





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

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


snake_case_				=			logging.getLogger(__name__)

@dataclass
class 		SCREAMING_SNAKE_CASE__				:
      A_						:				str							=    field(
          metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}  )
      A_						:				Optional[str]							=    field(
          default=_UpperCAmelCase						,      metadata={'help': 'Pretrained config name or path if not the same as model_name'}  )
      A_						:				Optional[str]							=    field(
          default=_UpperCAmelCase						,      metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}  )
      A_						:				Optional[str]							=    field(
          default=_UpperCAmelCase						,      metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}						,      )
      A_						:				bool							=    field(default=_UpperCAmelCase						,      metadata={'help': 'Whether tp freeze the encoder.'}  )
      A_						:				bool							=    field(default=_UpperCAmelCase						,      metadata={'help': 'Whether  to freeze the embeddings.'}  )






@dataclass
class 		SCREAMING_SNAKE_CASE__				:
      A_						:				str							=    field(
          metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}  )
      A_						:				Optional[str]							=    field(
          default='summarization'						,      metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}						,      )
      A_						:				Optional[int]							=    field(
          default=1_024						,      metadata={
              'help': (
                  'The maximum total input sequence length after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              )
          }						,      )
      A_						:				Optional[int]							=    field(
          default=128						,      metadata={
              'help': (
                  'The maximum total sequence length for target text after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              )
          }						,      )
      A_						:				Optional[int]							=    field(
          default=142						,      metadata={
              'help': (
                  'The maximum total sequence length for validation target text after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded. '
                  'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
                  'during ``evaluate`` and ``predict``.'
              )
          }						,      )
      A_						:				Optional[int]							=    field(
          default=142						,      metadata={
              'help': (
                  'The maximum total sequence length for test target text after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              )
          }						,      )
      A_						:				Optional[int]							=    field(default=-1						,      metadata={'help': '# training examples. -1 means use all.'}  )
      A_						:				Optional[int]							=    field(default=-1						,      metadata={'help': '# validation examples. -1 means use all.'}  )
      A_						:				Optional[int]							=    field(default=-1						,      metadata={'help': '# test examples. -1 means use all.'}  )
      A_						:				Optional[str]							=    field(default=_UpperCAmelCase						,      metadata={'help': 'Source language id for translation.'}  )
      A_						:				Optional[str]							=    field(default=_UpperCAmelCase						,      metadata={'help': 'Target language id for translation.'}  )
      A_						:				Optional[int]							=    field(default=_UpperCAmelCase						,      metadata={'help': '# num_beams to use for evaluation.'}  )
      A_						:				bool							=    field(
          default=_UpperCAmelCase						,      metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}						,      )






def lowerCamelCase__    (							snake_case_	:  List[Any]       ,      snake_case_	:  List[str]       ,      snake_case_	:  Dict       )      ->				str:
   logger.info(f"""***** {split} metrics *****"""       )
   for key in sorted(metrics.keys()       ):
      logger.info(f"""  {key} = {metrics[key]}"""       )
   save_json(snake_case_       ,      os.path.join(snake_case_       ,      f"""{split}_results.json"""       )       )






def lowerCamelCase__    (							)      ->				Optional[Any]:
   # See all possible arguments in src/transformers/training_args.py
   # or by passing the --help flag to this script.
   # We now keep distinct sets of args, for a cleaner separation of concerns.

   __snake_case							       =   HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)       )

   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.
      __snake_case       ,	__snake_case       ,	__snake_case							       =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
   else:
      __snake_case       ,	__snake_case       ,	__snake_case							       =   parser.parse_args_into_dataclasses()

   check_output_dir(snake_case_       )

