

import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


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






def        UpperCamelCase_(							_snake_case   :  Optional[Any]       ,     _snake_case   :  Any	):




			"""simple docstring"""






			__a        =np.argmax(_snake_case       ,     axis=1	)
			return np.sum(outputs == labels	)






def        UpperCamelCase_(							_snake_case   :  List[str]	):




			"""simple docstring"""






			with open(_snake_case       ,     encoding='utf_8'	) as f:
						__a        =csv.reader(_snake_case	)
						__a        =[]
						next(_snake_case	)  # skip the first line
						for line in tqdm(_snake_case	):
									output.append((' '.join(line[1:5]	), line[5], line[6], int(line[-1]	) - 1)	)
			return output






def        UpperCamelCase_(							_snake_case   :  Union[str, Any]       ,     _snake_case   :  Any       ,     _snake_case   :  Dict       ,     _snake_case   :  Any       ,     _snake_case   :  Union[str, Any]       ,     _snake_case   :  Tuple	):




			"""simple docstring"""






			__a        =[]
			for dataset in encoded_datasets:
						__a        =len(_snake_case	)
						__a        =np.zeros((n_batch, 2, input_len)       ,     dtype=np.intaa	)
						__a        =np.zeros((n_batch, 2)       ,     dtype=np.intaa	)
						__a        =np.full((n_batch, 2, input_len)       ,     fill_value=-100       ,     dtype=np.intaa	)
						__a        =np.zeros((n_batch,)       ,     dtype=np.intaa	)
						for (
						    i,
						    (story, conta, conta, mc_label),
						) in enumerate(_snake_case	):
									__a        =[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
									__a        =[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
									__a        =with_conta
									__a        =with_conta
									__a        =len(_snake_case	) - 1
									__a        =len(_snake_case	) - 1
									__a        =with_conta
									__a        =with_conta
									__a        =mc_label
						__a        =(input_ids, mc_token_ids, lm_labels, mc_labels)
						tensor_datasets.append(tuple(torch.tensor(_snake_case	) for t in all_inputs	)	)
			return tensor_datasets






def        UpperCamelCase_(							):




			"""simple docstring"""






			__a        =argparse.ArgumentParser()
			parser.add_argument('--model_name'       ,     type=_snake_case       ,     default='openai-gpt'       ,     help='pretrained model name'	)
			parser.add_argument('--do_train'       ,     action='store_true'       ,     help='Whether to run training.'	)
			parser.add_argument('--do_eval'       ,     action='store_true'       ,     help='Whether to run eval on the dev set.'	)
			parser.add_argument(
			    '--output_dir'       ,     default=_snake_case       ,     type=_snake_case       ,     required=_snake_case       ,     help='The output directory where the model predictions and checkpoints will be written.'       ,     )
			parser.add_argument('--train_dataset'       ,     type=_snake_case       ,     default=''	)
			parser.add_argument('--eval_dataset'       ,     type=_snake_case       ,     default=''	)
			parser.add_argument('--seed'       ,     type=_snake_case       ,     default=42	)
			parser.add_argument('--num_train_epochs'       ,     type=_snake_case       ,     default=3	)
			parser.add_argument('--train_batch_size'       ,     type=_snake_case       ,     default=8	)
			parser.add_argument('--eval_batch_size'       ,     type=_snake_case       ,     default=16	)
			parser.add_argument('--adam_epsilon'       ,     default=1e-8       ,     type=_snake_case       ,     help='Epsilon for Adam optimizer.'	)
			parser.add_argument('--max_grad_norm'       ,     type=_snake_case       ,     default=1	)
			parser.add_argument(
			    '--max_steps'       ,     default=-1       ,     type=_snake_case       ,     help=(
			        'If > 0: set total number of training                         steps to perform. Override num_train_epochs.'
			    )       ,     )
			parser.add_argument(
			    '--gradient_accumulation_steps'       ,     type=_snake_case       ,     default=1       ,     help='Number of updates steps to accumulate before                        performing a backward/update pass.'       ,     )
			parser.add_argument('--learning_rate'       ,     type=_snake_case       ,     default=6.2_5e-5	)
			parser.add_argument('--warmup_steps'       ,     default=0       ,     type=_snake_case       ,     help='Linear warmup over warmup_steps.'	)
			parser.add_argument('--lr_schedule'       ,     type=_snake_case       ,     default='warmup_linear'	)
			parser.add_argument('--weight_decay'       ,     type=_snake_case       ,     default=0.01	)
			parser.add_argument('--lm_coef'       ,     type=_snake_case       ,     default=0.9	)
			parser.add_argument('--n_valid'       ,     type=_snake_case       ,     default=374	)

