






"""simple docstring"""


from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block






@dataclass
class 		snake_case						(						UpperCAmelCase ):
    __magic_name__					  =  42






class 		snake_case						(						nn.Module ):





    def __init__(							self			:      Optional[int]       ,	A			:      Any=3       ,	A			:      Any=3       ,	A			:      Dict=("DownEncoderBlock2D",)       ,	A			:      Dict=(6_4,)       ,	A			:      Any=2       ,	A			:      int=3_2       ,	A			:      Any="silu"       ,	A			:      Tuple=True       ,	):


      '''simple docstring'''




      super().__init__()
      a  :    Tuple       =     layers_per_block

      a  :    List[Any]       =     torch.nn.Convad(
          A       ,	block_out_channels[0]       ,	kernel_size=3       ,	stride=1       ,	padding=1       ,	)

      a  :    List[str]       =     None
      a  :    Dict       =     nn.ModuleList([]    )

      # down
      a  :    Union[str, Any]       =     block_out_channels[0]
      for i, down_block_type in enumerate(A    ):
        a  :    Optional[int]       =     output_channel
        a  :    Union[str, Any]       =     block_out_channels[i]
        a  :    List[str]       =     i == len(A    ) - 1

        a  :    int       =     get_down_block(
            A       ,	num_layers=self.layers_per_block       ,	in_channels=A       ,	out_channels=A       ,	add_downsample=not is_final_block       ,	resnet_eps=1E-6       ,	downsample_padding=0       ,	resnet_act_fn=A       ,	resnet_groups=A       ,	attention_head_dim=A       ,	temb_channels=A       ,	)
        self.down_blocks.append(A    )

      # mid
      a  :    str       =     UNetMidBlockaD(
          in_channels=block_out_channels[-1]       ,	resnet_eps=1E-6       ,	resnet_act_fn=A       ,	output_scale_factor=1       ,	resnet_time_scale_shift='default'       ,	attention_head_dim=block_out_channels[-1]       ,	resnet_groups=A       ,	temb_channels=A       ,	)

      # out
      a  :    int       =     nn.GroupNorm(num_channels=block_out_channels[-1]       ,	num_groups=A       ,	eps=1E-6    )
      a  :    List[str]       =     nn.SiLU()

      a  :    int       =     2 * out_channels if double_z else out_channels
      a  :    Optional[Any]       =     nn.Convad(block_out_channels[-1]       ,	A       ,	3       ,	padding=1    )

      a  :    int       =     False





    def        lowerCamelCase__   (							self			:      int       ,	A			:      Dict    ):


      '''simple docstring'''




      a  :    Optional[Any]       =     x
      a  :    Any       =     self.conv_in(A    )

      if self.training and self.gradient_checkpointing:

        def create_custom_forward(A			:      List[Any]    ):
          def custom_forward(*A			:      int    ):
            return module(*A    )

          return custom_forward

        # down
        if is_torch_version('>='       ,	'1.11.0'    ):
          for down_block in self.down_blocks:
            a  :    List[str]       =     torch.utils.checkpoint.checkpoint(
                create_custom_forward(A    )       ,	A       ,	use_reentrant=A    )
          # middle
          a  :    Tuple       =     torch.utils.checkpoint.checkpoint(
              create_custom_forward(self.mid_block    )       ,	A       ,	use_reentrant=A    )
        else:
          for down_block in self.down_blocks:
            a  :    Dict       =     torch.utils.checkpoint.checkpoint(create_custom_forward(A    )       ,	A    )
          # middle
          a  :    Tuple       =     torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block    )       ,	A    )

      else:
        # down
        for down_block in self.down_blocks:
          a  :    str       =     down_block(A    )

        # middle
        a  :    Tuple       =     self.mid_block(A    )

      # post-process
      a  :    Optional[Any]       =     self.conv_norm_out(A    )
      a  :    Union[str, Any]       =     self.conv_act(A    )
      a  :    str       =     self.conv_out(A    )

      return sample






class 		snake_case						(						nn.Module ):





    def __init__(							self			:      Optional[int]       ,	A			:      Union[str, Any]=3       ,	A			:      Optional[Any]=3       ,	A			:      Optional[int]=("UpDecoderBlock2D",)       ,	A			:      Union[str, Any]=(6_4,)       ,	A			:      Optional[Any]=2       ,	A			:      int=3_2       ,	A			:      List[Any]="silu"       ,	A			:      str="group"       ,	):


