


'''simple docstring'''

import argparse

import torch

from diffusers import UNetaDConditionModel

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase=None,					lowerCamelCase=None,					lowerCamelCase=None):
						assert isinstance(lowerCamelCase,					lowerCamelCase), "Paths should be a list of dicts containing 'old' and 'new' keys."

						# Splits the attention layers into three variables.
						if attention_paths_to_split is not None:
												for path, path_map in attention_paths_to_split.items():
																		__lowerCAmelCase											=					old_checkpoint[path]
																		__lowerCAmelCase											=					old_tensor.shape[0] // 3

																		__lowerCAmelCase											=					(-1, channels) if len(old_tensor.shape) == 3 else (-1)

																		__lowerCAmelCase											=					old_tensor.shape[0] // config['''num_head_channels'''] // 3

																		__lowerCAmelCase											=					old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])
																		__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=					old_tensor.split(channels // num_heads,					dim=1)

																		__lowerCAmelCase											=					query.reshape(lowerCamelCase)
																		__lowerCAmelCase											=					key.reshape(lowerCamelCase)
																		__lowerCAmelCase											=					value.reshape(lowerCamelCase)

						for path in paths:
												__lowerCAmelCase											=					path['''new''']

												# These have already been assigned
												if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																		continue

												if additional_replacements is not None:
																		for replacement in additional_replacements:
																								__lowerCAmelCase											=					new_path.replace(replacement['''old'''],					replacement['''new'''])

        # proj_attn.weight has to be converted from conv 1D to linear
												__lowerCAmelCase											=					old_checkpoint[path['''old''']]
												__lowerCAmelCase											=					['''proj_attn.weight''']
												__lowerCAmelCase											=					['''proj_out.weight''', '''proj_in.weight''']
												if any(k in new_path for k in names):
																		__lowerCAmelCase											=					weight[:, :, 0]
												elif any(k in new_path for k in names_a) and len(weight.shape) > 2 and ".attentions." not in new_path:
																		__lowerCAmelCase											=					weight[:, :, 0]
												else:
																		__lowerCAmelCase											=					weight

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase=0):
						__lowerCAmelCase											=					[]
						for old_item in old_list:
												__lowerCAmelCase											=					old_item

												#         new_item = new_item.replace('norm.weight', 'group_norm.weight')
												#         new_item = new_item.replace('norm.bias', 'group_norm.bias')

												#         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
												#         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

												#         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

												mapping.append({'''old''': old_item, '''new''': new_item})

						return mapping

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase=1):
						if n_shave_prefix_segments >= 0:
												return ".".join(path.split('''.''')[n_shave_prefix_segments:])
						else:
												return ".".join(path.split('''.''')[:n_shave_prefix_segments])

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase=0):
						__lowerCAmelCase											=					[]
						for old_item in old_list:
												mapping.append({'''old''': old_item, '''new''': old_item})

						return mapping

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase=0):
						__lowerCAmelCase											=					[]
						for old_item in old_list:
												__lowerCAmelCase											=					old_item.replace('''in_layers.0''',					'''norm1''')
												__lowerCAmelCase											=					new_item.replace('''in_layers.2''',					'''conv1''')

												__lowerCAmelCase											=					new_item.replace('''out_layers.0''',					'''norm2''')
												__lowerCAmelCase											=					new_item.replace('''out_layers.3''',					'''conv2''')

												__lowerCAmelCase											=					new_item.replace('''emb_layers.1''',					'''time_emb_proj''')
												__lowerCAmelCase											=					new_item.replace('''skip_connection''',					'''conv_shortcut''')

												__lowerCAmelCase											=					shave_segments(lowerCamelCase,					n_shave_prefix_segments=lowerCamelCase)

												if "temopral_conv" not in old_item:
																		mapping.append({'''old''': old_item, '''new''': new_item})

						return mapping

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase=None,					lowerCamelCase=False):
						__lowerCAmelCase											=					{}
						__lowerCAmelCase											=					list(checkpoint.keys())

						__lowerCAmelCase											=					'''model.diffusion_model.'''

						# at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
						if sum(k.startswith('''model_ema''') for k in keys) > 1_0_0 and extract_ema:
												print(F"""Checkpoint {path} has both EMA and non-EMA weights.""")
												print(
												    '''In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA'''
												    ''' weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.''')
												for key in keys:
																		if key.startswith('''model.diffusion_model'''):
																								__lowerCAmelCase											=					'''model_ema.''' + ''''''.join(key.split('''.''')[1:])
																								__lowerCAmelCase											=					checkpoint.pop(lowerCamelCase)
						else:
												if sum(k.startswith('''model_ema''') for k in keys) > 1_0_0:
																		print(
																		    '''In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA'''
																		    ''' weights (usually better for inference), please make sure to add the `--extract_ema` flag.''')

