






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import List, Optional

import torch
from datasets import Dataset
from torch import nn
from tqdm.auto import tqdm

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
    utils,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


__snake_case     :     List[str]       =  """
Distills an NLI-based zero-shot classifier to a smaller, more efficient model with a fixed set of candidate class
names. Useful for speeding up zero-shot classification in cases where labeled training data is not available, but
when only a single fixed set of classes is needed. Takes a teacher NLI model, student classifier model, unlabeled
dataset, and set of K possible class names. Yields a single classifier with K outputs corresponding to the provided
class names.
"""

__snake_case     :     List[str]       =  logging.getLogger(__name__)


@dataclass
class      __SCREAMING_SNAKE_CASE       :
						_SCREAMING_SNAKE_CASE						:							Optional[str]				=       field(
						    default='''roberta-large-mnli'''		,       metadata={'''help''': '''The NLI/zero-shot teacher model to be distilled.'''})
						_SCREAMING_SNAKE_CASE						:							Optional[str]				=       field(
						    default='''This example is {}.'''		,       metadata={
						        '''help''': (
						            '''Template used to turn class names into mock hypotheses for teacher NLI model. Must include {{}}'''
						            '''where class name is inserted.'''
						        )
						    }		,       )
						_SCREAMING_SNAKE_CASE						:							Optional[int]				=       field(
						    default=32		,       metadata={'''help''': '''Batch size for generating teacher predictions.'''})
						_SCREAMING_SNAKE_CASE						:							Optional[bool]				=       field(
						    default=__lowercase		,       metadata={
						        '''help''': (
						            '''Allow multiple classes to be true rather than forcing them to sum to 1 (sometimes called'''
						            '''multi-class multi-label classification).'''
						        )
						    }		,       )
						_SCREAMING_SNAKE_CASE						:							Optional[float]				=       field(
						    default=1.0		,       metadata={'''help''': '''Temperature applied to teacher softmax for distillation.'''})




@dataclass
class      __SCREAMING_SNAKE_CASE       :
						_SCREAMING_SNAKE_CASE						:							Optional[str]				=       field(
						    default='''distilbert-base-uncased'''		,       metadata={'''help''': '''The NLI/zero-shot teacher model to be distilled.'''})




@dataclass
class      __SCREAMING_SNAKE_CASE       :
						_SCREAMING_SNAKE_CASE						:							str				=       field(metadata={'''help''': '''Text file with one unlabeled instance per line.'''})
						_SCREAMING_SNAKE_CASE						:							str				=       field(metadata={'''help''': '''Text file with one class name per line.'''})
						_SCREAMING_SNAKE_CASE						:							bool				=       field(
						    default=__lowercase		,       metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the Rust tokenizers library) or not.'''}		,       )




@dataclass
class      __SCREAMING_SNAKE_CASE       ( __lowercase):
						_SCREAMING_SNAKE_CASE						:							Optional[str]				=       field(
						    default=__lowercase		,       metadata={'''help''': '''The output directory where the model predictions and checkpoints will be written.'''}		,       )
						_SCREAMING_SNAKE_CASE						:							int				=       field(
						    default=32		,       metadata={'''help''': '''Batch size per GPU/TPU core/CPU for training.'''})
						_SCREAMING_SNAKE_CASE						:							int				=       field(
						    default=128		,       metadata={'''help''': '''Batch size per GPU/TPU core/CPU for evaluation.'''})
						_SCREAMING_SNAKE_CASE						:							float				=       field(default=1.0		,       metadata={'''help''': '''Total number of training epochs to perform.'''})
						_SCREAMING_SNAKE_CASE						:							bool				=       field(default=__lowercase		,       metadata={'''help''': '''Whether to run training of student model.'''})
						_SCREAMING_SNAKE_CASE						:							bool				=       field(
						    default=__lowercase		,       metadata={
						        '''help''': (
						            '''Whether to evaluate the agreement of the final student predictions and the teacher predictions'''
						            '''after training.'''
						        )
						    }		,       )
						_SCREAMING_SNAKE_CASE						:							Optional[int]				=       field(
						    default=0		,       metadata={
						        '''help''': (
						            '''Limit the total amount of checkpoints.'''
						            '''Deletes the older checkpoints in the output_dir. Default is 0 (no checkpoints).'''
						        )
						    }		,       )




class      __SCREAMING_SNAKE_CASE       ( __lowercase):






