


'''simple docstring'''

import warnings
from typing import List, Optional, Union

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





class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				['''image_processor''', '''tokenizer''']
     UpperCAmelCase 					=				'''FlavaImageProcessor'''
     UpperCAmelCase 					=				('''BertTokenizer''', '''BertTokenizerFast''')






     def __init__(			self     ,							__UpperCamelCase=None     ,							__UpperCamelCase=None     ,							**__UpperCamelCase		)       ->    str:
          _a						=		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     ,							)
               _a						=		kwargs.pop("feature_extractor"		)

          _a						=		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		)
          _a						=		self.image_processor






     def __call__(			self     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = True     ,							__UpperCamelCase = False     ,							__UpperCamelCase = False     ,							__UpperCamelCase = None     ,							__UpperCamelCase = 0     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = False     ,							__UpperCamelCase = False     ,							__UpperCamelCase = False     ,							__UpperCamelCase = False     ,							__UpperCamelCase = True     ,							__UpperCamelCase = None     ,							**__UpperCamelCase     ,							)       ->    List[Any]:

          if text is None and images is None:
               raise ValueError("You have to specify either text or images. Both cannot be none."		)

          if text is not None:
               _a						=		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     ,							)
          if images is not None:
               _a						=		self.image_processor(
                   __UpperCamelCase     ,							return_image_mask=__UpperCamelCase     ,							return_codebook_pixels=__UpperCamelCase     ,							return_tensors=__UpperCamelCase     ,							**__UpperCamelCase     ,							)

          if text is not None and images is not None:
               encoding.update(__UpperCamelCase		)
               return encoding
          elif text is not None:
               return encoding
          else:
               return BatchEncoding(data=dict(**__UpperCamelCase		)     ,							tensor_type=__UpperCamelCase		)






     def  a_					(			self     ,							*__UpperCamelCase     ,							**__UpperCamelCase		)       ->    int:
          return self.tokenizer.batch_decode(*__UpperCamelCase     ,							**__UpperCamelCase		)






     def  a_					(			self     ,							*__UpperCamelCase     ,							**__UpperCamelCase		)       ->    Optional[int]:
          return self.tokenizer.decode(*__UpperCamelCase     ,							**__UpperCamelCase		)






     @property
     def  a_					(			self		)       ->    Optional[int]:
          _a						=		self.tokenizer.model_input_names
          _a						=		self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)






     @property
     def  a_					(			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  a_					(			self		)       ->    Any:
          warnings.warn(
              "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."     ,							__UpperCamelCase     ,							)
          return self.image_processor





'''simple docstring'''

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


lowercase__				     =		False





class 						__SCREAMING_SNAKE_CASE					(      unittest.TestCase ):
     pass







@nightly
@require_torch_gpu
class 						__SCREAMING_SNAKE_CASE					(      unittest.TestCase ):






     def  a_					(			self		)       ->    Dict:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






     def  a_					(			self		)       ->    List[str]:
          _a						=		VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"		)
          # remove text_unet
          pipe.remove_unused_weights()
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a						=		"A painting of a squirrel eating a burger "
          _a						=		torch.manual_seed(0		)
          _a						=		pipe(
              prompt=__UpperCamelCase     ,							generator=__UpperCamelCase     ,							guidance_scale=7.5     ,							num_inference_steps=2     ,							output_type="numpy"		).images

          with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(__UpperCamelCase		)
               _a						=		VersatileDiffusionTextToImagePipeline.from_pretrained(__UpperCamelCase		)
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a						=		generator.manual_seed(0		)
          _a						=		pipe(
              prompt=__UpperCamelCase     ,							generator=__UpperCamelCase     ,							guidance_scale=7.5     ,							num_inference_steps=2     ,							output_type="numpy"		).images

          assert np.abs(image - new_image		).sum() < 1e-5, "Models don't have the same forward pass"






     def  a_					(			self		)       ->    List[Any]:
          _a						=		VersatileDiffusionTextToImagePipeline.from_pretrained(
              "shi-labs/versatile-diffusion"     ,							torch_dtype=torch.floataa		)
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a						=		"A painting of a squirrel eating a burger "
          _a						=		torch.manual_seed(0		)
          _a						=		pipe(
              prompt=__UpperCamelCase     ,							generator=__UpperCamelCase     ,							guidance_scale=7.5     ,							num_inference_steps=50     ,							output_type="numpy"		).images

          _a						=		image[0, 253:256, 253:256, -1]

          assert image.shape == (1, 512, 512, 3)
          _a						=		np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]		)

          assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2

