





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 MobileViTImageProcessor

class        __SCREAMING_SNAKE_CASE					(     unittest.TestCase       ):






							def __init__(						self  , __lowerCAmelCase  , __lowerCAmelCase=7  , __lowerCAmelCase=3  , __lowerCAmelCase=18  , __lowerCAmelCase=30  , __lowerCAmelCase=400  , __lowerCAmelCase=True  , __lowerCAmelCase=None  , __lowerCAmelCase=True  , __lowerCAmelCase=None  , __lowerCAmelCase=True  , ):
														UpperCamelCase__				     =			size if size is not None else {"""shortest_edge""": 20}
														UpperCamelCase__				     =			crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
														UpperCamelCase__				     =			parent
														UpperCamelCase__				     =			batch_size
														UpperCamelCase__				     =			num_channels
														UpperCamelCase__				     =			image_size
														UpperCamelCase__				     =			min_resolution
														UpperCamelCase__				     =			max_resolution
														UpperCamelCase__				     =			do_resize
														UpperCamelCase__				     =			size
														UpperCamelCase__				     =			do_center_crop
														UpperCamelCase__				     =			crop_size
														UpperCamelCase__				     =			do_flip_channel_order







							def 				_lowerCamelCase		(						self  ):
														return {
														    "do_resize": self.do_resize,
														    "size": self.size,
														    "do_center_crop": self.do_center_crop,
														    "crop_size": self.crop_size,
														    "do_flip_channel_order": self.do_flip_channel_order,
														}






@require_torch
@require_vision
class        __SCREAMING_SNAKE_CASE					(     _a   ,		unittest.TestCase       ):
							snake_case     :      Dict						     =       MobileViTImageProcessor if is_vision_available() else None






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			MobileViTImageProcessingTester(self  )






							@property
							def 				_lowerCamelCase		(						self  ):
														return self.image_processor_tester.prepare_image_processor_dict()






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			self.image_processing_class(**self.image_processor_dict  )
														self.assertTrue(hasattr(__lowerCAmelCase  , """do_resize"""  )  )
														self.assertTrue(hasattr(__lowerCAmelCase  , """size"""  )  )
														self.assertTrue(hasattr(__lowerCAmelCase  , """do_center_crop"""  )  )
														self.assertTrue(hasattr(__lowerCAmelCase  , """center_crop"""  )  )
														self.assertTrue(hasattr(__lowerCAmelCase  , """do_flip_channel_order"""  )  )






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			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}  )

														UpperCamelCase__				     =			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 				_lowerCamelCase		(						self  ):
														pass






							def 				_lowerCamelCase		(						self  ):
														# Initialize image_processing
														UpperCamelCase__				     =			self.image_processing_class(**self.image_processor_dict  )
														# create random PIL images
														UpperCamelCase__				     =			prepare_image_inputs(self.image_processor_tester  , equal_resolution=__lowerCAmelCase  )
														for image in image_inputs:
																					self.assertIsInstance(__lowerCAmelCase  , Image.Image  )

														# Test not batched input
														UpperCamelCase__				     =			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
														UpperCamelCase__				     =			image_processing(__lowerCAmelCase  , 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 				_lowerCamelCase		(						self  ):
														# Initialize image_processing
														UpperCamelCase__				     =			self.image_processing_class(**self.image_processor_dict  )
														# create random numpy tensors
														UpperCamelCase__				     =			prepare_image_inputs(self.image_processor_tester  , equal_resolution=__lowerCAmelCase  , numpify=__lowerCAmelCase  )
														for image in image_inputs:
																					self.assertIsInstance(__lowerCAmelCase  , np.ndarray  )

														# Test not batched input
														UpperCamelCase__				     =			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
														UpperCamelCase__				     =			image_processing(__lowerCAmelCase  , 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 				_lowerCamelCase		(						self  ):
														# Initialize image_processing
														UpperCamelCase__				     =			self.image_processing_class(**self.image_processor_dict  )
														# create random PyTorch tensors
														UpperCamelCase__				     =			prepare_image_inputs(self.image_processor_tester  , equal_resolution=__lowerCAmelCase  , torchify=__lowerCAmelCase  )
														for image in image_inputs:
																					self.assertIsInstance(__lowerCAmelCase  , torch.Tensor  )

														# Test not batched input
														UpperCamelCase__				     =			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
														UpperCamelCase__				     =			image_processing(__lowerCAmelCase  , 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"""],
														    )  , )











import math

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


UpperCamelCase__											= logging.get_logger(__name__)

