
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import LevitImageProcessor
class       _lowerCAmelCase  (		unittest.TestCase							):

 """simple docstring"""

 def __init__(						self			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			int=7					, SCREAMING_SNAKE_CASE__			:			Tuple=3					, SCREAMING_SNAKE_CASE__			:			str=18					, SCREAMING_SNAKE_CASE__			:			Optional[int]=30					, SCREAMING_SNAKE_CASE__			:			Dict=4_00					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=True					, SCREAMING_SNAKE_CASE__			:			Any=None					, SCREAMING_SNAKE_CASE__			:			Tuple=True					, SCREAMING_SNAKE_CASE__			:			str=None					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=True					, SCREAMING_SNAKE_CASE__			:			str=[0.5, 0.5, 0.5]					, SCREAMING_SNAKE_CASE__			:			Optional[int]=[0.5, 0.5, 0.5]					, ):






      """simple docstring"""



      UpperCamelCase						    =					size if size is not None else {'shortest_edge': 18}
      UpperCamelCase						    =					crop_size if crop_size is not None else {'height': 18, 'width': 18}
      UpperCamelCase						    =					parent
      UpperCamelCase						    =					batch_size
      UpperCamelCase						    =					num_channels
      UpperCamelCase						    =					image_size
      UpperCamelCase						    =					min_resolution
      UpperCamelCase						    =					max_resolution
      UpperCamelCase						    =					do_resize
      UpperCamelCase						    =					size
      UpperCamelCase						    =					do_center_crop
      UpperCamelCase						    =					crop_size
      UpperCamelCase						    =					do_normalize
      UpperCamelCase						    =					image_mean
      UpperCamelCase						    =					image_std






 def      __lowerCAmelCase  (						self			:			Union[str, Any]   ):






      """simple docstring"""



      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "do_center_crop": self.do_center_crop,
          "size": self.size,
          "crop_size": self.crop_size,
      }




@require_torch
@require_vision
class       _lowerCAmelCase  (		__magic_name__			,	unittest.TestCase							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Any       						=LevitImageProcessor if is_vision_available() else None

 def      __lowerCAmelCase  (						self			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase						    =					LevitImageProcessingTester(self   )

 @property
 def      __lowerCAmelCase  (						self			:			List[str]   ):






      """simple docstring"""



      return self.image_processor_tester.prepare_image_processor_dict()

 def      __lowerCAmelCase  (						self			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.image_processing_class(**self.image_processor_dict   )
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__					, 'image_mean'   )   )
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__					, 'image_std'   )   )
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__					, 'do_normalize'   )   )
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__					, 'do_resize'   )   )
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__					, 'do_center_crop'   )   )
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__					, 'size'   )   )

 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      UpperCamelCase						    =					self.image_processing_class.from_dict(self.image_processor_dict   )
      self.assertEqual(image_processor.size					, {'shortest_edge': 18}   )
      self.assertEqual(image_processor.crop_size					, {'height': 18, 'width': 18}   )

      UpperCamelCase						    =					self.image_processing_class.from_dict(self.image_processor_dict					, size=42					, crop_size=84   )
      self.assertEqual(image_processor.size					, {'shortest_edge': 42}   )
      self.assertEqual(image_processor.crop_size					, {'height': 84, 'width': 84}   )

 def      __lowerCAmelCase  (						self			:			Dict   ):






      """simple docstring"""



      pass

 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      UpperCamelCase						    =					self.image_processing_class(**self.image_processor_dict   )
      # create random PIL images
      UpperCamelCase						    =					prepare_image_inputs(self.image_processor_tester					, equal_resolution=SCREAMING_SNAKE_CASE__   )
      for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE__					, Image.Image   )

      # Test not batched input
      UpperCamelCase						    =					image_processing(image_inputs[0]					, return_tensors='pt'   ).pixel_values
      self.assertEqual(
          encoded_images.shape					, (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					, )

      # Test batched
      UpperCamelCase						    =					image_processing(SCREAMING_SNAKE_CASE__					, return_tensors='pt'   ).pixel_values
      self.assertEqual(
          encoded_images.shape					, (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					, )

 def      __lowerCAmelCase  (						self			:			Dict   ):






      """simple docstring"""



      UpperCamelCase						    =					self.image_processing_class(**self.image_processor_dict   )
      # create random numpy tensors
      UpperCamelCase						    =					prepare_image_inputs(self.image_processor_tester					, equal_resolution=SCREAMING_SNAKE_CASE__					, numpify=SCREAMING_SNAKE_CASE__   )
      for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE__					, np.ndarray   )

      # Test not batched input
      UpperCamelCase						    =					image_processing(image_inputs[0]					, return_tensors='pt'   ).pixel_values
      self.assertEqual(
          encoded_images.shape					, (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					, )

