


import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor





class   __a			(    unittest.TestCase      ):
 def __init__(   self				, lowerCAmelCase__				, lowerCAmelCase__ = True				, lowerCAmelCase__ = None				, lowerCAmelCase__ = 32				, lowerCAmelCase__ = True				, lowerCAmelCase__ = 1 / 255				, lowerCAmelCase__ = True				, lowerCAmelCase__ = True				, lowerCAmelCase__ = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]				, lowerCAmelCase__ = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]				, lowerCAmelCase__ = True				, lowerCAmelCase__=7				, lowerCAmelCase__=30				, lowerCAmelCase__=400				, lowerCAmelCase__=3				, )					->				Dict:




        '''simple docstring'''
        lowercase__:   Optional[int]  			=   parent
        lowercase__:   Tuple  			=   do_resize
        lowercase__:   Optional[Any]  			=   size if size is not None else {'shortest_edge': 288}
        lowercase__:   List[str]  			=   size_divisor
        lowercase__:   str  			=   do_rescale
        lowercase__:   int  			=   rescale_factor
        lowercase__:   List[Any]  			=   do_normalize
        lowercase__:   int  			=   do_center_crop
        lowercase__:   Optional[Any]  			=   image_mean
        lowercase__:   str  			=   image_std
        lowercase__:   Optional[int]  			=   do_pad
        lowercase__:   List[Any]  			=   batch_size
        lowercase__:   Optional[int]  			=   num_channels
        lowercase__:   int  			=   min_resolution
        lowercase__:   int  			=   max_resolution
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				str:




        '''simple docstring'''
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "size_divisor": self.size_divisor,
        }
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__=False )					->				str:




        '''simple docstring'''
        if not batched:
               lowercase__:   Optional[Any]  			=   self.size['shortest_edge']
               lowercase__:   List[str]  			=   image_inputs[0]
               if isinstance(lowerCAmelCase__				, Image.Image ):
                      lowercase__			,						lowercase__:   str  			=   image.size
               else:
                      lowercase__			,						lowercase__:   int  			=   image.shape[1], image.shape[2]
               lowercase__:   Dict  			=   size / min(lowerCAmelCase__				, lowerCAmelCase__ )
               if h < w:
                      lowercase__			,						lowercase__:   Union[str, Any]  			=   size, scale * w
               else:
                      lowercase__			,						lowercase__:   str  			=   scale * h, size

               lowercase__:   List[str]  			=   int((1_333 / 800) * size )
               if max(lowerCAmelCase__				, lowerCAmelCase__ ) > max_size:
                      lowercase__:   Optional[Any]  			=   max_size / max(lowerCAmelCase__				, lowerCAmelCase__ )
                      lowercase__:   List[Any]  			=   newh * scale
                      lowercase__:   List[str]  			=   neww * scale

               lowercase__			,						lowercase__:   Optional[Any]  			=   int(newh + 0.5 ), int(neww + 0.5 )
               lowercase__			,						lowercase__:   Union[str, Any]  			=   (
                   newh // self.size_divisor * self.size_divisor,
                   neww // self.size_divisor * self.size_divisor,
               )

        else:
               lowercase__:   Dict  			=   []
               for image in image_inputs:
                      lowercase__			,						lowercase__:   Dict  			=   self.get_expected_values([image] )
                      expected_values.append((expected_height, expected_width) )
               lowercase__:   Optional[int]  			=   max(lowerCAmelCase__				, key=lambda lowerCAmelCase__ : item[0] )[0]
               lowercase__:   Optional[int]  			=   max(lowerCAmelCase__				, key=lambda lowerCAmelCase__ : item[1] )[1]

        return expected_height, expected_width





@require_torch
@require_vision
class   __a			(    __UpperCamelCase       ,       unittest.TestCase      ):
 __lowercase :							Union[str, Any]        = BridgeTowerImageProcessor if is_vision_available() else None
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Tuple:




        '''simple docstring'''
        lowercase__:   List[Any]  			=   BridgeTowerImageProcessingTester(self )
 @property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Any:




        '''simple docstring'''
        return self.image_processor_tester.prepare_image_processor_dict()
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Optional[int]:




