





"""simple docstring"""
import unittest
from dataclasses import dataclass
from typing import List, Union

import numpy as np
import PIL.Image

from diffusers.utils.outputs import BaseOutput







@dataclass
class 	a				(					a__   ):
 snake_case__    	=    42







class 	a				(					unittest.TestCase   ):



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      CustomOutput(images=np.random.rand(1							,    3							,    4							,    4			)			)

  # check every way of getting the attribute
  assert isinstance(outputs.images							,    np.ndarray			)
  assert outputs.images.shape == (1, 3, 4, 4)
  assert isinstance(outputs['images']							,    np.ndarray			)
  assert outputs["images"].shape == (1, 3, 4, 4)
  assert isinstance(outputs[0]							,    np.ndarray			)
  assert outputs[0].shape == (1, 3, 4, 4)

  # test with a non-tensor attribute
  lowerCAmelCase  							=      CustomOutput(images=[PIL.Image.new('RGB'							,    (4, 4)			)]			)

  # check every way of getting the attribute
  assert isinstance(outputs.images							,    _snake_case			)
  assert isinstance(outputs.images[0]							,    PIL.Image.Image			)
  assert isinstance(outputs['images']							,    _snake_case			)
  assert isinstance(outputs['images'][0]							,    PIL.Image.Image			)
  assert isinstance(outputs[0]							,    _snake_case			)
  assert isinstance(outputs[0][0]							,    PIL.Image.Image			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      CustomOutput({'images': np.random.rand(1							,    3							,    4							,    4			)}			)

  # check every way of getting the attribute
  assert isinstance(outputs.images							,    np.ndarray			)
  assert outputs.images.shape == (1, 3, 4, 4)
  assert isinstance(outputs['images']							,    np.ndarray			)
  assert outputs["images"].shape == (1, 3, 4, 4)
  assert isinstance(outputs[0]							,    np.ndarray			)
  assert outputs[0].shape == (1, 3, 4, 4)

  # test with a non-tensor attribute
  lowerCAmelCase  							=      CustomOutput({'images': [PIL.Image.new('RGB'							,    (4, 4)			)]}			)

  # check every way of getting the attribute
  assert isinstance(outputs.images							,    _snake_case			)
  assert isinstance(outputs.images[0]							,    PIL.Image.Image			)
  assert isinstance(outputs['images']							,    _snake_case			)
  assert isinstance(outputs['images'][0]							,    PIL.Image.Image			)
  assert isinstance(outputs[0]							,    _snake_case			)
  assert isinstance(outputs[0][0]							,    PIL.Image.Image			)











import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 			a__       (					UpperCamelCase__   ,					unittest.TestCase      ):
     a		:			Dict							=	MPNetTokenizer
     a		:			Dict							=	MPNetTokenizerFast
     a		:			Optional[int]							=	True
     a		:			Any							=	True







     def 			lowerCAmelCase_				(	self )  ->    Any:



          '''simple docstring'''



          super().setUp()

          a      =      [
              "[UNK]",
              "[CLS]",
              "[SEP]",
              "[PAD]",
              "[MASK]",
              "want",
              "##want",
              "##ed",
              "wa",
              "un",
              "runn",
              "##ing",
              ",",
              "low",
              "lowest",
          ]
          a      =      os.path.join(self.tmpdirname  ,		VOCAB_FILES_NAMES["vocab_file"] )
          with open(self.vocab_file  ,		"w"  ,		encoding="utf-8" ) as vocab_writer:
               vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )







     def 			lowerCAmelCase_				(	self  ,		A )  ->    Any:



          '''simple docstring'''



          a      =      "UNwant\u00E9d,running"
          a      =      "unwanted, running"
          return input_text, output_text







     def 			lowerCAmelCase_				(	self )  ->    List[Any]:



          '''simple docstring'''



          a      =      self.tokenizer_class(self.vocab_file )

          a      =      tokenizer.tokenize("UNwant\u00E9d,running" )
          self.assertListEqual(A  ,		["un", "##want", "##ed", ",", "runn", "##ing"] )
          self.assertListEqual(tokenizer.convert_tokens_to_ids(A )  ,		[9, 6, 7, 12, 10, 11] )







     @slow
     def 			lowerCAmelCase_				(	self )  ->    Dict:



          '''simple docstring'''



          a      =      self.tokenizer_class.from_pretrained("microsoft/mpnet-base" )

          a      =      tokenizer.encode("sequence builders"  ,		add_special_tokens=A )
          a      =      tokenizer.encode("multi-sequence build"  ,		add_special_tokens=A )

          a      =      tokenizer.build_inputs_with_special_tokens(A )
          a      =      tokenizer.build_inputs_with_special_tokens(A  ,		A )

          assert encoded_sentence == [0] + text + [2]
          assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]

