






"""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


A_			: Optional[int]				=			[nn.LayerNorm]

A_			: int				=			logging.get_logger(__name__)

A_			: Optional[int]				=			version.parse(version.parse(torch.__version__).base_version)

A_			: int				=			parsed_torch_version_base >= version.parse("2.0")
A_			: Dict				=			parsed_torch_version_base >= version.parse("1.12")
A_			: List[Any]				=			parsed_torch_version_base >= version.parse("1.11")
A_			: int				=			parsed_torch_version_base < version.parse("1.11")






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   self				):
 from torch import _softmax_backward_data

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






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



class 		a_      (	nn.Module ):







      '''simple docstring'''

      def __init__(self,     lowerCamelCase_,     lowerCamelCase_					):

       '''simple docstring'''



       super().__init__()
       lowerCamelCase__     :			Tuple     				=						nf
       lowerCamelCase__     :			Any     				=						nn.Parameter(torch.empty(lowerCamelCase_,     lowerCamelCase_					)					)
       lowerCamelCase__     :			Dict     				=						nn.Parameter(torch.zeros(lowerCamelCase_					)					)
       nn.init.normal_(self.weight,     std=0.02					)





      def 			a__							(self,     lowerCamelCase_					):

       '''simple docstring'''



       lowerCamelCase__     :			Optional[Any]     				=						x.size()[:-1] + (self.nf,)
       lowerCamelCase__     :			Optional[Any]     				=						torch.addmm(self.bias,     x.view(-1,     x.size(-1					)					),     self.weight					)
       lowerCamelCase__     :			Dict     				=						x.view(lowerCamelCase_					)
       return x






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






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






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   *_lowerCamelCase				):
 assert len(_lowerCamelCase				) > 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__     :			Any     				=						len(inspect.signature(_lowerCamelCase				).parameters				)
 if num_args_in_forward_chunk_fn != len(_lowerCamelCase				):
  raise ValueError(
      f'''forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(_lowerCamelCase				)} 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__     :			Union[str, Any]     				=						input_tensors[0].shape[chunk_dim] // chunk_size

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

 return forward_fn(*_lowerCamelCase				)






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase				):
 lowerCamelCase__     :			Optional[Any]     				=						torch.ones(_lowerCamelCase			,   _lowerCamelCase				)
 lowerCamelCase__     :			Optional[Any]     				=						set(_lowerCamelCase				) - 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__     :			Dict     				=						head - sum(1 if h < head else 0 for h in already_pruned_heads				)
  lowerCamelCase__     :			Optional[int]     				=						0
 lowerCamelCase__     :			Dict     				=						mask.view(-1				).contiguous().eq(1				)
 lowerCamelCase__     :			torch.LongTensor     				=						torch.arange(len(_lowerCamelCase				)				)[mask].long()
 return heads, index






def        lowerCamelCase_  (						*_lowerCamelCase			,   _lowerCamelCase = None				):
 return torch.meshgrid(*_lowerCamelCase			,   indexing=_lowerCamelCase				)






def        lowerCamelCase_  (						_lowerCamelCase				):
 return tensor.device, storage_ptr(_lowerCamelCase				), storage_size(_lowerCamelCase				)









"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_			: Dict				=			{"configuration_qdqbert": ["QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "QDQBertConfig"]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_			: Tuple				=			[
          "QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "QDQBertForMaskedLM",
          "QDQBertForMultipleChoice",
          "QDQBertForNextSentencePrediction",
          "QDQBertForQuestionAnswering",
          "QDQBertForSequenceClassification",
          "QDQBertForTokenClassification",
          "QDQBertLayer",
          "QDQBertLMHeadModel",
          "QDQBertModel",
          "QDQBertPreTrainedModel",
          "load_tf_weights_in_qdqbert",
      ]


if TYPE_CHECKING:
      from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_qdqbert import (
                QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                QDQBertForMaskedLM,
                QDQBertForMultipleChoice,
                QDQBertForNextSentencePrediction,
                QDQBertForQuestionAnswering,
                QDQBertForSequenceClassification,
                QDQBertForTokenClassification,
                QDQBertLayer,
                QDQBertLMHeadModel,
                QDQBertModel,
                QDQBertPreTrainedModel,
                load_tf_weights_in_qdqbert,
            )


else:
      import sys

      A_			: Tuple				=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

