


import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel






def 	_A				(lowerCAmelCase__  :Optional[Any]				,						lowerCAmelCase__  :Any=1		)   ->  Tuple:




     '''simple docstring'''



     if n_shave_prefix_segments >= 0:
          return ".".join(path.split('.'		)[n_shave_prefix_segments:]		)
     else:
          return ".".join(path.split('.'		)[:n_shave_prefix_segments]		)






def 	_A				(lowerCAmelCase__  :Optional[int]				,						lowerCAmelCase__  :List[Any]=0		)   ->  Dict:




     '''simple docstring'''



     _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(__UpperCamelCase				,						n_shave_prefix_segments=__UpperCamelCase		)

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

     return mapping






def 	_A				(lowerCAmelCase__  :str				,						lowerCAmelCase__  :Any=0		)   ->  List[str]:




     '''simple docstring'''



     _a			      =							[]
     for old_item in old_list:
          _a			      =							old_item

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

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

          _a			      =							shave_segments(__UpperCamelCase				,						n_shave_prefix_segments=__UpperCamelCase		)

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

     return mapping






def 	_A				(lowerCAmelCase__  :str				,						lowerCAmelCase__  :Optional[Any]				,						lowerCAmelCase__  :str				,						lowerCAmelCase__  :List[Any]=None				,						lowerCAmelCase__  :Tuple=None				,						lowerCAmelCase__  :int=None		)   ->  str:




     '''simple docstring'''



     assert isinstance(__UpperCamelCase				,						__UpperCamelCase		), "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(__UpperCamelCase		)
               _a			      =							key.reshape(__UpperCamelCase		)
               _a			      =							value.reshape(__UpperCamelCase		)

     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

          # Global renaming happens here
          _a			      =							new_path.replace('middle_block.0'				,						'mid_block.resnets.0'		)
          _a			      =							new_path.replace('middle_block.1'				,						'mid_block.attentions.0'		)
          _a			      =							new_path.replace('middle_block.2'				,						'mid_block.resnets.1'		)

          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
          if "proj_attn.weight" in new_path:
               _a			      =							old_checkpoint[path['old']][:, :, 0]
          else:
               _a			      =							old_checkpoint[path['old']]






def 	_A				(lowerCAmelCase__  :Tuple				,						lowerCAmelCase__  :Tuple		)   ->  Optional[int]:




     '''simple docstring'''



     _a			      =							{}

     _a			      =							checkpoint['time_embed.0.weight']
     _a			      =							checkpoint['time_embed.0.bias']
     _a			      =							checkpoint['time_embed.2.weight']
     _a			      =							checkpoint['time_embed.2.bias']

     _a			      =							checkpoint['input_blocks.0.0.weight']
     _a			      =							checkpoint['input_blocks.0.0.bias']

     _a			      =							checkpoint['out.0.weight']
     _a			      =							checkpoint['out.0.bias']
     _a			      =							checkpoint['out.2.weight']
     _a			      =							checkpoint['out.2.bias']

     # Retrieves the keys for the input blocks only
     _a			      =							len({'.'.join(layer.split('.'		)[:2]		) for layer in checkpoint if 'input_blocks' in layer}		)
     _a			      =							{
         layer_id: [key for key in checkpoint if f'input_blocks.{layer_id}' in key]
         for layer_id in range(__UpperCamelCase		)
     }

     # Retrieves the keys for the middle blocks only
     _a			      =							len({'.'.join(layer.split('.'		)[:2]		) for layer in checkpoint if 'middle_block' in layer}		)
     _a			      =							{
         layer_id: [key for key in checkpoint if f'middle_block.{layer_id}' in key]
         for layer_id in range(__UpperCamelCase		)
     }

     # Retrieves the keys for the output blocks only
     _a			      =							len({'.'.join(layer.split('.'		)[:2]		) for layer in checkpoint if 'output_blocks' in layer}		)
     _a			      =							{
         layer_id: [key for key in checkpoint if f'output_blocks.{layer_id}' in key]
         for layer_id in range(__UpperCamelCase		)
     }

     for i in range(1				,						__UpperCamelCase		):
          _a			      =							(i - 1) // (config['num_res_blocks'] + 1)
          _a			      =							(i - 1) % (config['num_res_blocks'] + 1)

