


'''simple docstring'''
import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


UpperCAmelCase_ :     int								=      {
    'sample_size': 3_2,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': 1_0_0_0,
    'block_out_channels': [3_2, 6_4],
    '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_ :     int								=      {
    'sample_size': 6_4,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 3,
    'num_class_embeds': 1_0_0_0,
    'block_out_channels': [1_9_2, 1_9_2 * 2, 1_9_2 * 3, 1_9_2 * 4],
    'attention_head_dim': 6_4,
    '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_ :     Union[str, Any]								=      {
    'sample_size': 2_5_6,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': None,
    'block_out_channels': [2_5_6, 2_5_6, 2_5_6 * 2, 2_5_6 * 2, 2_5_6 * 4, 2_5_6 * 4],
    'attention_head_dim': 6_4,
    '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_ :     Any								=      {
    'num_train_timesteps': 4_0,
    'sigma_min': 0.0_02,
    'sigma_max': 80.0,
}

UpperCAmelCase_ :     Union[str, Any]								=      {
    'num_train_timesteps': 2_0_1,
    'sigma_min': 0.0_02,
    'sigma_max': 80.0,
}

UpperCAmelCase_ :     str								=      {
    'num_train_timesteps': 1_5_1,
    'sigma_min': 0.0_02,
    'sigma_max': 80.0,
}


def 	_lowercase       (				UpperCamelCase__       :					Tuple			):
 if isinstance(UpperCamelCase__,				UpperCamelCase__			):
  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 	_lowercase       (				UpperCamelCase__       :					List[Any],				UpperCamelCase__       :					Dict,				UpperCamelCase__       :					int,				UpperCamelCase__       :					Tuple,				UpperCamelCase__       :					Any=False			):
 __A  :		Tuple       =			checkpoint[f"""{old_prefix}.in_layers.0.weight"""]
 __A  :		Tuple       =			checkpoint[f"""{old_prefix}.in_layers.0.bias"""]
 __A  :		Union[str, Any]       =			checkpoint[f"""{old_prefix}.in_layers.2.weight"""]
 __A  :		List[Any]       =			checkpoint[f"""{old_prefix}.in_layers.2.bias"""]
 __A  :		Union[str, Any]       =			checkpoint[f"""{old_prefix}.emb_layers.1.weight"""]
 __A  :		int       =			checkpoint[f"""{old_prefix}.emb_layers.1.bias"""]
 __A  :		Tuple       =			checkpoint[f"""{old_prefix}.out_layers.0.weight"""]
 __A  :		List[Any]       =			checkpoint[f"""{old_prefix}.out_layers.0.bias"""]
 __A  :		List[Any]       =			checkpoint[f"""{old_prefix}.out_layers.3.weight"""]
 __A  :		Dict       =			checkpoint[f"""{old_prefix}.out_layers.3.bias"""]

 if has_skip:
  __A  :		Any       =			checkpoint[f"""{old_prefix}.skip_connection.weight"""]
  __A  :		List[Any]       =			checkpoint[f"""{old_prefix}.skip_connection.bias"""]

 return new_checkpoint


def 	_lowercase       (				UpperCamelCase__       :					Any,				UpperCamelCase__       :					str,				UpperCamelCase__       :					List[str],				UpperCamelCase__       :					Any,				UpperCamelCase__       :					Optional[Any]=None			):
 __A  :		Tuple       =			checkpoint[f"""{old_prefix}.qkv.weight"""].chunk(3,				dim=0			)
 __A  :		Tuple       =			checkpoint[f"""{old_prefix}.qkv.bias"""].chunk(3,				dim=0			)

 __A  :		Optional[int]       =			checkpoint[f"""{old_prefix}.norm.weight"""]
 __A  :		List[Any]       =			checkpoint[f"""{old_prefix}.norm.bias"""]

 __A  :		int       =			weight_q.squeeze(-1			).squeeze(-1			)
 __A  :		Tuple       =			bias_q.squeeze(-1			).squeeze(-1			)
 __A  :		List[str]       =			weight_k.squeeze(-1			).squeeze(-1			)
 __A  :		Any       =			bias_k.squeeze(-1			).squeeze(-1			)
 __A  :		Any       =			weight_v.squeeze(-1			).squeeze(-1			)
 __A  :		str       =			bias_v.squeeze(-1			).squeeze(-1			)

 __A  :		int       =			(
     checkpoint[f"""{old_prefix}.proj_out.weight"""].squeeze(-1			).squeeze(-1			)
 )
 __A  :		Tuple       =			checkpoint[f"""{old_prefix}.proj_out.bias"""].squeeze(-1			).squeeze(-1			)

 return new_checkpoint


def 	_lowercase       (				UpperCamelCase__       :					str,				UpperCamelCase__       :					Any			):
 __A  :		Tuple       =			torch.load(UpperCamelCase__,				map_location='cpu'			)
 __A  :		Dict       =			{}

 __A  :		List[str]       =			checkpoint['time_embed.0.weight']
 __A  :		List[str]       =			checkpoint['time_embed.0.bias']
 __A  :		List[Any]       =			checkpoint['time_embed.2.weight']
 __A  :		Tuple       =			checkpoint['time_embed.2.bias']

