

import math

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

def 		lowerCamelCase__		(					_lowerCamelCase = 3   ):

    '''simple docstring'''



    if isinstance(SCREAMING_SNAKE_CASE_ ,   SCREAMING_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(SCREAMING_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).'   )

    _lowerCAmelCase       :   Any  =    QuantumRegister(SCREAMING_SNAKE_CASE_ ,   'qr'   )
    _lowerCAmelCase       :   List[Any]  =    ClassicalRegister(SCREAMING_SNAKE_CASE_ ,   'cr'   )

    _lowerCAmelCase       :   Optional[int]  =    QuantumCircuit(SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_   )

    _lowerCAmelCase       :   Optional[Any]  =    number_of_qubits

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

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

    # measure all the qubits
    quantum_circuit.measure(SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_   )
    # simulate with 10000 shots
    _lowerCAmelCase       :   str  =    Aer.get_backend('qasm_simulator'   )
    _lowerCAmelCase       :   List[Any]  =    execute(SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_ ,   shots=10000   )

    return job.result().get_counts(SCREAMING_SNAKE_CASE_   )


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








"""simple docstring"""






import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin







class 	__UpperCamelCase    (    a__							,							a__						):

  @register_to_config
  def __init__(		self    ,_A = 128    ,_A = 256    ,_A = 2_0_0_0.0    ,_A = 768    ,_A = 12    ,_A = 12    ,_A = 64    ,_A = 2048    ,_A = 0.1    ,):
      '''simple docstring'''

      super().__init__()

      _lowerCAmelCase       :   int  =    nn.Sequential(
          nn.Linear(_A    ,d_model * 4    ,bias=_A      )    ,nn.SiLU()    ,nn.Linear(d_model * 4    ,d_model * 4    ,bias=_A      )    ,nn.SiLU()    ,)

      _lowerCAmelCase       :   Any  =    nn.Embedding(_A    ,_A      )
      _lowerCAmelCase       :   Tuple  =    False

      _lowerCAmelCase       :   Union[str, Any]  =    nn.Linear(_A    ,_A    ,bias=_A      )

      _lowerCAmelCase       :   int  =    nn.Dropout(p=_A      )

      _lowerCAmelCase       :   int  =    nn.ModuleList()
      for lyr_num in range(_A      ):
          # FiLM conditional T5 decoder
          _lowerCAmelCase       :   Any  =    DecoderLayer(d_model=_A    ,d_kv=_A    ,num_heads=_A    ,d_ff=_A    ,dropout_rate=_A      )
          self.decoders.append(_A      )

      _lowerCAmelCase       :   Optional[Any]  =    TaLayerNorm(_A      )

      _lowerCAmelCase       :   List[str]  =    nn.Dropout(p=_A      )
      _lowerCAmelCase       :   Optional[Any]  =    nn.Linear(_A    ,_A    ,bias=_A      )

  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Dict  =    torch.mul(query_input.unsqueeze(-1      )    ,key_input.unsqueeze(-2      )      )
      return mask.unsqueeze(-3      )







  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase       :   Dict  =    decoder_input_tokens.shape
      assert decoder_noise_time.shape == (batch,)

      # decoder_noise_time is in [0, 1), so rescale to expected timing range.
      _lowerCAmelCase       :   Any  =    get_timestep_embedding(
          decoder_noise_time * self.config.max_decoder_noise_time    ,embedding_dim=self.config.d_model    ,max_period=self.config.max_decoder_noise_time    ,).to(dtype=self.dtype      )

      _lowerCAmelCase       :   Union[str, Any]  =    self.conditioning_emb(_A      ).unsqueeze(1      )

      assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

      _lowerCAmelCase       :   str  =    decoder_input_tokens.shape[1]

