




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       a__	:







     def __init__(     self  ,		A  ,		A=sys.maxsize			)  ->   Optional[Any]:



          '''simple docstring'''



          a     =				"bilinear"
          a     =				max_size
          a     =				short_edge_length







     def __call__(     self  ,		A			)  ->   int:



          '''simple docstring'''



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

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

          return img_augs







class       a__	:







     def __init__(     self  ,		A			)  ->   str:



          '''simple docstring'''



          a     =				ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]  ,		cfg.INPUT.MAX_SIZE_TEST			)
          a     =				cfg.INPUT.FORMAT
          a     =				cfg.SIZE_DIVISIBILITY
          a     =				cfg.PAD_VALUE
          a     =				cfg.INPUT.MAX_SIZE_TEST
          a     =				cfg.MODEL.DEVICE
          a     =				torch.tensor(cfg.MODEL.PIXEL_STD			).to(self.device			).view(len(cfg.MODEL.PIXEL_STD			)  ,		1  ,		1			)
          a     =				torch.tensor(cfg.MODEL.PIXEL_MEAN			).to(self.device			).view(len(cfg.MODEL.PIXEL_STD			)  ,		1  ,		1			)
          a     =				lambda A			: (x - self.pixel_mean) / self.pixel_std







     def       lowerCAmelCase_    (     self  ,		A			)  ->   Any:



          '''simple docstring'''



          a     =				tuple(max(A			) for s in zip(*[img.shape for img in images]			)			)
          a     =				[im.shape[-2:] for im in images]
          a     =				[
              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  ,		A  ,		A=False			)  ->   List[str]:



          '''simple docstring'''



          with torch.no_grad():
               if not isinstance(A  ,		A			):
                    a     =				[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
               a     =				torch.tensor([im.shape[:2] for im in images]			)
               a     =				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
               a     =				[self.normalizer(A			) for x in images]
               # now pad them to do the following operations
               a  ,				a     =				self.pad(A			)
               # Normalize

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







def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase)      ->					Optional[int]:
     boxes[:, 0::2] *= scale_yx[:, 1]
     boxes[:, 1::2] *= scale_yx[:, 0]
     return boxes





def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase)      ->					Union[str, Any]:
     assert torch.isfinite(__UpperCamelCase).all(), "Box tensor contains infinite or NaN!"
     a  ,				a     =				box_size
     tensor[:, 0].clamp_(min=0		, max=__UpperCamelCase)
     tensor[:, 1].clamp_(min=0		, max=__UpperCamelCase)
     tensor[:, 2].clamp_(min=0		, max=__UpperCamelCase)
     tensor[:, 3].clamp_(min=0		, max=__UpperCamelCase)







import functools
import operator

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


lowercase__				:      Any	   = logging.get_logger(__name__)

lowercase__				:      int	   = {
    "asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}




class       a__	( UpperCamelCase__				):
     a		:      Optional[Any]		 =     """sew-d"""







     def __init__(     self  ,		A=32  ,		A=768  ,		A=12  ,		A=12  ,		A=3072  ,		A=2  ,		A=512  ,		A=256  ,		A=True  ,		A=True  ,		A=("p2c", "c2p")  ,		A="layer_norm"  ,		A="gelu_python"  ,		A=0.1  ,		A=0.1  ,		A=0.1  ,		A=0.0  ,		A=0.1  ,		A=0.0_2  ,		A=1e-7  ,		A=1e-5  ,		A="group"  ,		A="gelu"  ,		A=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)  ,		A=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)  ,		A=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)  ,		A=False  ,		A=128  ,		A=16  ,		A=True  ,		A=0.0_5  ,		A=10  ,		A=2  ,		A=0.0  ,		A=10  ,		A=0  ,		A="mean"  ,		A=False  ,		A=False  ,		A=256  ,		A=0  ,		A=1  ,		A=2  ,		**A  ,		)  ->   Dict:



          '''simple docstring'''



          super().__init__(**A  ,		pad_token_id=A  ,		bos_token_id=A  ,		eos_token_id=A			)
          a     =				hidden_size
          a     =				feat_extract_norm
          a     =				feat_extract_activation
          a     =				list(A			)
          a     =				list(A			)
          a     =				list(A			)
          a     =				conv_bias
          a     =				num_conv_pos_embeddings
          a     =				num_conv_pos_embedding_groups
          a     =				len(self.conv_dim			)
          a     =				num_hidden_layers
          a     =				intermediate_size
          a     =				squeeze_factor
          a     =				max_position_embeddings
          a     =				position_buckets
          a     =				share_att_key
          a     =				relative_attention
          a     =				norm_rel_ebd
          a     =				list(A			)
          a     =				hidden_act
          a     =				num_attention_heads
          a     =				hidden_dropout
          a     =				attention_dropout
          a     =				activation_dropout
          a     =				feat_proj_dropout
          a     =				final_dropout
          a     =				layer_norm_eps
          a     =				feature_layer_norm_eps
          a     =				initializer_range
          a     =				vocab_size

          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)`,"
                   F'''but is `len(config.conv_dim) = {len(self.conv_dim			)}`, `len(config.conv_stride)'''
                   F'''= {len(self.conv_stride			)}`, `len(config.conv_kernel) = {len(self.conv_kernel			)}`.'''			)

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

          # ctc loss
          a     =				ctc_loss_reduction
          a     =				ctc_zero_infinity

          # sequence classification
          a     =				use_weighted_layer_sum
          a     =				classifier_proj_size







     @property
     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          return functools.reduce(operator.mul  ,		self.conv_stride  ,		1			)

