
"""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


snake_case			       =		[nn.LayerNorm]

snake_case			       =		logging.get_logger(__name__)

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

snake_case			       =		parsed_torch_version_base >= version.parse('2.0')
snake_case			       =		parsed_torch_version_base >= version.parse('1.12')
snake_case			       =		parsed_torch_version_base >= version.parse('1.11')
snake_case			       =		parsed_torch_version_base < version.parse('1.11')

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




class 					UpperCamelCase     (	nn.Module				):

				"""simple docstring"""



				def __init__(    self		,     lowercase__		,     lowercase__      )   ->   List[Any]:
									"""simple docstring"""







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




				def 							A (    self		,     lowercase__      )   ->   Optional[Any]:
									"""simple docstring"""







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






def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_ = 1      ):
					SCREAMING_SNAKE_CASE           =     index.to(layer.weight.device      )
					SCREAMING_SNAKE_CASE           =     layer.weight.index_select(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ).clone().detach()
					if dim == 0:
										SCREAMING_SNAKE_CASE           =     layer.bias.clone().detach()
					else:
										SCREAMING_SNAKE_CASE           =     layer.bias[index].clone().detach()
					SCREAMING_SNAKE_CASE           =     list(layer.weight.size()      )
					SCREAMING_SNAKE_CASE           =     len(SCREAMING_SNAKE_CASE_      )
					SCREAMING_SNAKE_CASE           =     ConvaD(new_size[1],					new_size[0]      ).to(layer.weight.device      )
					SCREAMING_SNAKE_CASE           =     False
					new_layer.weight.copy_(W.contiguous()      )
					SCREAMING_SNAKE_CASE           =     True
					SCREAMING_SNAKE_CASE           =     False
					new_layer.bias.copy_(b.contiguous()      )
					SCREAMING_SNAKE_CASE           =     True
					return new_layer

def   UpperCamelCase_  (		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   UpperCamelCase_  (		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
					SCREAMING_SNAKE_CASE           =     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:
										SCREAMING_SNAKE_CASE           =     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}'''      )

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

										# chunk input tensor into tuples
										SCREAMING_SNAKE_CASE           =     tuple(input_tensor.chunk(SCREAMING_SNAKE_CASE_,					dim=SCREAMING_SNAKE_CASE_      ) for input_tensor in input_tensors      )
										# apply forward fn to every tuple
										SCREAMING_SNAKE_CASE           =     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   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     torch.ones(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )
					SCREAMING_SNAKE_CASE           =     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
										SCREAMING_SNAKE_CASE           =     head - sum(1 if h < head else 0 for h in already_pruned_heads      )
										SCREAMING_SNAKE_CASE           =     0
					SCREAMING_SNAKE_CASE           =     mask.view(-1      ).contiguous().eq(1      )
					SCREAMING_SNAKE_CASE           =     torch.arange(len(SCREAMING_SNAKE_CASE_      )      )[mask].long()
					return heads, index

def   UpperCamelCase_  (		*SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_ = None      ):
					return torch.meshgrid(*SCREAMING_SNAKE_CASE_,					indexing=SCREAMING_SNAKE_CASE_      )



def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_      ):
					return tensor.device, storage_ptr(SCREAMING_SNAKE_CASE_      ), storage_size(SCREAMING_SNAKE_CASE_      )


"""simple docstring"""




import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


snake_case			       =		TypeVar('T', bound='TaskTemplate')




@dataclass(frozen=__magic_name__				)
class 					UpperCamelCase     (	abc.ABC				):

				"""simple docstring"""



				UpperCAmelCase_    :				str
				UpperCAmelCase_    :				ClassVar[Features]
				UpperCAmelCase_    :				ClassVar[Features]


				def 							A (    self		,     lowercase__      )   ->   T:
									"""simple docstring"""







									return copy.deepcopy(self      )


				@property
				def 							A (    self      )   ->   Features:
									"""simple docstring"""







									return Features(**self.input_schema		,     **self.label_schema      )


				@property
				@abc.abstractmethod
				def 							A (    self      )   ->   Dict[str, str]:
									"""simple docstring"""







									raise NotImplementedError




				@classmethod
				def 							A (    cls		,     lowercase__      )   ->   T:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     {f.name for f in dataclasses.fields(cls      )}
									return cls(**{k: v for k, v in template_dict.items() if k in field_names}      )
