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					(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ,      unittest.TestCase					):
  """simple docstring"""







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







  def    __A     (							self :       Optional[Any]   )  ->	List[str]:
        torch.manual_seed(0   )
        SCREAMING_SNAKE_CASE_          =  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=__magic_name__  ,     )
        SCREAMING_SNAKE_CASE_          =  PNDMScheduler(skip_prk_steps=__magic_name__   )
        torch.manual_seed(0   )
        SCREAMING_SNAKE_CASE_          =  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=128  ,     )
        torch.manual_seed(0   )
        SCREAMING_SNAKE_CASE_          =  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=1_000  ,     hidden_act="gelu"  ,     projection_dim=512  ,     )
        SCREAMING_SNAKE_CASE_          =  CLIPTextModel(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"   )

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







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       Tuple  ,     __magic_name__ :       Tuple=0   )  ->	str:
        # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
        SCREAMING_SNAKE_CASE_          =  floats_tensor((1, 3, 32, 32)  ,     rng=random.Random(__magic_name__   )   ).to(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  image.cpu().permute(0  ,     2  ,     3  ,     1   )[0]
        SCREAMING_SNAKE_CASE_          =  Image.fromarray(np.uinta(__magic_name__   )   ).convert("RGB"   ).resize((64, 64)   )
        SCREAMING_SNAKE_CASE_          =  Image.fromarray(np.uinta(image + 4   )   ).convert("RGB"   ).resize((64, 64)   )
        if str(__magic_name__   ).startswith("mps"   ):
              SCREAMING_SNAKE_CASE_          =  torch.manual_seed(__magic_name__   )
        else:
              SCREAMING_SNAKE_CASE_          =  torch.Generator(device=__magic_name__   ).manual_seed(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  {
            "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    __A     (							self :       Any   )  ->	List[str]:
        SCREAMING_SNAKE_CASE_          =  "cpu"  # ensure determinism for the device-dependent torch.Generator
        SCREAMING_SNAKE_CASE_          =  self.get_dummy_components()
        SCREAMING_SNAKE_CASE_          =  StableDiffusionInpaintPipeline(**__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  sd_pipe.to(__magic_name__   )
        sd_pipe.set_progress_bar_config(disable=__magic_name__   )

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

        assert image.shape == (1, 64, 64, 3)
        SCREAMING_SNAKE_CASE_          =  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    __A     (							self :       str   )  ->	List[Any]:
        super().test_inference_batch_single_identical(expected_max_diff=3e-3   )



@slow
@require_torch_gpu
class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







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







  def    __A     (							self :       Optional[Any]   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/sd2-inpaint/init_image.png"   )
        SCREAMING_SNAKE_CASE_          =  load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"   )
        SCREAMING_SNAKE_CASE_          =  load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
            "/yellow_cat_sitting_on_a_park_bench.npy"   )

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

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

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

        assert image.shape == (512, 512, 3)
        assert np.abs(expected_image - image   ).max() < 9e-3







  def    __A     (							self :       Any   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/sd2-inpaint/init_image.png"   )
        SCREAMING_SNAKE_CASE_          =  load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"   )
        SCREAMING_SNAKE_CASE_          =  load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
            "/yellow_cat_sitting_on_a_park_bench_fp16.npy"   )

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

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

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

        assert image.shape == (512, 512, 3)
        assert np.abs(expected_image - image   ).max() < 5e-1







  def    __A     (							self :       Optional[int]   )  ->	List[str]:
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

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

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

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

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

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



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
      import torch

      from transformers import XLMRobertaModel


@require_sentencepiece
@require_tokenizers
@require_torch
class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  @slow
  def    __A     (							self :       Dict   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  XLMRobertaModel.from_pretrained("xlm-roberta-base"   )
        SCREAMING_SNAKE_CASE_          =  torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]   )
        # The dog is cute and lives in the garden house

        SCREAMING_SNAKE_CASE_          =  torch.Size((1, 12, 768)   )  # batch_size, sequence_length, embedding_vector_dim
        SCREAMING_SNAKE_CASE_          =  torch.tensor(
            [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]   )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )["last_hidden_state"].detach()
        self.assertEqual(output.shape  ,     __magic_name__   )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]  ,     __magic_name__  ,     atol=1e-3   )   )







  @slow
  def    __A     (							self :       Tuple   )  ->	List[str]:
        SCREAMING_SNAKE_CASE_          =  XLMRobertaModel.from_pretrained("xlm-roberta-large"   )
        SCREAMING_SNAKE_CASE_          =  torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]   )
        # The dog is cute and lives in the garden house

        SCREAMING_SNAKE_CASE_          =  torch.Size((1, 12, 1_024)   )  # batch_size, sequence_length, embedding_vector_dim
        SCREAMING_SNAKE_CASE_          =  torch.tensor(
            [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]   )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )["last_hidden_state"].detach()
        self.assertEqual(output.shape  ,     __magic_name__   )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]  ,     __magic_name__  ,     atol=1e-3   )   )


