






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					    =	[nn.LayerNorm]

lowerCamelCase					    =	logging.get_logger(__name__)

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

lowerCamelCase					    =	parsed_torch_version_base >= version.parse('''2.0''')
lowerCamelCase					    =	parsed_torch_version_base >= version.parse('''1.12''')
lowerCamelCase					    =	parsed_torch_version_base >= version.parse('''1.11''')
lowerCamelCase					    =	parsed_torch_version_base < version.parse('''1.11''')
def 							UpperCAmelCase   ( _lowercase,   _lowercase,   _lowercase,   _lowercase,   self   ):


    '''simple docstring'''



    from torch import _softmax_backward_data

    if is_torch_less_than_1_11:
        return _softmax_backward_data(_lowercase,   _lowercase,   parent.dim,   self   )
    else:
        return _softmax_backward_data(_lowercase,   _lowercase,   parent.dim,   self.dtype   )
def 							UpperCAmelCase   ( _lowercase,   _lowercase,   _lowercase = 0   ):


    '''simple docstring'''



    _lowercase     			=						index.to(layer.weight.device   )
    _lowercase     			=						layer.weight.index_select(_lowercase,   _lowercase   ).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(_lowercase   )
    _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        snake_case_				(							nn.Module   ):







   """simple docstring"""



   def __init__(     self:						Any ,							snake_case__:						Tuple ,							snake_case__:						Any						)	->		str:


       """simple docstring"""



       super().__init__()
       _lowercase     			=						nf
       _lowercase     			=						nn.Parameter(torch.empty(snake_case__ ,							snake_case__						)						)
       _lowercase     			=						nn.Parameter(torch.zeros(snake_case__						)						)
       nn.init.normal_(self.weight ,							std=0.02						)

   def       SCREAMING_SNAKE_CASE__					(     self:						int ,							snake_case__:						Dict						)	->		Optional[int]:


       """simple docstring"""



       _lowercase     			=						x.size()[:-1] + (self.nf,)
       _lowercase     			=						torch.addmm(self.bias ,							x.view(-1 ,							x.size(-1						)						) ,							self.weight						)
       _lowercase     			=						x.view(snake_case__						)
       return x






def 							UpperCAmelCase   ( _lowercase,   _lowercase,   _lowercase = 1   ):


    '''simple docstring'''



    _lowercase     			=						index.to(layer.weight.device   )
    _lowercase     			=						layer.weight.index_select(_lowercase,   _lowercase   ).clone().detach()
    if dim == 0:
        _lowercase     			=						layer.bias.clone().detach()
    else:
        _lowercase     			=						layer.bias[index].clone().detach()
    _lowercase     			=						list(layer.weight.size()   )
    _lowercase     			=						len(_lowercase   )
    _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   ( _lowercase,   _lowercase,   _lowercase = None   ):


    '''simple docstring'''



    if isinstance(_lowercase,   nn.Linear   ):
        return prune_linear_layer(_lowercase,   _lowercase,   dim=0 if dim is None else dim   )
    elif isinstance(_lowercase,   _lowercase   ):
        return prune_convad_layer(_lowercase,   _lowercase,   dim=1 if dim is None else dim   )
    else:
        raise ValueError(f'''Can\'t prune layer of class {layer.__class__}'''   )
def 							UpperCAmelCase   ( _lowercase,   _lowercase,   _lowercase,   *_lowercase   ):


    '''simple docstring'''



    assert len(_lowercase   ) > 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(_lowercase   ).parameters   )
    if num_args_in_forward_chunk_fn != len(_lowercase   ):
        raise ValueError(
            f'''forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(_lowercase   )} 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(_lowercase,   dim=_lowercase   ) for input_tensor in input_tensors   )
        # apply forward fn to every tuple
        _lowercase     			=						tuple(forward_fn(*_lowercase   ) for input_tensors_chunk in zip(*_lowercase   )   )
        # concatenate output at same dimension
        return torch.cat(_lowercase,   dim=_lowercase   )

    return forward_fn(*_lowercase   )
def 							UpperCAmelCase   ( _lowercase,   _lowercase,   _lowercase,   _lowercase   ):


    '''simple docstring'''



    _lowercase     			=						torch.ones(_lowercase,   _lowercase   )
    _lowercase     			=						set(_lowercase   ) - 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(_lowercase   )   )[mask].long()
    return heads, index
def 							UpperCAmelCase   ( *_lowercase,   _lowercase = None   ):


    '''simple docstring'''



    return torch.meshgrid(*_lowercase,   indexing=_lowercase   )
def 							UpperCAmelCase   ( _lowercase   ):


    '''simple docstring'''



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








import torch

from accelerate import Accelerator, DistributedType

class        snake_case_				:







   """simple docstring"""



   def __enter__(     self:						int						)	->		Tuple:


       """simple docstring"""



       if self.enabled:
           _lowercase     			=						self.model.no_sync()
           self.model_sync_obj.__enter__()

       return self

   def __exit__(     self:						str ,							snake_case__:						Tuple ,							snake_case__:						int ,							snake_case__:						Tuple						)	->		Any:


       """simple docstring"""



       if self.enabled:
           # Average all models on exit
           self._sync_and_avg_model_params()
           self.model_sync_obj.__exit__(snake_case__ ,							snake_case__ ,							snake_case__						)

   def __init__(     self:						int ,							snake_case__:						Accelerator ,							snake_case__:						torch.nn.Module ,							snake_case__:						int ,							snake_case__:						bool = True						)	->		Optional[Any]:


       """simple docstring"""



       if accelerator.distributed_type not in [
           DistributedType.NO,
           DistributedType.MULTI_CPU,
           DistributedType.MULTI_GPU,
       ]:
           raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'						)
       _lowercase     			=						enabled and accelerator.distributed_type != DistributedType.NO
       _lowercase     			=						0
       if self.enabled:
           _lowercase     			=						accelerator
           _lowercase     			=						model
           _lowercase     			=						local_sgd_steps

   def       SCREAMING_SNAKE_CASE__					(     self:						str						)	->		List[Any]:


       """simple docstring"""



       self.num_steps += 1
       if not self.enabled:
           return

       if self.num_steps % self.local_sgd_steps == 0:
           self._sync_and_avg_model_params()

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int]						)	->		Dict:


       """simple docstring"""



       self.accelerator.wait_for_everyone()
       with self.accelerator.autocast():
           for param in self.model.parameters():
               _lowercase     			=						self.accelerator.reduce(param.data ,							reduction='mean'						)
