'''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
_A								=   0B1_0_1_1_0_0_1_1_1_1_1_0_1_1_0_0_1_0_0_1_0_0_0_0_0_1_1_1_1_0_1_1_1_0_1_1_0_0_0_1_1_0_0_1_1_1_1_0
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
_A								=   [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]


class       UpperCAmelCase__  :






   """simple docstring"""
   def __init__(self				)    ->     Union[str, Any]:
        lowercase_					:       Any     				=					WATERMARK_BITS
        lowercase_					:       List[str]     				=					WatermarkEncoder()

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





   def 		_lowerCamelCase (self       ,				_a				)    ->     Dict:
        # can't encode images that are smaller than 256
        if images.shape[-1] < 256:
             return images

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

        lowercase_					:       Optional[Any]     				=					[self.encoder.encode(_a       ,				'dwtDct'				) for image in images]

        lowercase_					:       List[Any]     				=					torch.from_numpy(np.array(_a				)				).permute(0       ,				3       ,				1       ,				2				)

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





'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A								=   logging.get_logger(__name__)

_A								=   {
    'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json',
}


class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   A							:       Any     =    '''mra'''
   def __init__(self       ,				_a=50_265       ,				_a=768       ,				_a=12       ,				_a=12       ,				_a=3_072       ,				_a="gelu"       ,				_a=0.1       ,				_a=0.1       ,				_a=512       ,				_a=1       ,				_a=0.02       ,				_a=1e-5       ,				_a="absolute"       ,				_a=4       ,				_a="full"       ,				_a=0       ,				_a=0       ,				_a=1       ,				_a=0       ,				_a=2       ,				**_a       ,				)    ->     int:
        super().__init__(pad_token_id=_a       ,				bos_token_id=_a       ,				eos_token_id=_a       ,				**_a				)

        lowercase_					:       Union[str, Any]     				=					vocab_size
        lowercase_					:       List[str]     				=					max_position_embeddings
        lowercase_					:       Optional[Any]     				=					hidden_size
        lowercase_					:       Optional[int]     				=					num_hidden_layers
        lowercase_					:       Dict     				=					num_attention_heads
        lowercase_					:       Optional[int]     				=					intermediate_size
        lowercase_					:       Dict     				=					hidden_act
        lowercase_					:       str     				=					hidden_dropout_prob
        lowercase_					:       int     				=					attention_probs_dropout_prob
        lowercase_					:       str     				=					initializer_range
        lowercase_					:       Union[str, Any]     				=					type_vocab_size
        lowercase_					:       Any     				=					layer_norm_eps
        lowercase_					:       Union[str, Any]     				=					position_embedding_type
        lowercase_					:       Any     				=					block_per_row
        lowercase_					:       Optional[int]     				=					approx_mode
        lowercase_					:       int     				=					initial_prior_first_n_blocks
        lowercase_					:       str     				=					initial_prior_diagonal_n_blocks




