






'''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      A     :






 """simple docstring"""





 def __init__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase=sys.maxsize			):
       UpperCamelCase_					: Union[str, Any]            =				"""bilinear"""
       UpperCamelCase_					: Any            =				max_size
       UpperCamelCase_					: Tuple            =				short_edge_length





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

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

       return img_augs






class      A     :






 """simple docstring"""





 def __init__(				self      ,  __lowerCAmelCase			):
       UpperCamelCase_					: List[str]            =				ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]      ,  cfg.INPUT.MAX_SIZE_TEST			)
       UpperCamelCase_					: Optional[Any]            =				cfg.INPUT.FORMAT
       UpperCamelCase_					: str            =				cfg.SIZE_DIVISIBILITY
       UpperCamelCase_					: str            =				cfg.PAD_VALUE
       UpperCamelCase_					: Any            =				cfg.INPUT.MAX_SIZE_TEST
       UpperCamelCase_					: Dict            =				cfg.MODEL.DEVICE
       UpperCamelCase_					: Optional[int]            =				torch.tensor(cfg.MODEL.PIXEL_STD			).to(self.device			).view(len(cfg.MODEL.PIXEL_STD			)      ,  1      ,  1			)
       UpperCamelCase_					: Tuple            =				torch.tensor(cfg.MODEL.PIXEL_MEAN			).to(self.device			).view(len(cfg.MODEL.PIXEL_STD			)      ,  1      ,  1			)
       UpperCamelCase_					: Tuple            =				lambda __lowerCAmelCase			: (x - self.pixel_mean) / self.pixel_std
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase			):
       UpperCamelCase_					: Tuple            =				tuple(max(__lowerCAmelCase			) for s in zip(*[img.shape for img in images]			)			)
       UpperCamelCase_					: Optional[Any]            =				[im.shape[-2:] for im in images]
       UpperCamelCase_					: Tuple            =				[
           nn.functional.pad(
               __lowerCAmelCase      ,  [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]      ,  value=self.pad_value      ,  )
           for size, im in zip(__lowerCAmelCase      ,  __lowerCAmelCase			)
       ]

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





 def __call__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase=False			):
       with torch.no_grad():
             if not isinstance(__lowerCAmelCase      ,  __lowerCAmelCase			):
                   UpperCamelCase_					: str            =				[images]
             if single_image:
                   assert len(__lowerCAmelCase			) == 1
             for i in range(len(__lowerCAmelCase			)			):
                   if isinstance(images[i]      ,  torch.Tensor			):
                         images.insert(__lowerCAmelCase      ,  images.pop(__lowerCAmelCase			).to(self.device			).float()			)
                   elif not isinstance(images[i]      ,  torch.Tensor			):
                         images.insert(
                             __lowerCAmelCase      ,  torch.as_tensor(img_tensorize(images.pop(__lowerCAmelCase			)      ,  input_format=self.input_format			)			)
                             .to(self.device			)
                             .float()      ,  )
            # resize smallest edge
             UpperCamelCase_					: List[Any]            =				torch.tensor([im.shape[:2] for im in images]			)
             UpperCamelCase_					: int            =				self.aug(__lowerCAmelCase			)
             # 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
             UpperCamelCase_					: Optional[int]            =				[self.normalizer(__lowerCAmelCase			) for x in images]
             # now pad them to do the following operations
             UpperCamelCase_     ,      UpperCamelCase_					: Union[str, Any]            =				self.pad(__lowerCAmelCase			)
             # Normalize

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






def 				snake_case       (  a_				:			Optional[Any]     , a_				:			Tuple						)							->					Union[str, Any]:





      """simple docstring"""

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



def 				snake_case       (  a_				:			str     , a_				:			Tuple[int, int]						)							->					List[str]:





      """simple docstring"""

      assert torch.isfinite(a_						).all(), "Box tensor contains infinite or NaN!"
      UpperCamelCase_     ,      UpperCamelCase_					: Optional[int]            =				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_						)














'''simple docstring'''

import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


UpperCamelCase      					="\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n"




class      A     (   unittest.TestCase, SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[Any]            =				load_tool("""text-question-answering"""			)
       self.tool.setup()
       UpperCamelCase_					: List[str]            =				load_tool("""text-question-answering"""      ,  remote=__lowerCAmelCase			)
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Union[str, Any]            =				self.tool(__lowerCAmelCase      ,  """What did Hugging Face do in April 2021?"""			)
       self.assertEqual(__lowerCAmelCase      ,  """launched the BigScience Research Workshop"""			)
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[Any]            =				self.remote_tool(__lowerCAmelCase      ,  """What did Hugging Face do in April 2021?"""			)
       self.assertEqual(__lowerCAmelCase      ,  """launched the BigScience Research Workshop"""			)
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: List[Any]            =				self.tool(text=__lowerCAmelCase      ,  question="""What did Hugging Face do in April 2021?"""			)
       self.assertEqual(__lowerCAmelCase      ,  """launched the BigScience Research Workshop"""			)





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Dict            =				self.remote_tool(text=__lowerCAmelCase      ,  question="""What did Hugging Face do in April 2021?"""			)
       self.assertEqual(__lowerCAmelCase      ,  """launched the BigScience Research Workshop"""			)






