



'''simple docstring'''
import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel


def 	lowercase_						( lowerCAmelCase__						:	Tuple      ,				lowerCAmelCase__						:	Optional[Any]=1			):

			"""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 	lowercase_						( lowerCAmelCase__						:	List[Any]      ,				lowerCAmelCase__						:	str=0			):

			"""simple docstring"""





			__UpperCAmelCase :   int    					=  []
			for old_item in old_list:
						__UpperCAmelCase :   str    					=  old_item
						__UpperCAmelCase :   Dict    					=  new_item.replace("""block."""      ,				"""resnets."""			)
						__UpperCAmelCase :   Union[str, Any]    					=  new_item.replace("""conv_shorcut"""      ,				"""conv1"""			)
						__UpperCAmelCase :   List[str]    					=  new_item.replace("""in_shortcut"""      ,				"""conv_shortcut"""			)
						__UpperCAmelCase :   Optional[int]    					=  new_item.replace("""temb_proj"""      ,				"""time_emb_proj"""			)

						__UpperCAmelCase :   List[str]    					=  shave_segments(lowerCAmelCase__      ,				n_shave_prefix_segments=lowerCAmelCase__			)

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

			return mapping


def 	lowercase_						( lowerCAmelCase__						:	List[Any]      ,				lowerCAmelCase__						:	Any=0      ,				lowerCAmelCase__						:	Optional[Any]=False			):

			"""simple docstring"""





			__UpperCAmelCase :   Any    					=  []
			for old_item in old_list:
						__UpperCAmelCase :   List[Any]    					=  old_item

						# In `model.mid`, the layer is called `attn`.
						if not in_mid:
									__UpperCAmelCase :   List[Any]    					=  new_item.replace("""attn"""      ,				"""attentions"""			)
						__UpperCAmelCase :   str    					=  new_item.replace(""".k."""      ,				""".key."""			)
						__UpperCAmelCase :   Dict    					=  new_item.replace(""".v."""      ,				""".value."""			)
						__UpperCAmelCase :   Optional[int]    					=  new_item.replace(""".q."""      ,				""".query."""			)

						__UpperCAmelCase :   str    					=  new_item.replace("""proj_out"""      ,				"""proj_attn"""			)
						__UpperCAmelCase :   Optional[int]    					=  new_item.replace("""norm"""      ,				"""group_norm"""			)

						__UpperCAmelCase :   List[str]    					=  shave_segments(lowerCAmelCase__      ,				n_shave_prefix_segments=lowerCAmelCase__			)
						mapping.append({"""old""": old_item, """new""": new_item}			)

			return mapping


def 	lowercase_						( lowerCAmelCase__						:	Dict      ,				lowerCAmelCase__						:	Optional[Any]      ,				lowerCAmelCase__						:	List[Any]      ,				lowerCAmelCase__						:	int=None      ,				lowerCAmelCase__						:	List[str]=None      ,				lowerCAmelCase__						:	int=None			):

			"""simple docstring"""





			assert isinstance(lowerCAmelCase__      ,				lowerCAmelCase__			), "Paths should be a list of dicts containing 'old' and 'new' keys."

			if attention_paths_to_split is not None:
						if config is None:
									raise ValueError("""Please specify the config if setting 'attention_paths_to_split' to 'True'."""			)

						for path, path_map in attention_paths_to_split.items():
									__UpperCAmelCase :   Optional[int]    					=  old_checkpoint[path]
									__UpperCAmelCase :   Union[str, Any]    					=  old_tensor.shape[0] // 3

									__UpperCAmelCase :   Optional[int]    					=  (-1, channels) if len(old_tensor.shape			) == 3 else (-1)

									__UpperCAmelCase :   Optional[int]    					=  old_tensor.shape[0] // config.get("""num_head_channels"""      ,				1			) // 3

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

									__UpperCAmelCase :   Optional[Any]    					=  query.reshape(lowerCAmelCase__			).squeeze()
									__UpperCAmelCase :   Union[str, Any]    					=  key.reshape(lowerCAmelCase__			).squeeze()
									__UpperCAmelCase :   str    					=  value.reshape(lowerCAmelCase__			).squeeze()

			for path in paths:
						__UpperCAmelCase :   Optional[Any]    					=  path["""new"""]

						if attention_paths_to_split is not None and new_path in attention_paths_to_split:
									continue

