



from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings


@maybe_allow_in_graph
class 	lowerCamelCase__	(       nn.Module):



 """simple docstring"""

 def __init__(    self					:					int			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					Dict=0.0			,						__lowerCAmelCase					:					Optional[int] = None			,						__lowerCAmelCase					:					str = "geglu"			,						__lowerCAmelCase					:					Optional[int] = None			,						__lowerCAmelCase					:					bool = False			,						__lowerCAmelCase					:					bool = False			,						__lowerCAmelCase					:					bool = False			,						__lowerCAmelCase					:					bool = False			,						__lowerCAmelCase					:					bool = True			,						__lowerCAmelCase					:					str = "layer_norm"			,						__lowerCAmelCase					:					bool = False			,						)   ->	Tuple:
    super().__init__()
    _A      =       only_cross_attention

    _A      =       (num_embeds_ada_norm is not None) and norm_type == '''ada_norm_zero'''
    _A      =       (num_embeds_ada_norm is not None) and norm_type == '''ada_norm'''

    if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
       raise ValueError(
           f'''`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to'''
           f''' define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.'''      )

    # Define 3 blocks. Each block has its own normalization layer.
    # 1. Self-Attn
    if self.use_ada_layer_norm:
       _A      =       AdaLayerNorm(__lowerCAmelCase			,						__lowerCAmelCase      )
    elif self.use_ada_layer_norm_zero:
       _A      =       AdaLayerNormZero(__lowerCAmelCase			,						__lowerCAmelCase      )
    else:
       _A      =       nn.LayerNorm(__lowerCAmelCase			,						elementwise_affine=__lowerCAmelCase      )
    _A      =       Attention(
        query_dim=__lowerCAmelCase			,						heads=__lowerCAmelCase			,						dim_head=__lowerCAmelCase			,						dropout=__lowerCAmelCase			,						bias=__lowerCAmelCase			,						cross_attention_dim=cross_attention_dim if only_cross_attention else None			,						upcast_attention=__lowerCAmelCase			,						)

    # 2. Cross-Attn
    if cross_attention_dim is not None or double_self_attention:
       # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
       # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
       # the second cross attention block.
       _A      =       (
           AdaLayerNorm(__lowerCAmelCase			,						__lowerCAmelCase      )
           if self.use_ada_layer_norm
           else nn.LayerNorm(__lowerCAmelCase			,						elementwise_affine=__lowerCAmelCase      )
       )
       _A      =       Attention(
           query_dim=__lowerCAmelCase			,						cross_attention_dim=cross_attention_dim if not double_self_attention else None			,						heads=__lowerCAmelCase			,						dim_head=__lowerCAmelCase			,						dropout=__lowerCAmelCase			,						bias=__lowerCAmelCase			,						upcast_attention=__lowerCAmelCase			,						)  # is self-attn if encoder_hidden_states is none
    else:
       _A      =       None
       _A      =       None

    # 3. Feed-forward
    _A      =       nn.LayerNorm(__lowerCAmelCase			,						elementwise_affine=__lowerCAmelCase      )
    _A      =       FeedForward(__lowerCAmelCase			,						dropout=__lowerCAmelCase			,						activation_fn=__lowerCAmelCase			,						final_dropout=__lowerCAmelCase      )

    # let chunk size default to None
    _A      =       None
    _A      =       0

 def     snake_case_      (    self					:					str			,						__lowerCAmelCase					:					Optional[int]			,						__lowerCAmelCase					:					int      )   ->	Union[str, Any]:
    # Sets chunk feed-forward
    _A      =       chunk_size
    _A      =       dim

 def     snake_case_      (    self					:					int			,						__lowerCAmelCase					:					torch.FloatTensor			,						__lowerCAmelCase					:					Optional[torch.FloatTensor] = None			,						__lowerCAmelCase					:					Optional[torch.FloatTensor] = None			,						__lowerCAmelCase					:					Optional[torch.FloatTensor] = None			,						__lowerCAmelCase					:					Optional[torch.LongTensor] = None			,						__lowerCAmelCase					:					Dict[str, Any] = None			,						__lowerCAmelCase					:					Optional[torch.LongTensor] = None			,						)   ->	Dict:
    # Notice that normalization is always applied before the real computation in the following blocks.
    # 1. Self-Attention
    if self.use_ada_layer_norm:
       _A      =       self.norma(__lowerCAmelCase			,						__lowerCAmelCase      )
    elif self.use_ada_layer_norm_zero:
       _A  ,    _A  ,    _A  ,    _A  ,    _A      =       self.norma(
           __lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						hidden_dtype=hidden_states.dtype      )
    else:
       _A      =       self.norma(__lowerCAmelCase      )

    _A      =       cross_attention_kwargs if cross_attention_kwargs is not None else {}

    _A      =       self.attna(
        __lowerCAmelCase			,						encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None			,						attention_mask=__lowerCAmelCase			,						**__lowerCAmelCase			,						)
    if self.use_ada_layer_norm_zero:
       _A      =       gate_msa.unsqueeze(1      ) * attn_output
    _A      =       attn_output + hidden_states

