


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   __lowercase				(  unittest.TestCase  ):





      def        _lowercase				(		self						:					Union[str, Any]       )    ->   List[Any]:






         """simple docstring"""







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





      @property
      def        _lowercase				(		self						:					Union[str, Any]       )    ->   List[Any]:






         """simple docstring"""







         UpperCAmelCase							=     1
         UpperCAmelCase							=     3
         UpperCAmelCase							=     (3_2, 3_2)

         UpperCAmelCase							=     floats_tensor((batch_size, num_channels) + sizes					,       rng=random.Random(0       )       ).to(__lowerCamelCase       )
         return image





      @property
      def        _lowercase				(		self						:					Dict       )    ->   Tuple:






         """simple docstring"""







         torch.manual_seed(0       )
         UpperCAmelCase							=     UNetaDConditionModel(
             block_out_channels=(3_2, 6_4)					,       layers_per_block=2					,       sample_size=3_2					,       in_channels=4					,       out_channels=4					,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")					,       up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")					,       cross_attention_dim=3_2					,       )
         return model





      @property
      def        _lowercase				(		self						:					int       )    ->   Union[str, Any]:






         """simple docstring"""







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





      @property
      def        _lowercase				(		self						:					Union[str, Any]       )    ->   Any:






         """simple docstring"""







         torch.manual_seed(0       )
         UpperCAmelCase							=     RobertaSeriesConfig(
             hidden_size=3_2					,       project_dim=3_2					,       intermediate_size=3_7					,       layer_norm_eps=1e-0_5					,       num_attention_heads=4					,       num_hidden_layers=5					,       pad_token_id=1					,       vocab_size=5_0_0_6					,       )
         return RobertaSeriesModelWithTransformation(__lowerCamelCase       )





      @property
      def        _lowercase				(		self						:					Optional[int]       )    ->   Tuple:






         """simple docstring"""







         def extract(*__lowerCamelCase						:					Dict					,       **__lowerCamelCase						:					Dict       ):






            class   __lowercase				:





                  def __init__(		self						:					Optional[Any]       )    ->   str:






                     """simple docstring"""







                     UpperCAmelCase							=     torch.ones([0]       )





                  def        _lowercase				(		self						:					str					,       __lowerCamelCase						:					List[Any]       )    ->   Tuple:






                     """simple docstring"""







                     self.pixel_values.to(__lowerCamelCase       )
                     return self

            return Out()

         return extract





      def        _lowercase				(		self						:					Tuple       )    ->   Dict:






         """simple docstring"""







         UpperCAmelCase							=     """cpu"""  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase							=     self.dummy_cond_unet
         UpperCAmelCase							=     PNDMScheduler(skip_prk_steps=__lowerCamelCase       )
         UpperCAmelCase							=     self.dummy_vae
         UpperCAmelCase							=     self.dummy_text_encoder
         UpperCAmelCase							=     XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""       )
         UpperCAmelCase							=     7_7

         UpperCAmelCase							=     self.dummy_image.to(__lowerCamelCase       )
         UpperCAmelCase							=     init_image / 2 + 0.5

         # make sure here that pndm scheduler skips prk
         UpperCAmelCase							=     AltDiffusionImgaImgPipeline(
             unet=__lowerCamelCase					,       scheduler=__lowerCamelCase					,       vae=__lowerCamelCase					,       text_encoder=__lowerCamelCase					,       tokenizer=__lowerCamelCase					,       safety_checker=__lowerCamelCase					,       feature_extractor=self.dummy_extractor					,       )
         UpperCAmelCase							=     VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor					,       do_normalize=__lowerCamelCase       )
         UpperCAmelCase							=     alt_pipe.to(__lowerCamelCase       )
         alt_pipe.set_progress_bar_config(disable=__lowerCamelCase       )

         UpperCAmelCase							=     """A painting of a squirrel eating a burger"""
         UpperCAmelCase							=     torch.Generator(device=__lowerCamelCase       ).manual_seed(0       )
         UpperCAmelCase							=     alt_pipe(
             [prompt]					,       generator=__lowerCamelCase					,       guidance_scale=6.0					,       num_inference_steps=2					,       output_type="""np"""					,       image=__lowerCamelCase					,       )

         UpperCAmelCase							=     output.images

         UpperCAmelCase							=     torch.Generator(device=__lowerCamelCase       ).manual_seed(0       )
         UpperCAmelCase							=     alt_pipe(
             [prompt]					,       generator=__lowerCamelCase					,       guidance_scale=6.0					,       num_inference_steps=2					,       output_type="""np"""					,       image=__lowerCamelCase					,       return_dict=__lowerCamelCase					,       )[0]

         UpperCAmelCase							=     image[0, -3:, -3:, -1]
         UpperCAmelCase							=     image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 3_2, 3_2, 3)
         UpperCAmelCase							=     np.array([0.4_427, 0.3_731, 0.4_249, 0.4_941, 0.4_546, 0.4_148, 0.4_193, 0.4_666, 0.4_499]       )

