import argparse

import torch

from diffusers import UNetaDConditionModel
def 							lowerCAmelCase(	__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():
															__a							=   old_checkpoint[path]
															__a							=   old_tensor.shape[0] // 3

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

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

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

															__a							=   query.reshape(__lowerCamelCase						)
															__a							=   key.reshape(__lowerCamelCase						)
															__a							=   value.reshape(__lowerCamelCase						)

					for path in paths:
										__a							=   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:
																				__a							=   new_path.replace(replacement['old']      ,				replacement['new']						)

        # proj_attn.weight has to be converted from conv 1D to linear
										__a							=   old_checkpoint[path['old']]
										__a							=   ['proj_attn.weight']
										__a							=   ['proj_out.weight', 'proj_in.weight']
										if any(k in new_path for k in names						):
															__a							=   weight[:, :, 0]
										elif any(k in new_path for k in names_a						) and len(weight.shape						) > 2 and ".attentions." not in new_path:
															__a							=   weight[:, :, 0]
										else:
															__a							=   weight
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase=0						):
					__a							=   []
					for old_item in old_list:
										__a							=   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 							lowerCAmelCase(	__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 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase=0						):
					__a							=   []
					for old_item in old_list:
										mapping.append({'old': old_item, 'new': old_item}						)

					return mapping
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase=0						):
					__a							=   []
					for old_item in old_list:
										__a							=   old_item.replace('in_layers.0'      ,				'norm1'						)
										__a							=   new_item.replace('in_layers.2'      ,				'conv1'						)

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

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

										__a							=   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 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase=None      ,				__lowerCamelCase=False						):
					__a							=   {}
					__a							=   list(checkpoint.keys()						)

					__a							=   '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						) > 100 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'						):
																				__a							=   'model_ema.' + ''.join(key.split('.'						)[1:]						)
																				__a							=   checkpoint.pop(__lowerCamelCase						)
					else:
										if sum(k.startswith('model_ema'						) for k in keys						) > 100:
															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:
															__a							=   checkpoint.pop(__lowerCamelCase						)

					__a							=   {}

					__a							=   unet_state_dict['time_embed.0.weight']
					__a							=   unet_state_dict['time_embed.0.bias']
					__a							=   unet_state_dict['time_embed.2.weight']
					__a							=   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":
										__a							=   unet_state_dict['label_emb.0.0.weight']
										__a							=   unet_state_dict['label_emb.0.0.bias']
										__a							=   unet_state_dict['label_emb.0.2.weight']
										__a							=   unet_state_dict['label_emb.0.2.bias']
					else:
										raise NotImplementedError(f'''Not implemented `class_embed_type`: {config["class_embed_type"]}'''						)

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

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

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

					# Retrieves the keys for the input blocks only
					__a							=   len({'.'.join(layer.split('.'						)[:2]						) for layer in unet_state_dict if 'input_blocks' in layer}						)
					__a							=   {
					    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
					__a							=   len({'.'.join(layer.split('.'						)[:2]						) for layer in unet_state_dict if 'middle_block' in layer}						)
					__a							=   {
					    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
					__a							=   len({'.'.join(layer.split('.'						)[:2]						) for layer in unet_state_dict if 'output_blocks' in layer}						)
					__a							=   {
					    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						):
										__a							=   (i - 1) // (config['layers_per_block'] + 1)
										__a							=   (i - 1) % (config['layers_per_block'] + 1)

										__a							=   [
										    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
										]
										__a							=   [key for key in input_blocks[i] if f'''input_blocks.{i}.1''' in key]
										__a							=   [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:
															__a							=   unet_state_dict.pop(
															    f'''input_blocks.{i}.op.weight'''						)
															__a							=   unet_state_dict.pop(
															    f'''input_blocks.{i}.op.bias'''						)

										__a							=   renew_resnet_paths(__lowerCamelCase						)
										__a							=   {'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						)

										__a							=   [key for key in resnets if 'temopral_conv' in key]
										__a							=   renew_temp_conv_paths(__lowerCamelCase						)
										__a							=   {
										    '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						):
															__a							=   renew_attention_paths(__lowerCamelCase						)
															__a							=   {'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						):
															__a							=   renew_attention_paths(__lowerCamelCase						)
															__a							=   {
															    '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						)

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

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

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

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

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

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

					__a							=   renew_attention_paths(__lowerCamelCase						)
					__a							=   {'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						):
										__a							=   i // (config['layers_per_block'] + 1)
										__a							=   i % (config['layers_per_block'] + 1)
										__a							=   [shave_segments(__lowerCamelCase      ,				2						) for name in output_blocks[i]]
										__a							=   {}

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

										if len(__lowerCamelCase						) > 1:
															__a							=   [key for key in output_blocks[i] if f'''output_blocks.{i}.0''' in key]
															__a							=   [key for key in output_blocks[i] if f'''output_blocks.{i}.1''' in key]
															__a							=   [key for key in output_blocks[i] if f'''output_blocks.{i}.2''' in key]

															__a							=   renew_resnet_paths(__lowerCamelCase						)
															__a							=   renew_resnet_paths(__lowerCamelCase						)

