




import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


SCREAMING_SNAKE_CASE__	:				Any  =       [nn.LayerNorm]

SCREAMING_SNAKE_CASE__	:				Dict  =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__	:				Tuple  =       version.parse(version.parse(torch.__version__).base_version)

SCREAMING_SNAKE_CASE__	:				List[str]  =       parsed_torch_version_base >= version.parse('2.0')
SCREAMING_SNAKE_CASE__	:				List[str]  =       parsed_torch_version_base >= version.parse('1.12')
SCREAMING_SNAKE_CASE__	:				Optional[int]  =       parsed_torch_version_base >= version.parse('1.11')
SCREAMING_SNAKE_CASE__	:				str  =       parsed_torch_version_base < version.parse('1.11')

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,self   )     ->			str:
       from torch import _softmax_backward_data

       if is_torch_less_than_1_11:
              return _softmax_backward_data(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,parent.dim			,self   )
       else:
              return _softmax_backward_data(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,parent.dim			,self.dtype   )

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE = 0   )     ->			nn.Linear:
       lowerCamelCase       :				Dict      			= index.to(layer.weight.device   )
       lowerCamelCase       :				Dict      			= layer.weight.index_select(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   ).clone().detach()
       if layer.bias is not None:
              if dim == 1:
                     lowerCamelCase       :				Union[str, Any]      			= layer.bias.clone().detach()
              else:
                     lowerCamelCase       :				Union[str, Any]      			= layer.bias[index].clone().detach()
       lowerCamelCase       :				Optional[Any]      			= list(layer.weight.size()   )
       lowerCamelCase       :				str      			= len(_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				Union[str, Any]      			= nn.Linear(new_size[1]			,new_size[0]			,bias=layer.bias is not None   ).to(layer.weight.device   )
       lowerCamelCase       :				Optional[Any]      			= False
       new_layer.weight.copy_(W.contiguous()   )
       lowerCamelCase       :				Any      			= True
       if layer.bias is not None:
              lowerCamelCase       :				List[Any]      			= False
              new_layer.bias.copy_(b.contiguous()   )
              lowerCamelCase       :				Optional[Any]      			= True
       return new_layer




class 		UpperCamelCase__     (nn.Module					):

    '''simple docstring'''



    def __init__(   self					,  UpperCamelCase__					,  UpperCamelCase__					)					->				Tuple:
           super().__init__()
           lowerCamelCase       :				Any      			= nf
           lowerCamelCase       :				Any      			= nn.Parameter(torch.empty(UpperCamelCase__					,  UpperCamelCase__					)					)
           lowerCamelCase       :				List[Any]      			= nn.Parameter(torch.zeros(UpperCamelCase__					)					)
           nn.init.normal_(self.weight					,  std=0.02					)







    def    _lowercase					(   self					,  UpperCamelCase__					)					->				List[Any]:
           lowerCamelCase       :				Optional[Any]      			= x.size()[:-1] + (self.nf,)
           lowerCamelCase       :				int      			= torch.addmm(self.bias					,  x.view(-1					,  x.size(-1					)					)					,  self.weight					)
           lowerCamelCase       :				Any      			= x.view(UpperCamelCase__					)
           return x




def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE = 1   )     ->			ConvaD:
       lowerCamelCase       :				Optional[Any]      			= index.to(layer.weight.device   )
       lowerCamelCase       :				List[Any]      			= layer.weight.index_select(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   ).clone().detach()
       if dim == 0:
              lowerCamelCase       :				Tuple      			= layer.bias.clone().detach()
       else:
              lowerCamelCase       :				str      			= layer.bias[index].clone().detach()
       lowerCamelCase       :				Optional[Any]      			= list(layer.weight.size()   )
       lowerCamelCase       :				Tuple      			= len(_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				List[str]      			= ConvaD(new_size[1]			,new_size[0]   ).to(layer.weight.device   )
       lowerCamelCase       :				Tuple      			= False
       new_layer.weight.copy_(W.contiguous()   )
       lowerCamelCase       :				Dict      			= True
       lowerCamelCase       :				Union[str, Any]      			= False
       new_layer.bias.copy_(b.contiguous()   )
       lowerCamelCase       :				Any      			= True
       return new_layer

