



from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin


@dataclass
class      __lowerCAmelCase     (		_lowerCAmelCase      ):
					"""simple docstring"""






					A__				:		torch.FloatTensor







class      __lowerCAmelCase     (		_lowerCAmelCase							,     _lowerCAmelCase      ):
					"""simple docstring"""






					@register_to_config
					def __init__(     self					:       str				,					_snake_case					:       int = 16				,					_snake_case					:       int = 88				,					_snake_case					:       Optional[int] = None				,					_snake_case					:       Optional[int] = None				,					_snake_case					:       int = 1				,					_snake_case					:       float = 0.0				,					_snake_case					:       int = 32				,					_snake_case					:       Optional[int] = None				,					_snake_case					:       bool = False				,					_snake_case					:       Optional[int] = None				,					_snake_case					:       str = "geglu"				,					_snake_case					:       bool = True				,					_snake_case					:       bool = True				,					):






								"""simple docstring"""


								super().__init__()
								A__       =    num_attention_heads
								A__       =    attention_head_dim
								A__       =    num_attention_heads * attention_head_dim

								A__       =    in_channels

								A__       =    torch.nn.GroupNorm(num_groups=_lowerCAmelCase				,					num_channels=_lowerCAmelCase				,					eps=1E-6				,					affine=_lowerCAmelCase					)
								A__       =    nn.Linear(_lowerCAmelCase				,					_lowerCAmelCase					)

								# 3. Define transformers blocks
								A__       =    nn.ModuleList(
								    [
								        BasicTransformerBlock(
								            _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					dropout=_lowerCAmelCase				,					cross_attention_dim=_lowerCAmelCase				,					activation_fn=_lowerCAmelCase				,					attention_bias=_lowerCAmelCase				,					double_self_attention=_lowerCAmelCase				,					norm_elementwise_affine=_lowerCAmelCase				,					)
								        for d in range(_lowerCAmelCase					)
								    ]					)

								A__       =    nn.Linear(_lowerCAmelCase				,					_lowerCAmelCase					)



					def     _a							(     self					:       Optional[int]				,					_snake_case					:       str				,					_snake_case					:       List[Any]=None				,					_snake_case					:       int=None				,					_snake_case					:       Any=None				,					_snake_case					:       List[str]=1				,					_snake_case					:       Any=None				,					_snake_case					:       bool = True				,					):






								"""simple docstring"""


								A__     , A__     , A__     , A__       =    hidden_states.shape
								A__       =    batch_frames // num_frames

								A__       =    hidden_states

								A__       =    hidden_states[None, :].reshape(_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase					)
								A__       =    hidden_states.permute(0				,					2				,					1				,					3				,					4					)

								A__       =    self.norm(_lowerCAmelCase					)
								A__       =    hidden_states.permute(0				,					3				,					4				,					2				,					1					).reshape(batch_size * height * width				,					_lowerCAmelCase				,					_lowerCAmelCase					)

								A__       =    self.proj_in(_lowerCAmelCase					)

								# 2. Blocks
								for block in self.transformer_blocks:
											A__       =    block(
											    _lowerCAmelCase				,					encoder_hidden_states=_lowerCAmelCase				,					timestep=_lowerCAmelCase				,					cross_attention_kwargs=_lowerCAmelCase				,					class_labels=_lowerCAmelCase				,					)

								# 3. Output
								A__       =    self.proj_out(_lowerCAmelCase					)
								A__       =    (
								    hidden_states[None, None, :]
								    .reshape(_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase					)
								    .permute(0				,					3				,					4				,					1				,					2					)
								    .contiguous()
								)
								A__       =    hidden_states.reshape(_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase					)

								A__       =    hidden_states + residual

								if not return_dict:
											return (output,)

								return TransformerTemporalModelOutput(sample=_lowerCAmelCase					)









import math
import random


def A						(       __UpperCamelCase ,	__UpperCamelCase = False					)						->      float:
			if deriv:
						return value * (1 - value)
			return 1 / (1 + math.exp(-value					))


# Initial Value
SCREAMING_SNAKE_CASE__       =      0.02


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      float:
			A__       =    float(2 * (random.randint(1 ,	100					)) - 1					)

			for _ in range(__UpperCamelCase					):
						# Forward propagation
						A__       =    sigmoid_function(INITIAL_VALUE * weight					)
						# How much did we miss?
						A__       =    (expected / 100) - layer_a
						# Error delta
						A__       =    layer_1_error * sigmoid_function(__UpperCamelCase ,	__UpperCamelCase					)
						# Update weight
						weight += INITIAL_VALUE * layer_1_delta

			return layer_a * 100


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							SCREAMING_SNAKE_CASE__       =      int(input('''Expected value: '''))
							SCREAMING_SNAKE_CASE__       =      int(input('''Number of propagations: '''))
							print(forward_propagation(expected, number_propagations))




