


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 A__    ( SCREAMING_SNAKE_CASE__ ):



 """simple docstring"""







 _lowercase     :					int      =						['''image_processor''', '''tokenizer''']
 _lowercase     :					Any      =						'''FlavaImageProcessor'''
 _lowercase     :					str      =						('''BertTokenizer''', '''BertTokenizerFast''')

 def __init__(		self :	Optional[int]     ,						A_ :	Any=None     ,						A_ :	Tuple=None     ,						**A_ :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    None
      if "feature_extractor" in kwargs:
           warnings.warn(
               "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
               " instead."     ,						snake_case__     ,						)
           _lowerCAmelCase   :					int        =    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__(snake_case__     ,						snake_case__       )
      _lowerCAmelCase   :					Optional[Any]        =    self.image_processor

 def __call__(		self :	List[Any]     ,						A_ :	List[Any] = None     ,						A_ :	int = None     ,						A_ :	List[Any] = True     ,						A_ :	Tuple = False     ,						A_ :	Optional[Any] = False     ,						A_ :	List[str] = None     ,						A_ :	str = 0     ,						A_ :	Dict = None     ,						A_ :	Optional[int] = None     ,						A_ :	int = None     ,						A_ :	Tuple = None     ,						A_ :	List[str] = None     ,						A_ :	Tuple = False     ,						A_ :	Optional[Any] = False     ,						A_ :	List[str] = False     ,						A_ :	int = False     ,						A_ :	Dict = True     ,						A_ :	Optional[int] = None     ,						**A_ :	List[str]     ,						):






      '''simple docstring'''




      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:
           _lowerCAmelCase   :					Any        =    self.tokenizer(
               text=snake_case__     ,						add_special_tokens=snake_case__     ,						padding=snake_case__     ,						truncation=snake_case__     ,						max_length=snake_case__     ,						stride=snake_case__     ,						pad_to_multiple_of=snake_case__     ,						return_token_type_ids=snake_case__     ,						return_attention_mask=snake_case__     ,						return_overflowing_tokens=snake_case__     ,						return_special_tokens_mask=snake_case__     ,						return_offsets_mapping=snake_case__     ,						return_length=snake_case__     ,						verbose=snake_case__     ,						return_tensors=snake_case__     ,						**snake_case__     ,						)
      if images is not None:
           _lowerCAmelCase   :					Union[str, Any]        =    self.image_processor(
               snake_case__     ,						return_image_mask=snake_case__     ,						return_codebook_pixels=snake_case__     ,						return_tensors=snake_case__     ,						**snake_case__     ,						)

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

 def    __magic_name__       (		self :	List[Any]     ,						*A_ :	Dict     ,						**A_ :	Any       ):






      '''simple docstring'''




      return self.tokenizer.batch_decode(*snake_case__     ,						**snake_case__       )

 def    __magic_name__       (		self :	int     ,						*A_ :	Optional[Any]     ,						**A_ :	List[str]       ):






      '''simple docstring'''




      return self.tokenizer.decode(*snake_case__     ,						**snake_case__       )

 @property
 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    self.tokenizer.model_input_names
      _lowerCAmelCase   :					Union[str, Any]        =    self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )

 @property
 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      warnings.warn(
          "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."     ,						snake_case__     ,						)
      return self.image_processor_class


 @property
 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      warnings.warn(
          "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."     ,						snake_case__     ,						)
      return self.image_processor









import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()





class A__    ( unittest.TestCase ):



 """simple docstring"""







 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()

 @property
 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    1
      _lowerCAmelCase   :					List[str]        =    3
      _lowerCAmelCase   :					int        =    (3_2, 3_2)

      _lowerCAmelCase   :					Dict        =    floats_tensor((batch_size, num_channels) + sizes     ,						rng=random.Random(0       )       ).to(A_       )
      return image