						__UpperCAmelCase :   List[str]    					=  new_path.replace("""down."""      ,				"""down_blocks."""			)
						__UpperCAmelCase :   List[str]    					=  new_path.replace("""up."""      ,				"""up_blocks."""			)

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

						if "attentions" in new_path:
									__UpperCAmelCase :   int    					=  old_checkpoint[path["""old"""]].squeeze()
						else:
									__UpperCAmelCase :   str    					=  old_checkpoint[path["""old"""]]


def 	lowercase_						( lowerCAmelCase__						:	int      ,				lowerCAmelCase__						:	Tuple			):

			"""simple docstring"""





			__UpperCAmelCase :   Optional[int]    					=  {}

			__UpperCAmelCase :   Dict    					=  checkpoint["""temb.dense.0.weight"""]
			__UpperCAmelCase :   Tuple    					=  checkpoint["""temb.dense.0.bias"""]
			__UpperCAmelCase :   Dict    					=  checkpoint["""temb.dense.1.weight"""]
			__UpperCAmelCase :   Any    					=  checkpoint["""temb.dense.1.bias"""]

			__UpperCAmelCase :   Any    					=  checkpoint["""norm_out.weight"""]
			__UpperCAmelCase :   List[Any]    					=  checkpoint["""norm_out.bias"""]

			__UpperCAmelCase :   Optional[int]    					=  checkpoint["""conv_in.weight"""]
			__UpperCAmelCase :   Dict    					=  checkpoint["""conv_in.bias"""]
			__UpperCAmelCase :   Union[str, Any]    					=  checkpoint["""conv_out.weight"""]
			__UpperCAmelCase :   List[Any]    					=  checkpoint["""conv_out.bias"""]

			__UpperCAmelCase :   Tuple    					=  len({""".""".join(layer.split("""."""			)[:2]			) for layer in checkpoint if """down""" in layer}			)
			__UpperCAmelCase :   str    					=  {
			    layer_id: [key for key in checkpoint if f'down.{layer_id}' in key] for layer_id in range(lowerCAmelCase__			)
			}

			__UpperCAmelCase :   str    					=  len({""".""".join(layer.split("""."""			)[:2]			) for layer in checkpoint if """up""" in layer}			)
			__UpperCAmelCase :   Union[str, Any]    					=  {layer_id: [key for key in checkpoint if f'up.{layer_id}' in key] for layer_id in range(lowerCAmelCase__			)}

			for i in range(lowerCAmelCase__			):
						__UpperCAmelCase :   Any    					=  (i - 1) // (config["""layers_per_block"""] + 1)

						if any("""downsample""" in layer for layer in down_blocks[i]			):
									__UpperCAmelCase :   Dict    					=  checkpoint[
									    f'down.{i}.downsample.op.weight'
									]
									__UpperCAmelCase :   int    					=  checkpoint[f'down.{i}.downsample.op.bias']
						#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
						#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

						if any("""block""" in layer for layer in down_blocks[i]			):
									__UpperCAmelCase :   str    					=  len(
									    {""".""".join(shave_segments(lowerCAmelCase__      ,				2			).split("""."""			)[:2]			) for layer in down_blocks[i] if """block""" in layer}			)
									__UpperCAmelCase :   Tuple    					=  {
									    layer_id: [key for key in down_blocks[i] if f'block.{layer_id}' in key]
									    for layer_id in range(lowerCAmelCase__			)
									}

									if num_blocks > 0:
												for j in range(config["""layers_per_block"""]			):
															__UpperCAmelCase :   List[str]    					=  renew_resnet_paths(blocks[j]			)
															assign_to_checkpoint(lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__			)

						if any("""attn""" in layer for layer in down_blocks[i]			):
									__UpperCAmelCase :   Tuple    					=  len(
									    {""".""".join(shave_segments(lowerCAmelCase__      ,				2			).split("""."""			)[:2]			) for layer in down_blocks[i] if """attn""" in layer}			)
									__UpperCAmelCase :   str    					=  {
									    layer_id: [key for key in down_blocks[i] if f'attn.{layer_id}' in key]
									    for layer_id in range(lowerCAmelCase__			)
									}

									if num_attn > 0:
												for j in range(config["""layers_per_block"""]			):
															__UpperCAmelCase :   Dict    					=  renew_attention_paths(attns[j]			)
															assign_to_checkpoint(lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				config=lowerCAmelCase__			)