   # Setup logging
   logging.basicConfig(
       format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''       ,      datefmt='''%m/%d/%Y %H:%M:%S'''       ,      level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN       ,      )
   logger.warning(
       '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''       ,      training_args.local_rank       ,      training_args.device       ,      training_args.n_gpu       ,      bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED       )       ,      training_args.fpaa       ,      )
   transformers.utils.logging.enable_default_handler()
   transformers.utils.logging.enable_explicit_format()
   # Set the verbosity to info of the Transformers logger (on main process only):
   if is_main_process(training_args.local_rank       ):
      transformers.utils.logging.set_verbosity_info()
   logger.info('''Training/evaluation parameters %s'''       ,      snake_case_       )

   # Set seed
   set_seed(training_args.seed       )

   # Load pretrained model and tokenizer
   #
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.

   __snake_case							       =   AutoConfig.from_pretrained(
       model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,      cache_dir=model_args.cache_dir       ,      )

   __snake_case							       =   ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
   for p in extra_model_params:
      if getattr(snake_case_       ,      snake_case_       ,      snake_case_       ):
         assert hasattr(snake_case_       ,      snake_case_       ), f"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
         setattr(snake_case_       ,      snake_case_       ,      getattr(snake_case_       ,      snake_case_       )       )

   __snake_case							       =   AutoTokenizer.from_pretrained(
       model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path       ,      cache_dir=model_args.cache_dir       ,      )
   __snake_case							       =   AutoModelForSeqaSeqLM.from_pretrained(
       model_args.model_name_or_path       ,      from_tf='''.ckpt''' in model_args.model_name_or_path       ,      config=snake_case_       ,      cache_dir=model_args.cache_dir       ,      )

   # use task specific params
   use_task_specific_params(snake_case_       ,      data_args.task       )

   # set num_beams for evaluation
   if data_args.eval_beams is None:
      __snake_case							       =   model.config.num_beams

   # set decoder_start_token_id for MBart
   if model.config.decoder_start_token_id is None and isinstance(snake_case_       ,      (MBartTokenizer, MBartTokenizerFast)       ):
      assert (
          data_args.tgt_lang is not None and data_args.src_lang is not None
      ), "mBart requires --tgt_lang and --src_lang"
      if isinstance(snake_case_       ,      snake_case_       ):
         __snake_case							       =   tokenizer.lang_code_to_id[data_args.tgt_lang]
      else:
         __snake_case							       =   tokenizer.convert_tokens_to_ids(data_args.tgt_lang       )

   if model_args.freeze_embeds:
      freeze_embeds(snake_case_       )
   if model_args.freeze_encoder:
      freeze_params(model.get_encoder()       )
      assert_all_frozen(model.get_encoder()       )

   __snake_case							       =   SeqaSeqDataset

   # Get datasets
   __snake_case							       =   (
       dataset_class(
           snake_case_       ,      type_path='''train'''       ,      data_dir=data_args.data_dir       ,      n_obs=data_args.n_train       ,      max_target_length=data_args.max_target_length       ,      max_source_length=data_args.max_source_length       ,      prefix=model.config.prefix or ''''''       ,      )
       if training_args.do_train
       else None
   )
   __snake_case							       =   (
       dataset_class(
           snake_case_       ,      type_path='''val'''       ,      data_dir=data_args.data_dir       ,      n_obs=data_args.n_val       ,      max_target_length=data_args.val_max_target_length       ,      max_source_length=data_args.max_source_length       ,      prefix=model.config.prefix or ''''''       ,      )
       if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
       else None
   )
   __snake_case							       =   (
       dataset_class(
           snake_case_       ,      type_path='''test'''       ,      data_dir=data_args.data_dir       ,      n_obs=data_args.n_test       ,      max_target_length=data_args.test_max_target_length       ,      max_source_length=data_args.max_source_length       ,      prefix=model.config.prefix or ''''''       ,      )
       if training_args.do_predict
       else None
   )