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase=False ):






												"""simple docstring"""
												lowerCAmelCase__         =    inputs['labels']
												lowerCAmelCase__         =    model(inputs['input_ids']  ,    attention_mask=inputs['attention_mask'] )
												lowerCAmelCase__         =    outputs[0]

												lowerCAmelCase__         =    -torch.sum(target_p * logits.log_softmax(dim=-1 )  ,    axis=-1 ).mean()

												if return_outputs:
																		return loss, outputs

												return loss







def 						_UpperCamelCase (     UpperCamelCase_							:       Union[str, Any]		)	-> Tuple:

						"""simple docstring"""



						lowerCAmelCase__         =    []
						with open(UpperCamelCase_     ,							'r'		) as f:
												for line in f:
																		lowerCAmelCase__         =    line.strip()
																		if len(UpperCamelCase_		) > 0:
																								lines.append(UpperCamelCase_		)
						return lines







def 						_UpperCamelCase (     UpperCamelCase_							:       Optional[int]     ,							UpperCamelCase_							:       Dict     ,							UpperCamelCase_							:       Optional[Any]		)	-> Any:

						"""simple docstring"""



						lowerCAmelCase__         =    []
						lowerCAmelCase__         =    []
						for example in examples:
												for name in class_names:
																		premises.append(UpperCamelCase_		)
																		hypotheses.append(hypothesis_template.format(UpperCamelCase_		)		)
						return premises, hypotheses







def 						_UpperCamelCase (     UpperCamelCase_							:       List[Any]		)	-> Optional[Any]:

						"""simple docstring"""



						for label, ind in config.labelaid.items():
												if label.lower().startswith('entail'		):
																		return ind
						logger.warning('Could not identify entailment dimension from teacher config label2id. Setting to -1.'		)
						return -1







def 						_UpperCamelCase (     UpperCamelCase_							:       str     ,							UpperCamelCase_							:       List[str]     ,							UpperCamelCase_							:       List[str]     ,							UpperCamelCase_							:       str     ,							UpperCamelCase_							:       int     ,							UpperCamelCase_							:       float     ,							UpperCamelCase_							:       bool     ,							UpperCamelCase_							:       bool     ,							UpperCamelCase_							:       bool     ,							UpperCamelCase_							:       bool     ,							)	-> Optional[int]:

						"""simple docstring"""



						lowerCAmelCase__         =    AutoModelForSequenceClassification.from_pretrained(UpperCamelCase_		)
						lowerCAmelCase__         =    model.config
						if not no_cuda and torch.cuda.is_available():
												lowerCAmelCase__         =    nn.DataParallel(model.cuda()		)
												batch_size *= len(model.device_ids		)
						lowerCAmelCase__         =    AutoTokenizer.from_pretrained(UpperCamelCase_     ,							use_fast=UpperCamelCase_		)

						lowerCAmelCase__    ,							lowerCAmelCase__         =    get_premise_hypothesis_pairs(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_		)
						lowerCAmelCase__         =    []

						for i in tqdm(range(0     ,							len(UpperCamelCase_		)     ,							UpperCamelCase_		)		):
												lowerCAmelCase__         =    premises[i : i + batch_size]
												lowerCAmelCase__         =    hypotheses[i : i + batch_size]

												lowerCAmelCase__         =    tokenizer(
												    UpperCamelCase_     ,							UpperCamelCase_     ,							padding=UpperCamelCase_     ,							truncation='only_first'     ,							return_tensors='pt'     ,							)

												with torch.cuda.amp.autocast(enabled=UpperCamelCase_		):
																		with torch.no_grad():
																								lowerCAmelCase__         =    model(**UpperCamelCase_		)
												logits.append(outputs.logits.detach().cpu().float()		)