      # If we want to use relative positions for audio context, we can just offset
      # this sequence by the length of encodings_and_masks.
      _lowerCAmelCase       :   Union[str, Any]  =    torch.broadcast_to(
          torch.arange(_A    ,device=decoder_input_tokens.device      )    ,(batch, seq_length)    ,)

      _lowerCAmelCase       :   Any  =    self.position_encoding(_A      )

      _lowerCAmelCase       :   str  =    self.continuous_inputs_projection(_A      )
      inputs += position_encodings
      _lowerCAmelCase       :   int  =    self.dropout(_A      )

      # decoder: No padding present.
      _lowerCAmelCase       :   Union[str, Any]  =    torch.ones(
          decoder_input_tokens.shape[:2]    ,device=decoder_input_tokens.device    ,dtype=inputs.dtype      )

      # Translate encoding masks to encoder-decoder masks.
      _lowerCAmelCase       :   Optional[Any]  =    [(x, self.encoder_decoder_mask(_A    ,_A      )) for x, y in encodings_and_masks]

      # cross attend style: concat encodings
      _lowerCAmelCase       :   Dict  =    torch.cat([x[0] for x in encodings_and_encdec_masks]    ,dim=1      )
      _lowerCAmelCase       :   Tuple  =    torch.cat([x[1] for x in encodings_and_encdec_masks]    ,dim=-1      )

      for lyr in self.decoders:
          _lowerCAmelCase       :   Tuple  =    lyr(
              _A    ,conditioning_emb=_A    ,encoder_hidden_states=_A    ,encoder_attention_mask=_A    ,)[0]

      _lowerCAmelCase       :   Any  =    self.decoder_norm(_A      )
      _lowerCAmelCase       :   List[Any]  =    self.post_dropout(_A      )

      _lowerCAmelCase       :   int  =    self.spec_out(_A      )
      return spec_out







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A=1E-6      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Optional[Any]  =    nn.ModuleList()

      # cond self attention: layer 0
      self.layer.append(
          TaLayerSelfAttentionCond(d_model=_A    ,d_kv=_A    ,num_heads=_A    ,dropout_rate=_A      )      )

      # cross attention: layer 1
      self.layer.append(
          TaLayerCrossAttention(
              d_model=_A    ,d_kv=_A    ,num_heads=_A    ,dropout_rate=_A    ,layer_norm_epsilon=_A    ,)      )

      # Film Cond MLP + dropout: last layer
      self.layer.append(
          TaLayerFFCond(d_model=_A    ,d_ff=_A    ,dropout_rate=_A    ,layer_norm_epsilon=_A      )      )







  def __lowerCamelCase		(		self    ,_A    ,_A=None    ,_A=None    ,_A=None    ,_A=None    ,_A=None    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   Any  =    self.layer[0](
          _A    ,conditioning_emb=_A    ,attention_mask=_A    ,)

      if encoder_hidden_states is not None:
          _lowerCAmelCase       :   Any  =    torch.where(encoder_attention_mask > 0    ,0    ,-1E10      ).to(
              encoder_hidden_states.dtype      )

          _lowerCAmelCase       :   str  =    self.layer[1](
              _A    ,key_value_states=_A    ,attention_mask=_A    ,)

      # Apply Film Conditional Feed Forward layer
      _lowerCAmelCase       :   Optional[Any]  =    self.layer[-1](_A    ,_A      )

      return (hidden_states,)







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Union[str, Any]  =    TaLayerNorm(_A      )
      _lowerCAmelCase       :   Any  =    TaFiLMLayer(in_features=d_model * 4    ,out_features=_A      )
      _lowerCAmelCase       :   Dict  =    Attention(query_dim=_A    ,heads=_A    ,dim_head=_A    ,out_bias=_A    ,scale_qk=_A      )
      _lowerCAmelCase       :   Tuple  =    nn.Dropout(_A      )







  def __lowerCamelCase		(		self    ,_A    ,_A=None    ,_A=None    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    self.layer_norm(_A      )

      if conditioning_emb is not None:
          _lowerCAmelCase       :   Union[str, Any]  =    self.FiLMLayer(_A    ,_A      )