			__UpperCAmelCase :   List[Any]    					=  [key for key in checkpoint if """mid.block_1""" in key]
			__UpperCAmelCase :   Union[str, Any]    					=  [key for key in checkpoint if """mid.block_2""" in key]
			__UpperCAmelCase :   Optional[Any]    					=  [key for key in checkpoint if """mid.attn_1""" in key]

			# Mid new 2
			__UpperCAmelCase :   Optional[Any]    					=  renew_resnet_paths(lowerCAmelCase__			)
			assign_to_checkpoint(
			    lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}]      ,				)

			__UpperCAmelCase :   Union[str, Any]    					=  renew_resnet_paths(lowerCAmelCase__			)
			assign_to_checkpoint(
			    lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}]      ,				)

			__UpperCAmelCase :   Dict    					=  renew_attention_paths(lowerCAmelCase__      ,				in_mid=lowerCAmelCase__			)
			assign_to_checkpoint(
			    lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """attn_1""", """new""": """attentions.0"""}]      ,				)

			for i in range(lowerCAmelCase__			):
						__UpperCAmelCase :   List[Any]    					=  num_up_blocks - 1 - i

						if any("""upsample""" in layer for layer in up_blocks[i]			):
									__UpperCAmelCase :   str    					=  checkpoint[
									    f'up.{i}.upsample.conv.weight'
									]
									__UpperCAmelCase :   Optional[int]    					=  checkpoint[f'up.{i}.upsample.conv.bias']

						if any("""block""" in layer for layer in up_blocks[i]			):
									__UpperCAmelCase :   Any    					=  len(
									    {""".""".join(shave_segments(lowerCAmelCase__      ,				2			).split("""."""			)[:2]			) for layer in up_blocks[i] if """block""" in layer}			)
									__UpperCAmelCase :   Dict    					=  {
									    layer_id: [key for key in up_blocks[i] if f'block.{layer_id}' in key] for layer_id in range(lowerCAmelCase__			)
									}

									if num_blocks > 0:
												for j in range(config["""layers_per_block"""] + 1			):
															__UpperCAmelCase :   Any    					=  {"""old""": f'up_blocks.{i}', """new""": f'up_blocks.{block_id}'}
															__UpperCAmelCase :   Optional[Any]    					=  renew_resnet_paths(blocks[j]			)
															assign_to_checkpoint(lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				additional_replacements=[replace_indices]			)

						if any("""attn""" in layer for layer in up_blocks[i]			):
									__UpperCAmelCase :   List[str]    					=  len(
									    {""".""".join(shave_segments(lowerCAmelCase__      ,				2			).split("""."""			)[:2]			) for layer in up_blocks[i] if """attn""" in layer}			)
									__UpperCAmelCase :   str    					=  {
									    layer_id: [key for key in up_blocks[i] if f'attn.{layer_id}' in key] for layer_id in range(lowerCAmelCase__			)
									}

									if num_attn > 0:
												for j in range(config["""layers_per_block"""] + 1			):
															__UpperCAmelCase :   List[str]    					=  {"""old""": f'up_blocks.{i}', """new""": f'up_blocks.{block_id}'}
															__UpperCAmelCase :   Tuple    					=  renew_attention_paths(attns[j]			)
															assign_to_checkpoint(lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				additional_replacements=[replace_indices]			)

			__UpperCAmelCase :   Optional[int]    					=  {k.replace("""mid_new_2"""      ,				"""mid_block"""			): v for k, v in new_checkpoint.items()}
			return new_checkpoint


def 	lowercase_						( lowerCAmelCase__						:	Union[str, Any]      ,				lowerCAmelCase__						:	Optional[Any]			):

			"""simple docstring"""





			__UpperCAmelCase :   Tuple    					=  {}

			__UpperCAmelCase :   str    					=  checkpoint["""encoder.norm_out.weight"""]
			__UpperCAmelCase :   List[Any]    					=  checkpoint["""encoder.norm_out.bias"""]

			__UpperCAmelCase :   Union[str, Any]    					=  checkpoint["""encoder.conv_in.weight"""]
			__UpperCAmelCase :   Union[str, Any]    					=  checkpoint["""encoder.conv_in.bias"""]
			__UpperCAmelCase :   Union[str, Any]    					=  checkpoint["""encoder.conv_out.weight"""]
			__UpperCAmelCase :   int    					=  checkpoint["""encoder.conv_out.bias"""]