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






      '''simple docstring'''




      torch.manual_seed(0       )
      _lowerCAmelCase   :					Dict        =    UNetaDConditionModel(
          block_out_channels=(3_2, 3_2, 6_4)     ,						layers_per_block=2     ,						sample_size=3_2     ,						in_channels=7     ,						out_channels=4     ,						down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D")     ,						up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D")     ,						cross_attention_dim=3_2     ,						attention_head_dim=8     ,						use_linear_projection=A_     ,						only_cross_attention=(True, True, False)     ,						num_class_embeds=1_0_0     ,						)
      return model

 @property
 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      torch.manual_seed(0       )
      _lowerCAmelCase   :					Union[str, Any]        =    AutoencoderKL(
          block_out_channels=[3_2, 3_2, 6_4]     ,						in_channels=3     ,						out_channels=3     ,						down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"]     ,						up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"]     ,						latent_channels=4     ,						)
      return model

 @property
 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      torch.manual_seed(0       )
      _lowerCAmelCase   :					Optional[Any]        =    CLIPTextConfig(
          bos_token_id=0     ,						eos_token_id=2     ,						hidden_size=3_2     ,						intermediate_size=3_7     ,						layer_norm_eps=1E-05     ,						num_attention_heads=4     ,						num_hidden_layers=5     ,						pad_token_id=1     ,						vocab_size=1_0_0_0     ,						hidden_act="gelu"     ,						projection_dim=5_1_2     ,						)
      return CLIPTextModel(A_       )

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    "cpu"  # ensure determinism for the device-dependent torch.Generator
      _lowerCAmelCase   :					Tuple        =    self.dummy_cond_unet_upscale
      _lowerCAmelCase   :					Optional[int]        =    DDPMScheduler()
      _lowerCAmelCase   :					Optional[Any]        =    DDIMScheduler(prediction_type="v_prediction"       )
      _lowerCAmelCase   :					str        =    self.dummy_vae
      _lowerCAmelCase   :					Any        =    self.dummy_text_encoder
      _lowerCAmelCase   :					Dict        =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

      _lowerCAmelCase   :					List[Any]        =    self.dummy_image.cpu().permute(0     ,						2     ,						3     ,						1       )[0]
      _lowerCAmelCase   :					List[Any]        =    Image.fromarray(np.uinta(A_       )       ).convert("RGB"       ).resize((6_4, 6_4)       )

      # make sure here that pndm scheduler skips prk
      _lowerCAmelCase   :					List[Any]        =    StableDiffusionUpscalePipeline(
          unet=A_     ,						low_res_scheduler=A_     ,						scheduler=A_     ,						vae=A_     ,						text_encoder=A_     ,						tokenizer=A_     ,						max_noise_level=3_5_0     ,						)
      _lowerCAmelCase   :					Optional[int]        =    sd_pipe.to(A_       )
      sd_pipe.set_progress_bar_config(disable=A_       )

      _lowerCAmelCase   :					List[str]        =    "A painting of a squirrel eating a burger"
      _lowerCAmelCase   :					Tuple        =    torch.Generator(device=A_       ).manual_seed(0       )
      _lowerCAmelCase   :					Optional[int]        =    sd_pipe(
          [prompt]     ,						image=A_     ,						generator=A_     ,						guidance_scale=6.0     ,						noise_level=2_0     ,						num_inference_steps=2     ,						output_type="np"     ,						)

      _lowerCAmelCase   :					Tuple        =    output.images

      _lowerCAmelCase   :					Optional[int]        =    torch.Generator(device=A_       ).manual_seed(0       )
      _lowerCAmelCase   :					Union[str, Any]        =    sd_pipe(
          [prompt]     ,						image=A_     ,						generator=A_     ,						guidance_scale=6.0     ,						noise_level=2_0     ,						num_inference_steps=2     ,						output_type="np"     ,						return_dict=A_     ,						)[0]

      _lowerCAmelCase   :					List[Any]        =    image[0, -3:, -3:, -1]
      _lowerCAmelCase   :					List[str]        =    image_from_tuple[0, -3:, -3:, -1]

      _lowerCAmelCase   :					Optional[int]        =    low_res_image.size[0] * 4
      assert image.shape == (1, expected_height_width, expected_height_width, 3)
      _lowerCAmelCase   :					str        =    np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661]       )

