





import math

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




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE = 3		)    ->       qiskit.result.counts.Counts:
  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).'		)

  lowercase__           =  QuantumRegister(_SCREAMING_SNAKE_CASE		,						'qr'		)
  lowercase__           =  ClassicalRegister(_SCREAMING_SNAKE_CASE		,						'cr'		)

  lowercase__           =  QuantumCircuit(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)

  lowercase__           =  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
  lowercase__           =  Aer.get_backend('qasm_simulator'		)
  lowercase__           =  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)}'''
      )









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     SCREAMING_SNAKE_CASE       (UpperCAmelCase      ,     UpperCAmelCase				):


   @register_to_config
   def __init__(   self      :    str      ,					a      :    int = 128      ,					a      :    int = 256      ,					a      :    float = 2000.0      ,					a      :    int = 768      ,					a      :    int = 12      ,					a      :    int = 12      ,					a      :    int = 64      ,					a      :    int = 2_048      ,					a      :    float = 0.1      ,					)->       Any:





     """simple docstring"""







     super().__init__()

     lowercase__           =  nn.Sequential(
         nn.Linear(a      ,					d_model * 4      ,					bias=a	)      ,					nn.SiLU()      ,					nn.Linear(d_model * 4      ,					d_model * 4      ,					bias=a	)      ,					nn.SiLU()      ,					)

     lowercase__           =  nn.Embedding(a      ,					a	)
     lowercase__           =  False

     lowercase__           =  nn.Linear(a      ,					a      ,					bias=a	)

     lowercase__           =  nn.Dropout(p=a	)

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

     lowercase__           =  TaLayerNorm(a	)

     lowercase__           =  nn.Dropout(p=a	)
     lowercase__           =  nn.Linear(a      ,					a      ,					bias=a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    str      ,					a      :    int      ,					a      :    List[Any]	)->       str:





     """simple docstring"""







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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]      ,					a      :    Dict      ,					a      :    int      ,					a      :    int	)->       int:





     """simple docstring"""







     lowercase__      ,   lowercase__      ,   lowercase__           =  decoder_input_tokens.shape
     assert decoder_noise_time.shape == (batch,)

     # decoder_noise_time is in [0, 1), so rescale to expected timing range.
     lowercase__           =  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	)

     lowercase__           =  self.conditioning_emb(a	).unsqueeze(1	)

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

     lowercase__           =  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.
     lowercase__           =  torch.broadcast_to(
         torch.arange(a      ,					device=decoder_input_tokens.device	)      ,					(batch, seq_length)      ,					)

     lowercase__           =  self.position_encoding(a	)

     lowercase__           =  self.continuous_inputs_projection(a	)
     inputs += position_encodings
     lowercase__           =  self.dropout(a	)

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

     # Translate encoding masks to encoder-decoder masks.
     lowercase__           =  [(x, self.encoder_decoder_mask(a      ,					a	)) for x, y in encodings_and_masks]

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

     for lyr in self.decoders:
       lowercase__           =  lyr(
           a      ,					conditioning_emb=a      ,					encoder_hidden_states=a      ,					encoder_attention_mask=a      ,					)[0]

     lowercase__           =  self.decoder_norm(a	)
     lowercase__           =  self.post_dropout(a	)

     lowercase__           =  self.spec_out(a	)
     return spec_out






class     SCREAMING_SNAKE_CASE       (nn.Module				):


   def __init__(   self      :    Dict      ,					a      :    List[Any]      ,					a      :    str      ,					a      :    Union[str, Any]      ,					a      :    Optional[int]      ,					a      :    Optional[int]      ,					a      :    Tuple=1E-6	)->       Union[str, Any]:





     """simple docstring"""







     super().__init__()
     lowercase__           =  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 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple      ,					a      :    str      ,					a      :    Union[str, Any]=None      ,					a      :    Dict=None      ,					a      :    Union[str, Any]=None      ,					a      :    List[Any]=None      ,					a      :    List[str]=None      ,					)->       List[Any]:





     """simple docstring"""







     lowercase__           =  self.layer[0](
         a      ,					conditioning_emb=a      ,					attention_mask=a      ,					)

     if encoder_hidden_states is not None:
       lowercase__           =  torch.where(encoder_attention_mask > 0      ,					0      ,					-1E1_0	).to(
           encoder_hidden_states.dtype	)

       lowercase__           =  self.layer[1](
           a      ,					key_value_states=a      ,					attention_mask=a      ,					)

     # Apply Film Conditional Feed Forward layer
     lowercase__           =  self.layer[-1](a      ,					a	)

     return (hidden_states,)






class     SCREAMING_SNAKE_CASE       (nn.Module				):


   def __init__(   self      :    List[str]      ,					a      :    List[Any]      ,					a      :    Tuple      ,					a      :    Union[str, Any]      ,					a      :    int	)->       List[str]:





     """simple docstring"""







     super().__init__()
     lowercase__           =  TaLayerNorm(a	)
     lowercase__           =  TaFiLMLayer(in_features=d_model * 4      ,					out_features=a	)
     lowercase__           =  Attention(query_dim=a      ,					heads=a      ,					dim_head=a      ,					out_bias=a      ,					scale_qk=a	)
     lowercase__           =  nn.Dropout(a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple      ,					a      :    List[Any]      ,					a      :    Union[str, Any]=None      ,					a      :    Tuple=None      ,					)->       int:





     """simple docstring"""







     lowercase__           =  self.layer_norm(a	)

     if conditioning_emb is not None:
       lowercase__           =  self.FiLMLayer(a      ,					a	)

     # Self-attention block
     lowercase__           =  self.attention(a	)

     lowercase__           =  hidden_states + self.dropout(a	)

     return hidden_states






class     SCREAMING_SNAKE_CASE       (nn.Module				):


   def __init__(   self      :    Optional[int]      ,					a      :    Optional[int]      ,					a      :    int      ,					a      :    Optional[int]      ,					a      :    Dict      ,					a      :    Any	)->       Optional[int]:





     """simple docstring"""







     super().__init__()
     lowercase__           =  Attention(query_dim=a      ,					heads=a      ,					dim_head=a      ,					out_bias=a      ,					scale_qk=a	)
     lowercase__           =  TaLayerNorm(a      ,					eps=a	)
     lowercase__           =  nn.Dropout(a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    int      ,					a      :    List[Any]      ,					a      :    str=None      ,					a      :    Dict=None      ,					)->       int:





     """simple docstring"""







     lowercase__           =  self.layer_norm(a	)
     lowercase__           =  self.attention(
         a      ,					encoder_hidden_states=a      ,					attention_mask=attention_mask.squeeze(1	)      ,					)
     lowercase__           =  hidden_states + self.dropout(a	)
     return layer_output






class     SCREAMING_SNAKE_CASE       (nn.Module				):


   def __init__(   self      :    Dict      ,					a      :    str      ,					a      :    int      ,					a      :    List[Any]      ,					a      :    List[str]	)->       Dict:





     """simple docstring"""







     super().__init__()
     lowercase__           =  TaDenseGatedActDense(d_model=a      ,					d_ff=a      ,					dropout_rate=a	)
     lowercase__           =  TaFiLMLayer(in_features=d_model * 4      ,					out_features=a	)
     lowercase__           =  TaLayerNorm(a      ,					eps=a	)
     lowercase__           =  nn.Dropout(a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]      ,					a      :    List[Any]      ,					a      :    List[Any]=None	)->       List[Any]:





     """simple docstring"""







     lowercase__           =  self.layer_norm(a	)
     if conditioning_emb is not None:
       lowercase__           =  self.film(a      ,					a	)

     lowercase__           =  self.DenseReluDense(a	)
     lowercase__           =  hidden_states + self.dropout(a	)
     return hidden_states






class     SCREAMING_SNAKE_CASE       (nn.Module				):


   def __init__(   self      :    Optional[Any]      ,					a      :    Tuple      ,					a      :    Tuple      ,					a      :    Dict	)->       Union[str, Any]:





     """simple docstring"""







     super().__init__()
     lowercase__           =  nn.Linear(a      ,					a      ,					bias=a	)
     lowercase__           =  nn.Linear(a      ,					a      ,					bias=a	)
     lowercase__           =  nn.Linear(a      ,					a      ,					bias=a	)
     lowercase__           =  nn.Dropout(a	)
     lowercase__           =  NewGELUActivation()


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    str	)->       str:





     """simple docstring"""







     lowercase__           =  self.act(self.wi_a(a	)	)
     lowercase__           =  self.wi_a(a	)
     lowercase__           =  hidden_gelu * hidden_linear
     lowercase__           =  self.dropout(a	)

     lowercase__           =  self.wo(a	)
     return hidden_states






class     SCREAMING_SNAKE_CASE       (nn.Module				):


   def __init__(   self      :    Dict      ,					a      :    Dict      ,					a      :    Dict=1E-6	)->       Optional[int]:





     """simple docstring"""







     super().__init__()
     lowercase__           =  nn.Parameter(torch.ones(a	)	)
     lowercase__           =  eps


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    Optional[Any]	)->       List[str]:





     """simple docstring"""







     lowercase__           =  hidden_states.to(torch.floataa	).pow(2	).mean(-1      ,					keepdim=a	)
     lowercase__           =  hidden_states * torch.rsqrt(variance + self.variance_epsilon	)

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

     return self.weight * hidden_states






class     SCREAMING_SNAKE_CASE       (nn.Module				):


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple      ,					a      :    torch.Tensor	)->       torch.Tensor:





     """simple docstring"""







     return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi	) * (input + 0.044715 * torch.pow(a      ,					3.0	))	))






class     SCREAMING_SNAKE_CASE       (nn.Module				):


   def __init__(   self      :    int      ,					a      :    Optional[Any]      ,					a      :    Any	)->       Optional[Any]:





     """simple docstring"""







     super().__init__()
     lowercase__           =  nn.Linear(a      ,					out_features * 2      ,					bias=a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]      ,					a      :    Tuple      ,					a      :    Dict	)->       Optional[int]:





     """simple docstring"""







     lowercase__           =  self.scale_bias(a	)
     lowercase__      ,   lowercase__           =  torch.chunk(a      ,					2      ,					-1	)
     lowercase__           =  x * (1 + scale) + shift
     return x


