




'''simple docstring'''




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, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class   lowerCAmelCase_      (       snake_case_	,     snake_case_	,     snake_case_	,     unittest.TestCase      ):







  '''simple docstring'''







  lowerCAmelCase_ :    Dict    =  StableDiffusionInpaintPipeline
  lowerCAmelCase_ :    Any    =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
  lowerCAmelCase_ :    Optional[Any]    =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  lowerCAmelCase_ :    Optional[int]    =  frozenset(
      []      )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
  lowerCAmelCase_ :    Any    =  frozenset([]      )


  def 							SCREAMING_SNAKE_CASE__	(  self			:		str       ):






   """simple docstring"""






   torch.manual_seed(0       )
   UpperCAmelCase__								=			UNetaDConditionModel(
       block_out_channels=(32, 64)  ,						layers_per_block=2  ,						sample_size=32  ,						in_channels=9  ,						out_channels=4  ,						down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")  ,						up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")  ,						cross_attention_dim=32  ,						attention_head_dim=(2, 4)  ,						use_linear_projection=_UpperCAmelCase  ,						)
   UpperCAmelCase__								=			PNDMScheduler(skip_prk_steps=_UpperCAmelCase       )
   torch.manual_seed(0       )
   UpperCAmelCase__								=			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  ,						sample_size=1_28  ,						)
   torch.manual_seed(0       )
   UpperCAmelCase__								=			CLIPTextConfig(
       bos_token_id=0  ,						eos_token_id=2  ,						hidden_size=32  ,						intermediate_size=37  ,						layer_norm_eps=1E-05  ,						num_attention_heads=4  ,						num_hidden_layers=5  ,						pad_token_id=1  ,						vocab_size=10_00  ,						hidden_act="""gelu"""  ,						projection_dim=5_12  ,						)
   UpperCAmelCase__								=			CLIPTextModel(_UpperCAmelCase       )
   UpperCAmelCase__								=			CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )

   UpperCAmelCase__								=			{
       """unet""": unet,
       """scheduler""": scheduler,
       """vae""": vae,
       """text_encoder""": text_encoder,
       """tokenizer""": tokenizer,
       """safety_checker""": None,
       """feature_extractor""": None,
   }
   return components


  def 							SCREAMING_SNAKE_CASE__	(  self			:		Tuple  ,						_UpperCAmelCase			:		Optional[int]  ,						_UpperCAmelCase			:		List[Any]=0       ):






   """simple docstring"""






   UpperCAmelCase__								=			floats_tensor((1, 3, 32, 32)  ,						rng=random.Random(_UpperCAmelCase       )       ).to(_UpperCAmelCase       )
   UpperCAmelCase__								=			image.cpu().permute(0  ,						2  ,						3  ,						1       )[0]
   UpperCAmelCase__								=			Image.fromarray(np.uinta(_UpperCAmelCase       )       ).convert("""RGB"""       ).resize((64, 64)       )
   UpperCAmelCase__								=			Image.fromarray(np.uinta(image + 4       )       ).convert("""RGB"""       ).resize((64, 64)       )
   if str(_UpperCAmelCase       ).startswith("""mps"""       ):
    UpperCAmelCase__								=			torch.manual_seed(_UpperCAmelCase       )
   else:
    UpperCAmelCase__								=			torch.Generator(device=_UpperCAmelCase       ).manual_seed(_UpperCAmelCase       )
   UpperCAmelCase__								=			{
       """prompt""": """A painting of a squirrel eating a burger""",
       """image""": init_image,
       """mask_image""": mask_image,
       """generator""": generator,
       """num_inference_steps""": 2,
       """guidance_scale""": 6.0,
       """output_type""": """numpy""",
   }
   return inputs


  def 							SCREAMING_SNAKE_CASE__	(  self			:		Tuple       ):






   """simple docstring"""






   UpperCAmelCase__								=			"""cpu"""  # ensure determinism for the device-dependent torch.Generator
   UpperCAmelCase__								=			self.get_dummy_components()
   UpperCAmelCase__								=			StableDiffusionInpaintPipeline(**_UpperCAmelCase       )
   UpperCAmelCase__								=			sd_pipe.to(_UpperCAmelCase       )
   sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

   UpperCAmelCase__								=			self.get_dummy_inputs(_UpperCAmelCase       )
   UpperCAmelCase__								=			sd_pipe(**_UpperCAmelCase       ).images
   UpperCAmelCase__								=			image[0, -3:, -3:, -1]

   assert image.shape == (1, 64, 64, 3)
   UpperCAmelCase__								=			np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]       )

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



  def 							SCREAMING_SNAKE_CASE__	(  self			:		Optional[int]       ):






   """simple docstring"""






   super().test_inference_batch_single_identical(expected_max_diff=3E-3       )

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







  '''simple docstring'''







  def 							SCREAMING_SNAKE_CASE__	(  self			:		Optional[Any]       ):






   """simple docstring"""






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


  def 							SCREAMING_SNAKE_CASE__	(  self			:		Optional[Any]       ):






   """simple docstring"""






   UpperCAmelCase__								=			load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/sd2-inpaint/init_image.png"""       )
   UpperCAmelCase__								=			load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""       )
   UpperCAmelCase__								=			load_numpy(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
       """/yellow_cat_sitting_on_a_park_bench.npy"""       )