			__UpperCAmelCase :   str    					=  checkpoint["""decoder.norm_out.weight"""]
			__UpperCAmelCase :   Optional[int]    					=  checkpoint["""decoder.norm_out.bias"""]

			__UpperCAmelCase :   Dict    					=  checkpoint["""decoder.conv_in.weight"""]
			__UpperCAmelCase :   Dict    					=  checkpoint["""decoder.conv_in.bias"""]
			__UpperCAmelCase :   Tuple    					=  checkpoint["""decoder.conv_out.weight"""]
			__UpperCAmelCase :   str    					=  checkpoint["""decoder.conv_out.bias"""]

			__UpperCAmelCase :   str    					=  len({""".""".join(layer.split("""."""			)[:3]			) for layer in checkpoint if """down""" in layer}			)
			__UpperCAmelCase :   Tuple    					=  {
			    layer_id: [key for key in checkpoint if f'down.{layer_id}' in key] for layer_id in range(lowerCAmelCase__			)
			}

			__UpperCAmelCase :   Tuple    					=  len({""".""".join(layer.split("""."""			)[:3]			) for layer in checkpoint if """up""" in layer}			)
			__UpperCAmelCase :   List[Any]    					=  {layer_id: [key for key in checkpoint if f'up.{layer_id}' in key] for layer_id in range(lowerCAmelCase__			)}

			for i in range(lowerCAmelCase__			):
						__UpperCAmelCase :   Any    					=  (i - 1) // (config["""layers_per_block"""] + 1)

						if any("""downsample""" in layer for layer in down_blocks[i]			):
									__UpperCAmelCase :   Optional[Any]    					=  checkpoint[
									    f'encoder.down.{i}.downsample.conv.weight'
									]
									__UpperCAmelCase :   Union[str, Any]    					=  checkpoint[
									    f'encoder.down.{i}.downsample.conv.bias'
									]

						if any("""block""" in layer for layer in down_blocks[i]			):
									__UpperCAmelCase :   List[Any]    					=  len(
									    {""".""".join(shave_segments(lowerCAmelCase__      ,				3			).split("""."""			)[:3]			) for layer in down_blocks[i] if """block""" in layer}			)
									__UpperCAmelCase :   str    					=  {
									    layer_id: [key for key in down_blocks[i] if f'block.{layer_id}' in key]
									    for layer_id in range(lowerCAmelCase__			)
									}

									if num_blocks > 0:
												for j in range(config["""layers_per_block"""]			):
															__UpperCAmelCase :   List[Any]    					=  renew_resnet_paths(blocks[j]			)
															assign_to_checkpoint(lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__			)

						if any("""attn""" in layer for layer in down_blocks[i]			):
									__UpperCAmelCase :   List[str]    					=  len(
									    {""".""".join(shave_segments(lowerCAmelCase__      ,				3			).split("""."""			)[:3]			) for layer in down_blocks[i] if """attn""" in layer}			)
									__UpperCAmelCase :   Dict    					=  {
									    layer_id: [key for key in down_blocks[i] if f'attn.{layer_id}' in key]
									    for layer_id in range(lowerCAmelCase__			)
									}

									if num_attn > 0:
												for j in range(config["""layers_per_block"""]			):
															__UpperCAmelCase :   Optional[int]    					=  renew_attention_paths(attns[j]			)
															assign_to_checkpoint(lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				config=lowerCAmelCase__			)

			__UpperCAmelCase :   Optional[Any]    					=  [key for key in checkpoint if """mid.block_1""" in key]
			__UpperCAmelCase :   List[Any]    					=  [key for key in checkpoint if """mid.block_2""" in key]
			__UpperCAmelCase :   Dict    					=  [key for key in checkpoint if """mid.attn_1""" in key]

			# Mid new 2
			__UpperCAmelCase :   Any    					=  renew_resnet_paths(lowerCAmelCase__			)
			assign_to_checkpoint(
			    lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}]      ,				)

			__UpperCAmelCase :   Optional[Any]    					=  renew_resnet_paths(lowerCAmelCase__			)
			assign_to_checkpoint(
			    lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}]      ,				)

