code: 
  
  '''simple docstring'''
  
  
  
  
  
  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
  
  
  _lowerCAmelCase     :			Union[str, Any]    =[nn.LayerNorm]
  
  _lowerCAmelCase     :			List[Any]    =logging.get_logger(__name__)
  
  _lowerCAmelCase     :			Dict    =version.parse(version.parse(torch.__version__).base_version)
  
  _lowerCAmelCase     :			List[str]    =parsed_torch_version_base >= version.parse("""2.0""")
  _lowerCAmelCase     :			Optional[Any]    =parsed_torch_version_base >= version.parse("""1.12""")
  _lowerCAmelCase     :			Optional[int]    =parsed_torch_version_base >= version.parse("""1.11""")
  _lowerCAmelCase     :			Dict    =parsed_torch_version_base < version.parse("""1.11""")
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,self			):
  	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			):
  	UpperCAmelCase__:  Union[str, Any]		       =   index.to(layer.weight.device			)
  	UpperCAmelCase__:  int		       =   layer.weight.index_select(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			).clone().detach()
  	if layer.bias is not None:
  		if dim == 1:
  			UpperCAmelCase__:  Tuple		       =   layer.bias.clone().detach()
  		else:
  			UpperCAmelCase__:  Dict		       =   layer.bias[index].clone().detach()
  	UpperCAmelCase__:  Optional[Any]		       =   list(layer.weight.size()			)
  	UpperCAmelCase__:  Optional[int]		       =   len(SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  List[str]		       =   nn.Linear(new_size[1]  ,new_size[0]  ,bias=layer.bias is not None			).to(layer.weight.device			)
  	UpperCAmelCase__:  str		       =   False
  	new_layer.weight.copy_(W.contiguous()			)
  	UpperCAmelCase__:  Tuple		       =   True
  	if layer.bias is not None:
  		UpperCAmelCase__:  Tuple		       =   False
  		new_layer.bias.copy_(b.contiguous()			)
  		UpperCAmelCase__:  Tuple		       =   True
  	return new_layer
  
  class __UpperCamelCase    (							nn.Module		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					def __init__(							self							,			lowerCamelCase__							,			lowerCamelCase__	):
  						super().__init__()
  						UpperCAmelCase__:  List[str]		       =   nf
  						UpperCAmelCase__:  Any		       =   nn.Parameter(torch.empty(lowerCamelCase__							,			lowerCamelCase__	)	)
  						UpperCAmelCase__:  Union[str, Any]		       =   nn.Parameter(torch.zeros(lowerCamelCase__	)	)
  						nn.init.normal_(self.weight							,			std=0.02	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__	):
  						UpperCAmelCase__:  int		       =   x.size()[:-1] + (self.nf,)
  						UpperCAmelCase__:  int		       =   torch.addmm(self.bias							,			x.view(-1							,			x.size(-1	)	)							,			self.weight	)
  						UpperCAmelCase__:  Any		       =   x.view(lowerCamelCase__	)
  						return x
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE = 1			):
  	UpperCAmelCase__:  List[str]		       =   index.to(layer.weight.device			)
  	UpperCAmelCase__:  Optional[Any]		       =   layer.weight.index_select(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			).clone().detach()
  	if dim == 0:
  		UpperCAmelCase__:  Union[str, Any]		       =   layer.bias.clone().detach()
  	else:
  		UpperCAmelCase__:  List[str]		       =   layer.bias[index].clone().detach()
  	UpperCAmelCase__:  Any		       =   list(layer.weight.size()			)
  	UpperCAmelCase__:  List[str]		       =   len(SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  Union[str, Any]		       =   ConvaD(new_size[1]  ,new_size[0]			).to(layer.weight.device			)
  	UpperCAmelCase__:  List[str]		       =   False
  	new_layer.weight.copy_(W.contiguous()			)
  	UpperCAmelCase__:  Optional[Any]		       =   True
  	UpperCAmelCase__:  str		       =   False
  	new_layer.bias.copy_(b.contiguous()			)
  	UpperCAmelCase__:  List[Any]		       =   True
  	return new_layer
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE = None			):
  	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			):
  	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
  	UpperCAmelCase__:  Optional[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:
  		UpperCAmelCase__:  Dict		       =   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}"			)
  
  		UpperCAmelCase__:  List[str]		       =   input_tensors[0].shape[chunk_dim] // chunk_size
  
  		# chunk input tensor into tuples
  		UpperCAmelCase__:  List[Any]		       =   tuple(input_tensor.chunk(SCREAMING_SNAKE_CASE  ,dim=SCREAMING_SNAKE_CASE			) for input_tensor in input_tensors			)
  		# apply forward fn to every tuple
  		UpperCAmelCase__:  Optional[Any]		       =   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			):
  	UpperCAmelCase__:  Optional[int]		       =   torch.ones(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  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
  		UpperCAmelCase__:  Any		       =   head - sum(1 if h < head else 0 for h in already_pruned_heads			)
  		UpperCAmelCase__:  Optional[Any]		       =   0
  	UpperCAmelCase__:  Tuple		       =   mask.view(-1			).contiguous().eq(1			)
  	UpperCAmelCase__:  torch.LongTensor		       =   torch.arange(len(SCREAMING_SNAKE_CASE			)			)[mask].long()
  	return heads, index
  