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

 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    "cpu"  # ensure determinism for the device-dependent torch.Generator
      _lowerCAmelCase   :					List[str]        =    self.dummy_cond_unet_upscale
      _lowerCAmelCase   :					str        =    DDPMScheduler()
      _lowerCAmelCase   :					Union[str, Any]        =    DDIMScheduler(prediction_type="v_prediction"       )
      _lowerCAmelCase   :					Tuple        =    self.dummy_vae
      _lowerCAmelCase   :					Union[str, Any]        =    self.dummy_text_encoder
      _lowerCAmelCase   :					List[Any]        =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

      _lowerCAmelCase   :					Dict        =    self.dummy_image.cpu().permute(0     ,						2     ,						3     ,						1       )[0]
      _lowerCAmelCase   :					Tuple        =    Image.fromarray(np.uinta(A_       )       ).convert("RGB"       ).resize((6_4, 6_4)       )

      # make sure here that pndm scheduler skips prk
      _lowerCAmelCase   :					Optional[Any]        =    StableDiffusionUpscalePipeline(
          unet=A_     ,						low_res_scheduler=A_     ,						scheduler=A_     ,						vae=A_     ,						text_encoder=A_     ,						tokenizer=A_     ,						max_noise_level=3_5_0     ,						)
      _lowerCAmelCase   :					Dict        =    sd_pipe.to(A_       )
      sd_pipe.set_progress_bar_config(disable=A_       )

      _lowerCAmelCase   :					Union[str, Any]        =    "A painting of a squirrel eating a burger"
      _lowerCAmelCase   :					Tuple        =    sd_pipe(
          2 * [prompt]     ,						image=2 * [low_res_image]     ,						guidance_scale=6.0     ,						noise_level=2_0     ,						num_inference_steps=2     ,						output_type="np"     ,						)
      _lowerCAmelCase   :					List[str]        =    output.images
      assert image.shape[0] == 2

      _lowerCAmelCase   :					str        =    torch.Generator(device=A_       ).manual_seed(0       )
      _lowerCAmelCase   :					List[Any]        =    sd_pipe(
          [prompt]     ,						image=A_     ,						generator=A_     ,						num_images_per_prompt=2     ,						guidance_scale=6.0     ,						noise_level=2_0     ,						num_inference_steps=2     ,						output_type="np"     ,						)
      _lowerCAmelCase   :					int        =    output.images
      assert image.shape[0] == 2


 @unittest.skipIf(torch_device != "cuda"     ,						"This test requires a GPU"       )
 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    self.dummy_cond_unet_upscale
      _lowerCAmelCase   :					List[str]        =    DDPMScheduler()
      _lowerCAmelCase   :					Optional[int]        =    DDIMScheduler(prediction_type="v_prediction"       )
      _lowerCAmelCase   :					Optional[Any]        =    self.dummy_vae
      _lowerCAmelCase   :					List[str]        =    self.dummy_text_encoder
      _lowerCAmelCase   :					Union[str, Any]        =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

      _lowerCAmelCase   :					Any        =    self.dummy_image.cpu().permute(0     ,						2     ,						3     ,						1       )[0]
      _lowerCAmelCase   :					Any        =    Image.fromarray(np.uinta(A_       )       ).convert("RGB"       ).resize((6_4, 6_4)       )

      # put models in fp16, except vae as it overflows in fp16
      _lowerCAmelCase   :					Tuple        =    unet.half()
      _lowerCAmelCase   :					Optional[Any]        =    text_encoder.half()

      # make sure here that pndm scheduler skips prk
      _lowerCAmelCase   :					List[str]        =    StableDiffusionUpscalePipeline(
          unet=A_     ,						low_res_scheduler=A_     ,						scheduler=A_     ,						vae=A_     ,						text_encoder=A_     ,						tokenizer=A_     ,						max_noise_level=3_5_0     ,						)
      _lowerCAmelCase   :					Optional[Any]        =    sd_pipe.to(A_       )
      sd_pipe.set_progress_bar_config(disable=A_       )