          _a			      =							[key for key in input_blocks[i] if f'input_blocks.{i}.0' in key]
          _a			      =							[key for key in input_blocks[i] if f'input_blocks.{i}.1' in key]

          if f'input_blocks.{i}.0.op.weight' in checkpoint:
               _a			      =							checkpoint[
                   f'input_blocks.{i}.0.op.weight'
               ]
               _a			      =							checkpoint[
                   f'input_blocks.{i}.0.op.bias'
               ]
               continue

          _a			      =							renew_resnet_paths(__UpperCamelCase		)
          _a			      =							{'old': f'input_blocks.{i}.0', 'new': f'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
          _a			      =							{'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
          assign_to_checkpoint(
              __UpperCamelCase				,						__UpperCamelCase				,						__UpperCamelCase				,						additional_replacements=[meta_path, resnet_op]				,						config=__UpperCamelCase		)

          if len(__UpperCamelCase		):
               _a			      =							renew_attention_paths(__UpperCamelCase		)
               _a			      =							{
                   'old': f'input_blocks.{i}.1',
                   'new': f'down_blocks.{block_id}.attentions.{layer_in_block_id}',
               }
               _a			      =							{
                   f'input_blocks.{i}.1.qkv.bias': {
                       'key': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
                       'query': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
                       'value': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
                   },
                   f'input_blocks.{i}.1.qkv.weight': {
                       'key': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
                       'query': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
                       'value': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
                   },
               }
               assign_to_checkpoint(
                   __UpperCamelCase				,						__UpperCamelCase				,						__UpperCamelCase				,						additional_replacements=[meta_path]				,						attention_paths_to_split=__UpperCamelCase				,						config=__UpperCamelCase				,						)

     _a			      =							middle_blocks[0]
     _a			      =							middle_blocks[1]
     _a			      =							middle_blocks[2]

     _a			      =							renew_resnet_paths(__UpperCamelCase		)
     assign_to_checkpoint(__UpperCamelCase				,						__UpperCamelCase				,						__UpperCamelCase				,						config=__UpperCamelCase		)

     _a			      =							renew_resnet_paths(__UpperCamelCase		)
     assign_to_checkpoint(__UpperCamelCase				,						__UpperCamelCase				,						__UpperCamelCase				,						config=__UpperCamelCase		)

     _a			      =							renew_attention_paths(__UpperCamelCase		)
     _a			      =							{
         'middle_block.1.qkv.bias': {
             'key': 'mid_block.attentions.0.key.bias',
             'query': 'mid_block.attentions.0.query.bias',
             'value': 'mid_block.attentions.0.value.bias',
         },
         'middle_block.1.qkv.weight': {
             'key': 'mid_block.attentions.0.key.weight',
             'query': 'mid_block.attentions.0.query.weight',
             'value': 'mid_block.attentions.0.value.weight',
         },
     }
     assign_to_checkpoint(
         __UpperCamelCase				,						__UpperCamelCase				,						__UpperCamelCase				,						attention_paths_to_split=__UpperCamelCase				,						config=__UpperCamelCase		)

     for i in range(__UpperCamelCase		):
          _a			      =							i // (config['num_res_blocks'] + 1)
          _a			      =							i % (config['num_res_blocks'] + 1)
          _a			      =							[shave_segments(__UpperCamelCase				,						2		) for name in output_blocks[i]]
          _a			      =							{}

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

          if len(__UpperCamelCase		) > 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			      =							renew_resnet_paths(__UpperCamelCase		)
               _a			      =							renew_resnet_paths(__UpperCamelCase		)

               _a			      =							{'old': f'output_blocks.{i}.0', 'new': f'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
               assign_to_checkpoint(__UpperCamelCase				,						__UpperCamelCase				,						__UpperCamelCase				,						additional_replacements=[meta_path]				,						config=__UpperCamelCase		)

               if ["conv.weight", "conv.bias"] in output_block_list.values():
                    _a			      =							list(output_block_list.values()		).index(['conv.weight', 'conv.bias']		)
                    _a			      =							checkpoint[
                        f'output_blocks.{i}.{index}.conv.weight'
                    ]
                    _a			      =							checkpoint[
                        f'output_blocks.{i}.{index}.conv.bias'
                    ]

