




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
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block




@dataclass
class 		UpperCamelCase__     (lowerCAmelCase__					):

    '''simple docstring'''



    lowerCamelCase_				:     torch.FloatTensor




class 		UpperCamelCase__     (lowerCAmelCase__   ,    lowerCAmelCase__					):

    '''simple docstring'''



    @register_to_config
    def __init__(   self					,  UpperCamelCase__ = None					,  UpperCamelCase__ = 3					,  UpperCamelCase__ = 3					,  UpperCamelCase__ = False					,  UpperCamelCase__ = "positional"					,  UpperCamelCase__ = 0					,  UpperCamelCase__ = True					,  UpperCamelCase__ = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")					,  UpperCamelCase__ = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")					,  UpperCamelCase__ = (224, 448, 672, 896)					,  UpperCamelCase__ = 2					,  UpperCamelCase__ = 1					,  UpperCamelCase__ = 1					,  UpperCamelCase__ = "conv"					,  UpperCamelCase__ = "conv"					,  UpperCamelCase__ = "silu"					,  UpperCamelCase__ = 8					,  UpperCamelCase__ = 32					,  UpperCamelCase__ = 1e-5					,  UpperCamelCase__ = "default"					,  UpperCamelCase__ = True					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  )					->				List[Any]:
           super().__init__()

           lowerCamelCase       :				Optional[Any]      			= sample_size
           lowerCamelCase       :				List[Any]      			= block_out_channels[0] * 4

           # Check inputs
           if len(UpperCamelCase__					) != len(UpperCamelCase__					):
                  raise ValueError(
                      F'''Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.'''					)

           if len(UpperCamelCase__					) != len(UpperCamelCase__					):
                  raise ValueError(
                      F'''Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.'''					)

           # input
           lowerCamelCase       :				int      			= nn.Convad(UpperCamelCase__					,  block_out_channels[0]					,  kernel_size=3					,  padding=(1, 1)					)

           # time
           if time_embedding_type == "fourier":
                  lowerCamelCase       :				Dict      			= GaussianFourierProjection(embedding_size=block_out_channels[0]					,  scale=16					)
                  lowerCamelCase       :				int      			= 2 * block_out_channels[0]
           elif time_embedding_type == "positional":
                  lowerCamelCase       :				List[str]      			= Timesteps(block_out_channels[0]					,  UpperCamelCase__					,  UpperCamelCase__					)
                  lowerCamelCase       :				Optional[Any]      			= block_out_channels[0]

           lowerCamelCase       :				Optional[int]      			= TimestepEmbedding(UpperCamelCase__					,  UpperCamelCase__					)

           # class embedding
           if class_embed_type is None and num_class_embeds is not None:
                  lowerCamelCase       :				List[str]      			= nn.Embedding(UpperCamelCase__					,  UpperCamelCase__					)
           elif class_embed_type == "timestep":
                  lowerCamelCase       :				Dict      			= TimestepEmbedding(UpperCamelCase__					,  UpperCamelCase__					)
           elif class_embed_type == "identity":
                  lowerCamelCase       :				str      			= nn.Identity(UpperCamelCase__					,  UpperCamelCase__					)
           else:
                  lowerCamelCase       :				Dict      			= None

           lowerCamelCase       :				Optional[Any]      			= nn.ModuleList([]					)
           lowerCamelCase       :				List[str]      			= None
           lowerCamelCase       :				int      			= nn.ModuleList([]					)

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

                  lowerCamelCase       :				List[Any]      			= get_down_block(
                      UpperCamelCase__					,  num_layers=UpperCamelCase__					,  in_channels=UpperCamelCase__					,  out_channels=UpperCamelCase__					,  temb_channels=UpperCamelCase__					,  add_downsample=not is_final_block					,  resnet_eps=UpperCamelCase__					,  resnet_act_fn=UpperCamelCase__					,  resnet_groups=UpperCamelCase__					,  attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel					,  downsample_padding=UpperCamelCase__					,  resnet_time_scale_shift=UpperCamelCase__					,  downsample_type=UpperCamelCase__					,  )
                  self.down_blocks.append(UpperCamelCase__					)

           # mid
           lowerCamelCase       :				Optional[Any]      			= UNetMidBlockaD(
               in_channels=block_out_channels[-1]					,  temb_channels=UpperCamelCase__					,  resnet_eps=UpperCamelCase__					,  resnet_act_fn=UpperCamelCase__					,  output_scale_factor=UpperCamelCase__					,  resnet_time_scale_shift=UpperCamelCase__					,  attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]					,  resnet_groups=UpperCamelCase__					,  add_attention=UpperCamelCase__					,  )

