

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, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer

@dataclass
class 			a					(		UpperCAmelCase			):
						_lowercase        =     4_2







class 			a					(		UpperCAmelCase							,	UpperCAmelCase			):


						@register_to_config
						def __init__(    self  ,   A_ = 3  ,   A_ = 3  ,   A_ = ("DownEncoderBlock2D",)  ,   A_ = ("UpDecoderBlock2D",)  ,   A_ = (64,)  ,   A_ = 1  ,   A_ = "silu"  ,   A_ = 3  ,   A_ = 32  ,   A_ = 256  ,   A_ = 32  ,   A_ = None  ,   A_ = 0.1_82_15  ,   A_ = "group"  ,   ):







										'''simple docstring'''


										super().__init__()

										# pass init params to Encoder
										_UpperCAmelCase    :						Optional[Any]							=      Encoder(
										    in_channels=A_  ,   out_channels=A_  ,   down_block_types=A_  ,   block_out_channels=A_  ,   layers_per_block=A_  ,   act_fn=A_  ,   norm_num_groups=A_  ,   double_z=A_  ,   )

										_UpperCAmelCase    :						Union[str, Any]							=      vq_embed_dim if vq_embed_dim is not None else latent_channels

										_UpperCAmelCase    :						Optional[Any]							=      nn.Convad(A_  ,   A_  ,   1		)
										_UpperCAmelCase    :						Any							=      VectorQuantizer(A_  ,   A_  ,   beta=0.25  ,   remap=A_  ,   sane_index_shape=A_		)
										_UpperCAmelCase    :						Any							=      nn.Convad(A_  ,   A_  ,   1		)

										# pass init params to Decoder
										_UpperCAmelCase    :						Tuple							=      Decoder(
										    in_channels=A_  ,   out_channels=A_  ,   up_block_types=A_  ,   block_out_channels=A_  ,   layers_per_block=A_  ,   act_fn=A_  ,   norm_num_groups=A_  ,   norm_type=A_  ,   )


						@apply_forward_hook
						def     _UpperCAmelCase			(    self  ,   A_  ,   A_ = True		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      self.encoder(A_		)
										_UpperCAmelCase    :						List[str]							=      self.quant_conv(A_		)

										if not return_dict:
														return (h,)

										return VQEncoderOutput(latents=A_		)


						@apply_forward_hook
						def     _UpperCAmelCase			(    self  ,   A_  ,   A_ = False  ,   A_ = True		):







										'''simple docstring'''


										if not force_not_quantize:
														_UpperCAmelCase    :						Optional[int]							=      self.quantize(A_		)
										else:
														_UpperCAmelCase    :						List[str]							=      h
										_UpperCAmelCase    :						List[Any]							=      self.post_quant_conv(A_		)
										_UpperCAmelCase    :						Optional[int]							=      self.decoder(A_  ,   quant if self.config.norm_type == "spatial" else None		)

										if not return_dict:
														return (dec,)

										return DecoderOutput(sample=A_		)





						def     _UpperCAmelCase			(    self  ,   A_  ,   A_ = True		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      sample
										_UpperCAmelCase    :						Any							=      self.encode(A_		).latents
										_UpperCAmelCase    :						Optional[int]							=      self.decode(A_		).sample

										if not return_dict:
														return (dec,)

										return DecoderOutput(sample=A_		)





def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					str					, lowerCAmelCase:					str	)				->    bool:
				_UpperCAmelCase    :						Optional[Any]							=      len(lowerCAmelCase	) + 1
				_UpperCAmelCase    :						Optional[int]							=      len(lowerCAmelCase	) + 1

				# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
				# length i of input_string matches with prefix string of length j of
				# given pattern.
				# "dp" stands for dynamic programming.
				_UpperCAmelCase    :						List[str]							=      [[0 for i in range(lowerCAmelCase	)] for j in range(lowerCAmelCase	)]

				# since string of zero length match pattern of zero length
				_UpperCAmelCase    :						List[Any]							=      1

				# since pattern of zero length will never match with string of non-zero length
				for i in range(1					, lowerCAmelCase	):
								_UpperCAmelCase    :						Dict							=      0

				# since string of zero length will match with pattern where there
				# is at least one * alternatively
				for j in range(1					, lowerCAmelCase	):
								_UpperCAmelCase    :						Tuple							=      dp[0][j - 2] if pattern[j - 1] == "*" else 0

				# now using bottom-up approach to find for all remaining lengths
				for i in range(1					, lowerCAmelCase	):
								for j in range(1					, lowerCAmelCase	):
												if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
																_UpperCAmelCase    :						Optional[Any]							=      dp[i - 1][j - 1]

												elif pattern[j - 1] == "*":
																if dp[i][j - 2] == 1:
																				_UpperCAmelCase    :						List[str]							=      1
																elif pattern[j - 2] in (input_string[i - 1], "."):
																				_UpperCAmelCase    :						str							=      dp[i - 1][j]
																else:
																				_UpperCAmelCase    :						int							=      0
												else:
																_UpperCAmelCase    :						List[Any]							=      0

				return bool(dp[-1][-1]	)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					# inputing the strings
					# input_string = input("input a string :")
					# pattern = input("input a pattern :")

					SCREAMING_SNAKE_CASE_            = 'aab'
					SCREAMING_SNAKE_CASE_            = 'c*a*b'

					# using function to check whether given string matches the given pattern
					if match_pattern(input_string, pattern):
										print(F'''{input_string} matches the given pattern {pattern}''')
					else:
										print(F'''{input_string} does not match with the given pattern {pattern}''')


