





"""simple docstring"""


import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

A_     :      Union[str, Any]								=logging.getLogger()
A_     :      Optional[int]								=torch.cuda.is_available()
A_     :      Optional[Any]								={
    """max_tokens_per_batch""": None,
    """supervise_forward""": True,
    """normalize_hidden""": True,
    """label_smoothing""": 0.2,
    """eval_max_gen_length""": None,
    """eval_beams""": 1,
    """val_metric""": """loss""",
    """save_top_k""": 1,
    """adafactor""": True,
    """early_stopping_patience""": 2,
    """logger_name""": """default""",
    """length_penalty""": 0.5,
    """cache_dir""": """""",
    """task""": """summarization""",
    """num_workers""": 2,
    """alpha_hid""": 0,
    """freeze_embeds""": True,
    """enc_only""": False,
    """tgt_suffix""": """""",
    """resume_from_checkpoint""": None,
    """sortish_sampler""": True,
    """student_decoder_layers""": 1,
    """val_check_interval""": 1.0,
    """output_dir""": """""",
    """fp16""": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    """no_teacher""": False,
    """fp16_opt_level""": """O1""",
    """gpus""": 1 if CUDA_AVAILABLE else 0,
    """n_tpu_cores""": 0,
    """max_grad_norm""": 1.0,
    """do_train""": True,
    """do_predict""": True,
    """accumulate_grad_batches""": 1,
    """server_ip""": """""",
    """server_port""": """""",
    """seed""": 4_2,
    """model_name_or_path""": """sshleifer/bart-tiny-random""",
    """config_name""": """""",
    """tokenizer_name""": """facebook/bart-large""",
    """do_lower_case""": False,
    """learning_rate""": 0.3,
    """lr_scheduler""": """linear""",
    """weight_decay""": 0.0,
    """adam_epsilon""": 1E-08,
    """warmup_steps""": 0,
    """max_epochs""": 1,
    """train_batch_size""": 2,
    """eval_batch_size""": 2,
    """max_source_length""": 1_2,
    """max_target_length""": 1_2,
    """val_max_target_length""": 1_2,
    """test_max_target_length""": 1_2,
    """fast_dev_run""": False,
    """no_cache""": False,
    """n_train""": -1,
    """n_val""": -1,
    """n_test""": -1,
    """student_encoder_layers""": 1,
    """freeze_encoder""": False,
    """auto_scale_batch_size""": False,
    """overwrite_output_dir""": False,
    """student""": None,
}





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Path						,  snake_case   :					list		)->		List[Any]:
       _lowerCamelCase    	=					'\n'.join(snake_case		)
       Path(snake_case		).open('w'		).writelines(snake_case		)


A_     :      str								=[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
A_     :      Tuple								=["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
A_     :      List[Any]								="""patrickvonplaten/t5-tiny-random"""
A_     :      Optional[Any]								="""sshleifer/t5-tinier-random"""
A_     :      Dict								="""sshleifer/bart-tiny-random"""
A_     :      Union[str, Any]								="""sshleifer/tiny-mbart"""
A_     :      int								="""sshleifer/tiny-marian-en-de"""
A_     :      Dict								="""stas/tiny-wmt19-en-de"""


A_     :      Any								=logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks





def SCREAMING_SNAKE_CASE_   (       snake_case   :					List[str]		)->		Optional[int]:
       for split in ["train", "val", "test"]:
              _dump_articles(os.path.join(snake_case						,  f'{split}.source'		)						,  snake_case		)
              _dump_articles(os.path.join(snake_case						,  f'{split}.target'		)						,  snake_case		)
       return tmp_dir







class 	__a   (  lowerCAmelCase__		):



     @classmethod
     def 						snake_case_			(       cls   ):
            logging.disable(logging.CRITICAL   )  # remove noisy download output from tracebacks
            return cls



     @slow
     @require_torch_gpu
     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					list_models()
            _lowerCamelCase    	=					'sshleifer'
            _lowerCamelCase    	=					[x.modelId for x in model_list if x.modelId.startswith(a__   )]
            _lowerCamelCase    	=					['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
            _lowerCamelCase    	=					[]
            for m in model_ids:
                   if m in allowed_to_be_broken:
                          continue
                   try:
                          AutoConfig.from_pretrained(a__   )
                   except Exception:
                          failures.append(a__   )
            assert not failures, F'The following models could not be loaded through AutoConfig: {failures}'



     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					{'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
            self._test_distiller_cli(a__   )



     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					{
                'student_encoder_layers': 2,
                'student_decoder_layers': 1,
                'max_epochs': 4,
                'val_check_interval': 0.25,
                'alpha_hid': 2.0,
                'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
            }
            _lowerCamelCase    	=					self._test_distiller_cli(a__					,     check_contents=a__   )

