



'''simple docstring'''





import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				Optional[Any]           =				['''image_processor''', '''tokenizer''']
  __lowercase							:				List[Any]           =				'''ViltImageProcessor'''
  __lowercase							:				Dict           =				('''BertTokenizer''', '''BertTokenizerFast''')




  def __init__(   self  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,**__UpperCAmelCase				)   ->		int:
      lowerCAmelCase__			:  List[str]											= None
      if "feature_extractor" in kwargs:
          warnings.warn(
              """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
              """ instead."""  ,__UpperCAmelCase  ,)
          lowerCAmelCase__			:  Any											= kwargs.pop("""feature_extractor"""				)

      lowerCAmelCase__			:  Any											= image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
          raise ValueError("""You need to specify an `image_processor`."""				)
      if tokenizer is None:
          raise ValueError("""You need to specify a `tokenizer`."""				)

      super().__init__(__UpperCAmelCase  ,__UpperCAmelCase				)
      lowerCAmelCase__			:  Any											= self.image_processor




  def __call__(   self  ,__UpperCAmelCase  ,__UpperCAmelCase = None  ,__UpperCAmelCase = True  ,__UpperCAmelCase = False  ,__UpperCAmelCase = None  ,__UpperCAmelCase = None  ,__UpperCAmelCase = 0  ,__UpperCAmelCase = None  ,__UpperCAmelCase = None  ,__UpperCAmelCase = None  ,__UpperCAmelCase = False  ,__UpperCAmelCase = False  ,__UpperCAmelCase = False  ,__UpperCAmelCase = False  ,__UpperCAmelCase = True  ,__UpperCAmelCase = None  ,**__UpperCAmelCase  ,)   ->		BatchEncoding:
      lowerCAmelCase__			:  List[Any]											= self.tokenizer(
          text=__UpperCAmelCase  ,add_special_tokens=__UpperCAmelCase  ,padding=__UpperCAmelCase  ,truncation=__UpperCAmelCase  ,max_length=__UpperCAmelCase  ,stride=__UpperCAmelCase  ,pad_to_multiple_of=__UpperCAmelCase  ,return_token_type_ids=__UpperCAmelCase  ,return_attention_mask=__UpperCAmelCase  ,return_overflowing_tokens=__UpperCAmelCase  ,return_special_tokens_mask=__UpperCAmelCase  ,return_offsets_mapping=__UpperCAmelCase  ,return_length=__UpperCAmelCase  ,verbose=__UpperCAmelCase  ,return_tensors=__UpperCAmelCase  ,**__UpperCAmelCase  ,)
      # add pixel_values + pixel_mask
      lowerCAmelCase__			:  Dict											= self.image_processor(__UpperCAmelCase  ,return_tensors=__UpperCAmelCase				)
      encoding.update(__UpperCAmelCase				)

      return encoding




  def 							UpperCAmelCase_     (   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		Tuple:
      return self.tokenizer.batch_decode(*__UpperCAmelCase  ,**__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		str:
      return self.tokenizer.decode(*__UpperCAmelCase  ,**__UpperCAmelCase				)




  @property
  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      lowerCAmelCase__			:  Union[str, Any]											= self.tokenizer.model_input_names
      lowerCAmelCase__			:  List[Any]											= self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)




  @property
  def 							UpperCAmelCase_     (   self				)   ->		Dict:
      warnings.warn(
          """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""  ,__UpperCAmelCase  ,)
      return self.image_processor_class






  @property
  def 							UpperCAmelCase_     (   self				)   ->		List[str]:
      warnings.warn(
          """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""  ,__UpperCAmelCase  ,)
      return self.image_processor







'''simple docstring'''





import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()






class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			unittest.TestCase					):
  '''simple docstring'''




  __lowercase							:				Optional[Any]           =				VQModel
  __lowercase							:				Tuple           =				'''sample'''




  @property
  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase=(32, 32)				)   ->		List[Any]:
      lowerCAmelCase__			:  Dict											= 4
      lowerCAmelCase__			:  List[str]											= 3

      lowerCAmelCase__			:  Dict											= floats_tensor((batch_size, num_channels) + sizes				).to(__UpperCAmelCase				)

      return {"sample": image}




  @property
  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      return (3, 32, 32)




  @property
  def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
      return (3, 32, 32)




  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      lowerCAmelCase__			:  Dict											= {
          """block_out_channels""": [32, 64],
          """in_channels""": 3,
          """out_channels""": 3,
          """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
          """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
          """latent_channels""": 3,
      }
      lowerCAmelCase__			:  Any											= self.dummy_input
      return init_dict, inputs_dict




  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      pass




  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      pass




  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      lowerCAmelCase__				,       lowerCAmelCase__			:  Union[str, Any]											= VQModel.from_pretrained("""fusing/vqgan-dummy"""  ,output_loading_info=__UpperCAmelCase				)
      self.assertIsNotNone(__UpperCAmelCase				)
      self.assertEqual(len(loading_info["""missing_keys"""]				)  ,0				)

      model.to(__UpperCAmelCase				)
      lowerCAmelCase__			:  List[Any]											= model(**self.dummy_input				)

      assert image is not None, "Make sure output is not None"






  def 							UpperCAmelCase_     (   self				)   ->		List[str]:
      lowerCAmelCase__			:  Tuple											= VQModel.from_pretrained("""fusing/vqgan-dummy"""				)
      model.to(__UpperCAmelCase				).eval()

      torch.manual_seed(0				)
      if torch.cuda.is_available():
          torch.cuda.manual_seed_all(0				)

      lowerCAmelCase__			:  List[str]											= torch.randn(1  ,model.config.in_channels  ,model.config.sample_size  ,model.config.sample_size				)
      lowerCAmelCase__			:  Optional[Any]											= image.to(__UpperCAmelCase				)
      with torch.no_grad():
          lowerCAmelCase__			:  Optional[Any]											= model(__UpperCAmelCase				).sample

      lowerCAmelCase__			:  Tuple											= output[0, -1, -3:, -3:].flatten().cpu()
      # fmt: off
      lowerCAmelCase__			:  Tuple											= torch.tensor([-0.0_1_5_3, -0.4_0_4_4, -0.1_8_8_0, -0.5_1_6_1, -0.2_4_1_8, -0.4_0_7_2, -0.1_6_1_2, -0.0_6_3_3, -0.0_1_4_3]				)
      # fmt: on
      self.assertTrue(torch.allclose(__UpperCAmelCase  ,__UpperCAmelCase  ,atol=1E-3				)				)