   # Initialize our Trainer
   __snake_case							       =   (
       build_compute_metrics_fn(data_args.task       ,      snake_case_       ) if training_args.predict_with_generate else None
   )
   __snake_case							       =   SeqaSeqTrainer(
       model=snake_case_       ,      args=snake_case_       ,      data_args=snake_case_       ,      train_dataset=snake_case_       ,      eval_dataset=snake_case_       ,      data_collator=SeqaSeqDataCollator(
           snake_case_       ,      snake_case_       ,      model.config.decoder_start_token_id       ,      training_args.tpu_num_cores       )       ,      compute_metrics=snake_case_       ,      tokenizer=snake_case_       ,      )

   __snake_case							       =   {}
   # Training
   if training_args.do_train:
      logger.info('''*** Train ***'''       )

      __snake_case							       =   trainer.train(
          model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path       ) else None       )
      __snake_case							       =   train_result.metrics
      __snake_case							       =   data_args.n_train

      trainer.save_model()  # this also saves the tokenizer

      if trainer.is_world_process_zero():
         handle_metrics('''train'''       ,      snake_case_       ,      training_args.output_dir       )
         all_metrics.update(snake_case_       )

         # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
         trainer.state.save_to_json(os.path.join(training_args.output_dir       ,      '''trainer_state.json'''       )       )

         # For convenience, we also re-save the tokenizer to the same directory,
         # so that you can share your model easily on huggingface.co/models =)
         tokenizer.save_pretrained(training_args.output_dir       )

    # Evaluation
   if training_args.do_eval:
      logger.info('''*** Evaluate ***'''       )

      __snake_case							       =   trainer.evaluate(metric_key_prefix='''val'''       )
      __snake_case							       =   data_args.n_val
      __snake_case							       =   round(metrics['''val_loss''']       ,      4       )

      if trainer.is_world_process_zero():
         handle_metrics('''val'''       ,      snake_case_       ,      training_args.output_dir       )
         all_metrics.update(snake_case_       )

   if training_args.do_predict:
      logger.info('''*** Predict ***'''       )

      __snake_case							       =   trainer.predict(test_dataset=snake_case_       ,      metric_key_prefix='''test'''       )
      __snake_case							       =   test_output.metrics
      __snake_case							       =   data_args.n_test

      if trainer.is_world_process_zero():
         __snake_case							       =   round(metrics['''test_loss''']       ,      4       )
         handle_metrics('''test'''       ,      snake_case_       ,      training_args.output_dir       )
         all_metrics.update(snake_case_       )

         if training_args.predict_with_generate:
            __snake_case							       =   tokenizer.batch_decode(
                test_output.predictions       ,      skip_special_tokens=snake_case_       ,      clean_up_tokenization_spaces=snake_case_       )
            __snake_case							       =   lmap(str.strip       ,      snake_case_       )
            write_txt_file(snake_case_       ,      os.path.join(training_args.output_dir       ,      '''test_generations.txt'''       )       )

   if trainer.is_world_process_zero():
      save_json(snake_case_       ,      os.path.join(training_args.output_dir       ,      '''all_results.json'''       )       )

   return all_metrics






def lowerCamelCase__    (							snake_case_	:  Optional[Any]       )      ->				Tuple:
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
    main()






'''simple docstring'''

import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=1  )			->      Dict:
		if n_shave_prefix_segments >= 0:
				return ".".join(path.split('.'  )[n_shave_prefix_segments:]  )
		else:
				return ".".join(path.split('.'  )[:n_shave_prefix_segments]  )



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=0  )			->      Tuple:
		_a     :      Any        =							[]
		for old_item in old_list:
				_a     :      Union[str, Any]        =							old_item.replace('in_layers.0'						,			'norm1'  )
				_a     :      Optional[int]        =							new_item.replace('in_layers.2'						,			'conv1'  )

				_a     :      str        =							new_item.replace('out_layers.0'						,			'norm2'  )
				_a     :      List[str]        =							new_item.replace('out_layers.3'						,			'conv2'  )

				_a     :      str        =							new_item.replace('emb_layers.1'						,			'time_emb_proj'  )
				_a     :      Tuple        =							new_item.replace('skip_connection'						,			'conv_shortcut'  )