      '''simple docstring'''




      super().__init__()
      a  :    int       =     layers_per_block

      a  :    int       =     nn.Convad(
          A       ,	block_out_channels[-1]       ,	kernel_size=3       ,	stride=1       ,	padding=1       ,	)

      a  :    Optional[Any]       =     None
      a  :    Dict       =     nn.ModuleList([]    )

      a  :    Tuple       =     in_channels if norm_type == 'spatial' else None

      # mid
      a  :    List[Any]       =     UNetMidBlockaD(
          in_channels=block_out_channels[-1]       ,	resnet_eps=1E-6       ,	resnet_act_fn=A       ,	output_scale_factor=1       ,	resnet_time_scale_shift='default' if norm_type == 'group' else norm_type       ,	attention_head_dim=block_out_channels[-1]       ,	resnet_groups=A       ,	temb_channels=A       ,	)

      # up
      a  :    Optional[int]       =     list(reversed(A    )    )
      a  :    Any       =     reversed_block_out_channels[0]
      for i, up_block_type in enumerate(A    ):
        a  :    Optional[int]       =     output_channel
        a  :    Union[str, Any]       =     reversed_block_out_channels[i]

        a  :    Union[str, Any]       =     i == len(A    ) - 1

        a  :    List[str]       =     get_up_block(
            A       ,	num_layers=self.layers_per_block + 1       ,	in_channels=A       ,	out_channels=A       ,	prev_output_channel=A       ,	add_upsample=not is_final_block       ,	resnet_eps=1E-6       ,	resnet_act_fn=A       ,	resnet_groups=A       ,	attention_head_dim=A       ,	temb_channels=A       ,	resnet_time_scale_shift=A       ,	)
        self.up_blocks.append(A    )
        a  :    Optional[int]       =     output_channel

      # out
      if norm_type == "spatial":
        a  :    List[str]       =     SpatialNorm(block_out_channels[0]       ,	A    )
      else:
        a  :    Tuple       =     nn.GroupNorm(num_channels=block_out_channels[0]       ,	num_groups=A       ,	eps=1E-6    )
      a  :    List[Any]       =     nn.SiLU()
      a  :    Optional[int]       =     nn.Convad(block_out_channels[0]       ,	A       ,	3       ,	padding=1    )

      a  :    Optional[int]       =     False





    def        lowerCamelCase__   (							self			:      List[str]       ,	A			:      int       ,	A			:      Optional[int]=None    ):


      '''simple docstring'''




      a  :    Optional[Any]       =     z
      a  :    Optional[int]       =     self.conv_in(A    )

      a  :    Union[str, Any]       =     next(iter(self.up_blocks.parameters()    )    ).dtype
      if self.training and self.gradient_checkpointing:

        def create_custom_forward(A			:      Dict    ):
          def custom_forward(*A			:      Tuple    ):
            return module(*A    )

          return custom_forward

        if is_torch_version('>='       ,	'1.11.0'    ):
          # middle
          a  :    List[str]       =     torch.utils.checkpoint.checkpoint(
              create_custom_forward(self.mid_block    )       ,	A       ,	A       ,	use_reentrant=A    )
          a  :    Optional[Any]       =     sample.to(A    )

          # up
          for up_block in self.up_blocks:
            a  :    int       =     torch.utils.checkpoint.checkpoint(
                create_custom_forward(A    )       ,	A       ,	A       ,	use_reentrant=A    )
        else:
          # middle
          a  :    Tuple       =     torch.utils.checkpoint.checkpoint(
              create_custom_forward(self.mid_block    )       ,	A       ,	A    )
          a  :    Tuple       =     sample.to(A    )

          # up
          for up_block in self.up_blocks:
            a  :    Union[str, Any]       =     torch.utils.checkpoint.checkpoint(create_custom_forward(A    )       ,	A       ,	A    )
      else:
        # middle
        a  :    Optional[Any]       =     self.mid_block(A       ,	A    )
        a  :    Dict       =     sample.to(A    )

        # up
        for up_block in self.up_blocks:
          a  :    Tuple       =     up_block(A       ,	A    )

        # post-process
      if latent_embeds is None:
        a  :    Any       =     self.conv_norm_out(A    )
      else:
        a  :    str       =     self.conv_norm_out(A       ,	A    )
      a  :    Any       =     self.conv_act(A    )
      a  :    Dict       =     self.conv_out(A    )

      return sample






class 		snake_case						(						nn.Module ):





    def __init__(							self			:      Optional[int]       ,	A			:      str       ,	A			:      Optional[int]       ,	A			:      Tuple       ,	A			:      Dict=None       ,	A			:      str="random"       ,	A			:      Optional[int]=False       ,	A			:      Any=True    ):