           # up
           lowerCamelCase       :				Dict      			= list(reversed(UpperCamelCase__					)					)
           lowerCamelCase       :				List[str]      			= reversed_block_out_channels[0]
           for i, up_block_type in enumerate(UpperCamelCase__					):
                  lowerCamelCase       :				Any      			= output_channel
                  lowerCamelCase       :				Optional[Any]      			= reversed_block_out_channels[i]
                  lowerCamelCase       :				Optional[int]      			= reversed_block_out_channels[min(i + 1					,  len(UpperCamelCase__					) - 1					)]

                  lowerCamelCase       :				Optional[Any]      			= i == len(UpperCamelCase__					) - 1

                  lowerCamelCase       :				Tuple      			= get_up_block(
                      UpperCamelCase__					,  num_layers=layers_per_block + 1					,  in_channels=UpperCamelCase__					,  out_channels=UpperCamelCase__					,  prev_output_channel=UpperCamelCase__					,  temb_channels=UpperCamelCase__					,  add_upsample=not is_final_block					,  resnet_eps=UpperCamelCase__					,  resnet_act_fn=UpperCamelCase__					,  resnet_groups=UpperCamelCase__					,  attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel					,  resnet_time_scale_shift=UpperCamelCase__					,  upsample_type=UpperCamelCase__					,  )
                  self.up_blocks.append(UpperCamelCase__					)
                  lowerCamelCase       :				Optional[Any]      			= output_channel

           # out
           lowerCamelCase       :				Optional[int]      			= norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4					,  32					)
           lowerCamelCase       :				Optional[Any]      			= nn.GroupNorm(num_channels=block_out_channels[0]					,  num_groups=UpperCamelCase__					,  eps=UpperCamelCase__					)
           lowerCamelCase       :				Optional[Any]      			= nn.SiLU()
           lowerCamelCase       :				Dict      			= nn.Convad(block_out_channels[0]					,  UpperCamelCase__					,  kernel_size=3					,  padding=1					)







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__ = None					,  UpperCamelCase__ = True					,  )					->				Union[UNetaDOutput, Tuple]:
           # 0. center input if necessary
           if self.config.center_input_sample:
                  lowerCamelCase       :				Dict      			= 2 * sample - 1.0

           # 1. time
           lowerCamelCase       :				int      			= timestep
           if not torch.is_tensor(UpperCamelCase__					):
                  lowerCamelCase       :				Optional[int]      			= torch.tensor([timesteps]					,  dtype=torch.long					,  device=sample.device					)
           elif torch.is_tensor(UpperCamelCase__					) and len(timesteps.shape					) == 0:
                  lowerCamelCase       :				Dict      			= timesteps[None].to(sample.device					)

           # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
           lowerCamelCase       :				Tuple      			= timesteps * torch.ones(sample.shape[0]					,  dtype=timesteps.dtype					,  device=timesteps.device					)

           lowerCamelCase       :				Dict      			= self.time_proj(UpperCamelCase__					)

           # timesteps does not contain any weights and will always return f32 tensors
           # but time_embedding might actually be running in fp16. so we need to cast here.
           # there might be better ways to encapsulate this.
           lowerCamelCase       :				Optional[int]      			= t_emb.to(dtype=self.dtype					)
           lowerCamelCase       :				Dict      			= self.time_embedding(UpperCamelCase__					)

           if self.class_embedding is not None:
                  if class_labels is None:
                         raise ValueError("class_labels should be provided when doing class conditioning"					)

                  if self.config.class_embed_type == "timestep":
                         lowerCamelCase       :				List[Any]      			= self.time_proj(UpperCamelCase__					)

                  lowerCamelCase       :				Tuple      			= self.class_embedding(UpperCamelCase__					).to(dtype=self.dtype					)
                  lowerCamelCase       :				Tuple      			= emb + class_emb

           # 2. pre-process
           lowerCamelCase       :				str      			= sample
           lowerCamelCase       :				Union[str, Any]      			= self.conv_in(UpperCamelCase__					)

           # 3. down
           lowerCamelCase       :				int      			= (sample,)
           for downsample_block in self.down_blocks:
                  if hasattr(UpperCamelCase__					,  "skip_conv"					):
                         lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase       :				Optional[int]      			= downsample_block(
                             hidden_states=UpperCamelCase__					,  temb=UpperCamelCase__					,  skip_sample=UpperCamelCase__					)
                  else:
                         lowerCamelCase			,				lowerCamelCase       :				Union[str, Any]      			= downsample_block(hidden_states=UpperCamelCase__					,  temb=UpperCamelCase__					)

                  down_block_res_samples += res_samples

           # 4. mid
           lowerCamelCase       :				Dict      			= self.mid_block(UpperCamelCase__					,  UpperCamelCase__					)

