


"""simple docstring"""


import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
   import torch

   from transformers import AutoModelForImageClassification

if is_vision_available():
   from transformers import AutoImageProcessor



@require_torch
@require_vision
class   _a      (      unittest.TestCase ):

    '''simple docstring'''





    @slow
    def      _UpperCamelCase       ( self							:					int				):
          lowerCamelCase__           =		AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'				)
          lowerCamelCase__           =		AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'				)
          model.to(SCREAMING_SNAKE_CASE__				)

          from datasets import load_dataset

          lowerCamelCase__           =		load_dataset('nielsr/rvlcdip-demo'				)

          lowerCamelCase__           =		dataset['train'][0]['image'].convert('RGB'				)

          lowerCamelCase__           =		image_processor(SCREAMING_SNAKE_CASE__							,					return_tensors='pt'				).to(SCREAMING_SNAKE_CASE__				)

          # forward pass
          with torch.no_grad():
                lowerCamelCase__           =		model(**SCREAMING_SNAKE_CASE__				)
                lowerCamelCase__           =		outputs.logits

          lowerCamelCase__           =		torch.Size((1, 16)				)
          self.assertEqual(logits.shape							,					SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		torch.tensor(
              [-0.41_58, -0.40_92, -0.43_47]							,					device=SCREAMING_SNAKE_CASE__							,					dtype=torch.float							,					)
          self.assertTrue(torch.allclose(logits[0, :3]							,					SCREAMING_SNAKE_CASE__							,					atol=1e-4				)				)







"""simple docstring"""


def   snake_case						(				_a:     list[list[float]]							)->		list[list[float]]:
      '''simple docstring'''


      lowerCamelCase__           =		[]
      for data in source_data:
            for i, el in enumerate(_a							):
                  if len(_a							) < i + 1:
                        data_lists.append([]							)
                  data_lists[i].append(float(_a							)							)
      return data_lists



def   snake_case						(				_a:     list[list[float]]		,   _a:     list[int]							)->		list[list[float]]:
      '''simple docstring'''


      lowerCamelCase__           =		[]
      for dlist, weight in zip(_a		,   _a							):
            lowerCamelCase__           =		min(_a							)
            lowerCamelCase__           =		max(_a							)

            lowerCamelCase__           =		[]
            # for weight 0 score is 1 - actual score
            if weight == 0:
                  for item in dlist:
                        try:
                              score.append(1 - ((item - mind) / (maxd - mind))							)
                        except ZeroDivisionError:
                              score.append(1							)

            elif weight == 1:
                  for item in dlist:
                        try:
                              score.append((item - mind) / (maxd - mind)							)
                        except ZeroDivisionError:
                              score.append(0							)

        # weight not 0 or 1
            else:
                  lowerCamelCase__           =		F'Invalid weight of {weight:f} provided'
                  raise ValueError(_a							)

            score_lists.append(_a							)

      return score_lists



def   snake_case						(				_a:     list[list[float]]							)->		list[float]:
      '''simple docstring'''


      lowerCamelCase__           =		[0 for i in range(len(score_lists[0]							)							)]

      for slist in score_lists:
            for j, ele in enumerate(_a							):
                  lowerCamelCase__           =		final_scores[j] + ele

      return final_scores






def   snake_case						(				_a:     list[list[float]]		,   _a:     list[int]							)->		list[list[float]]:
      '''simple docstring'''


      lowerCamelCase__           =		get_data(_a							)
      lowerCamelCase__           =		calculate_each_score(_a		,   _a							)
      lowerCamelCase__           =		generate_final_scores(_a							)

      # append scores to source data
      for i, ele in enumerate(_a							):
            source_data[i].append(_a							)

      return source_data