      '''simple docstring'''




      super().__init__()
      a  :    Optional[Any]       =     n_e
      a  :    Any       =     vq_embed_dim
      a  :    Optional[int]       =     beta
      a  :    str       =     legacy

      a  :    Tuple       =     nn.Embedding(self.n_e       ,	self.vq_embed_dim    )
      self.embedding.weight.data.uniform_(-1.0 / self.n_e       ,	1.0 / self.n_e    )

      a  :    Tuple       =     remap
      if self.remap is not None:
        self.register_buffer('used'       ,	torch.tensor(np.load(self.remap    )    )    )
        a  :    Tuple       =     self.used.shape[0]
        a  :    Optional[Any]       =     unknown_index  # "random" or "extra" or integer
        if self.unknown_index == "extra":
          a  :    Union[str, Any]       =     self.re_embed
          a  :    int       =     self.re_embed + 1
        print(
            F'''Remapping {self.n_e} indices to {self.re_embed} indices. '''
            F'''Using {self.unknown_index} for unknown indices.'''    )
      else:
        a  :    List[Any]       =     n_e

      a  :    int       =     sane_index_shape





    def        lowerCamelCase__   (							self			:      Optional[int]       ,	A			:      Tuple    ):


      '''simple docstring'''




      a  :    Any       =     inds.shape
      assert len(A    ) > 1
      a  :    Union[str, Any]       =     inds.reshape(ishape[0]       ,	-1    )
      a  :    Dict       =     self.used.to(A    )
      a  :    Union[str, Any]       =     (inds[:, :, None] == used[None, None, ...]).long()
      a  :    List[Any]       =     match.argmax(-1    )
      a  :    Any       =     match.sum(2    ) < 1
      if self.unknown_index == "random":
        a  :    Union[str, Any]       =     torch.randint(0       ,	self.re_embed       ,	size=new[unknown].shape    ).to(device=new.device    )
      else:
        a  :    Dict       =     self.unknown_index
      return new.reshape(A    )





    def        lowerCamelCase__   (							self			:      Union[str, Any]       ,	A			:      str    ):


      '''simple docstring'''




      a  :    Any       =     inds.shape
      assert len(A    ) > 1
      a  :    List[str]       =     inds.reshape(ishape[0]       ,	-1    )
      a  :    str       =     self.used.to(A    )
      if self.re_embed > self.used.shape[0]:  # extra token
        a  :    Dict       =     0  # simply set to zero
      a  :    Optional[int]       =     torch.gather(used[None, :][inds.shape[0] * [0], :]       ,	1       ,	A    )
      return back.reshape(A    )





    def        lowerCamelCase__   (							self			:      Any       ,	A			:      Optional[Any]    ):


      '''simple docstring'''




      a  :    List[str]       =     z.permute(0       ,	2       ,	3       ,	1    ).contiguous()
      a  :    Tuple       =     z.view(-1       ,	self.vq_embed_dim    )

      # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
      a  :    List[str]       =     torch.argmin(torch.cdist(A       ,	self.embedding.weight    )       ,	dim=1    )

      a  :    Any       =     self.embedding(A    ).view(z.shape    )
      a  :    str       =     None
      a  :    List[str]       =     None

      # compute loss for embedding
      if not self.legacy:
        a  :    Optional[Any]       =     self.beta * torch.mean((z_q.detach() - z) ** 2    ) + torch.mean((z_q - z.detach()) ** 2    )
      else:
        a  :    Tuple       =     torch.mean((z_q.detach() - z) ** 2    ) + self.beta * torch.mean((z_q - z.detach()) ** 2    )

      # preserve gradients
      a  :    Any       =     z + (z_q - z).detach()

      # reshape back to match original input shape
      a  :    Union[str, Any]       =     z_q.permute(0       ,	3       ,	1       ,	2    ).contiguous()

      if self.remap is not None:
        a  :    Dict       =     min_encoding_indices.reshape(z.shape[0]       ,	-1    )  # add batch axis
        a  :    str       =     self.remap_to_used(A    )
        a  :    int       =     min_encoding_indices.reshape(-1       ,	1    )  # flatten

      if self.sane_index_shape:
        a  :    Any       =     min_encoding_indices.reshape(z_q.shape[0]       ,	z_q.shape[2]       ,	z_q.shape[3]    )

      return z_q, loss, (perplexity, min_encodings, min_encoding_indices)





    def        lowerCamelCase__   (							self			:      Tuple       ,	A			:      Any       ,	A			:      Optional[int]    ):


