


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'''







					__UpperCAmelCase							  =					42




class 						lowerCamelCase_							(					lowerCAmelCase__  ,  lowerCAmelCase__					):



					'''simple docstring'''







					@register_to_config
					def __init__(					self			,							snake_case_ = 1_6			,							snake_case_ = 8_8			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = 1			,							snake_case_ = 0.0			,							snake_case_ = 3_2			,							snake_case_ = None			,							snake_case_ = False			,							snake_case_ = None			,							snake_case_ = "geglu"			,							snake_case_ = True			,							snake_case_ = True			,							) ->      int:







								'''simple docstring'''

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

								__lowercase			       = in_channels

								__lowercase			       = torch.nn.GroupNorm(num_groups=snake_case_			,							num_channels=snake_case_			,							eps=1e-6			,							affine=snake_case_    )
								__lowercase			       = nn.Linear(snake_case_			,							snake_case_    )

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

								__lowercase			       = nn.Linear(snake_case_			,							snake_case_    )






					def A   (					self			,							snake_case_			,							snake_case_=None			,							snake_case_=None			,							snake_case_=None			,							snake_case_=1			,							snake_case_=None			,							snake_case_ = True			,							) ->      Any:







								'''simple docstring'''

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

								__lowercase			       = hidden_states

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

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

								__lowercase			       = self.proj_in(snake_case_    )

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

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

								__lowercase			       = hidden_states + residual

								if not return_dict:
											return (output,)

								return TransformerTemporalModelOutput(sample=snake_case_    )






import doctest
from collections import deque

import numpy as np
class 						lowerCamelCase_							:



					'''simple docstring'''







					def __init__(					self    ) ->      None:







								'''simple docstring'''

								__lowercase			       = [2, 1, 2, -1]
								__lowercase			       = [1, 2, 3, 4]






					def A   (					self    ) ->      list[float]:







								'''simple docstring'''

								__lowercase			       = len(self.first_signal    )
								__lowercase			       = len(self.second_signal    )

								__lowercase			       = max(snake_case_			,							snake_case_    )

								# create a zero matrix of max_length x max_length
								__lowercase			       = [[0] * max_length for i in range(snake_case_    )]

								# fills the smaller signal with zeros to make both signals of same length
								if length_first_signal < length_second_signal:
											self.first_signal += [0] * (max_length - length_first_signal)
								elif length_first_signal > length_second_signal:
											self.second_signal += [0] * (max_length - length_second_signal)
								for i in range(snake_case_    ):
											__lowercase			       = deque(self.second_signal    )
											rotated_signal.rotate(snake_case_    )
											for j, item in enumerate(snake_case_    ):
														matrix[i][j] += item

        # multiply the matrix with the first signal
								__lowercase			       = np.matmul(np.transpose(snake_case_    )			,							np.transpose(self.first_signal    )    )

								# rounding-off to two decimal places
								return [round(snake_case_			,							2    ) for i in final_signal]


if __name__ == "__main__":
	doctest.testmod()