			parser.add_argument('--server_ip'       ,     type=_snake_case       ,     default=''       ,     help='Can be used for distant debugging.'	)
			parser.add_argument('--server_port'       ,     type=_snake_case       ,     default=''       ,     help='Can be used for distant debugging.'	)
			__a        =parser.parse_args()
			print(_snake_case	)

			if args.server_ip and args.server_port:
						# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
						import ptvsd

						print('Waiting for debugger attach'	)
						ptvsd.enable_attach(address=(args.server_ip, args.server_port)       ,     redirect_output=_snake_case	)
						ptvsd.wait_for_attach()

			random.seed(args.seed	)
			np.random.seed(args.seed	)
			torch.manual_seed(args.seed	)
			torch.cuda.manual_seed_all(args.seed	)

			__a        =torch.device('cuda' if torch.cuda.is_available() else 'cpu'	)
			__a        =torch.cuda.device_count()
			logger.info('device: {}, n_gpu {}'.format(_snake_case       ,     _snake_case	)	)

			if not args.do_train and not args.do_eval:
						raise ValueError('At least one of `do_train` or `do_eval` must be True.'	)

			if not os.path.exists(args.output_dir	):
						os.makedirs(args.output_dir	)

			# Load tokenizer and model
			# This loading functions also add new tokens and embeddings called `special tokens`
			# These new embeddings will be fine-tuned on the RocStories dataset
			__a        =['_start_', '_delimiter_', '_classify_']
			__a        =OpenAIGPTTokenizer.from_pretrained(args.model_name	)
			tokenizer.add_tokens(_snake_case	)
			__a        =tokenizer.convert_tokens_to_ids(_snake_case	)
			__a        =OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name	)
			model.resize_token_embeddings(len(_snake_case	)	)
			model.to(_snake_case	)

			# Load and encode the datasets
			def tokenize_and_encode(_snake_case   :  int	):
						if isinstance(_snake_case       ,     _snake_case	):
									return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(_snake_case	)	)
						elif isinstance(_snake_case       ,     _snake_case	):
									return obj
						return [tokenize_and_encode(_snake_case	) for o in obj]

			logger.info('Encoding dataset...'	)
			__a        =load_rocstories_dataset(args.train_dataset	)
			__a        =load_rocstories_dataset(args.eval_dataset	)
			__a        =(train_dataset, eval_dataset)
			__a        =tokenize_and_encode(_snake_case	)

			# Compute the max input length for the Transformer
			__a        =model.config.n_positions // 2 - 2
			__a        =max(
			    len(story[:max_length]	) + max(len(conta[:max_length]	)       ,     len(conta[:max_length]	)	) + 3
			    for dataset in encoded_datasets
			    for story, conta, conta, _ in dataset	)
			__a        =min(_snake_case       ,     model.config.n_positions	)  # Max size of input for the pre-trained model

			# Prepare inputs tensors and dataloaders
			__a        =pre_process_datasets(_snake_case       ,     _snake_case       ,     _snake_case       ,     *_snake_case	)
			__a      ,    __a        =tensor_datasets[0], tensor_datasets[1]