												for key in keys:
																		__lowerCAmelCase											=					checkpoint.pop(lowerCamelCase)

						__lowerCAmelCase											=					{}

						__lowerCAmelCase											=					unet_state_dict['''time_embed.0.weight''']
						__lowerCAmelCase											=					unet_state_dict['''time_embed.0.bias''']
						__lowerCAmelCase											=					unet_state_dict['''time_embed.2.weight''']
						__lowerCAmelCase											=					unet_state_dict['''time_embed.2.bias''']

						if config["class_embed_type"] is None:
												# No parameters to port
												...
						elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
												__lowerCAmelCase											=					unet_state_dict['''label_emb.0.0.weight''']
												__lowerCAmelCase											=					unet_state_dict['''label_emb.0.0.bias''']
												__lowerCAmelCase											=					unet_state_dict['''label_emb.0.2.weight''']
												__lowerCAmelCase											=					unet_state_dict['''label_emb.0.2.bias''']
						else:
												raise NotImplementedError(F"""Not implemented `class_embed_type`: {config["class_embed_type"]}""")

						__lowerCAmelCase											=					unet_state_dict['''input_blocks.0.0.weight''']
						__lowerCAmelCase											=					unet_state_dict['''input_blocks.0.0.bias''']

						__lowerCAmelCase											=					[v for v in unet_state_dict if v.startswith('''input_blocks.0.1''')]
						__lowerCAmelCase											=					renew_attention_paths(lowerCamelCase)
						__lowerCAmelCase											=					{'''old''': '''input_blocks.0.1''', '''new''': '''transformer_in'''}
						assign_to_checkpoint(lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					additional_replacements=[meta_path],					config=lowerCamelCase)

						__lowerCAmelCase											=					unet_state_dict['''out.0.weight''']
						__lowerCAmelCase											=					unet_state_dict['''out.0.bias''']
						__lowerCAmelCase											=					unet_state_dict['''out.2.weight''']
						__lowerCAmelCase											=					unet_state_dict['''out.2.bias''']

						# Retrieves the keys for the input blocks only
						__lowerCAmelCase											=					len({'''.'''.join(layer.split('''.''')[:2]) for layer in unet_state_dict if '''input_blocks''' in layer})
						__lowerCAmelCase											=					{
						    layer_id: [key for key in unet_state_dict if F"""input_blocks.{layer_id}""" in key]
						    for layer_id in range(lowerCamelCase)
						}

						# Retrieves the keys for the middle blocks only
						__lowerCAmelCase											=					len({'''.'''.join(layer.split('''.''')[:2]) for layer in unet_state_dict if '''middle_block''' in layer})
						__lowerCAmelCase											=					{
						    layer_id: [key for key in unet_state_dict if F"""middle_block.{layer_id}""" in key]
						    for layer_id in range(lowerCamelCase)
						}

						# Retrieves the keys for the output blocks only
						__lowerCAmelCase											=					len({'''.'''.join(layer.split('''.''')[:2]) for layer in unet_state_dict if '''output_blocks''' in layer})
						__lowerCAmelCase											=					{
						    layer_id: [key for key in unet_state_dict if F"""output_blocks.{layer_id}""" in key]
						    for layer_id in range(lowerCamelCase)
						}

						for i in range(1,					lowerCamelCase):
												__lowerCAmelCase											=					(i - 1) // (config['''layers_per_block'''] + 1)
												__lowerCAmelCase											=					(i - 1) % (config['''layers_per_block'''] + 1)

												__lowerCAmelCase											=					[
												    key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key and F"""input_blocks.{i}.0.op""" not in key
												]
												__lowerCAmelCase											=					[key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]
												__lowerCAmelCase											=					[key for key in input_blocks[i] if F"""input_blocks.{i}.2""" in key]

												if F"""input_blocks.{i}.op.weight""" in unet_state_dict:
																		__lowerCAmelCase											=					unet_state_dict.pop(
																		    F"""input_blocks.{i}.op.weight""")
																		__lowerCAmelCase											=					unet_state_dict.pop(
																		    F"""input_blocks.{i}.op.bias""")

