

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   _SCREAMING_SNAKE_CASE   (   __snake_case     ):
 __SCREAMING_SNAKE_CASE					:List[Any]      	=					"""data2vec-audio"""



 def __init__(   self		:   Any			,				a__		:   Tuple=32			,				a__		:   Dict=768			,				a__		:   Union[str, Any]=12			,				a__		:   Any=12			,				a__		:   str=3072			,				a__		:   Union[str, Any]="gelu"			,				a__		:   List[Any]=0.1			,				a__		:   Tuple=0.1			,				a__		:   List[str]=0.1			,				a__		:   Tuple=0.0			,				a__		:   Any=0.1			,				a__		:   int=0.1			,				a__		:   Dict=0.02			,				a__		:   Any=1E-5			,				a__		:   List[str]="gelu"			,				a__		:   List[str]=(512, 512, 512, 512, 512, 512, 512)			,				a__		:   str=(5, 2, 2, 2, 2, 2, 2)			,				a__		:   Union[str, Any]=(10, 3, 3, 3, 3, 2, 2)			,				a__		:   str=False			,				a__		:   Union[str, Any]=16			,				a__		:   Optional[int]=19			,				a__		:   Dict=5			,				a__		:   List[Any]=0.05			,				a__		:   int=10			,				a__		:   Optional[Any]=2			,				a__		:   Dict=0.0			,				a__		:   Optional[Any]=10			,				a__		:   Dict=0			,				a__		:   int="sum"			,				a__		:   Dict=False			,				a__		:   Any=False			,				a__		:   Optional[int]=256			,				a__		:   Any=(512, 512, 512, 512, 1500)			,				a__		:   List[Any]=(5, 3, 3, 1, 1)			,				a__		:   List[str]=(1, 2, 3, 1, 1)			,				a__		:   Tuple=512			,				a__		:   int=0			,				a__		:   List[str]=1			,				a__		:   Optional[int]=2			,				a__		:   Optional[Any]=False			,				a__		:   Dict=3			,				a__		:   Optional[Any]=2			,				a__		:   Union[str, Any]=3			,				a__		:   int=None			,				**a__		:   List[Any]			,				):
      super().__init__(**UpperCamelCase__			,				pad_token_id=UpperCamelCase__			,				bos_token_id=UpperCamelCase__			,				eos_token_id=UpperCamelCase__      )
      __magic_name__						       =    hidden_size
      __magic_name__						       =    feat_extract_activation
      __magic_name__						       =    list(UpperCamelCase__      )
      __magic_name__						       =    list(UpperCamelCase__      )
      __magic_name__						       =    list(UpperCamelCase__      )
      __magic_name__						       =    conv_bias
      __magic_name__						       =    num_conv_pos_embeddings
      __magic_name__						       =    num_conv_pos_embedding_groups
      __magic_name__						       =    conv_pos_kernel_size
      __magic_name__						       =    len(self.conv_dim      )
      __magic_name__						       =    num_hidden_layers
      __magic_name__						       =    intermediate_size
      __magic_name__						       =    hidden_act
      __magic_name__						       =    num_attention_heads
      __magic_name__						       =    hidden_dropout
      __magic_name__						       =    attention_dropout
      __magic_name__						       =    activation_dropout
      __magic_name__						       =    feat_proj_dropout
      __magic_name__						       =    final_dropout
      __magic_name__						       =    layerdrop
      __magic_name__						       =    layer_norm_eps
      __magic_name__						       =    initializer_range
      __magic_name__						       =    vocab_size
      __magic_name__						       =    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
      __magic_name__						       =    mask_time_prob
      __magic_name__						       =    mask_time_length
      __magic_name__						       =    mask_time_min_masks
      __magic_name__						       =    mask_feature_prob
      __magic_name__						       =    mask_feature_length
      __magic_name__						       =    mask_feature_min_masks

      # ctc loss
      __magic_name__						       =    ctc_loss_reduction
      __magic_name__						       =    ctc_zero_infinity

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

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

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



 @property
 def 			snake_case__  (   self		:   Optional[Any]      ):
      return math.prod(self.conv_stride      )









'''simple docstring'''




import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   Optional[Any]			,				a__		:   Any			,				a__		:   Tuple=sys.maxsize      ):
      __magic_name__						       =    '''bilinear'''
      __magic_name__						       =    max_size
      __magic_name__						       =    short_edge_length



 def __call__(   self		:   Tuple			,				a__		:   List[str]      ):
      __magic_name__						       =    []
      for img in imgs:
           __magic_name__       ,  __magic_name__						       =    img.shape[:2]
           # later: provide list and randomly choose index for resize
           __magic_name__						       =    np.random.randint(self.short_edge_length[0]			,				self.short_edge_length[1] + 1      )
           if size == 0:
                return img
           __magic_name__						       =    size * 1.0 / min(a__			,				a__      )
           if h < w:
                __magic_name__       ,  __magic_name__						       =    size, scale * w
           else:
                __magic_name__       ,  __magic_name__						       =    scale * h, size
           if max(a__			,				a__      ) > self.max_size:
                __magic_name__						       =    self.max_size * 1.0 / max(a__			,				a__      )
                __magic_name__						       =    newh * scale
                __magic_name__						       =    neww * scale
           __magic_name__						       =    int(neww + 0.5      )
           __magic_name__						       =    int(newh + 0.5      )