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

               if len(__UpperCamelCase		):
                    _a			      =							renew_attention_paths(__UpperCamelCase		)
                    _a			      =							{
                        'old': f'output_blocks.{i}.1',
                        'new': f'up_blocks.{block_id}.attentions.{layer_in_block_id}',
                    }
                    _a			      =							{
                        f'output_blocks.{i}.1.qkv.bias': {
                            'key': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
                            'query': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
                            'value': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
                        },
                        f'output_blocks.{i}.1.qkv.weight': {
                            'key': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
                            'query': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
                            'value': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
                        },
                    }
                    assign_to_checkpoint(
                        __UpperCamelCase				,						__UpperCamelCase				,						__UpperCamelCase				,						additional_replacements=[meta_path]				,						attention_paths_to_split=to_split if any('qkv' in key for key in attentions		) else None				,						config=__UpperCamelCase				,						)
          else:
               _a			      =							renew_resnet_paths(__UpperCamelCase				,						n_shave_prefix_segments=1		)
               for path in resnet_0_paths:
                    _a			      =							'.'.join(['output_blocks', str(__UpperCamelCase		), path['old']]		)
                    _a			      =							'.'.join(['up_blocks', str(__UpperCamelCase		), 'resnets', str(__UpperCamelCase		), path['new']]		)

                    _a			      =							checkpoint[old_path]

     return new_checkpoint


if __name__ == "__main__":
 a_					:    int          =    argparse.ArgumentParser()

 parser.add_argument(
     "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
 )

 parser.add_argument(
     "--config_file",
     default=None,
     type=str,
     required=True,
     help="The config json file corresponding to the architecture.",
 )

 parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

 a_					:    Dict          =    parser.parse_args()

 a_					:    Any          =    torch.load(args.checkpoint_path)

 with open(args.config_file) as f:
  a_					:    List[Any]          =    json.loads(f.read())

 a_					:    Dict          =    convert_ldm_checkpoint(checkpoint, config)

 if "ldm" in config:
  del config["ldm"]

 a_					:    Union[str, Any]          =    UNetaDModel(**config)
 model.load_state_dict(converted_checkpoint)

 try:
  a_					:    List[Any]          =    DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
  a_					:    Union[str, Any]          =    VQModel.from_pretrained("/".join(args.checkpoint_path.split("/")[:-1]))

  a_					:    Dict          =    LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
  pipe.save_pretrained(args.dump_path)
 except:  # noqa: E722
  model.save_pretrained(args.dump_path)





'''simple docstring'''






import logging

import numpy as np
import pytest
from scipy.linalg import eigh

logging.basicConfig(level=logging.INFO, format="%(message)s")






def 	_A				(lowerCAmelCase__  :np.ndarray		)   ->  np.ndarray:




     '''simple docstring'''



     return input_array.reshape((input_array.size, 1)		)






def 	_A				(lowerCAmelCase__  :np.ndarray				,						lowerCAmelCase__  :np.ndarray				,						lowerCAmelCase__  :int		)   ->  np.ndarray:




     '''simple docstring'''



     _a			      =							np.nan
     for i in range(lowerCAmelCase__		):
          _a			      =							features[:, labels == i]
          _a			      =							data.mean(1		)
          # Centralize the data of class i
          _a			      =							data - column_reshape(lowerCAmelCase__		)
          if i > 0:
               # If covariance_sum is not None
               covariance_sum += np.dot(lowerCAmelCase__				,						centered_data.T		)
          else:
               # If covariance_sum is np.nan (i.e. first loop)
               _a			      =							np.dot(lowerCAmelCase__				,						centered_data.T		)

     return covariance_sum / features.shape[1]






def 	_A				(lowerCAmelCase__  :np.ndarray				,						lowerCAmelCase__  :np.ndarray				,						lowerCAmelCase__  :int		)   ->  np.ndarray:




     '''simple docstring'''