												__lowerCAmelCase											=					renew_resnet_paths(lowerCamelCase)
												__lowerCAmelCase											=					{'''old''': F"""input_blocks.{i}.0""", '''new''': F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
												assign_to_checkpoint(
												    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					additional_replacements=[meta_path],					config=lowerCamelCase)

												__lowerCAmelCase											=					[key for key in resnets if '''temopral_conv''' in key]
												__lowerCAmelCase											=					renew_temp_conv_paths(lowerCamelCase)
												__lowerCAmelCase											=					{
												    '''old''': F"""input_blocks.{i}.0.temopral_conv""",
												    '''new''': F"""down_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
												}
												assign_to_checkpoint(
												    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					additional_replacements=[meta_path],					config=lowerCamelCase)

												if len(lowerCamelCase):
																		__lowerCAmelCase											=					renew_attention_paths(lowerCamelCase)
																		__lowerCAmelCase											=					{'''old''': F"""input_blocks.{i}.1""", '''new''': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}"""}
																		assign_to_checkpoint(
																		    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					additional_replacements=[meta_path],					config=lowerCamelCase)

												if len(lowerCamelCase):
																		__lowerCAmelCase											=					renew_attention_paths(lowerCamelCase)
																		__lowerCAmelCase											=					{
																		    '''old''': F"""input_blocks.{i}.2""",
																		    '''new''': F"""down_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
																		}
																		assign_to_checkpoint(
																		    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					additional_replacements=[meta_path],					config=lowerCamelCase)

						__lowerCAmelCase											=					middle_blocks[0]
						__lowerCAmelCase											=					[key for key in resnet_a if '''temopral_conv''' in key]
						__lowerCAmelCase											=					middle_blocks[1]
						__lowerCAmelCase											=					middle_blocks[2]
						__lowerCAmelCase											=					middle_blocks[3]
						__lowerCAmelCase											=					[key for key in resnet_a if '''temopral_conv''' in key]

						__lowerCAmelCase											=					renew_resnet_paths(lowerCamelCase)
						__lowerCAmelCase											=					{'''old''': '''middle_block.0''', '''new''': '''mid_block.resnets.0'''}
						assign_to_checkpoint(
						    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					config=lowerCamelCase,					additional_replacements=[meta_path])

						__lowerCAmelCase											=					renew_temp_conv_paths(lowerCamelCase)
						__lowerCAmelCase											=					{'''old''': '''middle_block.0.temopral_conv''', '''new''': '''mid_block.temp_convs.0'''}
						assign_to_checkpoint(
						    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					config=lowerCamelCase,					additional_replacements=[meta_path])

						__lowerCAmelCase											=					renew_resnet_paths(lowerCamelCase)
						__lowerCAmelCase											=					{'''old''': '''middle_block.3''', '''new''': '''mid_block.resnets.1'''}
						assign_to_checkpoint(
						    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					config=lowerCamelCase,					additional_replacements=[meta_path])

						__lowerCAmelCase											=					renew_temp_conv_paths(lowerCamelCase)
						__lowerCAmelCase											=					{'''old''': '''middle_block.3.temopral_conv''', '''new''': '''mid_block.temp_convs.1'''}
						assign_to_checkpoint(
						    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					config=lowerCamelCase,					additional_replacements=[meta_path])

						__lowerCAmelCase											=					renew_attention_paths(lowerCamelCase)
						__lowerCAmelCase											=					{'''old''': '''middle_block.1''', '''new''': '''mid_block.attentions.0'''}
						assign_to_checkpoint(
						    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					additional_replacements=[meta_path],					config=lowerCamelCase)

						__lowerCAmelCase											=					renew_attention_paths(lowerCamelCase)
						__lowerCAmelCase											=					{'''old''': '''middle_block.2''', '''new''': '''mid_block.temp_attentions.0'''}
						assign_to_checkpoint(
						    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					additional_replacements=[meta_path],					config=lowerCamelCase)

						for i in range(lowerCamelCase):
												__lowerCAmelCase											=					i // (config['''layers_per_block'''] + 1)
												__lowerCAmelCase											=					i % (config['''layers_per_block'''] + 1)
												__lowerCAmelCase											=					[shave_segments(lowerCamelCase,					2) for name in output_blocks[i]]
												__lowerCAmelCase											=					{}

												for layer in output_block_layers:
																		__lowerCAmelCase							,       __lowerCAmelCase											=					layer.split('''.''')[0], shave_segments(lowerCamelCase,					1)
																		if layer_id in output_block_list:
																								output_block_list[layer_id].append(lowerCamelCase)
																		else:
																								__lowerCAmelCase											=					[layer_name]

												if len(lowerCamelCase) > 1:
																		__lowerCAmelCase											=					[key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
																		__lowerCAmelCase											=					[key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]
																		__lowerCAmelCase											=					[key for key in output_blocks[i] if F"""output_blocks.{i}.2""" in key]

