'''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       __UpperCAmelCase							:





       '''simple docstring'''



       def __init__( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE=sys.maxsize					)						->	str:
           A_      =  '''bilinear'''
           A_      =  max_size
           A_      =  short_edge_length

       def __call__( self       ,	_SCREAMING_SNAKE_CASE					)						->	str:
           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(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)
               if h < w:
                   A_  ,A_      =  size, scale * w
               else:
                   A_  ,A_      =  scale * h, size
               if max(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					) > self.max_size:
                   A_      =  self.max_size * 1.0 / max(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)
                   A_      =  newh * scale
                   A_      =  neww * scale
               A_      =  int(neww + 0.5					)
               A_      =  int(newh + 0.5					)

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

           return img_augs




class       __UpperCAmelCase							:





       '''simple docstring'''



       def __init__( self       ,	_SCREAMING_SNAKE_CASE					)						->	List[Any]:
           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 _SCREAMING_SNAKE_CASE					: (x - self.pixel_mean) / self.pixel_std

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE					)						->	Any:
           A_      =  tuple(max(_SCREAMING_SNAKE_CASE					) for s in zip(*[img.shape for img in images]					)					)
           A_      =  [im.shape[-2:] for im in images]
           A_      =  [
               nn.functional.pad(
                   _SCREAMING_SNAKE_CASE       ,	[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]       ,	value=self.pad_value       ,	)
               for size, im in zip(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)
           ]

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

       def __call__( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE=False					)						->	Dict:

           with torch.no_grad():
               if not isinstance(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					):
                   A_      =  [images]
               if single_image:
                   assert len(_SCREAMING_SNAKE_CASE					) == 1
               for i in range(len(_SCREAMING_SNAKE_CASE					)					):
                   if isinstance(images[i]       ,	torch.Tensor					):
                       images.insert(_SCREAMING_SNAKE_CASE       ,	images.pop(_SCREAMING_SNAKE_CASE					).to(self.device					).float()					)
                   elif not isinstance(images[i]       ,	torch.Tensor					):
                       images.insert(
                           _SCREAMING_SNAKE_CASE       ,	torch.as_tensor(img_tensorize(images.pop(_SCREAMING_SNAKE_CASE					)       ,	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(_SCREAMING_SNAKE_CASE					)
               # 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(_SCREAMING_SNAKE_CASE					) for x in images]
               # now pad them to do the following operations
               A_  ,A_      =  self.pad(_SCREAMING_SNAKE_CASE					)
               # Normalize

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





def 	_UpperCAmelCase       (				_UpperCamelCase       :      Optional[Any],  _UpperCamelCase       :      int  )      -> int:
    boxes[:, 0::2] *= scale_yx[:, 1]
    boxes[:, 1::2] *= scale_yx[:, 0]
    return boxes





def 	_UpperCAmelCase       (				_UpperCamelCase       :      Tuple,  _UpperCamelCase       :      Tuple[int, int]  )      -> str:
    assert torch.isfinite(lowercase_  ).all(), "Box tensor contains infinite or NaN!"
    A_  ,A_      =  box_size
    tensor[:, 0].clamp_(min=0,  max=lowercase_  )
    tensor[:, 1].clamp_(min=0,  max=lowercase_  )
    tensor[:, 2].clamp_(min=0,  max=lowercase_  )
    tensor[:, 3].clamp_(min=0,  max=lowercase_  )







"""simple docstring"""







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


_SCREAMING_SNAKE_CASE	:		str   =   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	:		Any   =   {
    '''facebook/opt-125m''': '''https://huggingface.co/facebook/opt-125m/blob/main/config.json''',
    '''facebook/opt-350m''': '''https://huggingface.co/facebook/opt-350m/blob/main/config.json''',
    '''facebook/opt-1.3b''': '''https://huggingface.co/facebook/opt-1.3b/blob/main/config.json''',
    '''facebook/opt-2.7b''': '''https://huggingface.co/facebook/opt-2.7b/blob/main/config.json''',
    '''facebook/opt-6.7b''': '''https://huggingface.co/facebook/opt-6.7b/blob/main/config.json''',
    '''facebook/opt-13b''': '''https://huggingface.co/facebook/opt-13b/blob/main/config.json''',
}






class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Tuple    =    "opt"
   __UpperCAmelCase	:							Any    =    ["past_key_values"]







   def __init__(	self ,				_SCREAMING_SNAKE_CASE=50272 ,				_SCREAMING_SNAKE_CASE=768 ,				_SCREAMING_SNAKE_CASE=12 ,				_SCREAMING_SNAKE_CASE=3072 ,				_SCREAMING_SNAKE_CASE=2048 ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=False ,				_SCREAMING_SNAKE_CASE=None ,				_SCREAMING_SNAKE_CASE=0.1 ,				_SCREAMING_SNAKE_CASE=0.0 ,				_SCREAMING_SNAKE_CASE=12 ,				_SCREAMING_SNAKE_CASE="relu" ,				_SCREAMING_SNAKE_CASE=0.0 ,				_SCREAMING_SNAKE_CASE=0.02 ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=1 ,				_SCREAMING_SNAKE_CASE=2 ,				_SCREAMING_SNAKE_CASE=2 ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=True ,				**_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

      super().__init__(
          pad_token_id=_SCREAMING_SNAKE_CASE ,				bos_token_id=_SCREAMING_SNAKE_CASE ,				eos_token_id=_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE ,				)
      __lowerCamelCase    		=					vocab_size
      __lowerCamelCase    		=					max_position_embeddings
      __lowerCamelCase    		=					num_attention_heads
      __lowerCamelCase    		=					word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
      __lowerCamelCase    		=					ffn_dim
      __lowerCamelCase    		=					hidden_size
      __lowerCamelCase    		=					num_hidden_layers
      __lowerCamelCase    		=					dropout
      __lowerCamelCase    		=					attention_dropout
      __lowerCamelCase    		=					activation_function
      __lowerCamelCase    		=					init_std
      __lowerCamelCase    		=					layerdrop
      __lowerCamelCase    		=					use_cache
      __lowerCamelCase    		=					do_layer_norm_before
      # We keep these variables at `True` for backward compatibility.
      __lowerCamelCase    		=					enable_bias
      __lowerCamelCase    		=					layer_norm_elementwise_affine

      # Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
      # with checkpoints that have been fine-tuned before transformers v4.20.1
      # see https://github.com/facebookresearch/metaseq/pull/164
      __lowerCamelCase    		=					_remove_final_layer_norm