           if img.dtype == np.uinta:
                __magic_name__						       =    Image.fromarray(a__      )
                __magic_name__						       =    pil_image.resize((neww, newh)			,				PILImageResampling.BILINEAR      )
                __magic_name__						       =    np.asarray(a__      )
           else:
                __magic_name__						       =    img.permute(2			,				0			,				1      ).unsqueeze(0      )  # 3, 0, 1)  # hw(c) -> nchw
                __magic_name__						       =    nn.functional.interpolate(
                    a__			,				(newh, neww)			,				mode=self.interp_method			,				align_corners=a__      ).squeeze(0      )
           img_augs.append(a__      )

      return img_augs






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   List[Any]			,				a__		:   Tuple      ):
      __magic_name__						       =    ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]			,				cfg.INPUT.MAX_SIZE_TEST      )
      __magic_name__						       =    cfg.INPUT.FORMAT
      __magic_name__						       =    cfg.SIZE_DIVISIBILITY
      __magic_name__						       =    cfg.PAD_VALUE
      __magic_name__						       =    cfg.INPUT.MAX_SIZE_TEST
      __magic_name__						       =    cfg.MODEL.DEVICE
      __magic_name__						       =    torch.tensor(cfg.MODEL.PIXEL_STD      ).to(self.device      ).view(len(cfg.MODEL.PIXEL_STD      )			,				1			,				1      )
      __magic_name__						       =    torch.tensor(cfg.MODEL.PIXEL_MEAN      ).to(self.device      ).view(len(cfg.MODEL.PIXEL_STD      )			,				1			,				1      )
      __magic_name__						       =    lambda a__      : (x - self.pixel_mean) / self.pixel_std



 def 			snake_case__  (   self		:   Union[str, Any]			,				a__		:   Dict      ):
      __magic_name__						       =    tuple(max(a__      ) for s in zip(*[img.shape for img in images]      )      )
      __magic_name__						       =    [im.shape[-2:] for im in images]
      __magic_name__						       =    [
          nn.functional.pad(
              a__			,				[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]			,				value=self.pad_value			,				)
          for size, im in zip(a__			,				a__      )
      ]

      return torch.stack(a__      ), torch.tensor(a__      )



 def __call__(   self		:   Dict			,				a__		:   Dict			,				a__		:   List[str]=False      ):
      with torch.no_grad():
           if not isinstance(a__			,				a__      ):
                __magic_name__						       =    [images]
           if single_image:
                assert len(a__      ) == 1
           for i in range(len(a__      )      ):
                if isinstance(images[i]			,				torch.Tensor      ):
                     images.insert(a__			,				images.pop(a__      ).to(self.device      ).float()      )
                elif not isinstance(images[i]			,				torch.Tensor      ):
                     images.insert(
                         a__			,				torch.as_tensor(img_tensorize(images.pop(a__      )			,				input_format=self.input_format      )      )
                         .to(self.device      )
                         .float()			,				)
            # resize smallest edge
           __magic_name__						       =    torch.tensor([im.shape[:2] for im in images]      )
           __magic_name__						       =    self.aug(a__      )
           # transpose images and convert to torch tensors
           # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
           # now normalize before pad to avoid useless arithmetic
           __magic_name__						       =    [self.normalizer(a__      ) for x in images]
           # now pad them to do the following operations
           __magic_name__       ,  __magic_name__						       =    self.pad(a__      )
           # Normalize

           if self.size_divisibility > 0:
                raise NotImplementedError()
           # pad
           __magic_name__						       =    torch.true_divide(a__			,				a__      )
           if single_image:
                return images[0], sizes[0], scales_yx[0]
           else:
                return images, sizes, scales_yx




def 						UpperCamelCase	(			a		,						a			)				->     List[Any]:






     '''simple docstring'''
     boxes[:, 0::2] *= scale_yx[:, 1]
     boxes[:, 1::2] *= scale_yx[:, 0]
     return boxes




def 						UpperCamelCase	(			a		,						a			)				->     Any:






     '''simple docstring'''
     assert torch.isfinite(a			).all(), "Box tensor contains infinite or NaN!"
     __magic_name__       ,  __magic_name__						       =    box_size
     tensor[:, 0].clamp_(min=0		,						max=a			)
     tensor[:, 1].clamp_(min=0		,						max=a			)
     tensor[:, 2].clamp_(min=0		,						max=a			)
     tensor[:, 3].clamp_(min=0		,						max=a			)






