



"""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								=   [nn.LayerNorm]

UpperCAmelCase								=   logging.get_logger(__name__)

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

UpperCAmelCase								=   parsed_torch_version_base >= version.parse("""2.0""")
UpperCAmelCase								=   parsed_torch_version_base >= version.parse("""1.12""")
UpperCAmelCase								=   parsed_torch_version_base >= version.parse("""1.11""")
UpperCAmelCase								=   parsed_torch_version_base < version.parse("""1.11""")

def      A_ (		__a       :			Optional[Any]	,						__a       :			Optional[int]	,						__a       :			str	,						__a       :			Optional[int]	,						self       :			Tuple						):

				"""simple docstring"""
				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      A_ (		__a       :			int	,						__a       :			Any	,						__a       :			Dict = 0						):

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

class 	__snake_case (				nn.Module):







							'''simple docstring'''






							def __init__(							self					,					a_					,					a_						):
											super().__init__()
											a__			 =						nf
											a__			 =						nn.Parameter(torch.empty(UpperCamelCase__					,					UpperCamelCase__						)						)
											a__			 =						nn.Parameter(torch.zeros(UpperCamelCase__						)						)
											nn.init.normal_(self.weight					,					std=0.02						)







							def _a (							self					,					a_						):
											a__			 =						x.size()[:-1] + (self.nf,)
											a__			 =						torch.addmm(self.bias					,					x.view(-1					,					x.size(-1						)						)					,					self.weight						)
											a__			 =						x.view(UpperCamelCase__						)
											return x




def      A_ (		__a       :			Any	,						__a       :			Dict	,						__a       :			Union[str, Any] = 1						):

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

def      A_ (		__a       :			Optional[Any]	,						__a       :			int	,						__a       :			Optional[Any] = None						):

				"""simple docstring"""
				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      A_ (		__a       :			Tuple	,						__a       :			str	,						__a       :			Tuple	,						*__a       :			Optional[Any]						):

				"""simple docstring"""
				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
				a__			 =						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:
								a__			 =						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}'''						)

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

								# chunk input tensor into tuples
								a__			 =						tuple(input_tensor.chunk(_SCREAMING_SNAKE_CASE	,						dim=_SCREAMING_SNAKE_CASE						) for input_tensor in input_tensors						)
								# apply forward fn to every tuple
								a__			 =						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      A_ (		__a       :			List[str]	,						__a       :			Optional[Any]	,						__a       :			str	,						__a       :			Union[str, Any]						):

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

def      A_ (		*__a       :			List[Any]	,						__a       :			Optional[Any] = None						):

				"""simple docstring"""
				return torch.meshgrid(*_SCREAMING_SNAKE_CASE	,						indexing=_SCREAMING_SNAKE_CASE						)




def      A_ (		__a       :			int						):

				"""simple docstring"""
				return tensor.device, storage_ptr(_SCREAMING_SNAKE_CASE						), storage_size(_SCREAMING_SNAKE_CASE						)






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase								=   logging.get_logger(__name__)

UpperCAmelCase								=   {
    """facebook/regnet-y-040""": """https://huggingface.co/facebook/regnet-y-040/blob/main/config.json""",
}

class 	__snake_case (				SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__			: Tuple             =   """regnet"""
							UpperCamelCase__			: int             =   ["""x""", """y"""]


							def __init__(							self					,					a_=3					,					a_=32					,					a_=[128, 192, 512, 1_088]					,					a_=[2, 6, 12, 2]					,					a_=64					,					a_="y"					,					a_="relu"					,					**a_					,					):
											super().__init__(**a_						)
											if layer_type not in self.layer_types:
															raise ValueError(F'''layer_type={layer_type} is not one of {",".join(self.layer_types						)}'''						)
											a__			 =						num_channels
											a__			 =						embedding_size
											a__			 =						hidden_sizes
											a__			 =						depths
											a__			 =						groups_width
											a__			 =						layer_type
											a__			 =						hidden_act
											# always downsample in the first stage
											a__			 =						True

