



"""simple docstring"""

import numpy as np
import torch
from imwatermark import WatermarkEncoder


# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
lowercase__			   =  0b10_11_00_11_11_10_11_00_10_01_00_00_01_11_10_11_10_11_00_01_10_01_11_10
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
lowercase__			   =  [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]




class SCREAMING_SNAKE_CASE__					:



						def __init__(self							):





													'''simple docstring'''

													__a		:		Any									=       WATERMARK_BITS
													__a		:		List[str]									=       WatermarkEncoder()

													self.encoder.set_watermark("""bits"""						,     self.watermark							)





						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

													if images.shape[-1] < 256:
																				return images

													__a		:		List[Any]									=       (255 * (images / 2 + 0.5)).cpu().permute(0						,     2						,     3						,     1							).float().numpy()

													__a		:		List[str]									=       [self.encoder.encode(_lowercase						,     """dwtDct"""							) for image in images]

													__a		:		List[str]									=       torch.from_numpy(np.array(_lowercase							)							).permute(0						,     3						,     1						,     2							)

													__a		:		Optional[Any]									=       torch.clamp(2 * (images / 255 - 0.5)						,     min=-1.0						,     max=1.0							)
													return images






"""simple docstring"""

from typing import TYPE_CHECKING

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


lowercase__			   =  {
    "configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}

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

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase__			   =  [
	    "LlamaForCausalLM",
	    "LlamaModel",
	    "LlamaPreTrainedModel",
	    "LlamaForSequenceClassification",
	]


if TYPE_CHECKING:
	from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

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

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

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
	import sys

	lowercase__			   =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

