





from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
				import torch

UpperCamelCase__ :			Optional[Any]       	=			logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
				from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):




				def  A_(      cls ):
					return cls




def  A_(      A					,   A = None					,   A = None					,   A = None					,   A = None					,   ):
	UpperCAmelCase_		    =  device
	UpperCAmelCase_		    =  shape[0]

	UpperCAmelCase_		    =  layout or torch.strided
	UpperCAmelCase_		    =  device or torch.device("""cpu""" )

	if generator is not None:
		UpperCAmelCase_		    =  generator.device.type if not isinstance(A					,   A ) else generator[0].device.type
		if gen_device_type != device.type and gen_device_type == "cpu":
			UpperCAmelCase_		    =  """cpu"""
			if device != "mps":
				logger.info(
				    f"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
				    f""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
				    f""" slighly speed up this function by passing a generator that was created on the {device} device.""" )
		elif gen_device_type != device.type and gen_device_type == "cuda":
			raise ValueError(f"""Cannot generate a {device} tensor from a generator of type {gen_device_type}.""" )

	if isinstance(A					,   A ):
		UpperCAmelCase_		    =  (1,) + shape[1:]
		UpperCAmelCase_		    =  [
		    torch.randn(A					,   generator=generator[i]					,   device=A					,   dtype=A					,   layout=A )
		    for i in range(A )
		]
		UpperCAmelCase_		    =  torch.cat(A					,   dim=0 ).to(A )
	else:
		UpperCAmelCase_		    =  torch.randn(A					,   generator=A					,   device=A					,   dtype=A					,   layout=A ).to(A )

	return latents




def  A_(      A ):
	if is_torch_version("""<"""					,   """2.0.0""" ) or not hasattr(A					,   """_dynamo""" ):
		return False
	return isinstance(A					,   torch._dynamo.eval_frame.OptimizedModule )









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

UpperCamelCase__ :			int       	=			logging.get_logger(__name__)

UpperCamelCase__ :			Dict       	=			version.parse(version.parse(torch.__version__).base_version)

UpperCamelCase__ :			str       	=			parsed_torch_version_base >= version.parse("""2.0""")
UpperCamelCase__ :			List[Any]       	=			parsed_torch_version_base >= version.parse("""1.12""")
UpperCamelCase__ :			List[Any]       	=			parsed_torch_version_base >= version.parse("""1.11""")
UpperCamelCase__ :			Any       	=			parsed_torch_version_base < version.parse("""1.11""")




def  A_(      A					,   A					,   A					,   A					,   self ):
	from torch import _softmax_backward_data

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




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







class 	_UpperCamelCase	(							nn.Module	):







					'''simple docstring'''

					def __init__(    self     :	Union[str, Any]			,       __lowercase     :	List[Any]			,       __lowercase     :	List[Any]		):






						'''simple docstring'''







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





					def 							SCREAMING_SNAKE_CASE				(    self     :	str			,       __lowercase     :	Tuple		):






						'''simple docstring'''







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




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




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




def  A_(      A					,   A					,   A					,   *A ):
	assert len(A ) > 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
	UpperCAmelCase_		    =  len(inspect.signature(A ).parameters )
	if num_args_in_forward_chunk_fn != len(A ):
		raise ValueError(
		    f"""forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(A )} input """
		    """tensors are given""" )

	if chunk_size > 0:
		UpperCAmelCase_		    =  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}""" )

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

		# chunk input tensor into tuples
		UpperCAmelCase_		    =  tuple(input_tensor.chunk(A					,   dim=A ) for input_tensor in input_tensors )
		# apply forward fn to every tuple
		UpperCAmelCase_		    =  tuple(forward_fn(*A ) for input_tensors_chunk in zip(*A ) )
		# concatenate output at same dimension
		return torch.cat(A					,   dim=A )

	return forward_fn(*A )




def  A_(      A					,   A					,   A					,   A ):
	UpperCAmelCase_		    =  torch.ones(A					,   A )
	UpperCAmelCase_		    =  set(A ) - 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
		UpperCAmelCase_		    =  head - sum(1 if h < head else 0 for h in already_pruned_heads )
		UpperCAmelCase_		    =  0
	UpperCAmelCase_		    =  mask.view(-1 ).contiguous().eq(1 )
	UpperCAmelCase_		    =  torch.arange(len(A ) )[mask].long()
	return heads, index




def  A_(      *A					,   A = None ):
	return torch.meshgrid(*A					,   indexing=A )




def  A_(      A ):
	return tensor.device, storage_ptr(A ), storage_size(A )