				_a     :      Any        =							shave_segments(lowerCAmelCase_						,			n_shave_prefix_segments=lowerCAmelCase_  )

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

		return mapping



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=0  )			->      Any:
		_a     :      List[str]        =							[]
		for old_item in old_list:
				_a     :      List[Any]        =							old_item

				_a     :      Optional[int]        =							new_item.replace('norm.weight'						,			'group_norm.weight'  )
				_a     :      Optional[Any]        =							new_item.replace('norm.bias'						,			'group_norm.bias'  )

				_a     :      Any        =							new_item.replace('proj_out.weight'						,			'proj_attn.weight'  )
				_a     :      Optional[Any]        =							new_item.replace('proj_out.bias'						,			'proj_attn.bias'  )

				_a     :      Optional[int]        =							shave_segments(lowerCAmelCase_						,			n_shave_prefix_segments=lowerCAmelCase_  )

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

		return mapping



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=None						,			lowerCAmelCase_=None						,			lowerCAmelCase_=None  )			->      Any:
		assert isinstance(lowerCAmelCase_						,			lowerCAmelCase_  ), "Paths should be a list of dicts containing 'old' and 'new' keys."

		# Splits the attention layers into three variables.
		if attention_paths_to_split is not None:
				for path, path_map in attention_paths_to_split.items():
						_a     :      Optional[Any]        =							old_checkpoint[path]
						_a     :      Optional[Any]        =							old_tensor.shape[0] // 3

						_a     :      Any        =							(-1, channels) if len(old_tensor.shape  ) == 3 else (-1)

						_a     :      int        =							old_tensor.shape[0] // config['num_head_channels'] // 3

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

						_a     :      Dict        =							query.reshape(lowerCAmelCase_  )
						_a     :      str        =							key.reshape(lowerCAmelCase_  )
						_a     :      Optional[int]        =							value.reshape(lowerCAmelCase_  )

		for path in paths:
				_a     :      Dict        =							path['new']

				# These have already been assigned
				if attention_paths_to_split is not None and new_path in attention_paths_to_split:
						continue

				# Global renaming happens here
				_a     :      Any        =							new_path.replace('middle_block.0'						,			'mid_block.resnets.0'  )
				_a     :      str        =							new_path.replace('middle_block.1'						,			'mid_block.attentions.0'  )
				_a     :      Union[str, Any]        =							new_path.replace('middle_block.2'						,			'mid_block.resnets.1'  )

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

        # proj_attn.weight has to be converted from conv 1D to linear
				if "proj_attn.weight" in new_path:
						_a     :      List[str]        =							old_checkpoint[path['old']][:, :, 0]
				else:
						_a     :      Dict        =							old_checkpoint[path['old']]



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      List[Any]:
		_a     :      Optional[int]        =							{}

		_a     :      Dict        =							checkpoint['time_embed.0.weight']
		_a     :      Tuple        =							checkpoint['time_embed.0.bias']
		_a     :      Union[str, Any]        =							checkpoint['time_embed.2.weight']
		_a     :      List[str]        =							checkpoint['time_embed.2.bias']

		_a     :      List[str]        =							checkpoint['input_blocks.0.0.weight']
		_a     :      Union[str, Any]        =							checkpoint['input_blocks.0.0.bias']

		_a     :      Optional[int]        =							checkpoint['out.0.weight']
		_a     :      int        =							checkpoint['out.0.bias']
		_a     :      List[str]        =							checkpoint['out.2.weight']
		_a     :      Optional[int]        =							checkpoint['out.2.bias']

		# Retrieves the keys for the input blocks only
		_a     :      Optional[int]        =							len({'.'.join(layer.split('.'  )[:2]  ) for layer in checkpoint if 'input_blocks' in layer}  )
		_a     :      Dict        =							{
		    layer_id: [key for key in checkpoint if f"""input_blocks.{layer_id}""" in key]
		    for layer_id in range(lowerCAmelCase_  )
		}

