

'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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  ):


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





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


   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				int      					=       1
      _lowercase      :				int      					=       3
      _lowercase      :				List[Any]      					=       (32, 32)

      _lowercase      :				int      					=       floats_tensor((batch_size, num_channels) + sizes      ,    rng=random.Random(0  )  ).to(_UpperCamelCase  )
      return image


   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      torch.manual_seed(0  )
      _lowercase      :				Union[str, Any]      					=       UNetaDConditionModel(
          block_out_channels=(32, 64)      ,    layers_per_block=2      ,    sample_size=32      ,    in_channels=4      ,    out_channels=4      ,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,    up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,    cross_attention_dim=32      ,    )
      return model


   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      torch.manual_seed(0  )
      _lowercase      :				int      					=       AutoencoderKL(
          block_out_channels=[32, 64]      ,    in_channels=3      ,    out_channels=3      ,    down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]      ,    up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]      ,    latent_channels=4      ,    )
      return model


   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      torch.manual_seed(0  )
      _lowercase      :				Tuple      					=       RobertaSeriesConfig(
          hidden_size=32      ,    project_dim=32      ,    intermediate_size=37      ,    layer_norm_eps=1E-0_5      ,    num_attention_heads=4      ,    num_hidden_layers=5      ,    pad_token_id=1      ,    vocab_size=5006      ,    )
      return RobertaSeriesModelWithTransformation(_UpperCamelCase  )


   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      def extract(*_UpperCamelCase      ,    **_UpperCamelCase  ):


         class        a__  :


            def __init__(			self  ):





               """simple docstring"""





               _lowercase      :				int      					=       torch.ones([0]  )






            def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





               """simple docstring"""





               self.pixel_values.to(_UpperCamelCase  )
               return self




         return Out()

      return extract


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       "cpu"  # ensure determinism for the device-dependent torch.Generator
      _lowercase      :				Optional[Any]      					=       self.dummy_cond_unet
      _lowercase      :				List[str]      					=       PNDMScheduler(skip_prk_steps=_UpperCamelCase  )
      _lowercase      :				Optional[int]      					=       self.dummy_vae
      _lowercase      :				Tuple      					=       self.dummy_text_encoder
      _lowercase      :				List[str]      					=       XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"  )
      _lowercase      :				Dict      					=       77

      _lowercase      :				int      					=       self.dummy_image.to(_UpperCamelCase  )
      _lowercase      :				int      					=       init_image / 2 + 0.5

      # make sure here that pndm scheduler skips prk
      _lowercase      :				Optional[int]      					=       AltDiffusionImgaImgPipeline(
          unet=_UpperCamelCase      ,    scheduler=_UpperCamelCase      ,    vae=_UpperCamelCase      ,    text_encoder=_UpperCamelCase      ,    tokenizer=_UpperCamelCase      ,    safety_checker=_UpperCamelCase      ,    feature_extractor=self.dummy_extractor      ,    )
      _lowercase      :				List[Any]      					=       VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor      ,    do_normalize=_UpperCamelCase  )
      _lowercase      :				Dict      					=       alt_pipe.to(_UpperCamelCase  )
      alt_pipe.set_progress_bar_config(disable=_UpperCamelCase  )

      _lowercase      :				Optional[Any]      					=       "A painting of a squirrel eating a burger"
      _lowercase      :				str      					=       torch.Generator(device=_UpperCamelCase  ).manual_seed(0  )
      _lowercase      :				str      					=       alt_pipe(
          [prompt]      ,    generator=_UpperCamelCase      ,    guidance_scale=6.0      ,    num_inference_steps=2      ,    output_type="np"      ,    image=_UpperCamelCase      ,    )

      _lowercase      :				Any      					=       output.images

      _lowercase      :				Dict      					=       torch.Generator(device=_UpperCamelCase  ).manual_seed(0  )
      _lowercase      :				Union[str, Any]      					=       alt_pipe(
          [prompt]      ,    generator=_UpperCamelCase      ,    guidance_scale=6.0      ,    num_inference_steps=2      ,    output_type="np"      ,    image=_UpperCamelCase      ,    return_dict=_UpperCamelCase      ,    )[0]

      _lowercase      :				Optional[Any]      					=       image[0, -3:, -3:, -1]
      _lowercase      :				Any      					=       image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _lowercase      :				List[str]      					=       np.array([0.4_4_2_7, 0.3_7_3_1, 0.4_2_4_9, 0.4_9_4_1, 0.4_5_4_6, 0.4_1_4_8, 0.4_1_9_3, 0.4_6_6_6, 0.4_4_9_9]  )

      assert np.abs(image_slice.flatten() - expected_slice  ).max() < 5E-3
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 5E-3