      # Test batched
      UpperCamelCase						    =					image_processing(SCREAMING_SNAKE_CASE__					, return_tensors='pt'   ).pixel_values
      self.assertEqual(
          encoded_images.shape					, (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					, )






 def      __lowerCAmelCase  (						self			:			Union[str, Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.image_processing_class(**self.image_processor_dict   )
      # create random PyTorch tensors
      UpperCamelCase						    =					prepare_image_inputs(self.image_processor_tester					, equal_resolution=SCREAMING_SNAKE_CASE__					, torchify=SCREAMING_SNAKE_CASE__   )
      for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE__					, torch.Tensor   )

      # Test not batched input
      UpperCamelCase						    =					image_processing(image_inputs[0]					, return_tensors='pt'   ).pixel_values
      self.assertEqual(
          encoded_images.shape					, (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					, )

      # Test batched
      UpperCamelCase						    =					image_processing(SCREAMING_SNAKE_CASE__					, return_tensors='pt'   ).pixel_values
      self.assertEqual(
          encoded_images.shape					, (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					, )




import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class       _lowerCAmelCase  (		unittest.TestCase							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					List[Any]       						=JukeboxTokenizer
 SCREAMING_SNAKE_CASE_				:					str       						={
     "artist": "Zac Brown Band",
     "genres": "Country",
     "lyrics": "I met a traveller from an antique land,\n        Who said \"Two vast and trunkless legs of stone\n        Stand in the desert. . . . Near them, on the sand,\n        Half sunk a shattered visage lies, whose frown,\n        And wrinkled lip, and sneer of cold command,\n        Tell that its sculptor well those passions read\n        Which yet survive, stamped on these lifeless things,\n        The hand that mocked them, and the heart that fed;\n        And on the pedestal, these words appear:\n        My name is Ozymandias, King of Kings;\n        Look on my Works, ye Mighty, and despair!\n        Nothing beside remains. Round the decay\n        Of that colossal Wreck, boundless and bare\n        The lone and level sands stretch far away\n        ",
 }

 @require_torch
 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



      import torch

      UpperCamelCase						    =					JukeboxTokenizer.from_pretrained('openai/jukebox-1b-lyrics'   )
      UpperCamelCase						    =					tokenizer(**self.metas   )['input_ids']
      # fmt: off
      UpperCamelCase						    =					[
          torch.tensor([[
              0, 0, 0, 71_69, 5_07, 9, 76, 39, 31, 46, 76, 27,
              76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
              44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
              47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
              76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
              30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
              27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
              45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
              41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
              19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
              76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
              76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
              64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
              30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
              27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
              34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
              27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
              41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
              76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
              44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
              76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
              32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
              40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
              20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
              45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
              31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
              45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
              76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
              34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
              31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
              40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
              38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
              78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
              76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
              41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
              27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
              46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
              76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
              41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
              46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
              41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
              78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
              40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
              27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
              76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
              76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
              41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
              76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
              27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
              78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
              34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
              44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
              40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
              78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
              46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
              38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
              40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
              27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
              20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
              76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
              76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
              76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
              76, 76]]   ),
          torch.tensor([[0, 0, 0, 10_69, 11]]   ),
          torch.tensor([[0, 0, 0, 10_69, 11]]   ),
      ]
      # fmt: on
      self.assertTrue(torch.allclose(tokens[0]					, EXPECTED_OUTPUT[0]   )   )
      self.assertTrue(torch.allclose(tokens[1]					, EXPECTED_OUTPUT[1]   )   )
      self.assertTrue(torch.allclose(tokens[2]					, EXPECTED_OUTPUT[2]   )   )






 @require_torch
 def      __lowerCAmelCase  (						self			:			Union[str, Any]   ):






      """simple docstring"""



      import torch

      UpperCamelCase						    =					JukeboxTokenizer.from_pretrained('openai/jukebox-5b-lyrics'   )
      UpperCamelCase						    =					tokenizer(**self.metas   )['input_ids']
      # fmt: off
      UpperCamelCase						    =					[
          torch.tensor([[
              0, 0, 0, 10_69, 11, -1, -1, -1, -1, 9, 77, 39,
              31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
              31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
              40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
              79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
              77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
              27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
              37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
              32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
              77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
              77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
              77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
              46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
              77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
              77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
              77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
              77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
              64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
              40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
              40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
              38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
              31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
              41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
              77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
              46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
              41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
              31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
              31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
              23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
              44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
              31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
              38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
              40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
              77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
              27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
              31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
              34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
              31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
              1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
              31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
              45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
              31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
              77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
              15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
              11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
              45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
              41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
              44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
              46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
              27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
              77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
              35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
              77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
              31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
              77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
              41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
              77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
              40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
              77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
              77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
              27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
              77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
              77, 77, 77, 77, 77, 77]]   ),
          torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]]   ),
          torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]]   ),
      ]
      # fmt: on
      self.assertTrue(torch.allclose(tokens[0]					, EXPECTED_OUTPUT[0]   )   )
      self.assertTrue(torch.allclose(tokens[1]					, EXPECTED_OUTPUT[1]   )   )
      self.assertTrue(torch.allclose(tokens[2]					, EXPECTED_OUTPUT[2]   )   )