      _lowerCAmelCase   :					str        =    "A painting of a squirrel eating a burger"
      _lowerCAmelCase   :					Any        =    torch.manual_seed(0       )
      _lowerCAmelCase   :					Optional[Any]        =    sd_pipe(
          [prompt]     ,						image=A_     ,						generator=A_     ,						num_inference_steps=2     ,						output_type="np"     ,						).images

      _lowerCAmelCase   :					List[str]        =    low_res_image.size[0] * 4
      assert image.shape == (1, expected_height_width, expected_height_width, 3)





@slow
@require_torch_gpu
class A__    ( unittest.TestCase ):



 """simple docstring"""







 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()

 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/sd2-upscale/low_res_cat.png"       )
      _lowerCAmelCase   :					Dict        =    load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
          "/upsampled_cat.npy"       )

      _lowerCAmelCase   :					Union[str, Any]        =    "stabilityai/stable-diffusion-x4-upscaler"
      _lowerCAmelCase   :					Optional[int]        =    StableDiffusionUpscalePipeline.from_pretrained(A_       )
      pipe.to(A_       )
      pipe.set_progress_bar_config(disable=A_       )
      pipe.enable_attention_slicing()

      _lowerCAmelCase   :					str        =    "a cat sitting on a park bench"

      _lowerCAmelCase   :					str        =    torch.manual_seed(0       )
      _lowerCAmelCase   :					Optional[int]        =    pipe(
          prompt=A_     ,						image=A_     ,						generator=A_     ,						output_type="np"     ,						)
      _lowerCAmelCase   :					int        =    output.images[0]

      assert image.shape == (5_1_2, 5_1_2, 3)
      assert np.abs(expected_image - image       ).max() < 1E-3

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/sd2-upscale/low_res_cat.png"       )
      _lowerCAmelCase   :					Dict        =    load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
          "/upsampled_cat_fp16.npy"       )

      _lowerCAmelCase   :					Union[str, Any]        =    "stabilityai/stable-diffusion-x4-upscaler"
      _lowerCAmelCase   :					Any        =    StableDiffusionUpscalePipeline.from_pretrained(
          A_     ,						torch_dtype=torch.floataa     ,						)
      pipe.to(A_       )
      pipe.set_progress_bar_config(disable=A_       )
      pipe.enable_attention_slicing()

      _lowerCAmelCase   :					Optional[Any]        =    "a cat sitting on a park bench"

      _lowerCAmelCase   :					Any        =    torch.manual_seed(0       )
      _lowerCAmelCase   :					int        =    pipe(
          prompt=A_     ,						image=A_     ,						generator=A_     ,						output_type="np"     ,						)
      _lowerCAmelCase   :					Union[str, Any]        =    output.images[0]

      assert image.shape == (5_1_2, 5_1_2, 3)
      assert np.abs(expected_image - image       ).max() < 5E-1


 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      _lowerCAmelCase   :					int        =    load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/sd2-upscale/low_res_cat.png"       )

      _lowerCAmelCase   :					Tuple        =    "stabilityai/stable-diffusion-x4-upscaler"
      _lowerCAmelCase   :					Any        =    StableDiffusionUpscalePipeline.from_pretrained(
          A_     ,						torch_dtype=torch.floataa     ,						)
      pipe.to(A_       )
      pipe.set_progress_bar_config(disable=A_       )
      pipe.enable_attention_slicing(1       )
      pipe.enable_sequential_cpu_offload()

      _lowerCAmelCase   :					List[Any]        =    "a cat sitting on a park bench"

      _lowerCAmelCase   :					Union[str, Any]        =    torch.manual_seed(0       )
      _lowerCAmelCase   :					List[Any]        =    pipe(
          prompt=A_     ,						image=A_     ,						generator=A_     ,						num_inference_steps=5     ,						output_type="np"     ,						)

      _lowerCAmelCase   :					Optional[Any]        =    torch.cuda.max_memory_allocated()
      # make sure that less than 2.9 GB is allocated
      assert mem_bytes < 2.9 * 1_0**9