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





      """simple docstring"""





      _lowercase      :				Optional[Any]      					=       self.dummy_cond_unet
      _lowercase      :				Optional[int]      					=       PNDMScheduler(skip_prk_steps=_UpperCamelCase  )
      _lowercase      :				Optional[Any]      					=       self.dummy_vae
      _lowercase      :				List[str]      					=       self.dummy_text_encoder
      _lowercase      :				int      					=       XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"  )
      _lowercase      :				List[str]      					=       77

      _lowercase      :				List[Any]      					=       self.dummy_image.to(_UpperCamelCase  )

      # put models in fp16
      _lowercase      :				Optional[Any]      					=       unet.half()
      _lowercase      :				List[Any]      					=       vae.half()
      _lowercase      :				Union[str, Any]      					=       bert.half()

      # make sure here that pndm scheduler skips prk
      _lowercase      :				List[Any]      					=       AltDiffusionImgaImgPipeline(
          unet=_UpperCamelCase      ,    scheduler=_UpperCamelCase      ,    vae=_UpperCamelCase      ,    text_encoder=_UpperCamelCase      ,    tokenizer=_UpperCamelCase      ,    safety_checker=_UpperCamelCase      ,    feature_extractor=self.dummy_extractor      ,    )
      _lowercase      :				Optional[Any]      					=       VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor      ,    do_normalize=_UpperCamelCase  )
      _lowercase      :				Tuple      					=       alt_pipe.to(_UpperCamelCase  )
      alt_pipe.set_progress_bar_config(disable=_UpperCamelCase  )

      _lowercase      :				str      					=       "A painting of a squirrel eating a burger"
      _lowercase      :				Any      					=       torch.manual_seed(0  )
      _lowercase      :				Tuple      					=       alt_pipe(
          [prompt]      ,    generator=_UpperCamelCase      ,    num_inference_steps=2      ,    output_type="np"      ,    image=_UpperCamelCase      ,    ).images

      assert image.shape == (1, 32, 32, 3)






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





      """simple docstring"""





      _lowercase      :				List[str]      					=       load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/img2img/sketch-mountains-input.jpg"  )
      # resize to resolution that is divisible by 8 but not 16 or 32
      _lowercase      :				Optional[Any]      					=       init_image.resize((760, 504)  )

      _lowercase      :				Any      					=       "BAAI/AltDiffusion"
      _lowercase      :				Optional[int]      					=       AltDiffusionImgaImgPipeline.from_pretrained(
          _UpperCamelCase      ,    safety_checker=_UpperCamelCase      ,    )
      pipe.to(_UpperCamelCase  )
      pipe.set_progress_bar_config(disable=_UpperCamelCase  )
      pipe.enable_attention_slicing()

      _lowercase      :				Dict      					=       "A fantasy landscape, trending on artstation"

      _lowercase      :				Any      					=       torch.manual_seed(0  )
      _lowercase      :				int      					=       pipe(
          prompt=_UpperCamelCase      ,    image=_UpperCamelCase      ,    strength=0.7_5      ,    guidance_scale=7.5      ,    generator=_UpperCamelCase      ,    output_type="np"      ,    )
      _lowercase      :				Optional[Any]      					=       output.images[0]

      _lowercase      :				Tuple      					=       image[255:258, 383:386, -1]

      assert image.shape == (504, 760, 3)
      _lowercase      :				Optional[int]      					=       np.array([0.9_3_5_8, 0.9_3_9_7, 0.9_5_9_9, 0.9_9_0_1, 1.0_0_0_0, 1.0_0_0_0, 0.9_8_8_2, 1.0_0_0_0, 1.0_0_0_0]  )

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




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


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





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






   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Union[str, Any]      					=       load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/img2img/sketch-mountains-input.jpg"  )
      _lowercase      :				str      					=       init_image.resize((768, 512)  )
      _lowercase      :				Union[str, Any]      					=       load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"  )

      _lowercase      :				List[Any]      					=       "BAAI/AltDiffusion"
      _lowercase      :				Optional[int]      					=       AltDiffusionImgaImgPipeline.from_pretrained(
          _UpperCamelCase      ,    safety_checker=_UpperCamelCase      ,    )
      pipe.to(_UpperCamelCase  )
      pipe.set_progress_bar_config(disable=_UpperCamelCase  )
      pipe.enable_attention_slicing()