            _lowerCamelCase    	=					list(Path(model.output_dir   ).glob('*.ckpt'   )   )
            self.assertEqual(1					,     len(a__   )   )
            _lowerCamelCase    	=					list(Path(model.output_dir   ).glob('**/*.bin'   )   )
            self.assertEqual(len(a__   )					,     2   )
            _lowerCamelCase    	=					lmap(str.strip					,     Path(model.hparams.data_dir   ).joinpath('test.source'   ).open().readlines()   )
            _lowerCamelCase    	=					tempfile.mktemp()  # XXX: not being cleaned up
            generate_summaries_or_translations(a__					,     a__					,     str(model.output_dir / 'best_tfmr'   )   )
            self.assertTrue(Path(a__   ).exists()   )

            _lowerCamelCase    	=					self.get_auto_remove_tmp_dir()
            convert_pl_to_hf(ckpts[0]					,     transformer_ckpts[0].parent					,     a__   )
            assert os.path.exists(os.path.join(a__					,     'pytorch_model.bin'   )   )



     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					AutoModelForSeqaSeqLM.from_pretrained(a__   )
            _lowerCamelCase	,	_lowerCamelCase    	=					model.dummy_inputs['input_ids'], model.dummy_inputs['attention_mask']
            _lowerCamelCase    	=					torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]					,     dtype=torch.long					,     device=model.device   )
            _lowerCamelCase    	=					target_ids[:, :-1].contiguous()  # Why this line?
            _lowerCamelCase    	=					target_ids[:, 1:].clone()  # why clone?
            _lowerCamelCase    	=					model(
                a__					,     attention_mask=a__					,     decoder_input_ids=a__					,     labels=a__					,     use_cache=a__   ).loss

            _lowerCamelCase    	=					model(a__					,     attention_mask=a__					,     decoder_input_ids=a__					,     use_cache=a__   ).logits

            _lowerCamelCase    	=					nn.functional.log_softmax(a__					,     dim=-1   )
            _lowerCamelCase	,	_lowerCamelCase    	=					label_smoothed_nll_loss(
                a__					,     a__					,     0.1					,     ignore_index=model.config.pad_token_id   )
            with self.assertRaises(a__   ):
                   # TODO: understand why this breaks
                   self.assertEqual(a__					,     a__   )



     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					{
                'student_encoder_layers': 2,
                'student_decoder_layers': 1,
                'num_train_epochs': 4,
                'val_check_interval': 0.25,
                'alpha_hid': 2.0,
                'task': 'translation',
                'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
                'tokenizer_name': MBART_TINY,
                'teacher': MBART_TINY,
                'src_lang': 'en_XX',
                'tgt_lang': 'ro_RO',
            }
            _lowerCamelCase    	=					self._test_distiller_cli(a__					,     check_contents=a__   )
            assert model.model.config.model_type == "mbart"

            _lowerCamelCase    	=					list(Path(model.output_dir   ).glob('*.ckpt'   )   )
            self.assertEqual(1					,     len(a__   )   )
            _lowerCamelCase    	=					list(Path(model.output_dir   ).glob('**/*.bin'   )   )
            _lowerCamelCase    	=					list(Path(model.output_dir   ).glob('best_tfmr/*'   )   )
            assert len(a__   ) > 2
            self.assertEqual(len(a__   )					,     2   )



     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					{
                'student_encoder_layers': 1,
                'student_decoder_layers': 1,
                'alpha_hid': 2.0,
                'teacher': T5_TINY,
                'model_name_or_path': T5_TINY,
                'tokenizer_name': T5_TINY,
            }
            self._test_distiller_cli(a__   )



     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					{
                'teacher': T5_TINY,
                'student': T5_TINIER,
                'model_name_or_path': T5_TINIER,
                'tokenizer_name': T5_TINIER,
            }
            self._test_distiller_cli(a__   )



     def 						snake_case_			(       self					,     a__					,     a__=True   ):
            _lowerCamelCase    	=					{
                'label_smoothing': 0.0,
                'early_stopping_patience': -1,
                'train_batch_size': 1,
                'eval_batch_size': 2,
                'max_epochs': 2,
                'alpha_mlm': 0.2,
                'alpha_ce': 0.8,
                'do_predict': True,
                'model_name_or_path': 'sshleifer/tinier_bart',
                'teacher': CHEAP_ARGS['model_name_or_path'],
                'val_check_interval': 0.5,
            }
            default_updates.update(a__   )
            _lowerCamelCase    	=					CHEAP_ARGS.copy()
            _lowerCamelCase    	=					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
            _lowerCamelCase    	=					self.get_auto_remove_tmp_dir()

            args_d.update(data_dir=a__					,     output_dir=a__					,     **a__   )
            _lowerCamelCase    	=					distill_main(argparse.Namespace(**a__   )   )
            if not check_contents:
                   return model
            _lowerCamelCase    	=					os.listdir(a__   )
            _lowerCamelCase    	=					{os.path.basename(a__   ) for p in contents}
            _lowerCamelCase    	=					[p for p in contents if p.endswith('ckpt'   )]
            assert len(a__   ) > 0

            self.assertIn('test_generations.txt'					,     a__   )
            self.assertIn('test_results.txt'					,     a__   )

