
"""simple docstring"""
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      _UpperCAmelCase				(					_snake_case):
    __lowercase							: torch.FloatTensor






class      _UpperCAmelCase				(					_snake_case		,					_snake_case):
    @register_to_config
    def __init__(    self						,    snake_case_ = 6_55_36						,    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__()
      _snake_case     :						Optional[Any]						=   sample_size

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

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

      _snake_case     :						int						=   nn.ModuleList([]     )
      _snake_case     :						Any						=   None
      _snake_case     :						str						=   nn.ModuleList([]     )
      _snake_case     :						Tuple						=   None

      # down
      _snake_case     :						str						=   in_channels
      for i, down_block_type in enumerate(snake_case_     ):
        _snake_case     :						Optional[int]						=   output_channel
        _snake_case     :						List[Any]						=   block_out_channels[i]

        if i == 0:
          input_channel += extra_in_channels

        _snake_case     :						Union[str, Any]						=   i == len(snake_case_     ) - 1

        _snake_case     :						Optional[Any]						=   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
      _snake_case     :						Any						=   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
      _snake_case     :						int						=   list(reversed(snake_case_     )     )
      _snake_case     :						List[Any]						=   reversed_block_out_channels[0]
      if out_block_type is None:
        _snake_case     :						Optional[Any]						=   out_channels
      else:
        _snake_case     :						List[str]						=   block_out_channels[0]

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

        _snake_case     :						str						=   i == len(snake_case_     ) - 1

        _snake_case     :						Union[str, Any]						=   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_     )
        _snake_case     :						Tuple						=   output_channel

      # out
      _snake_case     :						List[str]						=   norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4						,    32     )
      _snake_case     :						Any						=   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						,    ):
      _snake_case     :						Optional[int]						=   timestep
      if not torch.is_tensor(snake_case_     ):
        _snake_case     :						List[Any]						=   torch.tensor([timesteps]						,    dtype=torch.long						,    device=sample.device     )
      elif torch.is_tensor(snake_case_     ) and len(timesteps.shape     ) == 0:
        _snake_case     :						str						=   timesteps[None].to(sample.device     )

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

      # 2. down
      _snake_case     :						Optional[Any]						=   ()
      for downsample_block in self.down_blocks:
        _snake_case     :						str						=   downsample_block(hidden_states=snake_case_						,    temb=snake_case_     )
        down_block_res_samples += res_samples

      # 3. mid
      if self.mid_block:
        _snake_case     :						List[str]						=   self.mid_block(snake_case_						,    snake_case_     )

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

      # 5. post-process
      if self.out_block:
        _snake_case     :						List[Any]						=   self.out_block(snake_case_						,    snake_case_     )

      if not return_dict:
        return (sample,)

      return UNetaDOutput(sample=snake_case_     )



"""simple docstring"""
from __future__ import annotations






class      _UpperCAmelCase				:
    def __init__(    self						,    snake_case_						,    snake_case_     ):
      _snake_case			,					_snake_case     :						Dict						=   text, pattern
      _snake_case			,					_snake_case     :						int						=   len(snake_case_     ), len(snake_case_     )
    def 					lowerCamelCase__	(    self						,    snake_case_     ):

      for i in range(self.patLen - 1						,    -1						,    -1     ):
        if char == self.pattern[i]:
          return i
      return -1
    def 					lowerCamelCase__	(    self						,    snake_case_     ):

      for i in range(self.patLen - 1						,    -1						,    -1     ):
        if self.pattern[i] != self.text[current_pos + i]:
          return current_pos + i
      return -1


    def 					lowerCamelCase__	(    self     ):
      # searches pattern in text and returns index positions
      _snake_case     :						List[str]						=   []
      for i in range(self.textLen - self.patLen + 1     ):
        _snake_case     :						Union[str, Any]						=   self.mismatch_in_text(snake_case_     )
        if mismatch_index == -1:
          positions.append(snake_case_     )
        else:
          _snake_case     :						Tuple						=   self.match_in_pattern(self.text[mismatch_index]     )
          _snake_case     :						Tuple						=   (
              mismatch_index - match_index
          )  # shifting index lgtm [py/multiple-definition]
      return positions


_a      :			List[Any]					=							"""ABAABA"""
_a      :			str					=							"""AB"""
_a      :			List[Any]					=							BoyerMooreSearch(text, pattern)
_a      :			Any					=							bms.bad_character_heuristic()

if len(positions) == 0:
    print("""No match found""")
else:
    print("""Pattern found in following positions: """)
    print(positions)

