


import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=1      )->						Union[str, Any]:




						"""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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=0      )->						int:




						"""simple docstring"""

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

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

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

												snake_case_ =   shave_segments(SCREAMING_SNAKE_CASE      ,      n_shave_prefix_segments=SCREAMING_SNAKE_CASE      )

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

						return mapping






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=0      )->						Any:




						"""simple docstring"""

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

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

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

												snake_case_ =   shave_segments(SCREAMING_SNAKE_CASE      ,      n_shave_prefix_segments=SCREAMING_SNAKE_CASE      )

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

						return mapping






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=None      ,      SCREAMING_SNAKE_CASE=None      ,      SCREAMING_SNAKE_CASE=None      )->						Optional[Any]:




						"""simple docstring"""

						assert isinstance(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ), "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():
																		snake_case_ =   old_checkpoint[path]
																		snake_case_ =   old_tensor.shape[0] // 3

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

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

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

																		snake_case_ =   query.reshape(SCREAMING_SNAKE_CASE      )
																		snake_case_ =   key.reshape(SCREAMING_SNAKE_CASE      )
																		snake_case_ =   value.reshape(SCREAMING_SNAKE_CASE      )

						for path in paths:
												snake_case_ =   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
												snake_case_ =   new_path.replace('''middle_block.0'''      ,      '''mid_block.resnets.0'''      )
												snake_case_ =   new_path.replace('''middle_block.1'''      ,      '''mid_block.attentions.0'''      )
												snake_case_ =   new_path.replace('''middle_block.2'''      ,      '''mid_block.resnets.1'''      )

												if additional_replacements is not None:
																		for replacement in additional_replacements:
																								snake_case_ =   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:
																		snake_case_ =   old_checkpoint[path['''old''']][:, :, 0]
												else:
																		snake_case_ =   old_checkpoint[path['''old''']]






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Tuple:




						"""simple docstring"""

						snake_case_ =   {}

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

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

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

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

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

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

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

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

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

												if len(SCREAMING_SNAKE_CASE      ):
																		snake_case_ =   renew_attention_paths(SCREAMING_SNAKE_CASE      )
																		snake_case_ =   {
																		    '''old''': f'''input_blocks.{i}.1''',
																		    '''new''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
																		}
																		snake_case_ =   {
																		    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(
																		    SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      additional_replacements=[meta_path]      ,      attention_paths_to_split=SCREAMING_SNAKE_CASE      ,      config=SCREAMING_SNAKE_CASE      ,      )

						snake_case_ =   middle_blocks[0]
						snake_case_ =   middle_blocks[1]
						snake_case_ =   middle_blocks[2]

						snake_case_ =   renew_resnet_paths(SCREAMING_SNAKE_CASE      )
						assign_to_checkpoint(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      config=SCREAMING_SNAKE_CASE      )

						snake_case_ =   renew_resnet_paths(SCREAMING_SNAKE_CASE      )
						assign_to_checkpoint(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      config=SCREAMING_SNAKE_CASE      )

						snake_case_ =   renew_attention_paths(SCREAMING_SNAKE_CASE      )
						snake_case_ =   {
						    '''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(
						    SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      attention_paths_to_split=SCREAMING_SNAKE_CASE      ,      config=SCREAMING_SNAKE_CASE      )

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

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

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

																		snake_case_ =   renew_resnet_paths(SCREAMING_SNAKE_CASE      )
																		snake_case_ =   renew_resnet_paths(SCREAMING_SNAKE_CASE      )

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

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

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

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

																								snake_case_ =   checkpoint[old_path]

						return new_checkpoint


if __name__ == "__main__":
			UpperCAmelCase    =					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.""")

