"""simple docstring"""






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
       from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__lowerCAmelCase    :       List[Any]					     =logging.get_logger(__name__)

__lowerCAmelCase    :       List[str]					     ={
    """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
    ),
    """microsoft/deberta-v2-xxlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
    ),
}





class  _A						(       lowerCAmelCase	):
      snake_case__			:   Any							  =	'deberta-v2'

      def __init__(						self		,       __lowerCAmelCase=12_8100		,       __lowerCAmelCase=1536		,       __lowerCAmelCase=24		,       __lowerCAmelCase=24		,       __lowerCAmelCase=6144		,       __lowerCAmelCase="gelu"		,       __lowerCAmelCase=0.1		,       __lowerCAmelCase=0.1		,       __lowerCAmelCase=512		,       __lowerCAmelCase=0		,       __lowerCAmelCase=0.0_2		,       __lowerCAmelCase=1E-7		,       __lowerCAmelCase=False		,       __lowerCAmelCase=-1		,       __lowerCAmelCase=0		,       __lowerCAmelCase=True		,       __lowerCAmelCase=None		,       __lowerCAmelCase=0		,       __lowerCAmelCase="gelu"		,       **__lowerCAmelCase		,       ):







            """simple docstring"""



            super().__init__(**__lowerCAmelCase						)

            lowercase														=	hidden_size
            lowercase														=	num_hidden_layers
            lowercase														=	num_attention_heads
            lowercase														=	intermediate_size
            lowercase														=	hidden_act
            lowercase														=	hidden_dropout_prob
            lowercase														=	attention_probs_dropout_prob
            lowercase														=	max_position_embeddings
            lowercase														=	type_vocab_size
            lowercase														=	initializer_range
            lowercase														=	relative_attention
            lowercase														=	max_relative_positions
            lowercase														=	pad_token_id
            lowercase														=	position_biased_input

            # Backwards compatibility
            if type(__lowerCAmelCase						) == str:
                  lowercase														=	[x.strip() for x in pos_att_type.lower().split("""|"""						)]

            lowercase														=	pos_att_type
            lowercase														=	vocab_size
            lowercase														=	layer_norm_eps

            lowercase														=	kwargs.get("""pooler_hidden_size"""		,       __lowerCAmelCase						)
            lowercase														=	pooler_dropout
            lowercase														=	pooler_hidden_act






class  _A						(       lowerCAmelCase	):

      @property
      def 				A__       (						self						):







            """simple docstring"""



            if self.task == "multiple-choice":
                  lowercase														=	{0: """batch""", 1: """choice""", 2: """sequence"""}
            else:
                  lowercase														=	{0: """batch""", 1: """sequence"""}
            if self._config.type_vocab_size > 0:
                  return OrderedDict(
                      [("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)]						)
            else:
                  return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)]						)

      @property
      def 				A__       (						self						):







            """simple docstring"""



            return 12







      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase = -1		,       __lowerCAmelCase = -1		,       __lowerCAmelCase = -1		,       __lowerCAmelCase = False		,       __lowerCAmelCase = None		,       __lowerCAmelCase = 3		,       __lowerCAmelCase = 40		,       __lowerCAmelCase = 40		,       __lowerCAmelCase = None		,       ):







            """simple docstring"""



            lowercase														=	super().generate_dummy_inputs(preprocessor=__lowerCAmelCase		,       framework=__lowerCAmelCase						)
            if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                  del dummy_inputs["token_type_ids"]
            return dummy_inputs




"""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
__lowerCAmelCase    :       Union[str, Any]					     =0B1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
__lowerCAmelCase    :       Optional[int]					     =[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]





class  _A						:

      def __init__(						self						):







            """simple docstring"""



            lowercase														=	WATERMARK_BITS
            lowercase														=	WatermarkEncoder()

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







      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



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

            lowercase														=	(255 * (images / 2 + 0.5)).cpu().permute(0		,       2		,       3		,       1						).float().numpy()

            lowercase														=	[self.encoder.encode(__lowerCAmelCase		,       """dwtDct"""						) for image in images]

            lowercase														=	torch.from_numpy(np.array(__lowerCAmelCase						)						).permute(0		,       3		,       1		,       2						)

            lowercase														=	torch.clamp(2 * (images / 255 - 0.5)		,       min=-1.0		,       max=1.0						)
            return images