			__a        =TensorDataset(*_snake_case	)
			__a        =RandomSampler(_snake_case	)
			__a        =DataLoader(_snake_case       ,     sampler=_snake_case       ,     batch_size=args.train_batch_size	)

			__a        =TensorDataset(*_snake_case	)
			__a        =SequentialSampler(_snake_case	)
			__a        =DataLoader(_snake_case       ,     sampler=_snake_case       ,     batch_size=args.eval_batch_size	)

			# Prepare optimizer
			if args.do_train:
						if args.max_steps > 0:
									__a        =args.max_steps
									__a        =args.max_steps // (len(_snake_case	) // args.gradient_accumulation_steps) + 1
						else:
									__a        =len(_snake_case	) // args.gradient_accumulation_steps * args.num_train_epochs

						__a        =list(model.named_parameters()	)
						__a        =['bias', 'LayerNorm.bias', 'LayerNorm.weight']
						__a        =[
						    {
						        'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay	)],
						        'weight_decay': args.weight_decay,
						    },
						    {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay	)], 'weight_decay': 0.0},
						]
						__a        =AdamW(_snake_case       ,     lr=args.learning_rate       ,     eps=args.adam_epsilon	)
						__a        =get_linear_schedule_with_warmup(
						    _snake_case       ,     num_warmup_steps=args.warmup_steps       ,     num_training_steps=_snake_case	)

			if args.do_train:
						__a      ,    __a      ,    __a        =0, 0, None
						model.train()
						for _ in trange(int(args.num_train_epochs	)       ,     desc='Epoch'	):
									__a        =0
									__a        =0
									__a        =tqdm(_snake_case       ,     desc='Training'	)
									for step, batch in enumerate(_snake_case	):
												__a        =tuple(t.to(_snake_case	) for t in batch	)
												__a      ,    __a      ,    __a      ,    __a        =batch
												__a        =model(_snake_case       ,     mc_token_ids=_snake_case       ,     lm_labels=_snake_case       ,     mc_labels=_snake_case	)
												__a        =args.lm_coef * losses[0] + losses[1]
												loss.backward()
												optimizer.step()
												scheduler.step()
												optimizer.zero_grad()
												tr_loss += loss.item()
												__a        =(
												    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
												)
												nb_tr_steps += 1
												__a        ='Training loss: {:.2e} lr: {:.2e}'.format(_snake_case       ,     scheduler.get_lr()[0]	)

    # Save a trained model
			if args.do_train:
						# Save a trained model, configuration and tokenizer
						__a        =model.module if hasattr(_snake_case       ,     'module'	) else model  # Only save the model itself

						# If we save using the predefined names, we can load using `from_pretrained`
						__a        =os.path.join(args.output_dir       ,     _snake_case	)
						__a        =os.path.join(args.output_dir       ,     _snake_case	)

						torch.save(model_to_save.state_dict()       ,     _snake_case	)
						model_to_save.config.to_json_file(_snake_case	)
						tokenizer.save_vocabulary(args.output_dir	)

						# Load a trained model and vocabulary that you have fine-tuned
						__a        =OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir	)
						__a        =OpenAIGPTTokenizer.from_pretrained(args.output_dir	)
						model.to(_snake_case	)

			if args.do_eval:
						model.eval()
						__a      ,    __a        =0, 0
						__a      ,    __a        =0, 0
						for batch in tqdm(_snake_case       ,     desc='Evaluating'	):
									__a        =tuple(t.to(_snake_case	) for t in batch	)
									__a      ,    __a      ,    __a      ,    __a        =batch
									with torch.no_grad():
												__a      ,    __a      ,    __a      ,    __a        =model(
												    _snake_case       ,     mc_token_ids=_snake_case       ,     lm_labels=_snake_case       ,     mc_labels=_snake_case	)