																		__lowerCAmelCase											=					renew_resnet_paths(lowerCamelCase)
																		__lowerCAmelCase											=					renew_resnet_paths(lowerCamelCase)

																		__lowerCAmelCase											=					{'''old''': F"""output_blocks.{i}.0""", '''new''': F"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
																		assign_to_checkpoint(
																		    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					additional_replacements=[meta_path],					config=lowerCamelCase)

																		__lowerCAmelCase											=					[key for key in resnets if '''temopral_conv''' in key]
																		__lowerCAmelCase											=					renew_temp_conv_paths(lowerCamelCase)
																		__lowerCAmelCase											=					{
																		    '''old''': F"""output_blocks.{i}.0.temopral_conv""",
																		    '''new''': F"""up_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
																		}
																		assign_to_checkpoint(
																		    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					additional_replacements=[meta_path],					config=lowerCamelCase)

																		__lowerCAmelCase											=					{k: sorted(lowerCamelCase) for k, v in output_block_list.items()}
																		if ["conv.bias", "conv.weight"] in output_block_list.values():
																								__lowerCAmelCase											=					list(output_block_list.values()).index(['''conv.bias''', '''conv.weight'''])
																								__lowerCAmelCase											=					unet_state_dict[
																								    F"""output_blocks.{i}.{index}.conv.weight"""
																								]
																								__lowerCAmelCase											=					unet_state_dict[
																								    F"""output_blocks.{i}.{index}.conv.bias"""
																								]

																								# Clear attentions as they have been attributed above.
																								if len(lowerCamelCase) == 2:
																														__lowerCAmelCase											=					[]

																		if len(lowerCamelCase):
																								__lowerCAmelCase											=					renew_attention_paths(lowerCamelCase)
																								__lowerCAmelCase											=					{
																								    '''old''': F"""output_blocks.{i}.1""",
																								    '''new''': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
																								}
																								assign_to_checkpoint(
																								    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					additional_replacements=[meta_path],					config=lowerCamelCase)

																		if len(lowerCamelCase):
																								__lowerCAmelCase											=					renew_attention_paths(lowerCamelCase)
																								__lowerCAmelCase											=					{
																								    '''old''': F"""output_blocks.{i}.2""",
																								    '''new''': F"""up_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
																								}
																								assign_to_checkpoint(
																								    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					additional_replacements=[meta_path],					config=lowerCamelCase)
												else:
																		__lowerCAmelCase											=					renew_resnet_paths(lowerCamelCase,					n_shave_prefix_segments=1)
																		for path in resnet_0_paths:
																								__lowerCAmelCase											=					'''.'''.join(['''output_blocks''', str(lowerCamelCase), path['''old''']])
																								__lowerCAmelCase											=					'''.'''.join(['''up_blocks''', str(lowerCamelCase), '''resnets''', str(lowerCamelCase), path['''new''']])
																								__lowerCAmelCase											=					unet_state_dict[old_path]

																		__lowerCAmelCase											=					[l for l in output_block_layers if '''temopral_conv''' in l]
																		for path in temopral_conv_paths:
																								__lowerCAmelCase											=					path.split('''temopral_conv.''')[-1]
																								__lowerCAmelCase											=					'''.'''.join(['''output_blocks''', str(lowerCamelCase), str(lowerCamelCase), '''temopral_conv''', pruned_path])
																								__lowerCAmelCase											=					'''.'''.join(['''up_blocks''', str(lowerCamelCase), '''temp_convs''', str(lowerCamelCase), pruned_path])
																								__lowerCAmelCase											=					unet_state_dict[old_path]

						return new_checkpoint


if __name__ == "__main__":
	_UpperCAmelCase					:			Tuple				     =					argparse.ArgumentParser()

	parser.add_argument(
	    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
	)
	parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
	_UpperCAmelCase					:			Optional[Any]				     =					parser.parse_args()