      _lowercase      :				Optional[Any]      					=       "A fantasy landscape, trending on artstation"

      _lowercase      :				List[Any]      					=       torch.manual_seed(0  )
      _lowercase      :				Optional[Any]      					=       pipe(
          prompt=_UpperCamelCase      ,    image=_UpperCamelCase      ,    strength=0.7_5      ,    guidance_scale=7.5      ,    generator=_UpperCamelCase      ,    output_type="np"      ,    )
      _lowercase      :				str      					=       output.images[0]

      assert image.shape == (512, 768, 3)
      # img2img is flaky across GPUs even in fp32, so using MAE here
      assert np.abs(expected_image - image  ).max() < 1E-2









'''simple docstring'''
import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					Any     	=							['image_processor', 'tokenizer']
   _SCREAMING_SNAKE_CASE   :					Dict     	=							'BlipImageProcessor'
   _SCREAMING_SNAKE_CASE   :					Dict     	=							'AutoTokenizer'


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      super().__init__(_UpperCamelCase      ,    _UpperCamelCase  )

      # add QFormer tokenizer
      _lowercase      :				List[Any]      					=       qformer_tokenizer


   def __call__(			self      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = True      ,    _UpperCamelCase = False      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = 0      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = True      ,    _UpperCamelCase = None      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      if images is None and text is None:
         raise ValueError("You have to specify at least images or text."  )

      _lowercase      :				str      					=       BatchFeature()

      if text is not None:
         _lowercase      :				Dict      					=       self.tokenizer(
             text=_UpperCamelCase      ,    add_special_tokens=_UpperCamelCase      ,    padding=_UpperCamelCase      ,    truncation=_UpperCamelCase      ,    max_length=_UpperCamelCase      ,    stride=_UpperCamelCase      ,    pad_to_multiple_of=_UpperCamelCase      ,    return_attention_mask=_UpperCamelCase      ,    return_overflowing_tokens=_UpperCamelCase      ,    return_special_tokens_mask=_UpperCamelCase      ,    return_offsets_mapping=_UpperCamelCase      ,    return_token_type_ids=_UpperCamelCase      ,    return_length=_UpperCamelCase      ,    verbose=_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase      ,    )
         encoding.update(_UpperCamelCase  )
         _lowercase      :				Dict      					=       self.qformer_tokenizer(
             text=_UpperCamelCase      ,    add_special_tokens=_UpperCamelCase      ,    padding=_UpperCamelCase      ,    truncation=_UpperCamelCase      ,    max_length=_UpperCamelCase      ,    stride=_UpperCamelCase      ,    pad_to_multiple_of=_UpperCamelCase      ,    return_attention_mask=_UpperCamelCase      ,    return_overflowing_tokens=_UpperCamelCase      ,    return_special_tokens_mask=_UpperCamelCase      ,    return_offsets_mapping=_UpperCamelCase      ,    return_token_type_ids=_UpperCamelCase      ,    return_length=_UpperCamelCase      ,    verbose=_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase      ,    )
         _lowercase      :				Union[str, Any]      					=       qformer_text_encoding.pop("input_ids"  )
         _lowercase      :				List[Any]      					=       qformer_text_encoding.pop("attention_mask"  )

      if images is not None:
         _lowercase      :				List[Any]      					=       self.image_processor(_UpperCamelCase      ,    return_tensors=_UpperCamelCase  )
         encoding.update(_UpperCamelCase  )

      return encoding


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





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


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





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


   @property
   # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       self.tokenizer.model_input_names
      _lowercase      :				Optional[Any]      					=       self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      if os.path.isfile(_UpperCamelCase  ):
         raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file'''  )
      os.makedirs(_UpperCamelCase      ,    exist_ok=_UpperCamelCase  )
      _lowercase      :				Union[str, Any]      					=       os.path.join(_UpperCamelCase      ,    "qformer_tokenizer"  )
      self.qformer_tokenizer.save_pretrained(_UpperCamelCase  )
      return super().save_pretrained(_UpperCamelCase      ,    **_UpperCamelCase  )






   @classmethod
   def 						_lowerCamelCase       (			cls      ,    _UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       AutoTokenizer.from_pretrained(_UpperCamelCase      ,    subfolder="qformer_tokenizer"  )
      _lowercase      :				Optional[Any]      					=       cls._get_arguments_from_pretrained(_UpperCamelCase      ,    **_UpperCamelCase  )
      args.append(_UpperCamelCase  )
      return cls(*_UpperCamelCase  )






