


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


UpperCAmelCase__     			=							[nn.LayerNorm]

UpperCAmelCase__     			=							logging.get_logger(__name__)

UpperCAmelCase__     			=							version.parse(version.parse(torch.__version__).base_version)

UpperCAmelCase__     			=							parsed_torch_version_base >= version.parse('''2.0''')
UpperCAmelCase__     			=							parsed_torch_version_base >= version.parse('''1.12''')
UpperCAmelCase__     			=							parsed_torch_version_base >= version.parse('''1.11''')
UpperCAmelCase__     			=							parsed_torch_version_base < version.parse('''1.11''')
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case  ,		__snake_case  ,		__snake_case  ,		self		)  ->    Any:







 """simple docstring"""


 from torch import _softmax_backward_data

 if is_torch_less_than_1_11:
  return _softmax_backward_data(__snake_case  ,		__snake_case  ,		parent.dim  ,		self		)
 else:
  return _softmax_backward_data(__snake_case  ,		__snake_case  ,		parent.dim  ,		self.dtype		)
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case  ,		__snake_case = 0		)  ->    nn.Linear:







 """simple docstring"""


 _lowercase							   =index.to(layer.weight.device		)
 _lowercase							   =layer.weight.index_select(__snake_case  ,		__snake_case		).clone().detach()
 if layer.bias is not None:
  if dim == 1:
   _lowercase							   =layer.bias.clone().detach()
  else:
   _lowercase							   =layer.bias[index].clone().detach()
 _lowercase							   =list(layer.weight.size()		)
 _lowercase							   =len(__snake_case		)
 _lowercase							   =nn.Linear(new_size[1]  ,		new_size[0]  ,		bias=layer.bias is not None		).to(layer.weight.device		)
 _lowercase							   =False
 new_layer.weight.copy_(W.contiguous()		)
 _lowercase							   =True
 if layer.bias is not None:
  _lowercase							   =False
  new_layer.bias.copy_(b.contiguous()		)
  _lowercase							   =True
 return new_layer







class    lowerCamelCase__     ( nn.Module):





     def __init__(self	,  UpperCAmelCase	,  UpperCAmelCase )  ->     Optional[Any]:
      super().__init__()
      _lowercase							   =nf
      _lowercase							   =nn.Parameter(torch.empty(UpperCAmelCase	,  UpperCAmelCase ) )
      _lowercase							   =nn.Parameter(torch.zeros(UpperCAmelCase ) )
      nn.init.normal_(self.weight	,  std=0.02 )





     def   __A      (self	,  UpperCAmelCase )  ->     List[str]:
      _lowercase							   =x.size()[:-1] + (self.nf,)
      _lowercase							   =torch.addmm(self.bias	,  x.view(-1	,  x.size(-1 ) )	,  self.weight )
      _lowercase							   =x.view(UpperCAmelCase )
      return x



def        UpperCAmelCase_      (							__snake_case  ,		__snake_case  ,		__snake_case = 1		)  ->    ConvaD:







 """simple docstring"""


 _lowercase							   =index.to(layer.weight.device		)
 _lowercase							   =layer.weight.index_select(__snake_case  ,		__snake_case		).clone().detach()
 if dim == 0:
  _lowercase							   =layer.bias.clone().detach()
 else:
  _lowercase							   =layer.bias[index].clone().detach()
 _lowercase							   =list(layer.weight.size()		)
 _lowercase							   =len(__snake_case		)
 _lowercase							   =ConvaD(new_size[1]  ,		new_size[0]		).to(layer.weight.device		)
 _lowercase							   =False
 new_layer.weight.copy_(W.contiguous()		)
 _lowercase							   =True
 _lowercase							   =False
 new_layer.bias.copy_(b.contiguous()		)
 _lowercase							   =True
 return new_layer
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case  ,		__snake_case = None		)  ->    Union[nn.Linear, ConvaD]:







 """simple docstring"""


 if isinstance(__snake_case  ,		nn.Linear		):
  return prune_linear_layer(__snake_case  ,		__snake_case  ,		dim=0 if dim is None else dim		)
 elif isinstance(__snake_case  ,		__snake_case		):
  return prune_convad_layer(__snake_case  ,		__snake_case  ,		dim=1 if dim is None else dim		)
 else:
  raise ValueError(F"Can't prune layer of class {layer.__class__}"		)
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case  ,		__snake_case  ,		*__snake_case		)  ->    torch.Tensor:







 """simple docstring"""


 assert len(__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
 _lowercase							   =len(inspect.signature(__snake_case		).parameters		)
 if num_args_in_forward_chunk_fn != len(__snake_case		):
  raise ValueError(
      F"forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(__snake_case		)} input "
      '''tensors are given'''		)

 if chunk_size > 0:
  _lowercase							   =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}"		)

