






"""simple docstring"""







import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_UpperCamelCase					      =  {
    """sample_size""": 32,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": 1000,
    """block_out_channels""": [32, 64],
    """attention_head_dim""": 8,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """scale_shift""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

_UpperCamelCase					      =  {
    """sample_size""": 64,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 3,
    """num_class_embeds""": 1000,
    """block_out_channels""": [192, 192 * 2, 192 * 3, 192 * 4],
    """attention_head_dim""": 64,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """scale_shift""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

_UpperCamelCase					      =  {
    """sample_size""": 256,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": None,
    """block_out_channels""": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    """attention_head_dim""": 64,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """ResnetDownsampleBlock2D""",
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
        """ResnetUpsampleBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """default""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

_UpperCamelCase					      =  {
    """num_train_timesteps""": 40,
    """sigma_min""": 0.0_02,
    """sigma_max""": 80.0,
}

_UpperCamelCase					      =  {
    """num_train_timesteps""": 201,
    """sigma_min""": 0.0_02,
    """sigma_max""": 80.0,
}

_UpperCamelCase					      =  {
    """num_train_timesteps""": 151,
    """sigma_min""": 0.0_02,
    """sigma_max""": 80.0,
}

def _a			(		_snake_case							):







						"""simple docstring"""






						if isinstance(lowerCamelCase__				,	lowerCamelCase__							):
												return v
						if v.lower() in ("yes", "true", "t", "y", "1"):
												return True
						elif v.lower() in ("no", "false", "f", "n", "0"):
												return False
						else:
												raise argparse.ArgumentTypeError("""boolean value expected"""							)

def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case=False							):







						"""simple docstring"""






						UpperCAmelCase          =	checkpoint[F'''{old_prefix}.in_layers.0.weight''']
						UpperCAmelCase          =	checkpoint[F'''{old_prefix}.in_layers.0.bias''']
						UpperCAmelCase          =	checkpoint[F'''{old_prefix}.in_layers.2.weight''']
						UpperCAmelCase          =	checkpoint[F'''{old_prefix}.in_layers.2.bias''']
						UpperCAmelCase          =	checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
						UpperCAmelCase          =	checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
						UpperCAmelCase          =	checkpoint[F'''{old_prefix}.out_layers.0.weight''']
						UpperCAmelCase          =	checkpoint[F'''{old_prefix}.out_layers.0.bias''']
						UpperCAmelCase          =	checkpoint[F'''{old_prefix}.out_layers.3.weight''']
						UpperCAmelCase          =	checkpoint[F'''{old_prefix}.out_layers.3.bias''']

						if has_skip:
												UpperCAmelCase          =	checkpoint[F'''{old_prefix}.skip_connection.weight''']
												UpperCAmelCase          =	checkpoint[F'''{old_prefix}.skip_connection.bias''']

						return new_checkpoint

def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case=None							):







						"""simple docstring"""






						UpperCAmelCase          =	checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3				,	dim=0							)
						UpperCAmelCase          =	checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3				,	dim=0							)

						UpperCAmelCase          =	checkpoint[F'''{old_prefix}.norm.weight''']
						UpperCAmelCase          =	checkpoint[F'''{old_prefix}.norm.bias''']

						UpperCAmelCase          =	weight_q.squeeze(-1							).squeeze(-1							)
						UpperCAmelCase          =	bias_q.squeeze(-1							).squeeze(-1							)
						UpperCAmelCase          =	weight_k.squeeze(-1							).squeeze(-1							)
						UpperCAmelCase          =	bias_k.squeeze(-1							).squeeze(-1							)
						UpperCAmelCase          =	weight_v.squeeze(-1							).squeeze(-1							)
						UpperCAmelCase          =	bias_v.squeeze(-1							).squeeze(-1							)

						UpperCAmelCase          =	(
						    checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1							).squeeze(-1							)
						)
						UpperCAmelCase          =	checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1							).squeeze(-1							)

						return new_checkpoint

def _a			(		_snake_case				,	_snake_case							):







						"""simple docstring"""






						UpperCAmelCase          =	torch.load(lowerCamelCase__				,	map_location="""cpu"""							)
						UpperCAmelCase          =	{}