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

 __A  :		List[Any]       =			checkpoint['input_blocks.0.0.weight']
 __A  :		Tuple       =			checkpoint['input_blocks.0.0.bias']

 __A  :		Dict       =			unet_config['down_block_types']
 __A  :		Any       =			unet_config['layers_per_block']
 __A  :		Tuple       =			unet_config['attention_head_dim']
 __A  :		str       =			unet_config['block_out_channels']
 __A  :		Any       =			1
 __A  :		Any       =			channels_list[0]

 for i, layer_type in enumerate(UpperCamelCase__			):
  __A  :		Dict       =			channels_list[i]
  __A  :		Union[str, Any]       =			current_channels != prev_channels
  if layer_type == "ResnetDownsampleBlock2D":
   for j in range(UpperCamelCase__			):
    __A  :		Any       =			f"""down_blocks.{i}.resnets.{j}"""
    __A  :		int       =			f"""input_blocks.{current_layer}.0"""
    __A  :		List[str]       =			True if j == 0 and downsample_block_has_skip else False
    __A  :		Any       =			convert_resnet(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				has_skip=UpperCamelCase__			)
    current_layer += 1

  elif layer_type == "AttnDownBlock2D":
   for j in range(UpperCamelCase__			):
    __A  :		List[str]       =			f"""down_blocks.{i}.resnets.{j}"""
    __A  :		Optional[Any]       =			f"""input_blocks.{current_layer}.0"""
    __A  :		str       =			True if j == 0 and downsample_block_has_skip else False
    __A  :		Tuple       =			convert_resnet(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				has_skip=UpperCamelCase__			)
    __A  :		Dict       =			f"""down_blocks.{i}.attentions.{j}"""
    __A  :		Dict       =			f"""input_blocks.{current_layer}.1"""
    __A  :		Any       =			convert_attention(
        UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)
    current_layer += 1

  if i != len(UpperCamelCase__			) - 1:
   __A  :		Dict       =			f"""down_blocks.{i}.downsamplers.0"""
   __A  :		Dict       =			f"""input_blocks.{current_layer}.0"""
   __A  :		int       =			convert_resnet(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)
   current_layer += 1

  __A  :		Union[str, Any]       =			current_channels

 # hardcoded the mid-block for now
 __A  :		int       =			'mid_block.resnets.0'
 __A  :		int       =			'middle_block.0'
 __A  :		Optional[int]       =			convert_resnet(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)
 __A  :		List[Any]       =			'mid_block.attentions.0'
 __A  :		Union[str, Any]       =			'middle_block.1'
 __A  :		Union[str, Any]       =			convert_attention(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)
 __A  :		Union[str, Any]       =			'mid_block.resnets.1'
 __A  :		Dict       =			'middle_block.2'
 __A  :		List[str]       =			convert_resnet(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)

 __A  :		Union[str, Any]       =			0
 __A  :		Any       =			unet_config['up_block_types']

 for i, layer_type in enumerate(UpperCamelCase__			):
  if layer_type == "ResnetUpsampleBlock2D":
   for j in range(layers_per_block + 1			):
    __A  :		Any       =			f"""up_blocks.{i}.resnets.{j}"""
    __A  :		Optional[int]       =			f"""output_blocks.{current_layer}.0"""
    __A  :		Optional[int]       =			convert_resnet(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				has_skip=UpperCamelCase__			)
    current_layer += 1

   if i != len(UpperCamelCase__			) - 1:
    __A  :		Optional[int]       =			f"""up_blocks.{i}.upsamplers.0"""
    __A  :		Tuple       =			f"""output_blocks.{current_layer-1}.1"""
    __A  :		List[Any]       =			convert_resnet(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)
  elif layer_type == "AttnUpBlock2D":
   for j in range(layers_per_block + 1			):
    __A  :		Optional[int]       =			f"""up_blocks.{i}.resnets.{j}"""
    __A  :		List[str]       =			f"""output_blocks.{current_layer}.0"""
    __A  :		Any       =			convert_resnet(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				has_skip=UpperCamelCase__			)
    __A  :		int       =			f"""up_blocks.{i}.attentions.{j}"""
    __A  :		Optional[Any]       =			f"""output_blocks.{current_layer}.1"""
    __A  :		Tuple       =			convert_attention(
        UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)
    current_layer += 1

   if i != len(UpperCamelCase__			) - 1:
    __A  :		Dict       =			f"""up_blocks.{i}.upsamplers.0"""
    __A  :		List[Any]       =			f"""output_blocks.{current_layer-1}.2"""
    __A  :		Union[str, Any]       =			convert_resnet(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)

 __A  :		Tuple       =			checkpoint['out.0.weight']
 __A  :		Any       =			checkpoint['out.0.bias']
 __A  :		int       =			checkpoint['out.2.weight']
 __A  :		Tuple       =			checkpoint['out.2.bias']

 return new_checkpoint


if __name__ == "__main__":
 UpperCAmelCase_ :     Optional[int]								=      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_ :     List[Any]								=      parser.parse_args()
 UpperCAmelCase_ :     int								=      strabool(args.class_cond)