		# Retrieves the keys for the middle blocks only
		_a     :      List[Any]        =							len({'.'.join(layer.split('.'  )[:2]  ) for layer in checkpoint if 'middle_block' in layer}  )
		_a     :      Union[str, Any]        =							{
		    layer_id: [key for key in checkpoint if f"""middle_block.{layer_id}""" in key]
		    for layer_id in range(lowerCAmelCase_  )
		}

		# Retrieves the keys for the output blocks only
		_a     :      Optional[int]        =							len({'.'.join(layer.split('.'  )[:2]  ) for layer in checkpoint if 'output_blocks' in layer}  )
		_a     :      str        =							{
		    layer_id: [key for key in checkpoint if f"""output_blocks.{layer_id}""" in key]
		    for layer_id in range(lowerCAmelCase_  )
		}

		for i in range(1						,			lowerCAmelCase_  ):
				_a     :      List[Any]        =							(i - 1) // (config['num_res_blocks'] + 1)
				_a     :      Optional[int]        =							(i - 1) % (config['num_res_blocks'] + 1)

				_a     :      Optional[int]        =							[key for key in input_blocks[i] if f"""input_blocks.{i}.0""" in key]
				_a     :      Optional[Any]        =							[key for key in input_blocks[i] if f"""input_blocks.{i}.1""" in key]

				if f"""input_blocks.{i}.0.op.weight""" in checkpoint:
						_a     :      List[Any]        =							checkpoint[
						    f"""input_blocks.{i}.0.op.weight"""
						]
						_a     :      Union[str, Any]        =							checkpoint[
						    f"""input_blocks.{i}.0.op.bias"""
						]
						continue

				_a     :      Any        =							renew_resnet_paths(lowerCAmelCase_  )
				_a     :      List[str]        =							{'old': f"""input_blocks.{i}.0""", 'new': f"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
				_a     :      Optional[Any]        =							{'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
				assign_to_checkpoint(
				    lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			additional_replacements=[meta_path, resnet_op]						,			config=lowerCAmelCase_  )

				if len(lowerCAmelCase_  ):
						_a     :      List[str]        =							renew_attention_paths(lowerCAmelCase_  )
						_a     :      List[Any]        =							{
						    'old': f"""input_blocks.{i}.1""",
						    'new': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
						}
						_a     :      Optional[Any]        =							{
						    f"""input_blocks.{i}.1.qkv.bias""": {
						        'key': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
						        'query': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
						        'value': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
						    },
						    f"""input_blocks.{i}.1.qkv.weight""": {
						        'key': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
						        'query': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
						        'value': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
						    },
						}
						assign_to_checkpoint(
						    lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			additional_replacements=[meta_path]						,			attention_paths_to_split=lowerCAmelCase_						,			config=lowerCAmelCase_						,			)

		_a     :      str        =							middle_blocks[0]
		_a     :      Tuple        =							middle_blocks[1]
		_a     :      Any        =							middle_blocks[2]

		_a     :      List[Any]        =							renew_resnet_paths(lowerCAmelCase_  )
		assign_to_checkpoint(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			config=lowerCAmelCase_  )

		_a     :      Any        =							renew_resnet_paths(lowerCAmelCase_  )
		assign_to_checkpoint(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			config=lowerCAmelCase_  )

		_a     :      int        =							renew_attention_paths(lowerCAmelCase_  )
		_a     :      int        =							{
		    'middle_block.1.qkv.bias': {
		        'key': 'mid_block.attentions.0.key.bias',
		        'query': 'mid_block.attentions.0.query.bias',
		        'value': 'mid_block.attentions.0.value.bias',
		    },
		    'middle_block.1.qkv.weight': {
		        'key': 'mid_block.attentions.0.key.weight',
		        'query': 'mid_block.attentions.0.query.weight',
		        'value': 'mid_block.attentions.0.value.weight',
		    },
		}
		assign_to_checkpoint(
		    lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			attention_paths_to_split=lowerCAmelCase_						,			config=lowerCAmelCase_  )