									__a        =mc_logits.detach().cpu().numpy()
									__a        =mc_labels.to('cpu'	).numpy()
									__a        =accuracy(_snake_case       ,     _snake_case	)

									eval_loss += mc_loss.mean().item()
									eval_accuracy += tmp_eval_accuracy

									nb_eval_examples += input_ids.size(0	)
									nb_eval_steps += 1

						__a        =eval_loss / nb_eval_steps
						__a        =eval_accuracy / nb_eval_examples
						__a        =tr_loss / nb_tr_steps if args.do_train else None
						__a        ={'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'train_loss': train_loss}

						__a        =os.path.join(args.output_dir       ,     'eval_results.txt'	)
						with open(_snake_case       ,     'w'	) as writer:
									logger.info('***** Eval results *****'	)
									for key in sorted(result.keys()	):
												logger.info('  %s = %s'       ,     _snake_case       ,     str(result[key]	)	)
												writer.write('%s = %s\n' % (key, str(result[key]	))	)


if __name__ == "__main__":
						main()









import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_lowerCAmelCase					:			str  				=			{
    "sample_size": 32,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": 1_000,
    "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",
}

_lowerCAmelCase					:			List[Any]  				=			{
    "sample_size": 64,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 3,
    "num_class_embeds": 1_000,
    "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",
}

_lowerCAmelCase					:			str  				=			{
    "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",
}

_lowerCAmelCase					:			List[str]  				=			{
    "num_train_timesteps": 40,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

_lowerCAmelCase					:			Optional[Any]  				=			{
    "num_train_timesteps": 201,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

_lowerCAmelCase					:			List[str]  				=			{
    "num_train_timesteps": 151,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}






def        UpperCamelCase_(							_snake_case   :  str	):




			"""simple docstring"""






			if isinstance(_snake_case       ,     _snake_case	):
						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        UpperCamelCase_(							_snake_case   :  Tuple       ,     _snake_case   :  List[str]       ,     _snake_case   :  Any       ,     _snake_case   :  str       ,     _snake_case   :  Union[str, Any]=False	):




			"""simple docstring"""






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

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

			return new_checkpoint






def        UpperCamelCase_(							_snake_case   :  Dict       ,     _snake_case   :  Union[str, Any]       ,     _snake_case   :  Union[str, Any]       ,     _snake_case   :  Optional[Any]       ,     _snake_case   :  Optional[int]=None	):




			"""simple docstring"""






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

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

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

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

			return new_checkpoint






def        UpperCamelCase_(							_snake_case   :  str       ,     _snake_case   :  Tuple	):




			"""simple docstring"""






			__a        =torch.load(_snake_case       ,     map_location='cpu'	)
			__a        ={}

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

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

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

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

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

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

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

						__a        =current_channels

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

			__a        =0
			__a        =unet_config['up_block_types']

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

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

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

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

			return new_checkpoint


if __name__ == "__main__":
						_lowerCAmelCase					:			Optional[Any]  				=			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.")

						_lowerCAmelCase					:			Optional[Any]  				=			parser.parse_args()
						_lowerCAmelCase					:			Optional[Any]  				=			strabool(args.class_cond)

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

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

						if not args.class_cond:
												_lowerCAmelCase					:			List[Any]  				=			None

						_lowerCAmelCase					:			Tuple  				=			con_pt_to_diffuser(args.unet_path, unet_config)

						_lowerCAmelCase					:			Optional[int]  				=			UNetaDModel(**unet_config)
						image_unet.load_state_dict(converted_unet_ckpt)

						# Get scheduler config
						if "cd" in ckpt_name or "test" in ckpt_name:
												_lowerCAmelCase					:			int  				=			CD_SCHEDULER_CONFIG
						elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
												_lowerCAmelCase					:			Optional[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)):
												_lowerCAmelCase					:			List[str]  				=			CT_LSUN_256_SCHEDULER_CONFIG
						else:
												raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

						_lowerCAmelCase					:			Any  				=			CMStochasticIterativeScheduler(**scheduler_config)

						_lowerCAmelCase					:			str  				=			ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
						consistency_model.save_pretrained(args.dump_path)