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE = None   )     ->			Union[nn.Linear, ConvaD]:
       if isinstance(_SCREAMING_SNAKE_CASE			,nn.Linear   ):
              return prune_linear_layer(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,dim=0 if dim is None else dim   )
       elif isinstance(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   ):
              return prune_convad_layer(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,dim=1 if dim is None else dim   )
       else:
              raise ValueError(f'''Can\'t prune layer of class {layer.__class__}'''   )

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,*_SCREAMING_SNAKE_CASE   )     ->			torch.Tensor:
       assert len(_SCREAMING_SNAKE_CASE   ) > 0, f'''{input_tensors} has to be a tuple/list of tensors'''

       # inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
       lowerCamelCase       :				Union[str, Any]      			= len(inspect.signature(_SCREAMING_SNAKE_CASE   ).parameters   )
       if num_args_in_forward_chunk_fn != len(_SCREAMING_SNAKE_CASE   ):
              raise ValueError(
                  f'''forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(_SCREAMING_SNAKE_CASE   )} input '''
                  "tensors are given"   )

       if chunk_size > 0:
              lowerCamelCase       :				Any      			= input_tensors[0].shape[chunk_dim]
              for input_tensor in input_tensors:
                     if input_tensor.shape[chunk_dim] != tensor_shape:
                            raise ValueError(
                                f'''All input tenors have to be of the same shape: {tensor_shape}, '''
                                f'''found shape {input_tensor.shape[chunk_dim]}'''   )

              if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
                     raise ValueError(
                         f'''The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk '''
                         f'''size {chunk_size}'''   )

              lowerCamelCase       :				Dict      			= input_tensors[0].shape[chunk_dim] // chunk_size

              # chunk input tensor into tuples
              lowerCamelCase       :				Optional[Any]      			= tuple(input_tensor.chunk(_SCREAMING_SNAKE_CASE			,dim=_SCREAMING_SNAKE_CASE   ) for input_tensor in input_tensors   )
              # apply forward fn to every tuple
              lowerCamelCase       :				List[str]      			= tuple(forward_fn(*_SCREAMING_SNAKE_CASE   ) for input_tensors_chunk in zip(*_SCREAMING_SNAKE_CASE   )   )
              # concatenate output at same dimension
              return torch.cat(_SCREAMING_SNAKE_CASE			,dim=_SCREAMING_SNAKE_CASE   )

       return forward_fn(*_SCREAMING_SNAKE_CASE   )

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			Tuple[Set[int], torch.LongTensor]:
       lowerCamelCase       :				Any      			= torch.ones(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				int      			= set(_SCREAMING_SNAKE_CASE   ) - already_pruned_heads  # Convert to set and remove already pruned heads
       for head in heads:
              # Compute how many pruned heads are before the head and move the index accordingly
              lowerCamelCase       :				str      			= head - sum(1 if h < head else 0 for h in already_pruned_heads   )
              lowerCamelCase       :				Dict      			= 0
       lowerCamelCase       :				Dict      			= mask.view(-1   ).contiguous().eq(1   )
       lowerCamelCase       :				torch.LongTensor      			= torch.arange(len(_SCREAMING_SNAKE_CASE   )   )[mask].long()
       return heads, index

def 					A				(				*_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE = None   )     ->			Tuple[torch.Tensor, ...]:
       return torch.meshgrid(*_SCREAMING_SNAKE_CASE			,indexing=_SCREAMING_SNAKE_CASE   )


def 					A				(				_SCREAMING_SNAKE_CASE   )     ->			Tuple[torch.device, int, int]:
       return tensor.device, storage_ptr(_SCREAMING_SNAKE_CASE   ), storage_size(_SCREAMING_SNAKE_CASE   )












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)}''')






