





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class SCREAMING_SNAKE_CASE_ (					lowercase_							,       unittest.TestCase   ):





					"""simple docstring"""


					A__				 =    KandinskyVaaInpaintPipeline
					A__				 =    ["image_embeds", "negative_image_embeds", "image", "mask_image"]
					A__				 =    [
					    "image_embeds",
					    "negative_image_embeds",
					    "image",
					    "mask_image",
					]
					A__				 =    [
					    "generator",
					    "height",
					    "width",
					    "latents",
					    "guidance_scale",
					    "num_inference_steps",
					    "return_dict",
					    "guidance_scale",
					    "num_images_per_prompt",
					    "output_type",
					    "return_dict",
					]
					A__				 =    False





					@property
					def 					__magic_name__ ( self	):
										return 32





					@property
					def 					__magic_name__ ( self	):
										return 32





					@property
					def 					__magic_name__ ( self	):
										return self.time_input_dim





					@property
					def 					__magic_name__ ( self	):
										return self.time_input_dim * 4





					@property
					def 					__magic_name__ ( self	):
										return 100





					@property
					def 					__magic_name__ ( self	):
										torch.manual_seed(0	)

										lowerCamelCase__       =       {
										    "in_channels": 9,
										    # Out channels is double in channels because predicts mean and variance
										    "out_channels": 8,
										    "addition_embed_type": "image",
										    "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
										    "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
										    "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
										    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
										    "layers_per_block": 1,
										    "encoder_hid_dim": self.text_embedder_hidden_size,
										    "encoder_hid_dim_type": "image_proj",
										    "cross_attention_dim": self.cross_attention_dim,
										    "attention_head_dim": 4,
										    "resnet_time_scale_shift": "scale_shift",
										    "class_embed_type": None,
										}

										lowerCamelCase__       =       UNetaDConditionModel(**_lowerCAmelCase	)
										return model





					@property
					def 					__magic_name__ ( self	):
										return {
										    "block_out_channels": [32, 64],
										    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
										    "in_channels": 3,
										    "latent_channels": 4,
										    "layers_per_block": 1,
										    "norm_num_groups": 8,
										    "norm_type": "spatial",
										    "num_vq_embeddings": 12,
										    "out_channels": 3,
										    "up_block_types": [
										        "AttnUpDecoderBlock2D",
										        "UpDecoderBlock2D",
										    ],
										    "vq_embed_dim": 4,
										}





					@property
					def 					__magic_name__ ( self	):
										torch.manual_seed(0	)
										lowerCamelCase__       =       VQModel(**self.dummy_movq_kwargs	)
										return model





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.dummy_unet
										lowerCamelCase__       =       self.dummy_movq

										lowerCamelCase__       =       DDIMScheduler(
										    num_train_timesteps=1000	,  beta_schedule="linear"	,  beta_start=0.00_085	,  beta_end=0.012	,  clip_sample=_lowerCAmelCase	,  set_alpha_to_one=_lowerCAmelCase	,  steps_offset=1	,  prediction_type="epsilon"	,  thresholding=_lowerCAmelCase	,  )

										lowerCamelCase__       =       {
										    "unet": unet,
										    "scheduler": scheduler,
										    "movq": movq,
										}

										return components





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase=0	):
										lowerCamelCase__       =       floats_tensor((1, self.text_embedder_hidden_size)	,  rng=random.Random(_lowerCAmelCase	)	).to(_lowerCAmelCase	)
										lowerCamelCase__       =       floats_tensor((1, self.text_embedder_hidden_size)	,  rng=random.Random(seed + 1	)	).to(
										    _lowerCAmelCase	)
										# create init_image
										lowerCamelCase__       =       floats_tensor((1, 3, 64, 64)	,  rng=random.Random(_lowerCAmelCase	)	).to(_lowerCAmelCase	)
										lowerCamelCase__       =       image.cpu().permute(0	,  2	,  3	,  1	)[0]
										lowerCamelCase__       =       Image.fromarray(np.uinta(_lowerCAmelCase	)	).convert("RGB"	).resize((256, 256)	)
										# create mask
										lowerCamelCase__       =       np.ones((64, 64)	,  dtype=np.floataa	)
										lowerCamelCase__       =       0

										if str(_lowerCAmelCase	).startswith("mps"	):
															lowerCamelCase__       =       torch.manual_seed(_lowerCAmelCase	)
										else:
															lowerCamelCase__       =       torch.Generator(device=_lowerCAmelCase	).manual_seed(_lowerCAmelCase	)
										lowerCamelCase__       =       {
										    "image": init_image,
										    "mask_image": mask,
										    "image_embeds": image_embeds,
										    "negative_image_embeds": negative_image_embeds,
										    "generator": generator,
										    "height": 64,
										    "width": 64,
										    "num_inference_steps": 2,
										    "guidance_scale": 4.0,
										    "output_type": "np",
										}
										return inputs