			__UpperCAmelCase :   Tuple    					=  renew_attention_paths(lowerCAmelCase__      ,				in_mid=lowerCAmelCase__			)
			assign_to_checkpoint(
			    lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """attn_1""", """new""": """attentions.0"""}]      ,				)

			for i in range(lowerCAmelCase__			):
						__UpperCAmelCase :   Union[str, Any]    					=  num_up_blocks - 1 - i

						if any("""upsample""" in layer for layer in up_blocks[i]			):
									__UpperCAmelCase :   int    					=  checkpoint[
									    f'decoder.up.{i}.upsample.conv.weight'
									]
									__UpperCAmelCase :   Any    					=  checkpoint[
									    f'decoder.up.{i}.upsample.conv.bias'
									]

						if any("""block""" in layer for layer in up_blocks[i]			):
									__UpperCAmelCase :   List[str]    					=  len(
									    {""".""".join(shave_segments(lowerCAmelCase__      ,				3			).split("""."""			)[:3]			) for layer in up_blocks[i] if """block""" in layer}			)
									__UpperCAmelCase :   Union[str, Any]    					=  {
									    layer_id: [key for key in up_blocks[i] if f'block.{layer_id}' in key] for layer_id in range(lowerCAmelCase__			)
									}

									if num_blocks > 0:
												for j in range(config["""layers_per_block"""] + 1			):
															__UpperCAmelCase :   List[str]    					=  {"""old""": f'up_blocks.{i}', """new""": f'up_blocks.{block_id}'}
															__UpperCAmelCase :   Optional[int]    					=  renew_resnet_paths(blocks[j]			)
															assign_to_checkpoint(lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				additional_replacements=[replace_indices]			)

						if any("""attn""" in layer for layer in up_blocks[i]			):
									__UpperCAmelCase :   int    					=  len(
									    {""".""".join(shave_segments(lowerCAmelCase__      ,				3			).split("""."""			)[:3]			) for layer in up_blocks[i] if """attn""" in layer}			)
									__UpperCAmelCase :   List[str]    					=  {
									    layer_id: [key for key in up_blocks[i] if f'attn.{layer_id}' in key] for layer_id in range(lowerCAmelCase__			)
									}

									if num_attn > 0:
												for j in range(config["""layers_per_block"""] + 1			):
															__UpperCAmelCase :   List[str]    					=  {"""old""": f'up_blocks.{i}', """new""": f'up_blocks.{block_id}'}
															__UpperCAmelCase :   Union[str, Any]    					=  renew_attention_paths(attns[j]			)
															assign_to_checkpoint(lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				additional_replacements=[replace_indices]			)

			__UpperCAmelCase :   Optional[int]    					=  {k.replace("""mid_new_2"""      ,				"""mid_block"""			): v for k, v in new_checkpoint.items()}
			__UpperCAmelCase :   str    					=  checkpoint["""quant_conv.weight"""]
			__UpperCAmelCase :   str    					=  checkpoint["""quant_conv.bias"""]
			if "quantize.embedding.weight" in checkpoint:
						__UpperCAmelCase :   str    					=  checkpoint["""quantize.embedding.weight"""]
			__UpperCAmelCase :   Optional[Any]    					=  checkpoint["""post_quant_conv.weight"""]
			__UpperCAmelCase :   Optional[Any]    					=  checkpoint["""post_quant_conv.bias"""]

			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())

					# unet case
					_UpperCamelCase									=      {key.split('''.''')[0] for key in checkpoint.keys()}
					if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
										_UpperCamelCase									=      convert_vq_autoenc_checkpoint(checkpoint, config)
					else:
										_UpperCamelCase									=      convert_ddpm_checkpoint(checkpoint, config)

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

					if config["_class_name"] == "VQModel":
										_UpperCamelCase									=      VQModel(**config)
										model.load_state_dict(converted_checkpoint)
										model.save_pretrained(args.dump_path)
					elif config["_class_name"] == "AutoencoderKL":
										_UpperCamelCase									=      AutoencoderKL(**config)
										model.load_state_dict(converted_checkpoint)
										model.save_pretrained(args.dump_path)
					else:
										_UpperCamelCase									=      UNetaDModel(**config)
										model.load_state_dict(converted_checkpoint)

										_UpperCamelCase									=      DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

