



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






@dataclass
class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_					):
		'''simple docstring'''




		__lowercase							:				torch.FloatTensor







class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_					):
		'''simple docstring'''




		@register_to_config
		def __init__(   self  ,__UpperCAmelCase = 3  ,__UpperCAmelCase = 3  ,__UpperCAmelCase = ("DownEncoderBlock2D",)  ,__UpperCAmelCase = ("UpDecoderBlock2D",)  ,__UpperCAmelCase = (64,)  ,__UpperCAmelCase = 1  ,__UpperCAmelCase = "silu"  ,__UpperCAmelCase = 3  ,__UpperCAmelCase = 32  ,__UpperCAmelCase = 256  ,__UpperCAmelCase = 32  ,__UpperCAmelCase = None  ,__UpperCAmelCase = 0.1_8_2_1_5  ,__UpperCAmelCase = "group"  ,)   ->		List[str]:
						super().__init__()

						# pass init params to Encoder
						lowerCAmelCase__			:  Any											= Encoder(
						    in_channels=__UpperCAmelCase  ,out_channels=__UpperCAmelCase  ,down_block_types=__UpperCAmelCase  ,block_out_channels=__UpperCAmelCase  ,layers_per_block=__UpperCAmelCase  ,act_fn=__UpperCAmelCase  ,norm_num_groups=__UpperCAmelCase  ,double_z=__UpperCAmelCase  ,)

						lowerCAmelCase__			:  Any											= vq_embed_dim if vq_embed_dim is not None else latent_channels

						lowerCAmelCase__			:  List[Any]											= nn.Convad(__UpperCAmelCase  ,__UpperCAmelCase  ,1				)
						lowerCAmelCase__			:  Optional[Any]											= VectorQuantizer(__UpperCAmelCase  ,__UpperCAmelCase  ,beta=0.2_5  ,remap=__UpperCAmelCase  ,sane_index_shape=__UpperCAmelCase				)
						lowerCAmelCase__			:  str											= nn.Convad(__UpperCAmelCase  ,__UpperCAmelCase  ,1				)

						# pass init params to Decoder
						lowerCAmelCase__			:  Optional[Any]											= Decoder(
						    in_channels=__UpperCAmelCase  ,out_channels=__UpperCAmelCase  ,up_block_types=__UpperCAmelCase  ,block_out_channels=__UpperCAmelCase  ,layers_per_block=__UpperCAmelCase  ,act_fn=__UpperCAmelCase  ,norm_num_groups=__UpperCAmelCase  ,norm_type=__UpperCAmelCase  ,)




		@apply_forward_hook
		def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase = True				)   ->		VQEncoderOutput:
						lowerCAmelCase__			:  Any											= self.encoder(__UpperCAmelCase				)
						lowerCAmelCase__			:  List[str]											= self.quant_conv(__UpperCAmelCase				)

						if not return_dict:
										return (h,)

						return VQEncoderOutput(latents=__UpperCAmelCase				)




		@apply_forward_hook
		def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase = False  ,__UpperCAmelCase = True				)   ->		Union[DecoderOutput, torch.FloatTensor]:
						# also go through quantization layer
						if not force_not_quantize:
										lowerCAmelCase__			:  Dict											= self.quantize(__UpperCAmelCase				)
						else:
										lowerCAmelCase__			:  str											= h
						lowerCAmelCase__			:  Dict											= self.post_quant_conv(__UpperCAmelCase				)
						lowerCAmelCase__			:  str											= self.decoder(__UpperCAmelCase  ,quant if self.config.norm_type == """spatial""" else None				)

						if not return_dict:
										return (dec,)

						return DecoderOutput(sample=__UpperCAmelCase				)






		def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase = True				)   ->		Union[DecoderOutput, torch.FloatTensor]:
						lowerCAmelCase__			:  int											= sample
						lowerCAmelCase__			:  List[Any]											= self.encode(__UpperCAmelCase				).latents
						lowerCAmelCase__			:  str											= self.decode(__UpperCAmelCase				).sample

						if not return_dict:
										return (dec,)

						return DecoderOutput(sample=__UpperCAmelCase				)







'''simple docstring'''





from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_lowerCAmelCase										=     10
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    for i in range(UpperCamelCase							,   UpperCamelCase      ):
        if array[i] == target:
            return i
    return -1
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  Optional[Any]											= 0
    lowerCAmelCase__			:  Optional[int]											= len(UpperCamelCase      )
    while left <= right:
        if right - left < precision:
            return lin_search(UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      )

        lowerCAmelCase__			:  Tuple											= (left + right) // 3 + 1
        lowerCAmelCase__			:  List[str]											= 2 * (left + right) // 3 + 1

        if array[one_third] == target:
            return one_third
        elif array[two_third] == target:
            return two_third

        elif target < array[one_third]:
            lowerCAmelCase__			:  int											= one_third - 1
        elif array[two_third] < target:
            lowerCAmelCase__			:  Union[str, Any]											= two_third + 1

        else:
            lowerCAmelCase__			:  List[Any]											= one_third + 1
            lowerCAmelCase__			:  List[str]											= two_third - 1
    else:
        return -1
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    if left < right:
        if right - left < precision:
            return lin_search(UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      )
        lowerCAmelCase__			:  Dict											= (left + right) // 3 + 1
        lowerCAmelCase__			:  Tuple											= 2 * (left + right) // 3 + 1

        if array[one_third] == target:
            return one_third
        elif array[two_third] == target:
            return two_third

        elif target < array[one_third]:
            return rec_ternary_search(UpperCamelCase							,   one_third - 1							,   UpperCamelCase							,   UpperCamelCase      )
        elif array[two_third] < target:
            return rec_ternary_search(two_third + 1							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      )
        else:
            return rec_ternary_search(one_third + 1							,   two_third - 1							,   UpperCamelCase							,   UpperCamelCase      )
    else:
        return -1


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 _lowerCAmelCase										=     input('''Enter numbers separated by comma:\n''').strip()
 _lowerCAmelCase										=     [int(item.strip()) for item in user_input.split(''',''')]
 assert collection == sorted(collection), F"List must be ordered.\n{collection}."
 _lowerCAmelCase										=     int(input('''Enter the number to be found in the list:\n''').strip())
 _lowerCAmelCase										=     ite_ternary_search(collection, target)
 _lowerCAmelCase										=     rec_ternary_search(0, len(collection) - 1, collection, target)
 if resulta != -1:
  print(F"""Iterative search: {target} found at positions: {resulta}""")
  print(F"""Recursive search: {target} found at positions: {resulta}""")
 else:
  print('''Not found''')


