercase__    ,					return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape    ,					(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )    ,					)




	def 	__snake_case		(    self      :						int):







								'''simple docstring'''



								lowerCAmelCase__  			=      self.image_processing_class(**self.image_processor_dict)
								# create random PyTorch tensors
								lowerCAmelCase__  			=      prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=lowercase__    ,					torchify=lowercase__)
								for image in image_inputs:
															self.assertIsInstance(lowercase__    ,					torch.Tensor)

								# Test not batched input
								lowerCAmelCase__  			=      image_processing(image_inputs[0]    ,					return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape    ,					(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )    ,					)

								# Test batched
								lowerCAmelCase__  			=      image_processing(lowercase__    ,					return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape    ,					(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )    ,					)

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


lowerCAmelCase__					=				logging.get_logger(__name__)

lowerCAmelCase__					=				{
    'ZinengTang/tvlt-base': 'https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json',
}
class        a_				(		SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 UpperCAmelCase_							=      'tvlt'




 def __init__(    self      :						Dict    ,					lowercase__      :						str=224    ,					lowercase__      :						int=2_048    ,					lowercase__      :						int=128    ,					lowercase__      :						Optional[Any]=[16, 16]    ,					lowercase__      :						List[str]=[16, 16]    ,					lowercase__      :						Optional[int]=3    ,					lowercase__      :						Tuple=1    ,					lowercase__      :						Union[str, Any]=8    ,					lowercase__      :						int=768    ,					lowercase__      :						Tuple=12    ,					lowercase__      :						List[str]=12    ,					lowercase__      :						Union[str, Any]=3_072    ,					lowercase__      :						Union[str, Any]="gelu"    ,					lowercase__      :						Dict=0.0    ,					lowercase__      :						Tuple=0.0    ,					lowercase__      :						Union[str, Any]=0.02    ,					lowercase__      :						Any=1e-6    ,					lowercase__      :						Dict=True    ,					lowercase__      :						int=False    ,					lowercase__      :						Tuple=16    ,					lowercase__      :						Optional[int]=512    ,					lowercase__      :						int=8    ,					lowercase__      :						List[Any]=2_048    ,					lowercase__      :						Optional[int]=0.75    ,					lowercase__      :						Union[str, Any]=0.15    ,					lowercase__      :						List[str]="frame-level"    ,					lowercase__      :						Dict=True    ,					lowercase__      :						Tuple=True    ,					lowercase__      :						Any="classification"    ,					**lowercase__      :						Union[str, Any]    ,					):







        '''simple docstring'''



        super().__init__(**lowercase__)

        if audio_mask_type not in ("frame-level", "patch_level"):
               raise ValueError(
                   'audio_mask_type must be one of two acceptable strategies - {\'frame_level\', \'patch-level\') '
                   F"""got {audio_mask_type}""")

        lowerCAmelCase__  			=      image_size
        lowerCAmelCase__  			=      spectrogram_length
        lowerCAmelCase__  			=      frequency_length
        lowerCAmelCase__  			=      image_patch_size
        lowerCAmelCase__  			=      audio_patch_size
        lowerCAmelCase__  			=      num_image_channels
        lowerCAmelCase__  			=      num_audio_channels
        lowerCAmelCase__  			=      num_frames

        lowerCAmelCase__  			=      hidden_size
        lowerCAmelCase__  			=      num_hidden_layers
        lowerCAmelCase__  			=      num_attention_heads
        lowerCAmelCase__  			=      intermediate_size
        lowerCAmelCase__  			=      hidden_act
        lowerCAmelCase__  			=      hidden_dropout_prob
        lowerCAmelCase__  			=      attention_probs_dropout_prob
        lowerCAmelCase__  			=      initializer_range
        lowerCAmelCase__  			=      layer_norm_eps
        lowerCAmelCase__  			=      qkv_bias
        lowerCAmelCase__  			=      use_mean_pooling

        lowerCAmelCase__  			=      decoder_num_attention_heads
        lowerCAmelCase__  			=      decoder_hidden_size
        lowerCAmelCase__  			=      decoder_num_hidden_layers
        lowerCAmelCase__  			=      decoder_intermediate_size
        lowerCAmelCase__  			=      pixel_mask_ratio
        lowerCAmelCase__  			=      audio_mask_ratio
        lowerCAmelCase__  			=      audio_mask_type

        lowerCAmelCase__  			=      task_matching
        lowerCAmelCase__  			=      task_mae
        lowerCAmelCase__  			=      loss_type