           # 5. up
           lowerCamelCase       :				Tuple      			= None
           for upsample_block in self.up_blocks:
                  lowerCamelCase       :				List[Any]      			= down_block_res_samples[-len(upsample_block.resnets					) :]
                  lowerCamelCase       :				int      			= down_block_res_samples[: -len(upsample_block.resnets					)]

                  if hasattr(UpperCamelCase__					,  "skip_conv"					):
                         lowerCamelCase			,				lowerCamelCase       :				List[str]      			= upsample_block(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					)
                  else:
                         lowerCamelCase       :				Union[str, Any]      			= upsample_block(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					)

        # 6. post-process
           lowerCamelCase       :				Optional[int]      			= self.conv_norm_out(UpperCamelCase__					)
           lowerCamelCase       :				Tuple      			= self.conv_act(UpperCamelCase__					)
           lowerCamelCase       :				Optional[Any]      			= self.conv_out(UpperCamelCase__					)

           if skip_sample is not None:
                  sample += skip_sample

           if self.config.time_embedding_type == "fourier":
                  lowerCamelCase       :				Optional[int]      			= timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]					)))					)
                  lowerCamelCase       :				int      			= sample / timesteps

           if not return_dict:
                  return (sample,)

           return UNetaDOutput(sample=UpperCamelCase__					)












import numpy as np
import qiskit

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			qiskit.QuantumCircuit:
       lowerCamelCase       :				Optional[int]      			= qiskit.QuantumCircuit(num_qubits + 1   )

       # First, let's deal with the case in which oracle is balanced
       if case == "balanced":
              # First generate a random number that tells us which CNOTs to
              # wrap in X-gates:
              lowerCamelCase       :				Optional[int]      			= np.random.randint(1			,2**num_qubits   )
              # Next, format 'b' as a binary string of length 'n', padded with zeros:
              lowerCamelCase       :				str      			= format(_SCREAMING_SNAKE_CASE			,f'''0{num_qubits}b'''   )
              # Next, we place the first X-gates. Each digit in our binary string
              # corresponds to a qubit, if the digit is 0, we do nothing, if it's 1
              # we apply an X-gate to that qubit:
              for index, bit in enumerate(_SCREAMING_SNAKE_CASE   ):
                     if bit == "1":
                            oracle_qc.x(_SCREAMING_SNAKE_CASE   )
        # Do the controlled-NOT gates for each qubit, using the output qubit
        # as the target:
              for index in range(_SCREAMING_SNAKE_CASE   ):
                     oracle_qc.cx(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )
              # Next, place the final X-gates
              for index, bit in enumerate(_SCREAMING_SNAKE_CASE   ):
                     if bit == "1":
                            oracle_qc.x(_SCREAMING_SNAKE_CASE   )

    # Case in which oracle is constant
       if case == "constant":
              # First decide what the fixed output of the oracle will be
              # (either always 0 or always 1)
              lowerCamelCase       :				str      			= np.random.randint(2   )
              if output == 1:
                     oracle_qc.x(_SCREAMING_SNAKE_CASE   )

       lowerCamelCase       :				int      			= oracle_qc.to_gate()
       lowerCamelCase       :				Tuple      			= "Oracle"  # To show when we display the circuit
       return oracle_gate

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			qiskit.QuantumCircuit:
       lowerCamelCase       :				Union[str, Any]      			= qiskit.QuantumCircuit(num_qubits + 1			,_SCREAMING_SNAKE_CASE   )
       # Set up the output qubit:
       dj_circuit.x(_SCREAMING_SNAKE_CASE   )
       dj_circuit.h(_SCREAMING_SNAKE_CASE   )
       # And set up the input register:
       for qubit in range(_SCREAMING_SNAKE_CASE   ):
              dj_circuit.h(_SCREAMING_SNAKE_CASE   )
       # Let's append the oracle gate to our circuit:
       dj_circuit.append(_SCREAMING_SNAKE_CASE			,range(num_qubits + 1   )   )
       # Finally, perform the H-gates again and measure:
       for qubit in range(_SCREAMING_SNAKE_CASE   ):
              dj_circuit.h(_SCREAMING_SNAKE_CASE   )

       for i in range(_SCREAMING_SNAKE_CASE   ):
              dj_circuit.measure(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )

       return dj_circuit

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			qiskit.result.counts.Counts:
       lowerCamelCase       :				Tuple      			= qiskit.Aer.get_backend("aer_simulator"   )

       lowerCamelCase       :				Tuple      			= dj_oracle(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				List[Any]      			= dj_algorithm(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )

       # Execute the circuit on the simulator
       lowerCamelCase       :				List[Any]      			= qiskit.execute(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,shots=1000   )

       # Return the histogram data of the results of the experiment.
       return job.result().get_counts(_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
       print(f'''Deutsch Jozsa - Constant Oracle: {deutsch_jozsa("constant", 3)}''')
       print(f'''Deutsch Jozsa - Balanced Oracle: {deutsch_jozsa("balanced", 3)}''')