    # 2. Cross-Attention
    if self.attna is not None:
       _A      =       (
           self.norma(__lowerCAmelCase			,						__lowerCAmelCase      ) if self.use_ada_layer_norm else self.norma(__lowerCAmelCase      )
       )

       _A      =       self.attna(
           __lowerCAmelCase			,						encoder_hidden_states=__lowerCAmelCase			,						attention_mask=__lowerCAmelCase			,						**__lowerCAmelCase			,						)
       _A      =       attn_output + hidden_states

    # 3. Feed-forward
    _A      =       self.norma(__lowerCAmelCase      )

    if self.use_ada_layer_norm_zero:
       _A      =       norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

    if self._chunk_size is not None:
       # "feed_forward_chunk_size" can be used to save memory
       if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
          raise ValueError(
              f'''`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.'''      )

       _A      =       norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
       _A      =       torch.cat(
           [self.ff(__lowerCAmelCase      ) for hid_slice in norm_hidden_states.chunk(__lowerCAmelCase			,						dim=self._chunk_dim      )]			,						dim=self._chunk_dim			,						)
    else:
       _A      =       self.ff(__lowerCAmelCase      )

    if self.use_ada_layer_norm_zero:
       _A      =       gate_mlp.unsqueeze(1      ) * ff_output

    _A      =       ff_output + hidden_states

    return hidden_states


class 	lowerCamelCase__	(       nn.Module):



 """simple docstring"""

 def __init__(    self					:					Optional[Any]			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					Optional[int] = None			,						__lowerCAmelCase					:					int = 4			,						__lowerCAmelCase					:					float = 0.0			,						__lowerCAmelCase					:					str = "geglu"			,						__lowerCAmelCase					:					bool = False			,						)   ->	int:
    super().__init__()
    _A      =       int(dim * mult      )
    _A      =       dim_out if dim_out is not None else dim

    if activation_fn == "gelu":
       _A      =       GELU(__lowerCAmelCase			,						__lowerCAmelCase      )
    if activation_fn == "gelu-approximate":
       _A      =       GELU(__lowerCAmelCase			,						__lowerCAmelCase			,						approximate='''tanh'''      )
    elif activation_fn == "geglu":
       _A      =       GEGLU(__lowerCAmelCase			,						__lowerCAmelCase      )
    elif activation_fn == "geglu-approximate":
       _A      =       ApproximateGELU(__lowerCAmelCase			,						__lowerCAmelCase      )

    _A      =       nn.ModuleList([]      )
    # project in
    self.net.append(__lowerCAmelCase      )
    # project dropout
    self.net.append(nn.Dropout(__lowerCAmelCase      )      )
    # project out
    self.net.append(nn.Linear(__lowerCAmelCase			,						__lowerCAmelCase      )      )
    # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
    if final_dropout:
       self.net.append(nn.Dropout(__lowerCAmelCase      )      )

 def     snake_case_      (    self					:					Tuple			,						__lowerCAmelCase					:					List[Any]      )   ->	Dict:
    for module in self.net:
       _A      =       module(__lowerCAmelCase      )
    return hidden_states


class 	lowerCamelCase__	(       nn.Module):



 """simple docstring"""

 def __init__(    self					:					Tuple			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					str = "none"      )   ->	Optional[Any]:
    super().__init__()
    _A      =       nn.Linear(__lowerCAmelCase			,						__lowerCAmelCase      )
    _A      =       approximate

 def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					Union[str, Any]      )   ->	Optional[int]:
    if gate.device.type != "mps":
       return F.gelu(__lowerCAmelCase			,						approximate=self.approximate      )
    # mps: gelu is not implemented for float16
    return F.gelu(gate.to(dtype=torch.floataa      )			,						approximate=self.approximate      ).to(dtype=gate.dtype      )

 def     snake_case_      (    self					:					int			,						__lowerCAmelCase					:					str      )   ->	List[Any]:
    _A      =       self.proj(__lowerCAmelCase      )
    _A      =       self.gelu(__lowerCAmelCase      )
    return hidden_states


class 	lowerCamelCase__	(       nn.Module):



 """simple docstring"""

 def __init__(    self					:					int			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					int      )   ->	Any:
    super().__init__()
    _A      =       nn.Linear(__lowerCAmelCase			,						dim_out * 2      )

 def     snake_case_      (    self					:					Optional[Any]			,						__lowerCAmelCase					:					Optional[Any]      )   ->	Optional[int]:
    if gate.device.type != "mps":
       return F.gelu(__lowerCAmelCase      )
    # mps: gelu is not implemented for float16
    return F.gelu(gate.to(dtype=torch.floataa      )      ).to(dtype=gate.dtype      )

 def     snake_case_      (    self					:					Optional[Any]			,						__lowerCAmelCase					:					Any      )   ->	Optional[int]:
    _A  ,    _A      =       self.proj(__lowerCAmelCase      ).chunk(2			,						dim=-1      )
    return hidden_states * self.gelu(__lowerCAmelCase      )


class 	lowerCamelCase__	(       nn.Module):