        '''simple docstring'''
        lowercase__:   List[Any]  			=   self.image_processing_class(**self.image_processor_dict )
        self.assertTrue(hasattr(lowerCAmelCase__				, 'image_mean' ) )
        self.assertTrue(hasattr(lowerCAmelCase__				, 'image_std' ) )
        self.assertTrue(hasattr(lowerCAmelCase__				, 'do_normalize' ) )
        self.assertTrue(hasattr(lowerCAmelCase__				, 'do_resize' ) )
        self.assertTrue(hasattr(lowerCAmelCase__				, 'size' ) )
        self.assertTrue(hasattr(lowerCAmelCase__				, 'size_divisor' ) )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[Any]:




        '''simple docstring'''
        pass
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Union[str, Any]:




        '''simple docstring'''
        # Initialize image processor
        lowercase__:   Optional[int]  			=   self.image_processing_class(**self.image_processor_dict )
        # create random PIL images
        lowercase__:   Any  			=   prepare_image_inputs(self.image_processor_tester				, equal_resolution=lowerCAmelCase__ )
        for image in image_inputs:
               self.assertIsInstance(lowerCAmelCase__				, Image.Image )

        # Test not batched input
        lowercase__:   str  			=   image_processing(image_inputs[0]				, return_tensors='pt' ).pixel_values

        lowercase__			,						lowercase__:   List[str]  			=   self.image_processor_tester.get_expected_values(lowerCAmelCase__ )
        self.assertEqual(
            encoded_images.shape				, (1, self.image_processor_tester.num_channels, expected_height, expected_width)				, )

        # Test batched
        lowercase__:   List[str]  			=   image_processing(lowerCAmelCase__				, return_tensors='pt' ).pixel_values

        lowercase__			,						lowercase__:   Optional[int]  			=   self.image_processor_tester.get_expected_values(lowerCAmelCase__				, batched=lowerCAmelCase__ )
        self.assertEqual(
            encoded_images.shape				, (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )				, )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Union[str, Any]:




        '''simple docstring'''
        # Initialize image processor
        lowercase__:   List[str]  			=   self.image_processing_class(**self.image_processor_dict )
        # create random numpy tensors
        lowercase__:   Any  			=   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
        lowercase__:   List[Any]  			=   image_processing(image_inputs[0]				, return_tensors='pt' ).pixel_values

        lowercase__			,						lowercase__:   Union[str, Any]  			=   self.image_processor_tester.get_expected_values(lowerCAmelCase__ )
        self.assertEqual(
            encoded_images.shape				, (1, self.image_processor_tester.num_channels, expected_height, expected_width)				, )

        # Test batched
        lowercase__:   int  			=   image_processing(lowerCAmelCase__				, return_tensors='pt' ).pixel_values

        lowercase__			,						lowercase__:   Union[str, Any]  			=   self.image_processor_tester.get_expected_values(lowerCAmelCase__				, batched=lowerCAmelCase__ )
        self.assertEqual(
            encoded_images.shape				, (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )				, )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Optional[int]:




        '''simple docstring'''
        # Initialize image processor
        lowercase__:   List[str]  			=   self.image_processing_class(**self.image_processor_dict )
        # create random PyTorch tensors
        lowercase__:   Union[str, Any]  			=   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
        lowercase__:   Tuple  			=   image_processing(image_inputs[0]				, return_tensors='pt' ).pixel_values

        lowercase__			,						lowercase__:   Union[str, Any]  			=   self.image_processor_tester.get_expected_values(lowerCAmelCase__ )
        self.assertEqual(
            encoded_images.shape				, (1, self.image_processor_tester.num_channels, expected_height, expected_width)				, )

