import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


__lowercase										= logging.get_logger(__name__)  # pylint: disable=invalid-name


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    def __init__(     self			,			__lowercase			,			__lowercase=768)	->     str:
        super().__init__(__lowercase)
        __UpperCamelCase					:str        =		proj_size

        __UpperCamelCase					:str        =		CLIPVisionModel(__lowercase)
        __UpperCamelCase					:Optional[int]        =		PaintByExampleMapper(__lowercase)
        __UpperCamelCase					:List[str]        =		nn.LayerNorm(config.hidden_size)
        __UpperCamelCase					:Optional[Any]        =		nn.Linear(config.hidden_size			,			self.proj_size)

        # uncondition for scaling
        __UpperCamelCase					:int        =		nn.Parameter(torch.randn((1, 1, self.proj_size)))







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase=False)	->     Optional[Any]:
        __UpperCamelCase					:Optional[Any]        =		self.model(pixel_values=__lowercase)
        __UpperCamelCase					:Optional[int]        =		clip_output.pooler_output
        __UpperCamelCase					:int        =		self.mapper(latent_states[:, None])
        __UpperCamelCase					:Tuple        =		self.final_layer_norm(__lowercase)
        __UpperCamelCase					:Union[str, Any]        =		self.proj_out(__lowercase)
        if return_uncond_vector:
            return latent_states, self.uncond_vector

        return latent_states


class      lowerCamelCase_					(       nn.Module			):






    '''simple docstring'''






    def __init__(     self			,			__lowercase)	->     Dict:
        super().__init__()
        __UpperCamelCase					:Optional[int]        =		(config.num_hidden_layers + 1) // 5
        __UpperCamelCase					:str        =		config.hidden_size
        __UpperCamelCase					:Dict        =		1
        __UpperCamelCase					:List[Any]        =		nn.ModuleList(
            [
                BasicTransformerBlock(__lowercase			,			__lowercase			,			__lowercase			,			activation_fn='''gelu'''			,			attention_bias=__lowercase)
                for _ in range(__lowercase)
            ])







    def       UpperCamelCase__       (     self			,			__lowercase)	->     int:
        for block in self.blocks:
            __UpperCamelCase					:Optional[Any]        =		block(__lowercase)

        return hidden_states






def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    if not grid or not grid[0]:
        raise TypeError('''The grid does not contain the appropriate information'''     )

    for cell_n in range(1  ,      len(grid[0]     )     ):
        grid[0][cell_n] += grid[0][cell_n - 1]
    __UpperCamelCase					:Union[str, Any]        =		grid[0]

    for row_n in range(1  ,      len(SCREAMING_SNAKE_CASE     )     ):
        __UpperCamelCase					:Optional[int]        =		grid[row_n]
        __UpperCamelCase					:Dict        =		fill_row(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
        __UpperCamelCase					:Any        =		grid[row_n]

    return grid[-1][-1]



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    current_row[0] += row_above[0]
    for cell_n in range(1  ,      len(SCREAMING_SNAKE_CASE     )     ):
        current_row[cell_n] += min(current_row[cell_n - 1]  ,      row_above[cell_n]     )

    return current_row


if __name__ == "__main__":
    import doctest

    doctest.testmod()