										_UpperCamelCase									=      DDPMPipeline(unet=model, scheduler=scheduler)
										pipe.save_pretrained(args.dump_path)









'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation

_UpperCamelCase									=      logging.get_logger(__name__)

_UpperCamelCase									=      {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

_UpperCamelCase									=      {
    '''vocab_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/vocab.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/vocab.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/vocab.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/vocab.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/merges.txt''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/merges.txt''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/merges.txt''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/merges.txt''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/merges.txt''',
    },
}

_UpperCamelCase									=      {
    '''gpt2''': 1024,
    '''gpt2-medium''': 1024,
    '''gpt2-large''': 1024,
    '''gpt2-xl''': 1024,
    '''distilgpt2''': 1024,
}


@lru_cache()
def 	lowercase_						( ):

			"""simple docstring"""





			__UpperCAmelCase :   Dict    					=  (
			    list(range(ord("""!"""			)      ,				ord("""~"""			) + 1			)			) + list(range(ord("""¡"""			)      ,				ord("""¬"""			) + 1			)			) + list(range(ord("""®"""			)      ,				ord("""ÿ"""			) + 1			)			)
			)
			__UpperCAmelCase :   Union[str, Any]    					=  bs[:]
			__UpperCAmelCase :   Optional[int]    					=  0
			for b in range(2**8			):
						if b not in bs:
									bs.append(lowerCAmelCase__			)
									cs.append(2**8 + n			)
									n += 1
			__UpperCAmelCase :   List[str]    					=  [chr(lowerCAmelCase__			) for n in cs]
			return dict(zip(lowerCAmelCase__      ,				lowerCAmelCase__			)			)


def 	lowercase_						( lowerCAmelCase__						:	List[str]			):

			"""simple docstring"""





			__UpperCAmelCase :   Any    					=  set()
			__UpperCAmelCase :   Dict    					=  word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)			)
						__UpperCAmelCase :   Union[str, Any]    					=  char
			return pairs






class _A	(     __SCREAMING_SNAKE_CASE     ):
		_SCREAMING_SNAKE_CASE       :     Any       =      VOCAB_FILES_NAMES
		_SCREAMING_SNAKE_CASE       :     str       =      PRETRAINED_VOCAB_FILES_MAP
		_SCREAMING_SNAKE_CASE       :     str       =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_SCREAMING_SNAKE_CASE       :     Dict       =      ["input_ids", "attention_mask"]





		def __init__(  self			,							__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase="replace"			,							__UpperCAmelCase="<|endoftext|>"			,							__UpperCAmelCase="<|endoftext|>"			,							__UpperCAmelCase="<|endoftext|>"			,							__UpperCAmelCase=None			,							__UpperCAmelCase=False			,							__UpperCAmelCase=False			,							**__UpperCAmelCase			,							)							->  List[Any]:

					'''simple docstring'''
					__UpperCAmelCase :   List[str]    					=  AddedToken(__UpperCAmelCase			,							lstrip=__UpperCAmelCase			,							rstrip=__UpperCAmelCase					) if isinstance(__UpperCAmelCase			,							__UpperCAmelCase					) else bos_token
					__UpperCAmelCase :   List[str]    					=  AddedToken(__UpperCAmelCase			,							lstrip=__UpperCAmelCase			,							rstrip=__UpperCAmelCase					) if isinstance(__UpperCAmelCase			,							__UpperCAmelCase					) else eos_token
					__UpperCAmelCase :   Optional[Any]    					=  AddedToken(__UpperCAmelCase			,							lstrip=__UpperCAmelCase			,							rstrip=__UpperCAmelCase					) if isinstance(__UpperCAmelCase			,							__UpperCAmelCase					) else unk_token
					__UpperCAmelCase :   Union[str, Any]    					=  AddedToken(__UpperCAmelCase			,							lstrip=__UpperCAmelCase			,							rstrip=__UpperCAmelCase					) if isinstance(__UpperCAmelCase			,							__UpperCAmelCase					) else pad_token
					super().__init__(
					    errors=__UpperCAmelCase			,							unk_token=__UpperCAmelCase			,							bos_token=__UpperCAmelCase			,							eos_token=__UpperCAmelCase			,							pad_token=__UpperCAmelCase			,							add_prefix_space=__UpperCAmelCase			,							add_bos_token=__UpperCAmelCase			,							**__UpperCAmelCase			,							)
					__UpperCAmelCase :   Optional[Any]    					=  add_bos_token