						lowerCAmelCase__         =    get_entailment_id(UpperCamelCase_		)
						lowerCAmelCase__         =    -1 if entail_id == 0 else 0
						lowerCAmelCase__         =    torch.cat(UpperCamelCase_     ,							dim=0		)  # N*K x 3
						lowerCAmelCase__         =    logits.reshape(len(UpperCamelCase_		)     ,							len(UpperCamelCase_		)     ,							-1		)[..., [contr_id, entail_id]]  # N x K x 2

						if multi_label:
												# softmax over (contr, entail) logits for each class independently
												lowerCAmelCase__         =    (nli_logits / temperature).softmax(-1		)
						else:
												# softmax over entail logits across classes s.t. class probabilities sum to 1.
												lowerCAmelCase__         =    (nli_logits / temperature).softmax(1		)

						return nli_prob[..., 1]  # N x K







def 						_UpperCamelCase (     )	-> Tuple:

						"""simple docstring"""



						lowerCAmelCase__         =    HfArgumentParser(
						    (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments)     ,							description=UpperCamelCase_     ,							)

						if len(sys.argv		) == 2 and sys.argv[1].endswith('.json'		):
												# If we pass only one argument to the script and it's the path to a json file,
												# let's parse it to get our arguments.
												lowerCAmelCase__    ,							lowerCAmelCase__    ,							lowerCAmelCase__    ,							lowerCAmelCase__         =    parser.parse_json_file(
												    json_file=os.path.abspath(sys.argv[1]		)		)
						else:
												lowerCAmelCase__    ,							lowerCAmelCase__    ,							lowerCAmelCase__    ,							lowerCAmelCase__         =    parser.parse_args_into_dataclasses()

						# Detecting last checkpoint.
						lowerCAmelCase__         =    None
						if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
												lowerCAmelCase__         =    get_last_checkpoint(training_args.output_dir		)
												if last_checkpoint is None and len(os.listdir(training_args.output_dir		)		) > 0:
																		raise ValueError(
																		    F"Output directory ({training_args.output_dir}) already exists and is not empty. "
																		    'Use --overwrite_output_dir to overcome.'		)
												elif last_checkpoint is not None:
																		logger.info(
																		    F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
																		    'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'		)

    # Setup logging
						logging.basicConfig(
						    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'     ,							datefmt='%m/%d/%Y %H:%M:%S'     ,							handlers=[logging.StreamHandler(sys.stdout		)]     ,							)
						logger.setLevel(logging.INFO if is_main_process(training_args.local_rank		) else logging.WARN		)

						# Log on each process the small summary:
						logger.warning(
						    F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
						    + F"distributed training: {bool(training_args.local_rank != -1		)}, 16-bits training: {training_args.fpaa}"		)
						# Set the verbosity to info of the Transformers logger (on main process only):
						if is_main_process(training_args.local_rank		):
												utils.logging.set_verbosity_info()
												utils.logging.enable_default_handler()
												utils.logging.enable_explicit_format()

						if training_args.local_rank != -1:
												raise ValueError('Distributed training is not currently supported.'		)
						if training_args.tpu_num_cores is not None:
												raise ValueError('TPU acceleration is not currently supported.'		)

						logger.info(F"Training/evaluation parameters {training_args}"		)

						# Set seed before initializing model.
						set_seed(training_args.seed		)

						# 1. read in data
						lowerCAmelCase__         =    read_lines(data_args.data_file		)
						lowerCAmelCase__         =    read_lines(data_args.class_names_file		)

						# 2. get teacher predictions and load into dataset
						logger.info('Generating predictions from zero-shot teacher model'		)
						lowerCAmelCase__         =    get_teacher_predictions(
						    teacher_args.teacher_name_or_path     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							teacher_args.hypothesis_template     ,							teacher_args.teacher_batch_size     ,							teacher_args.temperature     ,							teacher_args.multi_label     ,							data_args.use_fast_tokenizer     ,							training_args.no_cuda     ,							training_args.fpaa     ,							)
						lowerCAmelCase__         =    Dataset.from_dict(
						    {
						        'text': examples,
						        'labels': teacher_soft_preds,
						    }		)