  
  
  def  _A  (			*SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE = None			):
  	return torch.meshgrid(*SCREAMING_SNAKE_CASE  ,indexing=SCREAMING_SNAKE_CASE			)
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE			):
  	return tensor.device, storage_ptr(SCREAMING_SNAKE_CASE			), storage_size(SCREAMING_SNAKE_CASE			)
style_context: 
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,):
  	UpperCAmelCase__:  Tuple		       =   list(SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  int		       =   list(SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  Dict		       =   len(SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  int		       =   len(SCREAMING_SNAKE_CASE			)
  
  	UpperCAmelCase__:  Any		       =   [
  	    [0 for _ in range(len_destination_seq + 1			)] for _ in range(len_source_seq + 1			)
  	]
  	UpperCAmelCase__:  List[Any]		       =   [
  	    ["0" for _ in range(len_destination_seq + 1			)] for _ in range(len_source_seq + 1			)
  	]
  
  	for i in range(1  ,len_source_seq + 1			):
  		UpperCAmelCase__:  List[Any]		       =   i * delete_cost
  		UpperCAmelCase__:  str		       =   f"D{source_seq[i - 1]:c}"
  
  	for i in range(1  ,len_destination_seq + 1			):
  		UpperCAmelCase__:  Union[str, Any]		       =   i * insert_cost
  		UpperCAmelCase__:  Optional[Any]		       =   f"I{destination_seq[i - 1]:c}"
  
  	for i in range(1  ,len_source_seq + 1			):
  		for j in range(1  ,len_destination_seq + 1			):
  			if source_seq[i - 1] == destination_seq[j - 1]:
  				UpperCAmelCase__:  Any		       =   costs[i - 1][j - 1] + copy_cost
  				UpperCAmelCase__:  Tuple		       =   f"C{source_seq[i - 1]:c}"
  			else:
  				UpperCAmelCase__:  Union[str, Any]		       =   costs[i - 1][j - 1] + replace_cost
  				UpperCAmelCase__:  Optional[int]		       =   f"R{source_seq[i - 1]:c}" + str(destination_seq[j - 1]			)
  
  			if costs[i - 1][j] + delete_cost < costs[i][j]:
  				UpperCAmelCase__:  Optional[int]		       =   costs[i - 1][j] + delete_cost
  				UpperCAmelCase__:  int		       =   f"D{source_seq[i - 1]:c}"
  
  			if costs[i][j - 1] + insert_cost < costs[i][j]:
  				UpperCAmelCase__:  Any		       =   costs[i][j - 1] + insert_cost
  				UpperCAmelCase__:  Tuple		       =   f"I{destination_seq[j - 1]:c}"
  
  	return costs, ops
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	if i == 0 and j == 0:
  		return []
  	else:
  		if ops[i][j][0] == "C" or ops[i][j][0] == "R":
  			UpperCAmelCase__:  Union[str, Any]		       =   assemble_transformation(SCREAMING_SNAKE_CASE  ,i - 1  ,j - 1			)
  			seq.append(ops[i][j]			)
  			return seq
  		elif ops[i][j][0] == "D":
  			UpperCAmelCase__:  Optional[Any]		       =   assemble_transformation(SCREAMING_SNAKE_CASE  ,i - 1  ,SCREAMING_SNAKE_CASE			)
  			seq.append(ops[i][j]			)
  			return seq
  		else:
  			UpperCAmelCase__:  Dict		       =   assemble_transformation(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,j - 1			)
  			seq.append(ops[i][j]			)
  			return seq
  
  
  if __name__ == "__main__":
  					_lowerCAmelCase   ,  _lowerCAmelCase     :			Dict    =compute_transform_tables("""Python""", """Algorithms""", -1, 1, 2, 2)
  
  					_lowerCAmelCase     :			List[Any]    =len(operations)
  					_lowerCAmelCase     :			Optional[int]    =len(operations[0])
  					_lowerCAmelCase     :			Dict    =assemble_transformation(operations, m - 1, n - 1)
  
  					_lowerCAmelCase     :			int    =list("""Python""")
  					_lowerCAmelCase     :			Optional[int]    =0
  					_lowerCAmelCase     :			List[Any]    =0
  
  					with open("""min_cost.txt""", """w""") as file:
  										for op in sequence:
  															print("""""".join(string))
  
  															if op[0] == "C":
  																				file.write("""%-16s""" % """Copy %c""" % op[1])
  																				file.write("""\t\t\t""" + """""".join(string))
  																				file.write("""\r\n""")
  
  																				cost -= 1
  															elif op[0] == "R":
  																				_lowerCAmelCase     :			Tuple    =op[2]
  
  																				file.write("""%-16s""" % ("""Replace %c""" % op[1] + """ with """ + str(op[2])))
  																				file.write("""\t\t""" + """""".join(string))
  																				file.write("""\r\n""")
  
  																				cost += 1
  															elif op[0] == "D":
  																				string.pop(i)
  
  																				file.write("""%-16s""" % """Delete %c""" % op[1])
  																				file.write("""\t\t\t""" + """""".join(string))
  																				file.write("""\r\n""")
  
  																				cost += 2
  															else:
  																				string.insert(i, op[1])
  
  																				file.write("""%-16s""" % """Insert %c""" % op[1])
  																				file.write("""\t\t\t""" + """""".join(string))
  																				file.write("""\r\n""")
  
  																				cost += 2
  
  															i += 1
  
  										print("""""".join(string))
  										print("""Cost: """, cost)
  
  										file.write("""\r\nMinimum cost: """ + str(cost))