  _lowercase							   =input_tensors[0].shape[chunk_dim] // chunk_size

  # chunk input tensor into tuples
  _lowercase							   =tuple(input_tensor.chunk(__snake_case  ,		dim=__snake_case		) for input_tensor in input_tensors		)
  # apply forward fn to every tuple
  _lowercase							   =tuple(forward_fn(*__snake_case		) for input_tensors_chunk in zip(*__snake_case		)		)
  # concatenate output at same dimension
  return torch.cat(__snake_case  ,		dim=__snake_case		)

 return forward_fn(*__snake_case		)
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case  ,		__snake_case  ,		__snake_case		)  ->    Tuple[Set[int], torch.LongTensor]:







 """simple docstring"""


 _lowercase							   =torch.ones(__snake_case  ,		__snake_case		)
 _lowercase							   =set(__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
  _lowercase							   =head - sum(1 if h < head else 0 for h in already_pruned_heads		)
  _lowercase							   =0
 _lowercase							   =mask.view(-1		).contiguous().eq(1		)
 _lowercase							   =torch.arange(len(__snake_case		)		)[mask].long()
 return heads, index
def        UpperCAmelCase_      (							*__snake_case  ,		__snake_case = None		)  ->    Tuple[torch.Tensor, ...]:







 """simple docstring"""


 return torch.meshgrid(*__snake_case  ,		indexing=__snake_case		)



def        UpperCAmelCase_      (							__snake_case		)  ->    Tuple[torch.device, int, int]:







 """simple docstring"""


 return tensor.device, storage_ptr(__snake_case		), storage_size(__snake_case		)





UpperCAmelCase__     			=							[0.33, 0.44, 0.55, 0.44, 0.33]
UpperCAmelCase__     			=							3
UpperCAmelCase__     			=							5

# Buffer Space (with Parameters Space)
UpperCAmelCase__     			=							[]
UpperCAmelCase__     			=							[]

# Machine Time
UpperCAmelCase__     			=							0
def        UpperCAmelCase_      (							__snake_case		)  ->    Tuple:







 """simple docstring"""


 global buffer_space, params_space, machine_time, K, m, t

 # Choosing Dynamical Systems (All)
 for key, value in enumerate(__snake_case		):
  # Evolution Parameter
  _lowercase							   =float(seed / value		)

  # Control Theory: Orbit Change
  _lowercase							   =(buffer_space[(key + 1) % m] + e) % 1

  # Control Theory: Trajectory Change
  _lowercase							   =(params_space[key] + e) % 1 + 3

  # Modification (Transition Function) - Jumps
  _lowercase							   =round(float(r * value * (1 - value)		)  ,		10		)
  _lowercase							   =r  # Saving to Parameters Space

 # Logistic Map
 assert max(__snake_case		) < 1
 assert max(__snake_case		) < 4

 # Machine Time
 machine_time += 1
def        UpperCAmelCase_      (							)  ->    Union[str, Any]:







 """simple docstring"""


 global buffer_space, params_space, machine_time, K, m, t

 # PRNG (Xorshift by George Marsaglia)
 def xorshift(__snake_case  ,		__snake_case		):
  x ^= y >> 13
  y ^= x << 17
  x ^= y >> 5
  return x

 # Choosing Dynamical Systems (Increment)
 _lowercase							   =machine_time % m

 # Evolution (Time Length)
 for _ in range(0  ,		__snake_case		):
  # Variables (Position + Parameters)
  _lowercase							   =params_space[key]
  _lowercase							   =buffer_space[key]

  # Modification (Transition Function) - Flow
  _lowercase							   =round(float(r * value * (1 - value)		)  ,		10		)
  _lowercase							   =(machine_time * 0.01 + r * 1.01) % 1 + 3

 # Choosing Chaotic Data
 _lowercase							   =int(buffer_space[(key + 2) % m] * (10**10)		)
 _lowercase							   =int(buffer_space[(key - 2) % m] * (10**10)		)

 # Machine Time
 machine_time += 1

 return xorshift(__snake_case  ,		__snake_case		) % 0XF_F_F_F_F_F_F_F
def        UpperCAmelCase_      (							)  ->    int:







 """simple docstring"""


 global buffer_space, params_space, machine_time, K, m, t

 _lowercase							   =K
 _lowercase							   =[0] * m
 _lowercase							   =0


if __name__ == "__main__":
  # Initialization
  reset()

  # Pushing Data (Input)
  import random

  UpperCAmelCase__     			=							random.sample(range(0xffff_ffff), 100)
  for chunk in message:
    push(chunk)

  # for controlling
  UpperCAmelCase__     			=							''''''

  # Pulling Data (Output)
  while inp in ("e", "E"):
    print(f'''{format(pull(), '#04x')}''')
    print(buffer_space)
    print(params_space)
    UpperCAmelCase__     			=							input('''(e)exit? ''').strip()

