





'''simple docstring'''

import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils


class      SCREAMING_SNAKE_CASE__      (   unittest.TestCase		):







					"""simple docstring"""






					@patch("diffusers.utils.hub_utils.get_full_repo_name"		)
					def 		lowerCamelCase_   (   self       :      Tuple		,	UpperCAmelCase_       :      Mock		):







							"""simple docstring"""
							__UpperCAmelCase	:							Dict     =     "full_repo_name"
							with TemporaryDirectory() as tmpdir:
									# Dummy args values
									__UpperCAmelCase	:							int     =     Mock()
									__UpperCAmelCase	:							List[str]     =     tmpdir
									__UpperCAmelCase	:							Union[str, Any]     =     0
									__UpperCAmelCase	:							Any     =     "hub_token"
									__UpperCAmelCase	:							Optional[int]     =     "dataset_name"
									__UpperCAmelCase	:							Any     =     0.01
									__UpperCAmelCase	:							Any     =     100_000
									__UpperCAmelCase	:							int     =     10_000
									__UpperCAmelCase	:							str     =     0.01
									__UpperCAmelCase	:							Union[str, Any]     =     0.02
									__UpperCAmelCase	:							Tuple     =     0.03
									__UpperCAmelCase	:							List[str]     =     0.0005
									__UpperCAmelCase	:							str     =     0.000001
									__UpperCAmelCase	:							str     =     1
									__UpperCAmelCase	:							int     =     10
									__UpperCAmelCase	:							int     =     0.001
									__UpperCAmelCase	:							int     =     0.1
									__UpperCAmelCase	:							Tuple     =     0.2
									__UpperCAmelCase	:							Optional[Any]     =     True

									# Model card mush be rendered and saved
									diffusers.utils.hub_utils.create_model_card(UpperCAmelCase_		,	model_name="model_name"		)
									self.assertTrue((Path(UpperCAmelCase_		) / "README.md").is_file()		)











'''simple docstring'''

from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION


class      SCREAMING_SNAKE_CASE__      (   snake_case__							,snake_case__		):







					"""simple docstring"""






					def __init__(   self       :      Any		):







							"""simple docstring"""
							super().__init__()

							self.register_buffer("watermark_image"		,	torch.zeros((62, 62, 4)		)		)
							__UpperCAmelCase	:							Optional[int]     =     None






					def 		lowerCamelCase_   (   self       :      List[Any]		,	UpperCAmelCase_       :      List[PIL.Image.Image]		,	UpperCAmelCase_       :      int=None		):







							"""simple docstring"""
							# copied from https://github.com/deep-floyd/IF/blob/b77482e36ca2031cb94dbca1001fc1e6400bf4ab/deepfloyd_if/modules/base.py#L287

							__UpperCAmelCase	:							int     =     images[0].height
							__UpperCAmelCase	:							Any     =     images[0].width

							__UpperCAmelCase	:							Union[str, Any]     =     sample_size or h

							__UpperCAmelCase	:							Dict     =     min(h / sample_size		,	w / sample_size		)
							__UpperCAmelCase  ,			__UpperCAmelCase	:							Optional[int]     =     (int(h / coef		), int(w / coef		)) if coef < 1 else (h, w)

							__UpperCAmelCase  ,			__UpperCAmelCase	:							Union[str, Any]     =     1_024**2, img_w * img_h
							__UpperCAmelCase	:							Optional[int]     =     (Sa / Sa) ** 0.5
							__UpperCAmelCase  ,			__UpperCAmelCase  ,			__UpperCAmelCase	:							str     =     int(K * 62		), img_w - int(14 * K		), img_h - int(14 * K		)

							if self.watermark_image_as_pil is None:
									__UpperCAmelCase	:							int     =     self.watermark_image.to(torch.uinta		).cpu().numpy()
									__UpperCAmelCase	:							Optional[int]     =     Image.fromarray(UpperCAmelCase_		,	mode="RGBA"		)
									__UpperCAmelCase	:							Optional[int]     =     watermark_image

							__UpperCAmelCase	:							List[str]     =     self.watermark_image_as_pil.resize(
							    (wm_size, wm_size)		,	PIL_INTERPOLATION["bicubic"]		,	reducing_gap=UpperCAmelCase_		)

							for pil_img in images:
									pil_img.paste(UpperCAmelCase_		,	box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)		,	mask=wm_img.split()[-1]		)

							return images