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

						if unet_config["num_class_embeds"] is not None:
												UpperCAmelCase          =	checkpoint['''label_emb.weight''']

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

						UpperCAmelCase          =	unet_config['''down_block_types''']
						UpperCAmelCase          =	unet_config['''layers_per_block''']
						UpperCAmelCase          =	unet_config['''attention_head_dim''']
						UpperCAmelCase          =	unet_config['''block_out_channels''']
						UpperCAmelCase          =	1
						UpperCAmelCase          =	channels_list[0]

						for i, layer_type in enumerate(lowerCamelCase__							):
												UpperCAmelCase          =	channels_list[i]
												UpperCAmelCase          =	current_channels != prev_channels
												if layer_type == "ResnetDownsampleBlock2D":
																		for j in range(lowerCamelCase__							):
																								UpperCAmelCase          =	F'''down_blocks.{i}.resnets.{j}'''
																								UpperCAmelCase          =	F'''input_blocks.{current_layer}.0'''
																								UpperCAmelCase          =	True if j == 0 and downsample_block_has_skip else False
																								UpperCAmelCase          =	convert_resnet(lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__				,	has_skip=lowerCamelCase__							)
																								current_layer += 1

												elif layer_type == "AttnDownBlock2D":
																		for j in range(lowerCamelCase__							):
																								UpperCAmelCase          =	F'''down_blocks.{i}.resnets.{j}'''
																								UpperCAmelCase          =	F'''input_blocks.{current_layer}.0'''
																								UpperCAmelCase          =	True if j == 0 and downsample_block_has_skip else False
																								UpperCAmelCase          =	convert_resnet(lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__				,	has_skip=lowerCamelCase__							)
																								UpperCAmelCase          =	F'''down_blocks.{i}.attentions.{j}'''
																								UpperCAmelCase          =	F'''input_blocks.{current_layer}.1'''
																								UpperCAmelCase          =	convert_attention(
																								    lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__							)
																								current_layer += 1

												if i != len(lowerCamelCase__							) - 1:
																		UpperCAmelCase          =	F'''down_blocks.{i}.downsamplers.0'''
																		UpperCAmelCase          =	F'''input_blocks.{current_layer}.0'''
																		UpperCAmelCase          =	convert_resnet(lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__							)
																		current_layer += 1

												UpperCAmelCase          =	current_channels

						# hardcoded the mid-block for now
						UpperCAmelCase          =	'''mid_block.resnets.0'''
						UpperCAmelCase          =	'''middle_block.0'''
						UpperCAmelCase          =	convert_resnet(lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__							)
						UpperCAmelCase          =	'''mid_block.attentions.0'''
						UpperCAmelCase          =	'''middle_block.1'''
						UpperCAmelCase          =	convert_attention(lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__							)
						UpperCAmelCase          =	'''mid_block.resnets.1'''
						UpperCAmelCase          =	'''middle_block.2'''
						UpperCAmelCase          =	convert_resnet(lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__							)

						UpperCAmelCase          =	0
						UpperCAmelCase          =	unet_config['''up_block_types''']

						for i, layer_type in enumerate(lowerCamelCase__							):
												if layer_type == "ResnetUpsampleBlock2D":
																		for j in range(layers_per_block + 1							):
																								UpperCAmelCase          =	F'''up_blocks.{i}.resnets.{j}'''
																								UpperCAmelCase          =	F'''output_blocks.{current_layer}.0'''
																								UpperCAmelCase          =	convert_resnet(lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__				,	has_skip=lowerCamelCase__							)
																								current_layer += 1

																		if i != len(lowerCamelCase__							) - 1:
																								UpperCAmelCase          =	F'''up_blocks.{i}.upsamplers.0'''
																								UpperCAmelCase          =	F'''output_blocks.{current_layer-1}.1'''
																								UpperCAmelCase          =	convert_resnet(lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__							)
												elif layer_type == "AttnUpBlock2D":
																		for j in range(layers_per_block + 1							):
																								UpperCAmelCase          =	F'''up_blocks.{i}.resnets.{j}'''
																								UpperCAmelCase          =	F'''output_blocks.{current_layer}.0'''
																								UpperCAmelCase          =	convert_resnet(lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__				,	has_skip=lowerCamelCase__							)
																								UpperCAmelCase          =	F'''up_blocks.{i}.attentions.{j}'''
																								UpperCAmelCase          =	F'''output_blocks.{current_layer}.1'''
																								UpperCAmelCase          =	convert_attention(
																								    lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__							)
																								current_layer += 1