      # Self-attention block
      _lowerCAmelCase       :   Union[str, Any]  =    self.attention(_A      )

      _lowerCAmelCase       :   Optional[Any]  =    hidden_states + self.dropout(_A      )

      return hidden_states







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   List[str]  =    Attention(query_dim=_A    ,heads=_A    ,dim_head=_A    ,out_bias=_A    ,scale_qk=_A      )
      _lowerCAmelCase       :   Optional[int]  =    TaLayerNorm(_A    ,eps=_A      )
      _lowerCAmelCase       :   Tuple  =    nn.Dropout(_A      )







  def __lowerCamelCase		(		self    ,_A    ,_A=None    ,_A=None    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    self.layer_norm(_A      )
      _lowerCAmelCase       :   str  =    self.attention(
          _A    ,encoder_hidden_states=_A    ,attention_mask=attention_mask.squeeze(1      )    ,)
      _lowerCAmelCase       :   Any  =    hidden_states + self.dropout(_A      )
      return layer_output







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Optional[int]  =    TaDenseGatedActDense(d_model=_A    ,d_ff=_A    ,dropout_rate=_A      )
      _lowerCAmelCase       :   Tuple  =    TaFiLMLayer(in_features=d_model * 4    ,out_features=_A      )
      _lowerCAmelCase       :   Any  =    TaLayerNorm(_A    ,eps=_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    nn.Dropout(_A      )







  def __lowerCamelCase		(		self    ,_A    ,_A=None      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    self.layer_norm(_A      )
      if conditioning_emb is not None:
          _lowerCAmelCase       :   Union[str, Any]  =    self.film(_A    ,_A      )

      _lowerCAmelCase       :   str  =    self.DenseReluDense(_A      )
      _lowerCAmelCase       :   Tuple  =    hidden_states + self.dropout(_A      )
      return hidden_states







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Union[str, Any]  =    nn.Linear(_A    ,_A    ,bias=_A      )
      _lowerCAmelCase       :   Any  =    nn.Linear(_A    ,_A    ,bias=_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    nn.Linear(_A    ,_A    ,bias=_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    nn.Dropout(_A      )
      _lowerCAmelCase       :   int  =    NewGELUActivation()







  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    self.act(self.wi_a(_A      )      )
      _lowerCAmelCase       :   Optional[int]  =    self.wi_a(_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    hidden_gelu * hidden_linear
      _lowerCAmelCase       :   Dict  =    self.dropout(_A      )

      _lowerCAmelCase       :   Dict  =    self.wo(_A      )
      return hidden_states







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A=1E-6      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Union[str, Any]  =    nn.Parameter(torch.ones(_A      )      )
      _lowerCAmelCase       :   Optional[int]  =    eps







  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    hidden_states.to(torch.floataa      ).pow(2      ).mean(-1    ,keepdim=_A      )
      _lowerCAmelCase       :   List[Any]  =    hidden_states * torch.rsqrt(variance + self.variance_epsilon      )

      # convert into half-precision if necessary
      if self.weight.dtype in [torch.floataa, torch.bfloataa]:
          _lowerCAmelCase       :   Optional[int]  =    hidden_states.to(self.weight.dtype      )

      return self.weight * hidden_states







class 	__UpperCamelCase    (    nn.Module						):

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi      ) * (input + 0.0_4_4_7_1_5 * torch.pow(_A    ,3.0      ))      ))







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   List[str]  =    nn.Linear(_A    ,out_features * 2    ,bias=_A      )







  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    self.scale_bias(_A      )
      _lowerCAmelCase,					_lowerCAmelCase       :   List[Any]  =    torch.chunk(_A    ,2    ,-1      )
      _lowerCAmelCase       :   List[Any]  =    x * (1 + scale) + shift
      return x





