





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase__ :			Union[str, Any]       	=			{
    """configuration_mobilevitv2""": [
        """MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileViTV2Config""",
        """MobileViTV2OnnxConfig""",
    ],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__ :			int       	=			[
				    """MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """MobileViTV2ForImageClassification""",
				    """MobileViTV2ForSemanticSegmentation""",
				    """MobileViTV2Model""",
				    """MobileViTV2PreTrainedModel""",
				]

if TYPE_CHECKING:
				from .configuration_mobilevitva import (
				    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    MobileViTVaConfig,
				    MobileViTVaOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mobilevitva import (
								    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MobileViTVaForImageClassification,
								    MobileViTVaForSemanticSegmentation,
								    MobileViTVaModel,
								    MobileViTVaPreTrainedModel,
								)

else:
				import sys

				UpperCamelCase__ :			List[Any]       	=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









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 )