		for i in range(lowerCAmelCase_  ):
				_a     :      List[str]        =							i // (config['num_res_blocks'] + 1)
				_a     :      Any        =							i % (config['num_res_blocks'] + 1)
				_a     :      Union[str, Any]        =							[shave_segments(lowerCAmelCase_						,			2  ) for name in output_blocks[i]]
				_a     :      Optional[Any]        =							{}

				for layer in output_block_layers:
						_a       ,					_a     :      str        =							layer.split('.'  )[0], shave_segments(lowerCAmelCase_						,			1  )
						if layer_id in output_block_list:
								output_block_list[layer_id].append(lowerCAmelCase_  )
						else:
								_a     :      str        =							[layer_name]

				if len(lowerCAmelCase_  ) > 1:
						_a     :      str        =							[key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
						_a     :      Optional[Any]        =							[key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]

						_a     :      Dict        =							renew_resnet_paths(lowerCAmelCase_  )
						_a     :      str        =							renew_resnet_paths(lowerCAmelCase_  )

						_a     :      Optional[int]        =							{'old': f"""output_blocks.{i}.0""", 'new': f"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
						assign_to_checkpoint(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			additional_replacements=[meta_path]						,			config=lowerCAmelCase_  )

						if ["conv.weight", "conv.bias"] in output_block_list.values():
								_a     :      List[Any]        =							list(output_block_list.values()  ).index(['conv.weight', 'conv.bias']  )
								_a     :      Tuple        =							checkpoint[
								    f"""output_blocks.{i}.{index}.conv.weight"""
								]
								_a     :      List[str]        =							checkpoint[
								    f"""output_blocks.{i}.{index}.conv.bias"""
								]

								# Clear attentions as they have been attributed above.
								if len(lowerCAmelCase_  ) == 2:
										_a     :      Union[str, Any]        =							[]

						if len(lowerCAmelCase_  ):
								_a     :      Tuple        =							renew_attention_paths(lowerCAmelCase_  )
								_a     :      str        =							{
								    'old': f"""output_blocks.{i}.1""",
								    'new': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
								}
								_a     :      List[Any]        =							{
								    f"""output_blocks.{i}.1.qkv.bias""": {
								        'key': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
								        'query': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
								        'value': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
								    },
								    f"""output_blocks.{i}.1.qkv.weight""": {
								        'key': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
								        'query': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
								        'value': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
								    },
								}
								assign_to_checkpoint(
								    lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			additional_replacements=[meta_path]						,			attention_paths_to_split=to_split if any('qkv' in key for key in attentions  ) else None						,			config=lowerCAmelCase_						,			)
				else:
						_a     :      List[Any]        =							renew_resnet_paths(lowerCAmelCase_						,			n_shave_prefix_segments=1  )
						for path in resnet_0_paths:
								_a     :      int        =							'.'.join(['output_blocks', str(lowerCAmelCase_  ), path['old']]  )
								_a     :      Union[str, Any]        =							'.'.join(['up_blocks', str(lowerCAmelCase_  ), 'resnets', str(lowerCAmelCase_  ), path['new']]  )

								_a     :      Union[str, Any]        =							checkpoint[old_path]

		return new_checkpoint


if __name__ == "__main__":
							__lowerCAmelCase									=   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.''')

							__lowerCAmelCase									=   parser.parse_args()

							__lowerCAmelCase									=   torch.load(args.checkpoint_path)

							with open(args.config_file) as f:
														__lowerCAmelCase									=   json.loads(f.read())

							__lowerCAmelCase									=   convert_ldm_checkpoint(checkpoint, config)

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

							__lowerCAmelCase									=   UNetaDModel(**config)
							model.load_state_dict(converted_checkpoint)

							try:
														__lowerCAmelCase									=   DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
														__lowerCAmelCase									=   VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

														__lowerCAmelCase									=   LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
														pipe.save_pretrained(args.dump_path)
							except:  # noqa: E722
														model.save_pretrained(args.dump_path)




