
'''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


UpperCAmelCase__   : Dict					=		[nn.LayerNorm]

UpperCAmelCase__   : Optional[Any]					=		logging.get_logger(__name__)

UpperCAmelCase__   : int					=		version.parse(version.parse(torch.__version__).base_version)

UpperCAmelCase__   : Tuple					=		parsed_torch_version_base >= version.parse("2.0")
UpperCAmelCase__   : List[Any]					=		parsed_torch_version_base >= version.parse("1.12")
UpperCAmelCase__   : Optional[int]					=		parsed_torch_version_base >= version.parse("1.11")
UpperCAmelCase__   : int					=		parsed_torch_version_base < version.parse("1.11")







def   A     (      UpperCamelCase_					:     Union[str, Any]						,	UpperCamelCase_					:     int						,	UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     Any						,	self					:     Tuple      )					->       Optional[int]:







			'''simple docstring'''



			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   A     (      UpperCamelCase_					:     nn.Linear						,	UpperCamelCase_					:     torch.LongTensor						,	UpperCamelCase_					:     int = 0      )					->       nn.Linear:







			'''simple docstring'''



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






class    A     (	nn.Module    ):





	def __init__(					self			:       Optional[Any]      ,							__magic_name__			:       Any      ,							__magic_name__			:       Tuple							):


				"""simple docstring"""

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







	def 							__SCREAMING_SNAKE_CASE  (					self			:       Union[str, Any]      ,							__magic_name__			:       Tuple							):


				"""simple docstring"""

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







def   A     (      UpperCamelCase_					:     ConvaD						,	UpperCamelCase_					:     torch.LongTensor						,	UpperCamelCase_					:     int = 1      )					->       ConvaD:







			'''simple docstring'''



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







def   A     (      UpperCamelCase_					:     Union[nn.Linear, ConvaD]						,	UpperCamelCase_					:     torch.LongTensor						,	UpperCamelCase_					:     Optional[int] = None      )					->       Union[nn.Linear, ConvaD]:







			'''simple docstring'''



			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   A     (      UpperCamelCase_					:     Callable[..., torch.Tensor]						,	UpperCamelCase_					:     int						,	UpperCamelCase_					:     int						,	*UpperCamelCase_					:     Dict      )					->       torch.Tensor:







			'''simple docstring'''



			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
			lowerCAmelCase__								=							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:
						lowerCAmelCase__								=							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__								=							input_tensors[0].shape[chunk_dim] // chunk_size

						# chunk input tensor into tuples
						lowerCAmelCase__								=							tuple(input_tensor.chunk(UpperCamelCase_						,	dim=UpperCamelCase_      ) for input_tensor in input_tensors      )
						# apply forward fn to every tuple
						lowerCAmelCase__								=							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   A     (      UpperCamelCase_					:     List[int]						,	UpperCamelCase_					:     int						,	UpperCamelCase_					:     int						,	UpperCamelCase_					:     Set[int]      )					->       Tuple[Set[int], torch.LongTensor]:







			'''simple docstring'''



			lowerCAmelCase__								=							torch.ones(UpperCamelCase_						,	UpperCamelCase_      )
			lowerCAmelCase__								=							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
						lowerCAmelCase__								=							head - sum(1 if h < head else 0 for h in already_pruned_heads      )
						lowerCAmelCase__								=							0
			lowerCAmelCase__								=							mask.view(-1      ).contiguous().eq(1      )
			lowerCAmelCase__								=							torch.arange(len(UpperCamelCase_      )      )[mask].long()
			return heads, index







def   A     (      *UpperCamelCase_					:     Union[torch.Tensor, List[torch.Tensor]]						,	UpperCamelCase_					:     Optional[str] = None      )					->       Tuple[torch.Tensor, ...]:







			'''simple docstring'''



			return torch.meshgrid(*UpperCamelCase_						,	indexing=UpperCamelCase_      )







def   A     (      UpperCamelCase_					:     torch.Tensor      )					->       Tuple[torch.device, int, int]:







			'''simple docstring'''



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







'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__   : Any					=		{
    "configuration_fsmt": ["FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP", "FSMTConfig"],
    "tokenization_fsmt": ["FSMTTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase__   : str					=		["FSMTForConditionalGeneration", "FSMTModel", "PretrainedFSMTModel"]


if TYPE_CHECKING:
		from .configuration_fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig
		from .tokenization_fsmt import FSMTTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_fsmt import FSMTForConditionalGeneration, FSMTModel, PretrainedFSMTModel

else:
		import sys

		UpperCAmelCase__   : int					=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