UpperCamelCase__											= {
    "facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}

class        __SCREAMING_SNAKE_CASE					(     _a       ):
							snake_case     :      Optional[Any]						     =       """data2vec-audio"""






							def __init__(						self  , __lowerCAmelCase=32  , __lowerCAmelCase=768  , __lowerCAmelCase=12  , __lowerCAmelCase=12  , __lowerCAmelCase=3072  , __lowerCAmelCase="gelu"  , __lowerCAmelCase=0.1  , __lowerCAmelCase=0.1  , __lowerCAmelCase=0.1  , __lowerCAmelCase=0.0  , __lowerCAmelCase=0.1  , __lowerCAmelCase=0.1  , __lowerCAmelCase=0.02  , __lowerCAmelCase=1E-5  , __lowerCAmelCase="gelu"  , __lowerCAmelCase=(512, 512, 512, 512, 512, 512, 512)  , __lowerCAmelCase=(5, 2, 2, 2, 2, 2, 2)  , __lowerCAmelCase=(10, 3, 3, 3, 3, 2, 2)  , __lowerCAmelCase=False  , __lowerCAmelCase=16  , __lowerCAmelCase=19  , __lowerCAmelCase=5  , __lowerCAmelCase=0.05  , __lowerCAmelCase=10  , __lowerCAmelCase=2  , __lowerCAmelCase=0.0  , __lowerCAmelCase=10  , __lowerCAmelCase=0  , __lowerCAmelCase="sum"  , __lowerCAmelCase=False  , __lowerCAmelCase=False  , __lowerCAmelCase=256  , __lowerCAmelCase=(512, 512, 512, 512, 1500)  , __lowerCAmelCase=(5, 3, 3, 1, 1)  , __lowerCAmelCase=(1, 2, 3, 1, 1)  , __lowerCAmelCase=512  , __lowerCAmelCase=0  , __lowerCAmelCase=1  , __lowerCAmelCase=2  , __lowerCAmelCase=False  , __lowerCAmelCase=3  , __lowerCAmelCase=2  , __lowerCAmelCase=3  , __lowerCAmelCase=None  , **__lowerCAmelCase  , ):
														super().__init__(**__lowerCAmelCase  , pad_token_id=__lowerCAmelCase  , bos_token_id=__lowerCAmelCase  , eos_token_id=__lowerCAmelCase  )
														UpperCamelCase__				     =			hidden_size
														UpperCamelCase__				     =			feat_extract_activation
														UpperCamelCase__				     =			list(__lowerCAmelCase  )
														UpperCamelCase__				     =			list(__lowerCAmelCase  )
														UpperCamelCase__				     =			list(__lowerCAmelCase  )
														UpperCamelCase__				     =			conv_bias
														UpperCamelCase__				     =			num_conv_pos_embeddings
														UpperCamelCase__				     =			num_conv_pos_embedding_groups
														UpperCamelCase__				     =			conv_pos_kernel_size
														UpperCamelCase__				     =			len(self.conv_dim  )
														UpperCamelCase__				     =			num_hidden_layers
														UpperCamelCase__				     =			intermediate_size
														UpperCamelCase__				     =			hidden_act
														UpperCamelCase__				     =			num_attention_heads
														UpperCamelCase__				     =			hidden_dropout
														UpperCamelCase__				     =			attention_dropout
														UpperCamelCase__				     =			activation_dropout
														UpperCamelCase__				     =			feat_proj_dropout
														UpperCamelCase__				     =			final_dropout
														UpperCamelCase__				     =			layerdrop
														UpperCamelCase__				     =			layer_norm_eps
														UpperCamelCase__				     =			initializer_range
														UpperCamelCase__				     =			vocab_size
														UpperCamelCase__				     =			use_weighted_layer_sum

														if (
														    (len(self.conv_stride  ) != self.num_feat_extract_layers)
														    or (len(self.conv_kernel  ) != self.num_feat_extract_layers)
														    or (len(self.conv_dim  ) != self.num_feat_extract_layers)
														):
																					raise ValueError(
																					    """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
																					    """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
																					    f""" {len(self.conv_dim  )}`, `len(config.conv_stride) = {len(self.conv_stride  )}`,"""
																					    f""" `len(config.conv_kernel) = {len(self.conv_kernel  )}`."""  )

														# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
														UpperCamelCase__				     =			mask_time_prob
														UpperCamelCase__				     =			mask_time_length
														UpperCamelCase__				     =			mask_time_min_masks
														UpperCamelCase__				     =			mask_feature_prob
														UpperCamelCase__				     =			mask_feature_length
														UpperCamelCase__				     =			mask_feature_min_masks

														# ctc loss
														UpperCamelCase__				     =			ctc_loss_reduction
														UpperCamelCase__				     =			ctc_zero_infinity

														# adapter
														UpperCamelCase__				     =			add_adapter
														UpperCamelCase__				     =			adapter_kernel_size
														UpperCamelCase__				     =			adapter_stride
														UpperCamelCase__				     =			num_adapter_layers
														UpperCamelCase__				     =			output_hidden_size or hidden_size

														# SequenceClassification-specific parameter. Feel free to ignore for other classes.
														UpperCamelCase__				     =			classifier_proj_size

														# XVector-specific parameters. Feel free to ignore for other classes.
														UpperCamelCase__				     =			list(__lowerCAmelCase  )
														UpperCamelCase__				     =			list(__lowerCAmelCase  )
														UpperCamelCase__				     =			list(__lowerCAmelCase  )
														UpperCamelCase__				     =			xvector_output_dim







							@property
							def 				_lowerCamelCase		(						self  ):
														return math.prod(self.conv_stride  )