						# 3. create student
						logger.info('Initializing student model'		)
						lowerCAmelCase__         =    AutoModelForSequenceClassification.from_pretrained(
						    student_args.student_name_or_path     ,							num_labels=len(UpperCamelCase_		)		)
						lowerCAmelCase__         =    AutoTokenizer.from_pretrained(student_args.student_name_or_path     ,							use_fast=data_args.use_fast_tokenizer		)
						lowerCAmelCase__         =    dict(enumerate(UpperCamelCase_		)		)
						lowerCAmelCase__         =    {label: i for i, label in enumerate(UpperCamelCase_		)}

						# 4. train student on teacher predictions
						lowerCAmelCase__         =    dataset.map(UpperCamelCase_     ,							input_columns='text'		)
						dataset.set_format('torch'		)

						def compute_metrics(UpperCamelCase_							:       List[Any]     ,							UpperCamelCase_							:       List[Any]=False		):
												lowerCAmelCase__         =    p.predictions.argmax(-1		)
												lowerCAmelCase__         =    p.label_ids.argmax(-1		)  # "label_ids" are actually distributions
												return {"agreement": (preds == proxy_labels).mean().item()}

						lowerCAmelCase__         =    DistillationTrainer(
						    model=UpperCamelCase_     ,							tokenizer=UpperCamelCase_     ,							args=UpperCamelCase_     ,							train_dataset=UpperCamelCase_     ,							compute_metrics=UpperCamelCase_     ,							)

						if training_args.do_train:
												logger.info('Training student model on teacher predictions'		)
												trainer.train()

						if training_args.do_eval:
												lowerCAmelCase__         =    trainer.evaluate(eval_dataset=UpperCamelCase_		)['eval_agreement']
												logger.info(F"Agreement of student and teacher predictions: {agreement * 100:0.2f}%"		)

						trainer.save_model()


if __name__ == "__main__":
					main()













import argparse
import json

import torch

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







def 						_UpperCamelCase (     UpperCamelCase_							:       Tuple     ,							UpperCamelCase_							:       Optional[Any]=1		)	-> Optional[int]:

						"""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 						_UpperCamelCase (     UpperCamelCase_							:       Union[str, Any]     ,							UpperCamelCase_							:       int=0		)	-> Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    []
						for old_item in old_list:
												lowerCAmelCase__         =    old_item
												lowerCAmelCase__         =    new_item.replace('block.'     ,							'resnets.'		)
												lowerCAmelCase__         =    new_item.replace('conv_shorcut'     ,							'conv1'		)
												lowerCAmelCase__         =    new_item.replace('in_shortcut'     ,							'conv_shortcut'		)
												lowerCAmelCase__         =    new_item.replace('temb_proj'     ,							'time_emb_proj'		)

												lowerCAmelCase__         =    shave_segments(UpperCamelCase_     ,							n_shave_prefix_segments=UpperCamelCase_		)

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

						return mapping







def 						_UpperCamelCase (     UpperCamelCase_							:       str     ,							UpperCamelCase_							:       Union[str, Any]=0     ,							UpperCamelCase_							:       Dict=False		)	-> List[Any]:

						"""simple docstring"""



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

												# In `model.mid`, the layer is called `attn`.
												if not in_mid:
																		lowerCAmelCase__         =    new_item.replace('attn'     ,							'attentions'		)
												lowerCAmelCase__         =    new_item.replace('.k.'     ,							'.key.'		)
												lowerCAmelCase__         =    new_item.replace('.v.'     ,							'.value.'		)
												lowerCAmelCase__         =    new_item.replace('.q.'     ,							'.query.'		)

												lowerCAmelCase__         =    new_item.replace('proj_out'     ,							'proj_attn'		)
												lowerCAmelCase__         =    new_item.replace('norm'     ,							'group_norm'		)

												lowerCAmelCase__         =    shave_segments(UpperCamelCase_     ,							n_shave_prefix_segments=UpperCamelCase_		)
												mapping.append({'old': old_item, 'new': new_item}		)

						return mapping







def 						_UpperCamelCase (     UpperCamelCase_							:       int     ,							UpperCamelCase_							:       Dict     ,							UpperCamelCase_							:       Union[str, Any]     ,							UpperCamelCase_							:       Any=None     ,							UpperCamelCase_							:       str=None     ,							UpperCamelCase_							:       str=None		)	-> Dict:

						"""simple docstring"""



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

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

																		lowerCAmelCase__         =    old_tensor.shape[0] // config.get('num_head_channels'     ,							1		) // 3

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

																		lowerCAmelCase__         =    query.reshape(UpperCamelCase_		).squeeze()
																		lowerCAmelCase__         =    key.reshape(UpperCamelCase_		).squeeze()
																		lowerCAmelCase__         =    value.reshape(UpperCamelCase_		).squeeze()

						for path in paths:
												lowerCAmelCase__         =    path['new']

												if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																		continue

												lowerCAmelCase__         =    new_path.replace('down.'     ,							'down_blocks.'		)
												lowerCAmelCase__         =    new_path.replace('up.'     ,							'up_blocks.'		)

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

												if "attentions" in new_path:
																		lowerCAmelCase__         =    old_checkpoint[path['old']].squeeze()
												else:
																		lowerCAmelCase__         =    old_checkpoint[path['old']]







def 						_UpperCamelCase (     UpperCamelCase_							:       str     ,							UpperCamelCase_							:       Dict		)	-> Any:

						"""simple docstring"""



						lowerCAmelCase__         =    {}

						lowerCAmelCase__         =    checkpoint['temb.dense.0.weight']
						lowerCAmelCase__         =    checkpoint['temb.dense.0.bias']
						lowerCAmelCase__         =    checkpoint['temb.dense.1.weight']
						lowerCAmelCase__         =    checkpoint['temb.dense.1.bias']

						lowerCAmelCase__         =    checkpoint['norm_out.weight']
						lowerCAmelCase__         =    checkpoint['norm_out.bias']

						lowerCAmelCase__         =    checkpoint['conv_in.weight']
						lowerCAmelCase__         =    checkpoint['conv_in.bias']
						lowerCAmelCase__         =    checkpoint['conv_out.weight']
						lowerCAmelCase__         =    checkpoint['conv_out.bias']

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

						lowerCAmelCase__         =    len({'.'.join(layer.split('.'		)[:2]		) for layer in checkpoint if 'up' in layer}		)
						lowerCAmelCase__         =    {layer_id: [key for key in checkpoint if F"up.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)}

						for i in range(UpperCamelCase_		):
												lowerCAmelCase__         =    (i - 1) // (config['layers_per_block'] + 1)

												if any('downsample' in layer for layer in down_blocks[i]		):
																		lowerCAmelCase__         =    checkpoint[
																		    F"down.{i}.downsample.op.weight"
																		]
																		lowerCAmelCase__         =    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]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							2		).split('.'		)[:2]		) for layer in down_blocks[i] if 'block' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in down_blocks[i] if F"block.{layer_id}" in key]
																		    for layer_id in range(UpperCamelCase_		)
																		}

																		if num_blocks > 0:
																								for j in range(config['layers_per_block']		):
																														lowerCAmelCase__         =    renew_resnet_paths(blocks[j]		)
																														assign_to_checkpoint(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_		)

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

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

						lowerCAmelCase__         =    [key for key in checkpoint if 'mid.block_1' in key]
						lowerCAmelCase__         =    [key for key in checkpoint if 'mid.block_2' in key]
						lowerCAmelCase__         =    [key for key in checkpoint if 'mid.attn_1' in key]

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

						lowerCAmelCase__         =    renew_resnet_paths(UpperCamelCase_		)
						assign_to_checkpoint(
						    UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]     ,							)

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

						for i in range(UpperCamelCase_		):
												lowerCAmelCase__         =    num_up_blocks - 1 - i

												if any('upsample' in layer for layer in up_blocks[i]		):
																		lowerCAmelCase__         =    checkpoint[
																		    F"up.{i}.upsample.conv.weight"
																		]
																		lowerCAmelCase__         =    checkpoint[F"up.{i}.upsample.conv.bias"]