			UpperCAmelCase    =					parser.parse_args()

			UpperCAmelCase    =					torch.load(args.checkpoint_path)

			with open(args.config_file) as f:
						UpperCAmelCase    =					json.loads(f.read())

			UpperCAmelCase    =					convert_ldm_checkpoint(checkpoint, config)

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

			UpperCAmelCase    =					UNetaDModel(**config)
			model.load_state_dict(converted_checkpoint)

			try:
						UpperCAmelCase    =					DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
						UpperCAmelCase    =					VQModel.from_pretrained("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

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



from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput







@dataclass
class lowerCAmelCase_		(				lowerCamelCase__ ):

 '''simple docstring'''

 __snake_case				=						None
 __snake_case				=						None
 __snake_case				=						None
 __snake_case				=						None







class lowerCAmelCase_		(				lowerCamelCase__ ):

 '''simple docstring'''

 def __init__(			self	,   _UpperCAmelCase=1	,   _UpperCAmelCase=0	,   _UpperCAmelCase=2	,   _UpperCAmelCase=5_12	,   _UpperCAmelCase="cls"	,   _UpperCAmelCase=False	,   _UpperCAmelCase=True	,   **_UpperCAmelCase	,   ):
       super().__init__(pad_token_id=_UpperCAmelCase	,   bos_token_id=_UpperCAmelCase	,   eos_token_id=_UpperCAmelCase	,   **_UpperCAmelCase      )
       snake_case_ =   project_dim
       snake_case_ =   pooler_fn
       snake_case_ =   learn_encoder
       snake_case_ =   use_attention_mask







class lowerCAmelCase_		(				lowerCamelCase__ ):

 '''simple docstring'''

 __snake_case				=						[R"pooler", R"logit_scale"]
 __snake_case				=						[R"position_ids", R"predictions.decoder.bias"]
 __snake_case				=						"roberta"
 __snake_case				=						RobertaSeriesConfig



 def __init__(			self	,   _UpperCAmelCase      ):
       super().__init__(_UpperCAmelCase      )
       snake_case_ =   XLMRobertaModel(_UpperCAmelCase      )
       snake_case_ =   nn.Linear(config.hidden_size	,   config.project_dim      )
       snake_case_ =   getattr(_UpperCAmelCase	,   '''has_pre_transformation'''	,   _UpperCAmelCase      )
       if self.has_pre_transformation:
             snake_case_ =   nn.Linear(config.hidden_size	,   config.project_dim      )
             snake_case_ =   nn.LayerNorm(config.hidden_size	,   eps=config.layer_norm_eps      )
       self.post_init()





 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   ):
       snake_case_ =   return_dict if return_dict is not None else self.config.use_return_dict

       snake_case_ =   self.base_model(
           input_ids=_UpperCAmelCase	,   attention_mask=_UpperCAmelCase	,   token_type_ids=_UpperCAmelCase	,   position_ids=_UpperCAmelCase	,   head_mask=_UpperCAmelCase	,   inputs_embeds=_UpperCAmelCase	,   encoder_hidden_states=_UpperCAmelCase	,   encoder_attention_mask=_UpperCAmelCase	,   output_attentions=_UpperCAmelCase	,   output_hidden_states=True if self.has_pre_transformation else output_hidden_states	,   return_dict=_UpperCAmelCase	,   )

       if self.has_pre_transformation:
             snake_case_ =   outputs['''hidden_states'''][-2]
             snake_case_ =   self.pre_LN(_UpperCAmelCase      )
             snake_case_ =   self.transformation_pre(_UpperCAmelCase      )

             return TransformationModelOutput(
                 projection_state=_UpperCAmelCase	,   last_hidden_state=outputs.last_hidden_state	,   hidden_states=outputs.hidden_states	,   attentions=outputs.attentions	,   )
       else:
             snake_case_ =   self.transformation(outputs.last_hidden_state      )
             return TransformationModelOutput(
                 projection_state=_UpperCAmelCase	,   last_hidden_state=outputs.last_hidden_state	,   hidden_states=outputs.hidden_states	,   attentions=outputs.attentions	,   )