					with open(__UpperCAmelCase			,							encoding="""utf-8"""					) as vocab_handle:
								__UpperCAmelCase :   str    					=  json.load(__UpperCAmelCase					)
					__UpperCAmelCase :   List[str]    					=  {v: k for k, v in self.encoder.items()}
					__UpperCAmelCase :   Tuple    					=  errors  # how to handle errors in decoding
					__UpperCAmelCase :   List[Any]    					=  bytes_to_unicode()
					__UpperCAmelCase :   Optional[Any]    					=  {v: k for k, v in self.byte_encoder.items()}
					with open(__UpperCAmelCase			,							encoding="""utf-8"""					) as merges_handle:
								__UpperCAmelCase :   Optional[int]    					=  merges_handle.read().split("""\n"""					)[1:-1]
					__UpperCAmelCase :   List[str]    					=  [tuple(merge.split()					) for merge in bpe_merges]
					__UpperCAmelCase :   int    					=  dict(zip(__UpperCAmelCase			,							range(len(__UpperCAmelCase					)					)					)					)
					__UpperCAmelCase :   Tuple    					=  {}
					__UpperCAmelCase :   List[Any]    					=  add_prefix_space

					# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
					__UpperCAmelCase :   Tuple    					=  re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""					)





		@property
		def 				__A							(  self					)							->  List[str]:

					'''simple docstring'''
					return len(self.encoder					)





		def 				__A							(  self					)							->  Tuple:

					'''simple docstring'''
					return dict(self.encoder			,							**self.added_tokens_encoder					)





		def 				__A							(  self			,							__UpperCAmelCase					)							->  int:

					'''simple docstring'''
					if token in self.cache:
								return self.cache[token]
					__UpperCAmelCase :   str    					=  tuple(__UpperCAmelCase					)
					__UpperCAmelCase :   Optional[int]    					=  get_pairs(__UpperCAmelCase					)

					if not pairs:
								return token

					while True:
								__UpperCAmelCase :   Dict    					=  min(__UpperCAmelCase			,							key=lambda __UpperCAmelCase					: self.bpe_ranks.get(__UpperCAmelCase			,							float("""inf"""					)					)					)
								if bigram not in self.bpe_ranks:
											break
								__UpperCAmelCase					,	__UpperCAmelCase :   Union[str, Any]    					=  bigram
								__UpperCAmelCase :   Optional[Any]    					=  []
								__UpperCAmelCase :   Any    					=  0
								while i < len(__UpperCAmelCase					):
											try:
														__UpperCAmelCase :   Optional[Any]    					=  word.index(__UpperCAmelCase			,							__UpperCAmelCase					)
											except ValueError:
														new_word.extend(word[i:]					)
														break
											else:
														new_word.extend(word[i:j]					)
														__UpperCAmelCase :   Tuple    					=  j

											if word[i] == first and i < len(__UpperCAmelCase					) - 1 and word[i + 1] == second:
														new_word.append(first + second					)
														i += 2
											else:
														new_word.append(word[i]					)
														i += 1
								__UpperCAmelCase :   int    					=  tuple(__UpperCAmelCase					)
								__UpperCAmelCase :   Optional[int]    					=  new_word
								if len(__UpperCAmelCase					) == 1:
											break
								else:
											__UpperCAmelCase :   Union[str, Any]    					=  get_pairs(__UpperCAmelCase					)
					__UpperCAmelCase :   List[str]    					=  """ """.join(__UpperCAmelCase					)
					__UpperCAmelCase :   List[Any]    					=  word
					return word





		def 				__A							(  self			,							__UpperCAmelCase			,							__UpperCAmelCase=None					)							->  Tuple:

					'''simple docstring'''
					if self.add_bos_token:
								__UpperCAmelCase :   List[Any]    					=  [self.bos_token_id]
					else:
								__UpperCAmelCase :   List[Any]    					=  []

					__UpperCAmelCase :   Optional[Any]    					=  bos_token_ids + token_ids_a

					if token_ids_a is None:
								return output

					return output + bos_token_ids + token_ids_a





		def 				__A							(  self			,							__UpperCAmelCase			,							__UpperCAmelCase = None			,							__UpperCAmelCase = False					)							->  List[int]:

					'''simple docstring'''
					if already_has_special_tokens:
								return super().get_special_tokens_mask(
								    token_ids_a=__UpperCAmelCase			,							token_ids_a=__UpperCAmelCase			,							already_has_special_tokens=__UpperCAmelCase					)

					if not self.add_bos_token:
								return super().get_special_tokens_mask(
								    token_ids_a=__UpperCAmelCase			,							token_ids_a=__UpperCAmelCase			,							already_has_special_tokens=__UpperCAmelCase					)

					if token_ids_a is None:
								return [1] + ([0] * len(__UpperCAmelCase					))
					return [1] + ([0] * len(__UpperCAmelCase					)) + [1] + ([0] * len(__UpperCAmelCase					))





		def 				__A							(  self			,							__UpperCAmelCase					)							->  int:

					'''simple docstring'''
					__UpperCAmelCase :   Optional[Any]    					=  []
					for token in re.findall(self.pat			,							__UpperCAmelCase					):
								__UpperCAmelCase :   str    					=  """""".join(
								    self.byte_encoder[b] for b in token.encode("""utf-8"""					)					)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
								bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__UpperCAmelCase					).split(""" """					)					)
					return bpe_tokens





		def 				__A							(  self			,							__UpperCAmelCase					)							->  Tuple:

					'''simple docstring'''
					return self.encoder.get(__UpperCAmelCase			,							self.encoder.get(self.unk_token					)					)





		def 				__A							(  self			,							__UpperCAmelCase					)							->  Dict:

					'''simple docstring'''
					return self.decoder.get(__UpperCAmelCase					)





		def 				__A							(  self			,							__UpperCAmelCase					)							->  Any:

					'''simple docstring'''
					__UpperCAmelCase :   int    					=  """""".join(__UpperCAmelCase					)
					__UpperCAmelCase :   Dict    					=  bytearray([self.byte_decoder[c] for c in text]					).decode("""utf-8"""			,							errors=self.errors					)
					return text





		def 				__A							(  self			,							__UpperCAmelCase			,							__UpperCAmelCase = None					)							->  Tuple[str]:

					'''simple docstring'''
					if not os.path.isdir(__UpperCAmelCase					):
								logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
								return
					__UpperCAmelCase :   Tuple    					=  os.path.join(
					    __UpperCAmelCase			,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)
					__UpperCAmelCase :   str    					=  os.path.join(
					    __UpperCAmelCase			,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]					)

					with open(__UpperCAmelCase			,							"""w"""			,							encoding="""utf-8"""					) as f:
								f.write(json.dumps(self.encoder			,							indent=2			,							sort_keys=__UpperCAmelCase			,							ensure_ascii=__UpperCAmelCase					) + """\n"""					)

					__UpperCAmelCase :   List[Any]    					=  0
					with open(__UpperCAmelCase			,							"""w"""			,							encoding="""utf-8"""					) as writer:
								writer.write("""#version: 0.2\n"""					)
								for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,							key=lambda __UpperCAmelCase					: kv[1]					):
											if index != token_index:
														logger.warning(
														    f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
														    """ Please check that the tokenizer is not corrupted!"""					)
														__UpperCAmelCase :   List[Any]    					=  token_index
											writer.write(""" """.join(__UpperCAmelCase					) + """\n"""					)
											index += 1

					return vocab_file, merge_file





		def 				__A							(  self			,							__UpperCAmelCase			,							__UpperCAmelCase=False			,							**__UpperCAmelCase					)							->  Tuple:

					'''simple docstring'''
					__UpperCAmelCase :   Dict    					=  kwargs.pop("""add_prefix_space"""			,							self.add_prefix_space					)
					if is_split_into_words or add_prefix_space:
								__UpperCAmelCase :   int    					=  """ """ + text
					return (text, kwargs)





		def 				__A							(  self			,							__UpperCAmelCase					)							->  List[int]:

					'''simple docstring'''
					__UpperCAmelCase :   List[str]    					=  []
					for is_user, text in conversation.iter_texts():
								input_ids.extend(self.encode(__UpperCAmelCase			,							add_special_tokens=__UpperCAmelCase					) + [self.eos_token_id]					)
					if len(__UpperCAmelCase					) > self.model_max_length:
								__UpperCAmelCase :   Optional[int]    					=  input_ids[-self.model_max_length :]
					return input_ids