         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        _lowercase				(		self						:					Dict       )    ->   Tuple:






         """simple docstring"""







         UpperCAmelCase							=     self.dummy_cond_unet
         UpperCAmelCase							=     PNDMScheduler(skip_prk_steps=__lowerCamelCase       )
         UpperCAmelCase							=     self.dummy_vae
         UpperCAmelCase							=     self.dummy_text_encoder
         UpperCAmelCase							=     XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""       )
         UpperCAmelCase							=     7_7

         UpperCAmelCase							=     self.dummy_image.to(__lowerCamelCase       )

         # put models in fp16
         UpperCAmelCase							=     unet.half()
         UpperCAmelCase							=     vae.half()
         UpperCAmelCase							=     bert.half()

         # make sure here that pndm scheduler skips prk
         UpperCAmelCase							=     AltDiffusionImgaImgPipeline(
             unet=__lowerCamelCase					,       scheduler=__lowerCamelCase					,       vae=__lowerCamelCase					,       text_encoder=__lowerCamelCase					,       tokenizer=__lowerCamelCase					,       safety_checker=__lowerCamelCase					,       feature_extractor=self.dummy_extractor					,       )
         UpperCAmelCase							=     VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor					,       do_normalize=__lowerCamelCase       )
         UpperCAmelCase							=     alt_pipe.to(__lowerCamelCase       )
         alt_pipe.set_progress_bar_config(disable=__lowerCamelCase       )

         UpperCAmelCase							=     """A painting of a squirrel eating a burger"""
         UpperCAmelCase							=     torch.manual_seed(0       )
         UpperCAmelCase							=     alt_pipe(
             [prompt]					,       generator=__lowerCamelCase					,       num_inference_steps=2					,       output_type="""np"""					,       image=__lowerCamelCase					,       ).images

         assert image.shape == (1, 3_2, 3_2, 3)





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






         """simple docstring"""







         UpperCAmelCase							=     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
         UpperCAmelCase							=     init_image.resize((7_6_0, 5_0_4)       )

         UpperCAmelCase							=     """BAAI/AltDiffusion"""
         UpperCAmelCase							=     AltDiffusionImgaImgPipeline.from_pretrained(
             __lowerCamelCase					,       safety_checker=__lowerCamelCase					,       )
         pipe.to(__lowerCamelCase       )
         pipe.set_progress_bar_config(disable=__lowerCamelCase       )
         pipe.enable_attention_slicing()

         UpperCAmelCase							=     """A fantasy landscape, trending on artstation"""

         UpperCAmelCase							=     torch.manual_seed(0       )
         UpperCAmelCase							=     pipe(
             prompt=__lowerCamelCase					,       image=__lowerCamelCase					,       strength=0.75					,       guidance_scale=7.5					,       generator=__lowerCamelCase					,       output_type="""np"""					,       )
         UpperCAmelCase							=     output.images[0]

         UpperCAmelCase							=     image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

         assert image.shape == (5_0_4, 7_6_0, 3)
         UpperCAmelCase							=     np.array([0.9_358, 0.9_397, 0.9_599, 0.9_901, 1.0_000, 1.0_000, 0.9_882, 1.0_000, 1.0_000]       )

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






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





      def        _lowercase				(		self						:					str       )    ->   Tuple:






         """simple docstring"""







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





      def        _lowercase				(		self						:					Union[str, Any]       )    ->   Optional[Any]:






         """simple docstring"""







         UpperCAmelCase							=     load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/img2img/sketch-mountains-input.jpg"""       )
         UpperCAmelCase							=     init_image.resize((7_6_8, 5_1_2)       )
         UpperCAmelCase							=     load_numpy(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"""       )

         UpperCAmelCase							=     """BAAI/AltDiffusion"""
         UpperCAmelCase							=     AltDiffusionImgaImgPipeline.from_pretrained(
             __lowerCamelCase					,       safety_checker=__lowerCamelCase					,       )
         pipe.to(__lowerCamelCase       )
         pipe.set_progress_bar_config(disable=__lowerCamelCase       )
         pipe.enable_attention_slicing()

         UpperCAmelCase							=     """A fantasy landscape, trending on artstation"""

         UpperCAmelCase							=     torch.manual_seed(0       )
         UpperCAmelCase							=     pipe(
             prompt=__lowerCamelCase					,       image=__lowerCamelCase					,       strength=0.75					,       guidance_scale=7.5					,       generator=__lowerCamelCase					,       output_type="""np"""					,       )
         UpperCAmelCase							=     output.images[0]

         assert image.shape == (5_1_2, 7_6_8, 3)
         # img2img is flaky across GPUs even in fp32, so using MAE here
         assert np.abs(expected_image - image       ).max() < 1e-2







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


__a     =			{"""tokenization_herbert""": ["""HerbertTokenizer"""]}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __a     =			["""HerbertTokenizerFast"""]


if TYPE_CHECKING:
 from .tokenization_herbert import HerbertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_herbert_fast import HerbertTokenizerFast

else:
 import sys

 __a     =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