	_UpperCAmelCase					:			Optional[int]				     =					torch.load(args.checkpoint_path, map_location="""cpu""")
	_UpperCAmelCase					:			Any				     =					UNetaDConditionModel()

	_UpperCAmelCase					:			List[Any]				     =					convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

	_UpperCAmelCase					:			List[str]				     =					set(unet.state_dict().keys()) - set(converted_ckpt.keys())
	_UpperCAmelCase					:			List[Any]				     =					set(converted_ckpt.keys()) - set(unet.state_dict().keys())

	assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

	# load state_dict
	unet.load_state_dict(converted_ckpt)

	unet.save_pretrained(args.dump_path)

	# -- finish converting the unet --





'''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					:			Optional[Any]				     =					[nn.LayerNorm]

_UpperCAmelCase					:			Dict				     =					logging.get_logger(__name__)

_UpperCAmelCase					:			Tuple				     =					version.parse(version.parse(torch.__version__).base_version)

_UpperCAmelCase					:			Optional[int]				     =					parsed_torch_version_base >= version.parse("""2.0""")
_UpperCAmelCase					:			Optional[int]				     =					parsed_torch_version_base >= version.parse("""1.12""")
_UpperCAmelCase					:			List[str]				     =					parsed_torch_version_base >= version.parse("""1.11""")
_UpperCAmelCase					:			Optional[int]				     =					parsed_torch_version_base < version.parse("""1.11""")

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					self):
						from torch import _softmax_backward_data

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

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase = 0):
						__lowerCAmelCase											=					index.to(layer.weight.device)
						__lowerCAmelCase											=					layer.weight.index_select(lowerCamelCase,					lowerCamelCase).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(lowerCamelCase)
						__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   ):







				"""simple docstring"""



				def __init__(self							,      __lowercase							,      __lowercase		):
										super().__init__()
										__lowerCAmelCase											=					nf
										__lowerCAmelCase											=					nn.Parameter(torch.empty(__lowercase							,      __lowercase		)		)
										__lowerCAmelCase											=					nn.Parameter(torch.zeros(__lowercase		)		)
										nn.init.normal_(self.weight							,      std=0.0_2		)




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

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase = 1):
						__lowerCAmelCase											=					index.to(layer.weight.device)
						__lowerCAmelCase											=					layer.weight.index_select(lowerCamelCase,					lowerCamelCase).clone().detach()
						if dim == 0:
												__lowerCAmelCase											=					layer.bias.clone().detach()
						else:
												__lowerCAmelCase											=					layer.bias[index].clone().detach()
						__lowerCAmelCase											=					list(layer.weight.size())
						__lowerCAmelCase											=					len(lowerCamelCase)
						__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 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase = None):
						if isinstance(lowerCamelCase,					nn.Linear):
												return prune_linear_layer(lowerCamelCase,					lowerCamelCase,					dim=0 if dim is None else dim)
						elif isinstance(lowerCamelCase,					lowerCamelCase):
												return prune_convad_layer(lowerCamelCase,					lowerCamelCase,					dim=1 if dim is None else dim)
						else:
												raise ValueError(F"""Can't prune layer of class {layer.__class__}""")

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					*lowerCamelCase):
						assert len(lowerCamelCase) > 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(lowerCamelCase).parameters)
						if num_args_in_forward_chunk_fn != len(lowerCamelCase):
												raise ValueError(
												    F"""forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(lowerCamelCase)} 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(lowerCamelCase,					dim=lowerCamelCase) for input_tensor in input_tensors)
												# apply forward fn to every tuple
												__lowerCAmelCase											=					tuple(forward_fn(*lowerCamelCase) for input_tensors_chunk in zip(*lowerCamelCase))
												# concatenate output at same dimension
												return torch.cat(lowerCamelCase,					dim=lowerCamelCase)

						return forward_fn(*lowerCamelCase)

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase):
						__lowerCAmelCase											=					torch.ones(lowerCamelCase,					lowerCamelCase)
						__lowerCAmelCase											=					set(lowerCamelCase) - 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(lowerCamelCase))[mask].long()
						return heads, index

def 				__magic_name__(    *lowerCamelCase,					lowerCamelCase = None):
						return torch.meshgrid(*lowerCamelCase,					indexing=lowerCamelCase)




def 				__magic_name__(    lowerCamelCase):
						return tensor.device, storage_ptr(lowerCamelCase), storage_size(lowerCamelCase)