   UpperCAmelCase__								=			"""stabilityai/stable-diffusion-2-inpainting"""
   UpperCAmelCase__								=			StableDiffusionInpaintPipeline.from_pretrained(_UpperCAmelCase  ,						safety_checker=_UpperCAmelCase       )
   pipe.to(_UpperCAmelCase       )
   pipe.set_progress_bar_config(disable=_UpperCAmelCase       )
   pipe.enable_attention_slicing()

   UpperCAmelCase__								=			"""Face of a yellow cat, high resolution, sitting on a park bench"""

   UpperCAmelCase__								=			torch.manual_seed(0       )
   UpperCAmelCase__								=			pipe(
       prompt=_UpperCAmelCase  ,						image=_UpperCAmelCase  ,						mask_image=_UpperCAmelCase  ,						generator=_UpperCAmelCase  ,						output_type="""np"""  ,						)
   UpperCAmelCase__								=			output.images[0]

   assert image.shape == (5_12, 5_12, 3)
   assert np.abs(expected_image - image       ).max() < 9E-3


  def 							SCREAMING_SNAKE_CASE__	(  self			:		str       ):






   """simple docstring"""






   UpperCAmelCase__								=			load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/sd2-inpaint/init_image.png"""       )
   UpperCAmelCase__								=			load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""       )
   UpperCAmelCase__								=			load_numpy(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
       """/yellow_cat_sitting_on_a_park_bench_fp16.npy"""       )

   UpperCAmelCase__								=			"""stabilityai/stable-diffusion-2-inpainting"""
   UpperCAmelCase__								=			StableDiffusionInpaintPipeline.from_pretrained(
       _UpperCAmelCase  ,						torch_dtype=torch.floataa  ,						safety_checker=_UpperCAmelCase  ,						)
   pipe.to(_UpperCAmelCase       )
   pipe.set_progress_bar_config(disable=_UpperCAmelCase       )
   pipe.enable_attention_slicing()

   UpperCAmelCase__								=			"""Face of a yellow cat, high resolution, sitting on a park bench"""

   UpperCAmelCase__								=			torch.manual_seed(0       )
   UpperCAmelCase__								=			pipe(
       prompt=_UpperCAmelCase  ,						image=_UpperCAmelCase  ,						mask_image=_UpperCAmelCase  ,						generator=_UpperCAmelCase  ,						output_type="""np"""  ,						)
   UpperCAmelCase__								=			output.images[0]

   assert image.shape == (5_12, 5_12, 3)
   assert np.abs(expected_image - image       ).max() < 5E-1



  def 							SCREAMING_SNAKE_CASE__	(  self			:		str       ):






   """simple docstring"""






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

   UpperCAmelCase__								=			load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/sd2-inpaint/init_image.png"""       )
   UpperCAmelCase__								=			load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""       )

   UpperCAmelCase__								=			"""stabilityai/stable-diffusion-2-inpainting"""
   UpperCAmelCase__								=			PNDMScheduler.from_pretrained(_UpperCAmelCase  ,						subfolder="""scheduler"""       )
   UpperCAmelCase__								=			StableDiffusionInpaintPipeline.from_pretrained(
       _UpperCAmelCase  ,						safety_checker=_UpperCAmelCase  ,						scheduler=_UpperCAmelCase  ,						torch_dtype=torch.floataa  ,						)
   pipe.to(_UpperCAmelCase       )
   pipe.set_progress_bar_config(disable=_UpperCAmelCase       )
   pipe.enable_attention_slicing(1       )
   pipe.enable_sequential_cpu_offload()

   UpperCAmelCase__								=			"""Face of a yellow cat, high resolution, sitting on a park bench"""

   UpperCAmelCase__								=			torch.manual_seed(0       )
   UpperCAmelCase__								=			pipe(
       prompt=_UpperCAmelCase  ,						image=_UpperCAmelCase  ,						mask_image=_UpperCAmelCase  ,						generator=_UpperCAmelCase  ,						num_inference_steps=2  ,						output_type="""np"""  ,						)

   UpperCAmelCase__								=			torch.cuda.max_memory_allocated()
   # make sure that less than 2.65 GB is allocated
   assert mem_bytes < 2.65 * 10**9







'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase_					= {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					= ["""MBartTokenizer"""]

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					= [
          """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MBartForCausalLM""",
          """MBartForConditionalGeneration""",
          """MBartForQuestionAnswering""",
          """MBartForSequenceClassification""",
          """MBartModel""",
          """MBartPreTrainedModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					= [
          """TFMBartForConditionalGeneration""",
          """TFMBartModel""",
          """TFMBartPreTrainedModel""",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					= [
          """FlaxMBartForConditionalGeneration""",
          """FlaxMBartForQuestionAnswering""",
          """FlaxMBartForSequenceClassification""",
          """FlaxMBartModel""",
          """FlaxMBartPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mbart import MBartTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mbart_fast import MBartTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mbart import (
                MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
                MBartForCausalLM,
                MBartForConditionalGeneration,
                MBartForQuestionAnswering,
                MBartForSequenceClassification,
                MBartModel,
                MBartPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_mbart import (
                FlaxMBartForConditionalGeneration,
                FlaxMBartForQuestionAnswering,
                FlaxMBartForSequenceClassification,
                FlaxMBartModel,
                FlaxMBartPreTrainedModel,
            )

else:
      import sys

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



