





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block







@dataclass
class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			42







class      lowerCamelCase(						__snake_case			, __snake_case	):
     '''simple docstring'''


     @register_to_config
     def __init__(    self				,     snake_case_ = 6_5536				,     snake_case_ = None				,     snake_case_ = 2				,     snake_case_ = 2				,     snake_case_ = 0				,     snake_case_ = "fourier"				,     snake_case_ = True				,     snake_case_ = False				,     snake_case_ = 0.0				,     snake_case_ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")				,     snake_case_ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")				,     snake_case_ = "UNetMidBlock1D"				,     snake_case_ = None				,     snake_case_ = (32, 32, 64)				,     snake_case_ = None				,     snake_case_ = 8				,     snake_case_ = 1				,     snake_case_ = False				,     ):
         super().__init__()
         _A     =							sample_size

         # time
         if time_embedding_type == "fourier":
             _A     =							GaussianFourierProjection(
                 embedding_size=8				,     set_W_to_weight=snake_case_				,     log=snake_case_				,     flip_sin_to_cos=snake_case_							)
             _A     =							2 * block_out_channels[0]
         elif time_embedding_type == "positional":
             _A     =							Timesteps(
                 block_out_channels[0]				,     flip_sin_to_cos=snake_case_				,     downscale_freq_shift=snake_case_							)
             _A     =							block_out_channels[0]

         if use_timestep_embedding:
             _A     =							block_out_channels[0] * 4
             _A     =							TimestepEmbedding(
                 in_channels=snake_case_				,     time_embed_dim=snake_case_				,     act_fn=snake_case_				,     out_dim=block_out_channels[0]				,     )

         _A     =							nn.ModuleList([]							)
         _A     =							None
         _A     =							nn.ModuleList([]							)
         _A     =							None

         # down
         _A     =							in_channels
         for i, down_block_type in enumerate(snake_case_							):
             _A     =							output_channel
             _A     =							block_out_channels[i]

             if i == 0:
                 input_channel += extra_in_channels

             _A     =							i == len(snake_case_							) - 1

             _A     =							get_down_block(
                 snake_case_				,     num_layers=snake_case_				,     in_channels=snake_case_				,     out_channels=snake_case_				,     temb_channels=block_out_channels[0]				,     add_downsample=not is_final_block or downsample_each_block				,     )
             self.down_blocks.append(snake_case_							)

         # mid
         _A     =							get_mid_block(
             snake_case_				,     in_channels=block_out_channels[-1]				,     mid_channels=block_out_channels[-1]				,     out_channels=block_out_channels[-1]				,     embed_dim=block_out_channels[0]				,     num_layers=snake_case_				,     add_downsample=snake_case_				,     )

         # up
         _A     =							list(reversed(snake_case_							)							)
         _A     =							reversed_block_out_channels[0]
         if out_block_type is None:
             _A     =							out_channels
         else:
             _A     =							block_out_channels[0]

         for i, up_block_type in enumerate(snake_case_							):
             _A     =							output_channel
             _A     =							(
                 reversed_block_out_channels[i + 1] if i < len(snake_case_							) - 1 else final_upsample_channels
             )

             _A     =							i == len(snake_case_							) - 1

             _A     =							get_up_block(
                 snake_case_				,     num_layers=snake_case_				,     in_channels=snake_case_				,     out_channels=snake_case_				,     temb_channels=block_out_channels[0]				,     add_upsample=not is_final_block				,     )
             self.up_blocks.append(snake_case_							)
             _A     =							output_channel

         # out
         _A     =							norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4				,     32							)
         _A     =							get_out_block(
             out_block_type=snake_case_				,     num_groups_out=snake_case_				,     embed_dim=block_out_channels[0]				,     out_channels=snake_case_				,     act_fn=snake_case_				,     fc_dim=block_out_channels[-1] // 4				,     )




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_ = True				,     ):
         _A     =							timestep
         if not torch.is_tensor(snake_case_							):
             _A     =							torch.tensor([timesteps]				,     dtype=torch.long				,     device=sample.device							)
         elif torch.is_tensor(snake_case_							) and len(timesteps.shape							) == 0:
             _A     =							timesteps[None].to(sample.device							)

         _A     =							self.time_proj(snake_case_							)
         if self.config.use_timestep_embedding:
             _A     =							self.time_mlp(snake_case_							)
         else:
             _A     =							timestep_embed[..., None]
             _A     =							timestep_embed.repeat([1, 1, sample.shape[2]]							).to(sample.dtype							)
             _A     =							timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])							)

         # 2. down
         _A     =							()
         for downsample_block in self.down_blocks:
             _A,			_A     =							downsample_block(hidden_states=snake_case_				,     temb=snake_case_							)
             down_block_res_samples += res_samples

         # 3. mid
         if self.mid_block:
             _A     =							self.mid_block(snake_case_				,     snake_case_							)

         # 4. up
         for i, upsample_block in enumerate(self.up_blocks							):
             _A     =							down_block_res_samples[-1:]
             _A     =							down_block_res_samples[:-1]
             _A     =							upsample_block(snake_case_				,     res_hidden_states_tuple=snake_case_				,     temb=snake_case_							)

         # 5. post-process
         if self.out_block:
             _A     =							self.out_block(snake_case_				,     snake_case_							)

         if not return_dict:
             return (sample,)

         return UNetaDOutput(sample=snake_case_							)












from collections.abc import Callable





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> float:




    """simple docstring"""




    _A     =							a
    _A     =							b
    if function(_SCREAMING_SNAKE_CASE				) == 0:  # one of the a or b is a root for the function
        return a
    elif function(_SCREAMING_SNAKE_CASE				) == 0:
        return b
    elif (
        function(_SCREAMING_SNAKE_CASE				) * function(_SCREAMING_SNAKE_CASE				) > 0
    ):  # if none of these are root and they are both positive or negative,
        # then this algorithm can't find the root
        raise ValueError('could not find root in given interval.'				)
    else:
        _A     =							start + (end - start) / 2.0
        while abs(start - mid				) > 10**-7:  # until precisely equals to 10^-7
            if function(_SCREAMING_SNAKE_CASE				) == 0:
                return mid
            elif function(_SCREAMING_SNAKE_CASE				) * function(_SCREAMING_SNAKE_CASE				) < 0:
                _A     =							mid
            else:
                _A     =							mid
            _A     =							start + (end - start) / 2.0
        return mid





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> float:




    """simple docstring"""




    return x**3 - 2 * x - 5


if __name__ == "__main__":
 print(bisection(f, 1, 1_000))

 import doctest

 doctest.testmod()