															__a							=   {'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						)

															__a							=   [key for key in resnets if 'temopral_conv' in key]
															__a							=   renew_temp_conv_paths(__lowerCamelCase						)
															__a							=   {
															    '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						)

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

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

															if len(__lowerCamelCase						):
																				__a							=   renew_attention_paths(__lowerCamelCase						)
																				__a							=   {
																				    '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						):
																				__a							=   renew_attention_paths(__lowerCamelCase						)
																				__a							=   {
																				    '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:
															__a							=   renew_resnet_paths(__lowerCamelCase      ,				n_shave_prefix_segments=1						)
															for path in resnet_0_paths:
																				__a							=   '.'.join(['output_blocks', str(__lowerCamelCase						), path['old']]						)
																				__a							=   '.'.join(['up_blocks', str(__lowerCamelCase						), 'resnets', str(__lowerCamelCase						), path['new']]						)
																				__a							=   unet_state_dict[old_path]

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

					return new_checkpoint


if __name__ == "__main__":
	lowerCamelCase_				:		Dict       						=       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.""")
	lowerCamelCase_				:		List[str]       						=       parser.parse_args()

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

	lowerCamelCase_				:		Tuple       						=       convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

	lowerCamelCase_				:		List[Any]       						=       set(unet.state_dict().keys()) - set(converted_ckpt.keys())
	lowerCamelCase_				:		Dict       						=       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 --





from typing import Any

import numpy as np
def 							lowerCAmelCase(	__lowerCamelCase						):
					assert np.shape(__lowerCamelCase						)[0] == np.shape(__lowerCamelCase						)[1]

					# If matrix not symmetric, exit right away.
					if np.allclose(__lowerCamelCase      ,				matrix.T						) is False:
										return False

					# Get eigenvalues and eignevectors for a symmetric matrix.
					__a					,      __a							=   np.linalg.eigh(__lowerCamelCase						)

					# Check sign of all eigenvalues.
					# np.all returns a value of type np.bool_
					return bool(np.all(eigen_values > 0						)						)
def 							lowerCAmelCase(	__lowerCamelCase						):
					__a							=   np.random.randn(__lowerCamelCase      ,				__lowerCamelCase						)
					__a							=   np.dot(__lowerCamelCase      ,				random_matrix.T						)
					assert _is_matrix_spd(__lowerCamelCase						)
					return spd_matrix
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase = 1000      ,				__lowerCamelCase = 1e-8      ,				):
					assert np.shape(__lowerCamelCase						)[0] == np.shape(__lowerCamelCase						)[1]
					assert np.shape(__lowerCamelCase						)[0] == np.shape(__lowerCamelCase						)[0]
					assert _is_matrix_spd(__lowerCamelCase						)

					# Initialize solution guess, residual, search direction.
					__a							=   np.zeros((np.shape(__lowerCamelCase						)[0], 1)						)
					__a							=   np.copy(__lowerCamelCase						)
					__a							=   np.copy(__lowerCamelCase						)

					# Set initial errors in solution guess and residual.
					__a							=   1e9
					__a							=   1e9
					__a							=   1e9

					# Set iteration counter to threshold number of iterations.
					__a							=   0

					while error > tol:
										# Save this value so we only calculate the matrix-vector product once.
										__a							=   np.dot(__lowerCamelCase      ,				__lowerCamelCase						)

										# The main algorithm.

										# Update search direction magnitude.
										__a							=   np.dot(ra.T      ,				__lowerCamelCase						) / np.dot(pa.T      ,				__lowerCamelCase						)
										# Update solution guess.
										__a							=   xa + alpha * pa
										# Calculate new residual.
										__a							=   ra - alpha * w
										# Calculate new Krylov subspace scale.
										__a							=   np.dot(r.T      ,				__lowerCamelCase						) / np.dot(ra.T      ,				__lowerCamelCase						)
										# Calculate new A conjuage search direction.
										__a							=   r + beta * pa

										# Calculate errors.
										__a							=   np.linalg.norm(r - ra						)
										__a							=   np.linalg.norm(x - xa						)
										__a							=   np.maximum(__lowerCamelCase      ,				__lowerCamelCase						)

										# Update variables.
										__a							=   np.copy(__lowerCamelCase						)
										__a							=   np.copy(__lowerCamelCase						)
										__a							=   np.copy(__lowerCamelCase						)

										# Update number of iterations.
										iterations += 1
										if iterations > max_iterations:
															break

					return x
def 							lowerCAmelCase(	):
					__a							=   3
					__a							=   _create_spd_matrix(__lowerCamelCase						)
					__a							=   np.random.randn(__lowerCamelCase      ,				1						)
					__a							=   np.dot(__lowerCamelCase      ,				__lowerCamelCase						)

					# Numpy solution.
					__a							=   np.linalg.solve(__lowerCamelCase      ,				__lowerCamelCase						)

					# Our implementation.
					__a							=   conjugate_gradient(__lowerCamelCase      ,				__lowerCamelCase						)

					# Ensure both solutions are close to x_true (and therefore one another).
					assert np.linalg.norm(x_numpy - x_true						) <= 1e-6
					assert np.linalg.norm(x_conjugate_gradient - x_true						) <= 1e-6


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	test_conjugate_gradient()