      '''simple docstring'''




      if self.remap is not None:
        a  :    Union[str, Any]       =     indices.reshape(shape[0]       ,	-1    )  # add batch axis
        a  :    Tuple       =     self.unmap_to_all(A    )
        a  :    Union[str, Any]       =     indices.reshape(-1    )  # flatten again

      # get quantized latent vectors
      a  :    str       =     self.embedding(A    )

      if shape is not None:
        a  :    List[str]       =     z_q.view(A    )
        # reshape back to match original input shape
        a  :    Optional[Any]       =     z_q.permute(0       ,	3       ,	1       ,	2    ).contiguous()

      return z_q






class 		snake_case						(						UpperCAmelCase ):





    def __init__(							self			:      Any       ,	A			:      Union[str, Any]       ,	A			:      int=False    ):


      '''simple docstring'''




      a  :    Any       =     parameters
      a,  a  :    List[str]       =     torch.chunk(A       ,	2       ,	dim=1    )
      a  :    int       =     torch.clamp(self.logvar       ,	-30.0       ,	20.0    )
      a  :    List[str]       =     deterministic
      a  :    Dict       =     torch.exp(0.5 * self.logvar    )
      a  :    Tuple       =     torch.exp(self.logvar    )
      if self.deterministic:
        a  :    List[Any]       =     torch.zeros_like(
            self.mean       ,	device=self.parameters.device       ,	dtype=self.parameters.dtype    )





    def        lowerCamelCase__   (							self			:      str       ,	A			:      Optional[torch.Generator] = None    ):


      '''simple docstring'''




      a  :    List[str]       =     randn_tensor(
          self.mean.shape       ,	generator=A       ,	device=self.parameters.device       ,	dtype=self.parameters.dtype    )
      a  :    Dict       =     self.mean + self.std * sample
      return x





    def        lowerCamelCase__   (							self			:      List[str]       ,	A			:      Optional[Any]=None    ):


      '''simple docstring'''




      if self.deterministic:
        return torch.Tensor([0.0]    )
      else:
        if other is None:
          return 0.5 * torch.sum(torch.pow(self.mean       ,	2    ) + self.var - 1.0 - self.logvar       ,	dim=[1, 2, 3]    )
        else:
          return 0.5 * torch.sum(
              torch.pow(self.mean - other.mean       ,	2    ) / other.var
              + self.var / other.var
              - 1.0
              - self.logvar
              + other.logvar       ,	dim=[1, 2, 3]       ,	)





    def        lowerCamelCase__   (							self			:      List[Any]       ,	A			:      Optional[int]       ,	A			:      Dict=[1, 2, 3]    ):


      '''simple docstring'''




      if self.deterministic:
        return torch.Tensor([0.0]    )
      a  :    Dict       =     np.log(2.0 * np.pi    )
      return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean       ,	2    ) / self.var       ,	dim=A    )





    def        lowerCamelCase__   (							self			:      Union[str, Any]    ):


      '''simple docstring'''




      return self.mean












"""simple docstring"""


import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute




def     snake_case						(A_   :int = 3   ):







  '''simple docstring'''

  if isinstance(A_			,    A_   ):
    raise TypeError('number of qubits must be a integer.'   )
  if number_of_qubits <= 0:
    raise ValueError('number of qubits must be > 0.'   )
  if math.floor(A_   ) != number_of_qubits:
    raise ValueError('number of qubits must be exact integer.'   )
  if number_of_qubits > 1_0:
    raise ValueError('number of qubits too large to simulate(>10).'   )

  a  :    str       =     QuantumRegister(A_			,    'qr'   )
  a  :    List[Any]       =     ClassicalRegister(A_			,    'cr'   )

  a  :    Optional[Any]       =     QuantumCircuit(A_			,    A_   )

  a  :    Optional[Any]       =     number_of_qubits

  for i in range(A_   ):
    quantum_circuit.h(number_of_qubits - i - 1   )
    counter -= 1
    for j in range(A_   ):
      quantum_circuit.cp(np.pi / 2 ** (counter - j)			,    A_			,    A_   )

  for k in range(number_of_qubits // 2   ):
    quantum_circuit.swap(A_			,    number_of_qubits - k - 1   )

  # measure all the qubits
  quantum_circuit.measure(A_			,    A_   )
  # simulate with 10000 shots
  a  :    Optional[int]       =     Aer.get_backend('qasm_simulator'   )
  a  :    Any       =     execute(A_			,    A_			,    shots=1_0_0_0_0   )

  return job.result().get_counts(A_   )


if __name__ == "__main__":
   print(
       f'''Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}'''
   )