        # Test batched
        lowercase__:   int  			=   image_processing(lowerCAmelCase__				, return_tensors='pt' ).pixel_values

        lowercase__			,						lowercase__:   List[Any]  			=   self.image_processor_tester.get_expected_values(lowerCAmelCase__				, batched=lowerCAmelCase__ )
        self.assertEqual(
            encoded_images.shape				, (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )				, )






import math

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


__lowerCAmelCase		       =							logging.get_logger(__name__)

__lowerCAmelCase		       =							{
    '''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   __a			(    __UpperCamelCase      ):
 __lowercase :							Optional[int]        = 'data2vec-audio'
 def __init__(   self				, lowerCAmelCase__=32				, lowerCAmelCase__=768				, lowerCAmelCase__=12				, lowerCAmelCase__=12				, lowerCAmelCase__=3_072				, lowerCAmelCase__="gelu"				, lowerCAmelCase__=0.1				, lowerCAmelCase__=0.1				, lowerCAmelCase__=0.1				, lowerCAmelCase__=0.0				, lowerCAmelCase__=0.1				, lowerCAmelCase__=0.1				, lowerCAmelCase__=0.0_2				, 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__=0.0_5				, lowerCAmelCase__=10				, lowerCAmelCase__=2				, lowerCAmelCase__=0.0				, lowerCAmelCase__=10				, lowerCAmelCase__=0				, lowerCAmelCase__="sum"				, lowerCAmelCase__=False				, lowerCAmelCase__=False				, lowerCAmelCase__=256				, lowerCAmelCase__=(512, 512, 512, 512, 1_500)				, 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__				, )					->				Optional[int]:




        '''simple docstring'''
        super().__init__(**lowerCAmelCase__				, pad_token_id=lowerCAmelCase__				, bos_token_id=lowerCAmelCase__				, eos_token_id=lowerCAmelCase__ )
        lowercase__:   int  			=   hidden_size
        lowercase__:   str  			=   feat_extract_activation
        lowercase__:   List[Any]  			=   list(lowerCAmelCase__ )
        lowercase__:   Optional[int]  			=   list(lowerCAmelCase__ )
        lowercase__:   int  			=   list(lowerCAmelCase__ )
        lowercase__:   Union[str, Any]  			=   conv_bias
        lowercase__:   int  			=   num_conv_pos_embeddings
        lowercase__:   List[str]  			=   num_conv_pos_embedding_groups
        lowercase__:   List[Any]  			=   conv_pos_kernel_size
        lowercase__:   Optional[Any]  			=   len(self.conv_dim )
        lowercase__:   List[str]  			=   num_hidden_layers
        lowercase__:   List[str]  			=   intermediate_size
        lowercase__:   Tuple  			=   hidden_act
        lowercase__:   Any  			=   num_attention_heads
        lowercase__:   Optional[int]  			=   hidden_dropout
        lowercase__:   List[str]  			=   attention_dropout
        lowercase__:   int  			=   activation_dropout
        lowercase__:   Dict  			=   feat_proj_dropout
        lowercase__:   str  			=   final_dropout
        lowercase__:   List[str]  			=   layerdrop
        lowercase__:   str  			=   layer_norm_eps
        lowercase__:   Union[str, Any]  			=   initializer_range
        lowercase__:   Union[str, Any]  			=   vocab_size
        lowercase__:   Any  			=   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
        lowercase__:   List[str]  			=   mask_time_prob
        lowercase__:   Tuple  			=   mask_time_length
        lowercase__:   List[Any]  			=   mask_time_min_masks
        lowercase__:   Optional[int]  			=   mask_feature_prob
        lowercase__:   Union[str, Any]  			=   mask_feature_length
        lowercase__:   List[str]  			=   mask_feature_min_masks

        # ctc loss
        lowercase__:   Union[str, Any]  			=   ctc_loss_reduction
        lowercase__:   str  			=   ctc_zero_infinity

        # adapter
        lowercase__:   str  			=   add_adapter
        lowercase__:   List[Any]  			=   adapter_kernel_size
        lowercase__:   Tuple  			=   adapter_stride
        lowercase__:   Dict  			=   num_adapter_layers
        lowercase__:   Optional[Any]  			=   output_hidden_size or hidden_size

        # SequenceClassification-specific parameter. Feel free to ignore for other classes.
        lowercase__:   List[Any]  			=   classifier_proj_size

        # XVector-specific parameters. Feel free to ignore for other classes.
        lowercase__:   int  			=   list(lowerCAmelCase__ )
        lowercase__:   Dict  			=   list(lowerCAmelCase__ )
        lowercase__:   int  			=   list(lowerCAmelCase__ )
        lowercase__:   str  			=   xvector_output_dim
 @property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Optional[int]:




        '''simple docstring'''
        return math.prod(self.conv_stride )