     _a			      =							features.mean(1		)
     _a			      =							np.nan
     for i in range(lowerCAmelCase__		):
          _a			      =							features[:, labels == i]
          _a			      =							data.shape[1]
          _a			      =							data.mean(1		)
          if i > 0:
               # If covariance_sum is not None
               covariance_sum += device_data * np.dot(
                   column_reshape(lowerCAmelCase__		) - column_reshape(lowerCAmelCase__		)				,						(column_reshape(lowerCAmelCase__		) - column_reshape(lowerCAmelCase__		)).T				,						)
          else:
               # If covariance_sum is np.nan (i.e. first loop)
               _a			      =							device_data * np.dot(
                   column_reshape(lowerCAmelCase__		) - column_reshape(lowerCAmelCase__		)				,						(column_reshape(lowerCAmelCase__		) - column_reshape(lowerCAmelCase__		)).T				,						)

     return covariance_sum / features.shape[1]






def 	_A				(lowerCAmelCase__  :np.ndarray				,						lowerCAmelCase__  :int		)   ->  np.ndarray:




     '''simple docstring'''



     if features.any():
          _a			      =							features.mean(1		)
          # Center the dataset
          _a			      =							features - np.reshape(lowerCAmelCase__				,						(data_mean.size, 1)		)
          _a			      =							np.dot(lowerCAmelCase__				,						centered_data.T		) / features.shape[1]
          _a      ,   _a			      =							np.linalg.eigh(lowerCAmelCase__		)
          # Take all the columns in the reverse order (-1), and then takes only the first
          _a			      =							eigenvectors[:, ::-1][:, 0:dimensions]
          # Project the database on the new space
          _a			      =							np.dot(filtered_eigenvectors.T				,						lowerCAmelCase__		)
          logging.info('Principal Component Analysis computed'		)

          return projected_data
     else:
          logging.basicConfig(level=logging.ERROR				,						format='%(message)s'				,						force=lowerCAmelCase__		)
          logging.error('Dataset empty'		)
          raise AssertionError






def 	_A				(lowerCAmelCase__  :np.ndarray				,						lowerCAmelCase__  :np.ndarray				,						lowerCAmelCase__  :int				,						lowerCAmelCase__  :int		)   ->  np.ndarray:




     '''simple docstring'''



     assert classes > dimensions

     # Check if features have been already loaded
     if features.any:
          _a      ,   _a			      =							eigh(
              covariance_between_classes(lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__		)				,						covariance_within_classes(lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__		)				,						)
          _a			      =							eigenvectors[:, ::-1][:, :dimensions]
          _a      ,   _a      ,   _a			      =							np.linalg.svd(lowerCAmelCase__		)
          _a			      =							svd_matrix[:, 0:dimensions]
          _a			      =							np.dot(filtered_svd_matrix.T				,						lowerCAmelCase__		)
          logging.info('Linear Discriminant Analysis computed'		)

          return projected_data
     else:
          logging.basicConfig(level=logging.ERROR				,						format='%(message)s'				,						force=lowerCAmelCase__		)
          logging.error('Dataset empty'		)
          raise AssertionError






def 	_A				()   ->  None:




     '''simple docstring'''



     _a			      =							np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]		)
     _a			      =							np.array([0, 0, 0, 1, 1]		)
     _a			      =							2
     _a			      =							2

     # Assert that the function raises an AssertionError if dimensions > classes
     with pytest.raises(lowerCAmelCase__		) as error_info:
          _a			      =							linear_discriminant_analysis(
              lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__		)
          if isinstance(lowerCAmelCase__				,						np.ndarray		):
               raise AssertionError(
                   'Did not raise AssertionError for dimensions > classes'		)
          assert error_info.type is AssertionError






def 	_A				()   ->  None:




     '''simple docstring'''



     _a			      =							np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]		)
     _a			      =							2
     _a			      =							np.array([[6.9_2_8_2_0_3_2_3, 8.6_6_0_2_5_4_0_4, 1_0.3_9_2_3_0_4_8_5], [3.0, 3.0, 3.0]]		)

     with pytest.raises(lowerCAmelCase__		) as error_info:
          _a			      =							principal_component_analysis(lowerCAmelCase__				,						lowerCAmelCase__		)
          if not np.allclose(lowerCAmelCase__				,						lowerCAmelCase__		):
               raise AssertionError
          assert error_info.type is AssertionError


if __name__ == "__main__":
 import doctest

 doctest.testmod()