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       "cpu"

										lowerCamelCase__       =       self.get_dummy_components()

										lowerCamelCase__       =       self.pipeline_class(**_lowerCAmelCase	)
										lowerCamelCase__       =       pipe.to(_lowerCAmelCase	)

										pipe.set_progress_bar_config(disable=_lowerCAmelCase	)

										lowerCamelCase__       =       pipe(**self.get_dummy_inputs(_lowerCAmelCase	)	)
										lowerCamelCase__       =       output.images

										lowerCamelCase__       =       pipe(
										    **self.get_dummy_inputs(_lowerCAmelCase	)	,  return_dict=_lowerCAmelCase	,  )[0]

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

										print(F"image.shape {image.shape}"	)

										assert image.shape == (1, 64, 64, 3)

										lowerCamelCase__       =       np.array(
										    [0.50_775_903, 0.49_527_195, 0.48_824_543, 0.50_192_237, 0.48_644_906, 0.49_373_814, 0.4_780_598, 0.47_234_827, 0.48_327_848]	)

										assert (
										    np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
										), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
										assert (
										    np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1E-2
										), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"






					def 					__magic_name__ ( self	):
										super().test_inference_batch_single_identical(expected_max_diff=3E-3	)

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





					"""simple docstring"""


					def 					__magic_name__ ( self	):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()






					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"	)

										lowerCamelCase__       =       load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"	)
										lowerCamelCase__       =       np.ones((768, 768)	,  dtype=np.floataa	)
										lowerCamelCase__       =       0

										lowerCamelCase__       =       "a hat"

										lowerCamelCase__       =       KandinskyVaaPriorPipeline.from_pretrained(
										    "kandinsky-community/kandinsky-2-2-prior"	,  torch_dtype=torch.floataa	)
										pipe_prior.to(_lowerCAmelCase	)

										lowerCamelCase__       =       KandinskyVaaInpaintPipeline.from_pretrained(
										    "kandinsky-community/kandinsky-2-2-decoder-inpaint"	,  torch_dtype=torch.floataa	)
										lowerCamelCase__       =       pipeline.to(_lowerCAmelCase	)
										pipeline.set_progress_bar_config(disable=_lowerCAmelCase	)

										lowerCamelCase__       =       torch.Generator(device="cpu"	).manual_seed(0	)
										lowerCamelCase__    ,		lowerCamelCase__       =       pipe_prior(
										    _lowerCAmelCase	,  generator=_lowerCAmelCase	,  num_inference_steps=5	,  negative_prompt=""	,  ).to_tuple()

										lowerCamelCase__       =       pipeline(
										    image=_lowerCAmelCase	,  mask_image=_lowerCAmelCase	,  image_embeds=_lowerCAmelCase	,  negative_image_embeds=_lowerCAmelCase	,  generator=_lowerCAmelCase	,  num_inference_steps=100	,  height=768	,  width=768	,  output_type="np"	,  )

										lowerCamelCase__       =       output.images[0]

										assert image.shape == (768, 768, 3)

										assert_mean_pixel_difference(_lowerCAmelCase	,  _lowerCAmelCase	)








import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin

class SCREAMING_SNAKE_CASE_ (					unittest.TestCase							,       lowercase_   ):





					"""simple docstring"""


					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       load_tool("text-classification"	)
										self.tool.setup()
										lowerCamelCase__       =       load_tool("text-classification"	,  remote=_lowerCAmelCase	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.tool("That's quite cool"	,  ["positive", "negative"]	)
										self.assertEqual(_lowerCAmelCase	,  "positive"	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.remote_tool("That's quite cool"	,  ["positive", "negative"]	)
										self.assertEqual(_lowerCAmelCase	,  "positive"	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.tool(text="That's quite cool"	,  labels=["positive", "negative"]	)
										self.assertEqual(_lowerCAmelCase	,  "positive"	)






					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.remote_tool(text="That's quite cool"	,  labels=["positive", "negative"]	)
										self.assertEqual(_lowerCAmelCase	,  "positive"	)

