

'''simple docstring'''




import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
      pass

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


_lowerCAmelCase         =						logging.get_logger(__name__)

_lowerCAmelCase         =						{
    "kakaobrain/align-base": "https://huggingface.co/kakaobrain/align-base/resolve/main/config.json",
}






class   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:Tuple      	=					"""align_text_model"""



 def __init__(   self		:   Union[str, Any]			,				a__		:   Union[str, Any]=3_0522			,				a__		:   Union[str, Any]=768			,				a__		:   Optional[Any]=12			,				a__		:   Optional[int]=12			,				a__		:   Union[str, Any]=3072			,				a__		:   Optional[Any]="gelu"			,				a__		:   Optional[int]=0.1			,				a__		:   List[str]=0.1			,				a__		:   str=512			,				a__		:   List[Any]=2			,				a__		:   Optional[Any]=0.02			,				a__		:   Optional[int]=1E-12			,				a__		:   Optional[int]=0			,				a__		:   Union[str, Any]="absolute"			,				a__		:   Union[str, Any]=True			,				**a__		:   Union[str, Any]			,				):
      super().__init__(**a__      )

      __magic_name__						       =    vocab_size
      __magic_name__						       =    hidden_size
      __magic_name__						       =    num_hidden_layers
      __magic_name__						       =    num_attention_heads
      __magic_name__						       =    hidden_act
      __magic_name__						       =    intermediate_size
      __magic_name__						       =    hidden_dropout_prob
      __magic_name__						       =    attention_probs_dropout_prob
      __magic_name__						       =    max_position_embeddings
      __magic_name__						       =    type_vocab_size
      __magic_name__						       =    initializer_range
      __magic_name__						       =    layer_norm_eps
      __magic_name__						       =    position_embedding_type
      __magic_name__						       =    use_cache
      __magic_name__						       =    pad_token_id



 @classmethod
 def 			snake_case__  (   cls		:   Dict			,				a__		:   Union[str, os.PathLike]			,				**a__		:   List[str]      ):
      cls._set_token_in_kwargs(a__      )

      __magic_name__       ,  __magic_name__						       =    cls.get_config_dict(a__			,				**a__      )

      # get the text config dict if we are loading from AlignConfig
      if config_dict.get('''model_type'''      ) == "align":
           __magic_name__						       =    config_dict['''text_config''']

      if "model_type" in config_dict and hasattr(cls			,				'''model_type'''      ) and config_dict["model_type"] != cls.model_type:
           logger.warning(
               F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
               F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''      )

      return cls.from_dict(a__			,				**a__      )






class   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:Optional[int]      	=					"""align_vision_model"""



 def __init__(   self		:   Union[str, Any]			,				a__		:   int = 3			,				a__		:   int = 600			,				a__		:   float = 2.0			,				a__		:   float = 3.1			,				a__		:   int = 8			,				a__		:   List[int] = [3, 3, 5, 3, 5, 5, 3]			,				a__		:   List[int] = [32, 16, 24, 40, 80, 112, 192]			,				a__		:   List[int] = [16, 24, 40, 80, 112, 192, 320]			,				a__		:   List[int] = []			,				a__		:   List[int] = [1, 2, 2, 2, 1, 2, 1]			,				a__		:   List[int] = [1, 2, 2, 3, 3, 4, 1]			,				a__		:   List[int] = [1, 6, 6, 6, 6, 6, 6]			,				a__		:   float = 0.25			,				a__		:   str = "swish"			,				a__		:   int = 2560			,				a__		:   str = "mean"			,				a__		:   float = 0.02			,				a__		:   float = 0.001			,				a__		:   float = 0.99			,				a__		:   float = 0.2			,				**a__		:   int			,				):
      super().__init__(**a__      )

      __magic_name__						       =    num_channels
      __magic_name__						       =    image_size
      __magic_name__						       =    width_coefficient
      __magic_name__						       =    depth_coefficient
      __magic_name__						       =    depth_divisor
      __magic_name__						       =    kernel_sizes
      __magic_name__						       =    in_channels
      __magic_name__						       =    out_channels
      __magic_name__						       =    depthwise_padding
      __magic_name__						       =    strides
      __magic_name__						       =    num_block_repeats
      __magic_name__						       =    expand_ratios
      __magic_name__						       =    squeeze_expansion_ratio
      __magic_name__						       =    hidden_act
      __magic_name__						       =    hidden_dim
      __magic_name__						       =    pooling_type
      __magic_name__						       =    initializer_range
      __magic_name__						       =    batch_norm_eps
      __magic_name__						       =    batch_norm_momentum
      __magic_name__						       =    drop_connect_rate
      __magic_name__						       =    sum(a__      ) * 4



 @classmethod
 def 			snake_case__  (   cls		:   Optional[int]			,				a__		:   Union[str, os.PathLike]			,				**a__		:   List[str]      ):
      cls._set_token_in_kwargs(a__      )

      __magic_name__       ,  __magic_name__						       =    cls.get_config_dict(a__			,				**a__      )

      # get the vision config dict if we are loading from AlignConfig
      if config_dict.get('''model_type'''      ) == "align":
           __magic_name__						       =    config_dict['''vision_config''']

      if "model_type" in config_dict and hasattr(cls			,				'''model_type'''      ) and config_dict["model_type"] != cls.model_type:
           logger.warning(
               F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
               F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''      )

      return cls.from_dict(a__			,				**a__      )






class   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:Tuple      	=					"""align"""
 __SCREAMING_SNAKE_CASE					:Dict      	=					True



 def __init__(   self		:   List[str]			,				a__		:   List[str]=None			,				a__		:   Optional[int]=None			,				a__		:   int=640			,				a__		:   Any=1.0			,				a__		:   Optional[int]=0.02			,				**a__		:   str			,				):
      super().__init__(**a__      )

      if text_config is None:
           __magic_name__						       =    {}
           logger.info('''text_config is None. Initializing the AlignTextConfig with default values.'''      )

      if vision_config is None:
           __magic_name__						       =    {}
           logger.info('''vision_config is None. Initializing the AlignVisionConfig with default values.'''      )

      __magic_name__						       =    AlignTextConfig(**a__      )
      __magic_name__						       =    AlignVisionConfig(**a__      )

      __magic_name__						       =    projection_dim
      __magic_name__						       =    temperature_init_value
      __magic_name__						       =    initializer_range



 @classmethod
 def 			snake_case__  (   cls		:   Tuple			,				a__		:   AlignTextConfig			,				a__		:   AlignVisionConfig			,				**a__		:   str      ):
      return cls(text_config=text_config.to_dict()			,				vision_config=vision_config.to_dict()			,				**a__      )



 def 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    copy.deepcopy(self.__dict__      )
      __magic_name__						       =    self.text_config.to_dict()
      __magic_name__						       =    self.vision_config.to_dict()
      __magic_name__						       =    self.__class__.model_type
      return output









'''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			)