 UpperCAmelCase_ :     str								=      os.path.basename(args.unet_path)
 print(f'''Checkpoint: {ckpt_name}''')

 # Get U-Net config
 if "imagenet64" in ckpt_name:
  UpperCAmelCase_ :     Any								=      IMAGENET_64_UNET_CONFIG
 elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
  UpperCAmelCase_ :     Any								=      LSUN_256_UNET_CONFIG
 elif "test" in ckpt_name:
  UpperCAmelCase_ :     List[str]								=      TEST_UNET_CONFIG
 else:
  raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

 if not args.class_cond:
  UpperCAmelCase_ :     int								=      None

 UpperCAmelCase_ :     int								=      con_pt_to_diffuser(args.unet_path, unet_config)

 UpperCAmelCase_ :     Dict								=      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_ :     Dict								=      CD_SCHEDULER_CONFIG
 elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
  UpperCAmelCase_ :     int								=      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_ :     Dict								=      CT_LSUN_256_SCHEDULER_CONFIG
 else:
  raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

 UpperCAmelCase_ :     List[str]								=      CMStochasticIterativeScheduler(**scheduler_config)

 UpperCAmelCase_ :     Tuple								=      ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
 consistency_model.save_pretrained(args.dump_path)










'''simple docstring'''
import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
UpperCAmelCase_ :     Any								=      logging.getLogger(__name__)


def 			_lowercase       (				):
 __A  :		Tuple       =			argparse.ArgumentParser(
     description='Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'			)
 parser.add_argument('--file_path',				type=UpperCamelCase__,				default='data/dump.txt',				help='The path to the data.'			)
 parser.add_argument('--tokenizer_type',				type=UpperCamelCase__,				default='bert',				choices=['bert', 'roberta', 'gpt2']			)
 parser.add_argument('--tokenizer_name',				type=UpperCamelCase__,				default='bert-base-uncased',				help='The tokenizer to use.'			)
 parser.add_argument('--dump_file',				type=UpperCamelCase__,				default='data/dump',				help='The dump file prefix.'			)
 __A  :		Tuple       =			parser.parse_args()

 logger.info(f"""Loading Tokenizer ({args.tokenizer_name})"""			)
 if args.tokenizer_type == "bert":
  __A  :		Any       =			BertTokenizer.from_pretrained(args.tokenizer_name			)
  __A  :		Any       =			tokenizer.special_tokens_map['cls_token']  # `[CLS]`
  __A  :		List[Any]       =			tokenizer.special_tokens_map['sep_token']  # `[SEP]`
 elif args.tokenizer_type == "roberta":
  __A  :		Any       =			RobertaTokenizer.from_pretrained(args.tokenizer_name			)
  __A  :		List[Any]       =			tokenizer.special_tokens_map['cls_token']  # `<s>`
  __A  :		Dict       =			tokenizer.special_tokens_map['sep_token']  # `</s>`
 elif args.tokenizer_type == "gpt2":
  __A  :		List[str]       =			GPTaTokenizer.from_pretrained(args.tokenizer_name			)
  __A  :		Any       =			tokenizer.special_tokens_map['bos_token']  # `<|endoftext|>`
  __A  :		Union[str, Any]       =			tokenizer.special_tokens_map['eos_token']  # `<|endoftext|>`

 logger.info(f"""Loading text from {args.file_path}"""			)
 with open(args.file_path,				'r',				encoding='utf8'			) as fp:
  __A  :		Optional[int]       =			fp.readlines()

 logger.info('Start encoding'			)
 logger.info(f"""{len(UpperCamelCase__			)} examples to process."""			)

 __A  :		int       =			[]
 __A  :		List[str]       =			0
 __A  :		Tuple       =			10000
 __A  :		List[str]       =			time.time()
 for text in data:
  __A  :		int       =			f"""{bos} {text.strip()} {sep}"""
  __A  :		List[Any]       =			tokenizer.encode(UpperCamelCase__,				add_special_tokens=UpperCamelCase__			)
  rslt.append(UpperCamelCase__			)

  iter += 1
  if iter % interval == 0:
   __A  :		Any       =			time.time()
   logger.info(f"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl"""			)
   __A  :		Any       =			time.time()
 logger.info('Finished binarization'			)
 logger.info(f"""{len(UpperCamelCase__			)} examples processed."""			)

 __A  :		List[str]       =			f"""{args.dump_file}.{args.tokenizer_name}.pickle"""
 __A  :		str       =			tokenizer.vocab_size
 if vocab_size < (1 << 16):
  __A  :		Optional[int]       =			[np.uintaa(UpperCamelCase__			) for d in rslt]
 else:
  __A  :		int       =			[np.intaa(UpperCamelCase__			) for d in rslt]
 random.shuffle(rslt_			)
 logger.info(f"""Dump to {dp_file}"""			)
 with open(UpperCamelCase__,				'wb'			) as handle:
  pickle.dump(rslt_,				UpperCamelCase__,				protocol=pickle.HIGHEST_PROTOCOL			)


if __name__ == "__main__":
 main()