 """simple docstring"""

 def __init__(    self					:					Tuple			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					int      )   ->	Optional[int]:
    super().__init__()
    _A      =       nn.Linear(__lowerCAmelCase			,						__lowerCAmelCase      )

 def     snake_case_      (    self					:					int			,						__lowerCAmelCase					:					Optional[Any]      )   ->	Union[str, Any]:
    _A      =       self.proj(__lowerCAmelCase      )
    return x * torch.sigmoid(1.702 * x      )


class 	lowerCamelCase__	(       nn.Module):



 """simple docstring"""

 def __init__(    self					:					List[Any]			,						__lowerCAmelCase					:					List[Any]			,						__lowerCAmelCase					:					int      )   ->	str:
    super().__init__()
    _A      =       nn.Embedding(__lowerCAmelCase			,						__lowerCAmelCase      )
    _A      =       nn.SiLU()
    _A      =       nn.Linear(__lowerCAmelCase			,						embedding_dim * 2      )
    _A      =       nn.LayerNorm(__lowerCAmelCase			,						elementwise_affine=__lowerCAmelCase      )

 def     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					Optional[int]			,						__lowerCAmelCase					:					Optional[Any]      )   ->	int:
    _A      =       self.linear(self.silu(self.emb(__lowerCAmelCase      )      )      )
    _A  ,    _A      =       torch.chunk(__lowerCAmelCase			,						2      )
    _A      =       self.norm(__lowerCAmelCase      ) * (1 + scale) + shift
    return x


class 	lowerCamelCase__	(       nn.Module):



 """simple docstring"""

 def __init__(    self					:					str			,						__lowerCAmelCase					:					Optional[Any]			,						__lowerCAmelCase					:					Tuple      )   ->	str:
    super().__init__()

    _A      =       CombinedTimestepLabelEmbeddings(__lowerCAmelCase			,						__lowerCAmelCase      )

    _A      =       nn.SiLU()
    _A      =       nn.Linear(__lowerCAmelCase			,						6 * embedding_dim			,						bias=__lowerCAmelCase      )
    _A      =       nn.LayerNorm(__lowerCAmelCase			,						elementwise_affine=__lowerCAmelCase			,						eps=1E-6      )

 def     snake_case_      (    self					:					Optional[Any]			,						__lowerCAmelCase					:					Tuple			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					Any			,						__lowerCAmelCase					:					str=None      )   ->	Optional[Any]:
    _A      =       self.linear(self.silu(self.emb(__lowerCAmelCase			,						__lowerCAmelCase			,						hidden_dtype=__lowerCAmelCase      )      )      )
    _A  ,    _A  ,    _A  ,    _A  ,    _A  ,    _A      =       emb.chunk(6			,						dim=1      )
    _A      =       self.norm(__lowerCAmelCase      ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
    return x, gate_msa, shift_mlp, scale_mlp, gate_mlp


class 	lowerCamelCase__	(       nn.Module):



 """simple docstring"""

 def __init__(    self					:					Tuple			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					Optional[str] = None			,						__lowerCAmelCase					:					float = 1E-5      )   ->	Optional[Any]:
    super().__init__()
    _A      =       num_groups
    _A      =       eps

    if act_fn is None:
       _A      =       None
    else:
       _A      =       get_activation(__lowerCAmelCase      )

    _A      =       nn.Linear(__lowerCAmelCase			,						out_dim * 2      )

 def     snake_case_      (    self					:					List[Any]			,						__lowerCAmelCase					:					Union[str, Any]			,						__lowerCAmelCase					:					Any      )   ->	Union[str, Any]:
    if self.act:
       _A      =       self.act(__lowerCAmelCase      )
    _A      =       self.linear(__lowerCAmelCase      )
    _A      =       emb[:, :, None, None]
    _A  ,    _A      =       emb.chunk(2			,						dim=1      )

    _A      =       F.group_norm(__lowerCAmelCase			,						self.num_groups			,						eps=self.eps      )
    _A      =       x * (1 + scale) + shift
    return x








import math

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

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int = 3				)  ->     qiskit.result.counts.Counts:
   if isinstance(_snake_case	,   _snake_case				):
      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(_snake_case				) != number_of_qubits:
      raise ValueError('''number of qubits must be exact integer.'''				)
   if number_of_qubits > 10:
      raise ValueError('''number of qubits too large to simulate(>10).'''				)

   _A      =       QuantumRegister(_snake_case	,   '''qr'''				)
   _A      =       ClassicalRegister(_snake_case	,   '''cr'''				)

   _A      =       QuantumCircuit(_snake_case	,   _snake_case				)

   _A      =       number_of_qubits

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

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

   # measure all the qubits
   quantum_circuit.measure(_snake_case	,   _snake_case				)
   # simulate with 10000 shots
   _A      =       Aer.get_backend('''qasm_simulator'''				)
   _A      =       execute(_snake_case	,   _snake_case	,   shots=10_000				)

   return job.result().get_counts(_snake_case				)


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