																		if i != len(lowerCamelCase__							) - 1:
																								UpperCAmelCase          =	F'''up_blocks.{i}.upsamplers.0'''
																								UpperCAmelCase          =	F'''output_blocks.{current_layer-1}.2'''
																								UpperCAmelCase          =	convert_resnet(lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__							)

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

						return new_checkpoint


if __name__ == "__main__":
		_UpperCamelCase					      =  argparse.ArgumentParser()

		parser.add_argument("""--unet_path""", default=None, type=str, required=True, help="""Path to the unet.pt to convert.""")
		parser.add_argument(
		    """--dump_path""", default=None, type=str, required=True, help="""Path to output the converted UNet model."""
		)
		parser.add_argument("""--class_cond""", default=True, type=str, help="""Whether the model is class-conditional.""")

		_UpperCamelCase					      =  parser.parse_args()
		_UpperCamelCase					      =  strabool(args.class_cond)

		_UpperCamelCase					      =  os.path.basename(args.unet_path)
		print(F"""Checkpoint: {ckpt_name}""")

		# Get U-Net config
		if "imagenet64" in ckpt_name:
				_UpperCamelCase					      =  IMAGENET_64_UNET_CONFIG
		elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
				_UpperCamelCase					      =  LSUN_256_UNET_CONFIG
		elif "test" in ckpt_name:
				_UpperCamelCase					      =  TEST_UNET_CONFIG
		else:
				raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

		if not args.class_cond:
				_UpperCamelCase					      =  None

		_UpperCamelCase					      =  con_pt_to_diffuser(args.unet_path, unet_config)

		_UpperCamelCase					      =  UNetaDModel(**unet_config)
		image_unet.load_state_dict(converted_unet_ckpt)

		# Get scheduler config
		if "cd" in ckpt_name or "test" in ckpt_name:
				_UpperCamelCase					      =  CD_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
				_UpperCamelCase					      =  CT_IMAGENET_64_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
				_UpperCamelCase					      =  CT_LSUN_256_SCHEDULER_CONFIG
		else:
				raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

		_UpperCamelCase					      =  CMStochasticIterativeScheduler(**scheduler_config)

		_UpperCamelCase					      =  ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
		consistency_model.save_pretrained(args.dump_path)











"""simple docstring"""







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


_UpperCamelCase					      =  logging.get_logger(__name__)

_UpperCamelCase					      =  """▁"""

_UpperCamelCase					      =  {"""vocab_file""": """sentencepiece.bpe.model""", """monolingual_vocab_file""": """dict.txt"""}

_UpperCamelCase					      =  {
    """vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model""",
    },
    """monolingual_vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt""",
    },
}

_UpperCamelCase					      =  {"""vinai/bartpho-syllable""": 1024}







class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE		=      PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE		=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE		=      ['''input_ids''', '''attention_mask''']






    def __init__(			self     ,A     ,A     ,A="<s>"     ,A="</s>"     ,A="</s>"     ,A="<s>"     ,A="<unk>"     ,A="<pad>"     ,A="<mask>"     ,A = None     ,**A     ,):
          # Mask token behave like a normal word, i.e. include the space before it
          UpperCAmelCase          =	AddedToken(A     ,lstrip=A     ,rstrip=A ) if isinstance(A     ,A ) else mask_token

          UpperCAmelCase          =	{} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              bos_token=A     ,eos_token=A     ,unk_token=A     ,sep_token=A     ,cls_token=A     ,pad_token=A     ,mask_token=A     ,sp_model_kwargs=self.sp_model_kwargs     ,**A     ,)

          UpperCAmelCase          =	vocab_file
          UpperCAmelCase          =	monolingual_vocab_file
          UpperCAmelCase          =	spm.SentencePieceProcessor(**self.sp_model_kwargs )
          self.sp_model.Load(str(A ) )

