




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				(						lowerCamelCase  ,	lowerCamelCase    ):

					def __init__(							self   ):







						'''simple docstring'''




						super().__init__()

						self.register_buffer('watermark_image'					,			torch.zeros((62, 62, 4)   )   )
						lowerCamelCase_				:						Any				  =				None





					def       lowercase_				(							self					,			__lowercase					,			__lowercase=None   ):







						'''simple docstring'''




						lowerCamelCase_				:						Tuple				  =				images[0].height
						lowerCamelCase_				:						List[Any]				  =				images[0].width

						lowerCamelCase_				:						Tuple				  =				sample_size or h

						lowerCamelCase_				:						List[str]				  =				min(h / sample_size					,			w / sample_size   )
						lowerCamelCase_  ,     lowerCamelCase_				:						int				  =				(int(h / coef   ), int(w / coef   )) if coef < 1 else (h, w)

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

						if self.watermark_image_as_pil is None:
							lowerCamelCase_				:						Dict				  =				self.watermark_image.to(torch.uinta   ).cpu().numpy()
							lowerCamelCase_				:						str				  =				Image.fromarray(__lowercase					,			mode='RGBA'   )
							lowerCamelCase_				:						Optional[Any]				  =				watermark_image

						lowerCamelCase_				:						Tuple				  =				self.watermark_image_as_pil.resize(
						    (wm_size, wm_size)					,			PIL_INTERPOLATION['bicubic']					,			reducing_gap=__lowercase   )

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

						return images




"""simple docstring"""






from typing import TYPE_CHECKING

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


_lowerCamelCase  =	{
    'configuration_open_llama': ['OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenLlamaConfig'],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase  =	['LlamaTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase  =	['LlamaTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase  =	[
	    'OpenLlamaForCausalLM',
	    'OpenLlamaModel',
	    'OpenLlamaPreTrainedModel',
	    'OpenLlamaForSequenceClassification',
	]


if TYPE_CHECKING:
	from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from transformers import LlamaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from transformers import LlamaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_open_llama import (
		    OpenLlamaForCausalLM,
		    OpenLlamaForSequenceClassification,
		    OpenLlamaModel,
		    OpenLlamaPreTrainedModel,
		)


else:
	import sys

	_lowerCamelCase  =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
