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							(		lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   self							)						->						Dict:
     from torch import _softmax_backward_data

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







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






class 				_SCREAMING_SNAKE_CASE( nn.Module						):
   def __init__(     self				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->Union[str, Any]:
        super().__init__()
        a	:     Optional[Any]						      =    nf
        a	:     str						      =    nn.Parameter(torch.empty(_lowercase				,  _lowercase     )     )
        a	:     int						      =    nn.Parameter(torch.zeros(_lowercase     )     )
        nn.init.normal_(self.weight				,  std=0.02     )


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







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







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







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   *lowerCAmelCase_							)						->						torch.Tensor:
     assert len(UpperCAmelCase__							) > 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
     a	:     Optional[int]						      =    len(inspect.signature(UpperCAmelCase__							).parameters							)
     if num_args_in_forward_chunk_fn != len(UpperCAmelCase__							):
          raise ValueError(
              f"""forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(UpperCAmelCase__							)} input """
              '''tensors are given'''							)

     if chunk_size > 0:
          a	:     List[str]						      =    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}"""							)

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

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

     return forward_fn(*UpperCAmelCase__							)







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)						->						Tuple[Set[int], torch.LongTensor]:
     a	:     Dict						      =    torch.ones(UpperCAmelCase__    ,   UpperCAmelCase__							)
     a	:     Dict						      =    set(UpperCAmelCase__							) - 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
          a	:     List[str]						      =    head - sum(1 if h < head else 0 for h in already_pruned_heads							)
          a	:     int						      =    0
     a	:     int						      =    mask.view(-1							).contiguous().eq(1							)
     a	:     Any						      =    torch.arange(len(UpperCAmelCase__							)							)[mask].long()
     return heads, index







def 						UpperCAmelCase							(		*lowerCAmelCase_    ,   lowerCAmelCase_ = None							)						->						Tuple[torch.Tensor, ...]:
     return torch.meshgrid(*UpperCAmelCase__    ,   indexing=UpperCAmelCase__							)







def 						UpperCAmelCase							(		lowerCAmelCase_							)						->						Tuple[torch.device, int, int]:
     return tensor.device, storage_ptr(UpperCAmelCase__							), storage_size(UpperCAmelCase__							)
"""simple docstring"""







import numpy as np


def 	a__   (		UpperCAmelCase__							:      List[str]		,      UpperCAmelCase__							:      int		,      UpperCAmelCase__							:      str      )->						Optional[int]:
						UpperCAmelCase__       						=		n_draws * class_counts / class_counts.sum()
						# floored means we don't overshoot n_samples, but probably undershoot
						UpperCAmelCase__       						=		np.floor(UpperCAmelCase__      )
						# we add samples according to how much "left over" probability
						# they had, until we arrive at n_samples
						UpperCAmelCase__       						=		int(n_draws - floored.sum()      )
						if need_to_add > 0:
												UpperCAmelCase__       						=		continuous - floored
												UpperCAmelCase__       						=		np.sort(np.unique(UpperCAmelCase__      )      )[::-1]
												# add according to remainder, but break ties
												# randomly to avoid biases
												for value in values:
																		((UpperCAmelCase__)						,   )       						=		np.where(remainder == value      )
																		# if we need_to_add less than what's in inds
																		# we draw randomly from them.
																		# if we need to add more, we add them all and
																		# go to the next value
																		UpperCAmelCase__       						=		min(len(UpperCAmelCase__      )		,      UpperCAmelCase__      )
																		UpperCAmelCase__       						=		rng.choice(UpperCAmelCase__		,      size=UpperCAmelCase__		,      replace=UpperCAmelCase__      )
																		floored[inds] += 1
																		need_to_add -= add_now
																		if need_to_add == 0:
																								break
						return floored.astype(np.intaa      )







def 	a__   (		UpperCAmelCase__							:      Optional[int]		,      UpperCAmelCase__							:      Optional[int]		,      UpperCAmelCase__							:      Dict		,      UpperCAmelCase__							:      Any		,      UpperCAmelCase__							:      Union[str, Any]=1_0      )->						Tuple:
						UpperCAmelCase__						,   UpperCAmelCase__       						=		np.unique(UpperCAmelCase__		,      return_inverse=UpperCAmelCase__      )
						UpperCAmelCase__       						=		classes.shape[0]
						UpperCAmelCase__       						=		np.bincount(UpperCAmelCase__      )
						if np.min(UpperCAmelCase__      ) < 2:
												raise ValueError("""Minimum class count error"""      )
						if n_train < n_classes:
												raise ValueError(
												    """The train_size = %d should be greater or """ """equal to the number of classes = %d""" % (n_train, n_classes)      )
						if n_test < n_classes:
												raise ValueError(
												    """The test_size = %d should be greater or """ """equal to the number of classes = %d""" % (n_test, n_classes)      )
						UpperCAmelCase__       						=		np.split(np.argsort(UpperCAmelCase__		,      kind="""mergesort"""      )		,      np.cumsum(UpperCAmelCase__      )[:-1]      )
						for _ in range(UpperCAmelCase__      ):
												UpperCAmelCase__       						=		approximate_mode(UpperCAmelCase__		,      UpperCAmelCase__		,      UpperCAmelCase__      )
												UpperCAmelCase__       						=		class_counts - n_i
												UpperCAmelCase__       						=		approximate_mode(UpperCAmelCase__		,      UpperCAmelCase__		,      UpperCAmelCase__      )

												UpperCAmelCase__       						=		[]
												UpperCAmelCase__       						=		[]

												for i in range(UpperCAmelCase__      ):
																		UpperCAmelCase__       						=		rng.permutation(class_counts[i]      )
																		UpperCAmelCase__       						=		class_indices[i].take(UpperCAmelCase__		,      mode="""clip"""      )
																		train.extend(perm_indices_class_i[: n_i[i]]      )
																		test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]      )
												UpperCAmelCase__       						=		rng.permutation(UpperCAmelCase__      )
												UpperCAmelCase__       						=		rng.permutation(UpperCAmelCase__      )

												yield train, test