          # Load the reduced vocab

          # Keep order of special tokens for backward compatibility
          UpperCAmelCase          =	{}
          UpperCAmelCase          =	0
          for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
                if str(A ) not in self.fairseq_tokens_to_ids:
                      UpperCAmelCase          =	cnt
                      cnt += 1
          with open(A     ,"""r"""     ,encoding="""utf-8""" ) as f:
                for line in f.readlines():
                      UpperCAmelCase          =	line.strip().split()[0]
                      UpperCAmelCase          =	len(self.fairseq_tokens_to_ids )
          if str(A ) not in self.fairseq_tokens_to_ids:
                UpperCAmelCase          =	len(self.fairseq_tokens_to_ids )

          UpperCAmelCase          =	{v: k for k, v in self.fairseq_tokens_to_ids.items()}






    def __getstate__(			self ):
          UpperCAmelCase          =	self.__dict__.copy()
          UpperCAmelCase          =	None
          UpperCAmelCase          =	self.sp_model.serialized_model_proto()
          return state






    def __setstate__(			self     ,A ):
          UpperCAmelCase          =	d

          # for backward compatibility
          if not hasattr(self     ,"""sp_model_kwargs""" ):
                UpperCAmelCase          =	{}

          UpperCAmelCase          =	spm.SentencePieceProcessor(**self.sp_model_kwargs )
          self.sp_model.LoadFromSerializedProto(self.sp_model_proto )






    def     _UpperCamelCase							(			self     ,A     ,A = None ):

          if token_ids_a is None:
                return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          UpperCAmelCase          =	[self.cls_token_id]
          UpperCAmelCase          =	[self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep






    def     _UpperCamelCase							(			self     ,A     ,A = None     ,A = False ):

          if already_has_special_tokens:
                return super().get_special_tokens_mask(
                    token_ids_a=A     ,token_ids_a=A     ,already_has_special_tokens=A )

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






    def     _UpperCamelCase							(			self     ,A     ,A = None ):
          UpperCAmelCase          =	[self.sep_token_id]
          UpperCAmelCase          =	[self.cls_token_id]

          if token_ids_a is None:
                return len(cls + token_ids_a + sep ) * [0]
          return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]






    @property
    def     _UpperCamelCase							(			self ):
          return len(self.fairseq_ids_to_tokens )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	{self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
          vocab.update(self.added_tokens_encoder )
          return vocab






    def     _UpperCamelCase							(			self     ,A ):
          return self.sp_model.encode(A     ,out_type=A )






    def     _UpperCamelCase							(			self     ,A ):
          if token in self.fairseq_tokens_to_ids:
                return self.fairseq_tokens_to_ids[token]
          else:
                return self.unk_token_id






    def     _UpperCamelCase							(			self     ,A ):
          return self.fairseq_ids_to_tokens[index]






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	"""""".join(A ).replace(A     ,""" """ ).strip()
          return out_string






    def     _UpperCamelCase							(			self     ,A     ,A = None ):
          if not os.path.isdir(A ):
                logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
                return
          UpperCAmelCase          =	os.path.join(
              A     ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
          UpperCAmelCase          =	os.path.join(
              A     ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""monolingual_vocab_file"""]     ,)

          if os.path.abspath(self.vocab_file ) != os.path.abspath(A ) and os.path.isfile(self.vocab_file ):
                copyfile(self.vocab_file     ,A )
          elif not os.path.isfile(self.vocab_file ):
                with open(A     ,"""wb""" ) as fi:
                      UpperCAmelCase          =	self.sp_model.serialized_model_proto()
                      fi.write(A )

          if os.path.abspath(self.monolingual_vocab_file ) != os.path.abspath(
              A ) and os.path.isfile(self.monolingual_vocab_file ):
                copyfile(self.monolingual_vocab_file     ,A )
          elif not os.path.isfile(self.monolingual_vocab_file ):
                with open(A     ,"""w"""     ,encoding="""utf-8""" ) as fp:
                      for token in self.fairseq_tokens_to_ids:
                            if token not in self.all_special_tokens:
                                  fp.write(F'''{str(A )} \n''' )

          return out_vocab_file, out_monolingual_vocab_file



