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      A_	(a_					,							a_      ):
	def __init__(   self		):






				'''simple docstring'''







				super().__init__()

				self.register_buffer('''watermark_image'''							, torch.zeros((6_2, 6_2, 4)		)		)
				UpperCAmelCase       		=    None







	def 					_lowercase    (   self							, _A							, _A=None		):






				'''simple docstring'''







				UpperCAmelCase       		=    images[0].height
				UpperCAmelCase       		=    images[0].width

				UpperCAmelCase       		=    sample_size or h

				UpperCAmelCase       		=    min(h / sample_size							, w / sample_size		)
				UpperCAmelCase		,      UpperCAmelCase       		=    (int(h / coef		), int(w / coef		)) if coef < 1 else (h, w)

				UpperCAmelCase		,      UpperCAmelCase       		=    1_0_2_4**2, img_w * img_h
				UpperCAmelCase       		=    (Sa / Sa) ** 0.5
				UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase       		=    int(K * 6_2		), img_w - int(1_4 * K		), img_h - int(1_4 * K		)

				if self.watermark_image_as_pil is None:
							UpperCAmelCase       		=    self.watermark_image.to(torch.uinta		).cpu().numpy()
							UpperCAmelCase       		=    Image.fromarray(_A							, mode='''RGBA'''		)
							UpperCAmelCase       		=    watermark_image

				UpperCAmelCase       		=    self.watermark_image_as_pil.resize(
				    (wm_size, wm_size)							, PIL_INTERPOLATION['''bicubic''']							, reducing_gap=_A		)

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

				return images










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


__A    :   Any          =       {}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A    :   Union[str, Any]          =       ["CpmTokenizer"]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A    :   List[Any]          =       ["CpmTokenizerFast"]


if TYPE_CHECKING:
				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_cpm import CpmTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_cpm_fast import CpmTokenizerFast

else:
				import sys

				__A    :   str          =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