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

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

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

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

						lowerCAmelCase__         =    {k.replace('mid_new_2'     ,							'mid_block'		): v for k, v in new_checkpoint.items()}
						return new_checkpoint







def 						_UpperCamelCase (     UpperCamelCase_							:       Tuple     ,							UpperCamelCase_							:       int		)	-> Optional[int]:

						"""simple docstring"""



						lowerCAmelCase__         =    {}

						lowerCAmelCase__         =    checkpoint['encoder.norm_out.weight']
						lowerCAmelCase__         =    checkpoint['encoder.norm_out.bias']

						lowerCAmelCase__         =    checkpoint['encoder.conv_in.weight']
						lowerCAmelCase__         =    checkpoint['encoder.conv_in.bias']
						lowerCAmelCase__         =    checkpoint['encoder.conv_out.weight']
						lowerCAmelCase__         =    checkpoint['encoder.conv_out.bias']

						lowerCAmelCase__         =    checkpoint['decoder.norm_out.weight']
						lowerCAmelCase__         =    checkpoint['decoder.norm_out.bias']

						lowerCAmelCase__         =    checkpoint['decoder.conv_in.weight']
						lowerCAmelCase__         =    checkpoint['decoder.conv_in.bias']
						lowerCAmelCase__         =    checkpoint['decoder.conv_out.weight']
						lowerCAmelCase__         =    checkpoint['decoder.conv_out.bias']

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

						lowerCAmelCase__         =    len({'.'.join(layer.split('.'		)[:3]		) for layer in checkpoint if 'up' in layer}		)
						lowerCAmelCase__         =    {layer_id: [key for key in checkpoint if F"up.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)}

						for i in range(UpperCamelCase_		):
												lowerCAmelCase__         =    (i - 1) // (config['layers_per_block'] + 1)

												if any('downsample' in layer for layer in down_blocks[i]		):
																		lowerCAmelCase__         =    checkpoint[
																		    F"encoder.down.{i}.downsample.conv.weight"
																		]
																		lowerCAmelCase__         =    checkpoint[
																		    F"encoder.down.{i}.downsample.conv.bias"
																		]

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

																		if num_blocks > 0:
																								for j in range(config['layers_per_block']		):
																														lowerCAmelCase__         =    renew_resnet_paths(blocks[j]		)
																														assign_to_checkpoint(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_		)

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

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

						lowerCAmelCase__         =    [key for key in checkpoint if 'mid.block_1' in key]
						lowerCAmelCase__         =    [key for key in checkpoint if 'mid.block_2' in key]
						lowerCAmelCase__         =    [key for key in checkpoint if 'mid.attn_1' in key]

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

						lowerCAmelCase__         =    renew_resnet_paths(UpperCamelCase_		)
						assign_to_checkpoint(
						    UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]     ,							)

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

						for i in range(UpperCamelCase_		):
												lowerCAmelCase__         =    num_up_blocks - 1 - i

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

												if any('block' in layer for layer in up_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							3		).split('.'		)[:3]		) for layer in up_blocks[i] if 'block' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in up_blocks[i] if F"block.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)
																		}

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

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

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

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

						return new_checkpoint


if __name__ == "__main__":
					__snake_case     :     Tuple       =  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.""")

					__snake_case     :     str       =  parser.parse_args()
					__snake_case     :     Any       =  torch.load(args.checkpoint_path)

					with open(args.config_file) as f:
										__snake_case     :     Optional[int]       =  json.loads(f.read())

					# unet case
					__snake_case     :     Any       =  {key.split(""".""")[0] for key in checkpoint.keys()}
					if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
										__snake_case     :     Optional[int]       =  convert_vq_autoenc_checkpoint(checkpoint, config)
					else:
										__snake_case     :     List[str]       =  convert_ddpm_checkpoint(checkpoint, config)

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

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

										__snake_case     :     List[str]       =  DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

										__snake_case     :     Tuple       =  DDPMPipeline(unet=model, scheduler=scheduler)
										pipe.save_pretrained(args.dump_path)