            _lowerCamelCase    	=					load_json(model.metrics_save_path   )
            _lowerCamelCase    	=					metrics['val'][-1]
            self.assertGreaterEqual(last_step_stats['val_avg_gen_time']					,     0.01   )
            self.assertGreaterEqual(1.0					,     last_step_stats['val_avg_gen_time']   )
            self.assertIsInstance(last_step_stats[F'val_avg_{model.val_metric}']					,     a__   )
            _lowerCamelCase    	=					int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) + 1   )
            self.assertEqual(len(metrics['val']   )					,     a__   )
            self.assertEqual(len(metrics['test']   )					,     1   )
            return model







class 	__a   (  lowerCAmelCase__		):



     @parameterized.expand(
         [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]					,     )
     def 						snake_case_			(       self					,     a__   ):
            _lowerCamelCase    	=					CHEAP_ARGS.copy()
            _lowerCamelCase    	=					'translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
            _lowerCamelCase    	=					0.1 if task == 'translation' else 0

            _lowerCamelCase    	=					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
            _lowerCamelCase    	=					self.get_auto_remove_tmp_dir()
            args_d.update(
                data_dir=a__					,     model_name_or_path=a__					,     tokenizer_name=a__					,     train_batch_size=2					,     eval_batch_size=2					,     output_dir=a__					,     do_predict=a__					,     task=a__					,     src_lang='en_XX'					,     tgt_lang='ro_RO'					,     freeze_encoder=a__					,     freeze_embeds=a__					,     )
            assert "n_train" in args_d
            _lowerCamelCase    	=					argparse.Namespace(**a__   )
            _lowerCamelCase    	=					main(a__   )

            _lowerCamelCase    	=					module.model.get_input_embeddings()
            assert not input_embeds.weight.requires_grad
            if model == T5_TINY:
                   _lowerCamelCase    	=					module.model.lm_head
                   assert not lm_head.weight.requires_grad
                   assert (lm_head.weight == input_embeds.weight).all().item()
            elif model == FSMT_TINY:
                   _lowerCamelCase    	=					module.model.model
                   _lowerCamelCase    	=					fsmt.decoder.embed_positions
                   assert not embed_pos.weight.requires_grad
                   assert not fsmt.decoder.embed_tokens.weight.requires_grad
                   # check that embeds are not the same
                   assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
            else:
                   _lowerCamelCase    	=					module.model.model
                   _lowerCamelCase    	=					bart.decoder.embed_positions
                   assert not embed_pos.weight.requires_grad
                   assert not bart.shared.weight.requires_grad
                   # check that embeds are the same
                   assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
                   assert bart.decoder.embed_tokens == bart.shared

            _lowerCamelCase    	=					load_json(module.output_dir / 'text_batch.json'   )
            assert isinstance(a__					,     a__   )
            assert len(a__   ) >= 4



     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					CHEAP_ARGS.copy()

            _lowerCamelCase    	=					'summarization'
            _lowerCamelCase    	=					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )

            args_d.update(
                data_dir=a__					,     tokenizer_name=a__					,     train_batch_size=2					,     eval_batch_size=2					,     do_predict=a__					,     task=a__					,     src_lang='en_XX'					,     tgt_lang='ro_RO'					,     freeze_encoder=a__					,     freeze_embeds=a__					,     )

            # test models whose config includes the extra_model_args
            _lowerCamelCase    	=					BART_TINY
            _lowerCamelCase    	=					self.get_auto_remove_tmp_dir()
            _lowerCamelCase    	=					args_d.copy()
            args_da.update(
                model_name_or_path=a__					,     output_dir=a__					,     )
            _lowerCamelCase    	=					('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
            for p in extra_model_params:
                   _lowerCamelCase    	=					0.5
            _lowerCamelCase    	=					argparse.Namespace(**a__   )
            _lowerCamelCase    	=					main(a__   )
            for p in extra_model_params:
                   assert getattr(model.config					,     a__   ) == 0.5, F'failed to override the model config for param {p}'

            # test models whose config doesn't include the extra_model_args
            _lowerCamelCase    	=					T5_TINY
            _lowerCamelCase    	=					self.get_auto_remove_tmp_dir()
            _lowerCamelCase    	=					args_d.copy()
            args_da.update(
                model_name_or_path=a__					,     output_dir=a__					,     )
            _lowerCamelCase    	=					'encoder_layerdrop'
            _lowerCamelCase    	=					0.5
            _lowerCamelCase    	=					argparse.Namespace(**a__   )
            with pytest.raises(a__   ) as excinfo:
                   _lowerCamelCase    	=					main(a__   )
            assert str(excinfo.value   ) == F'model config doesn\'t have a `{unsupported_param}` attribute'



     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					CHEAP_ARGS.copy()

            _lowerCamelCase    	=					'summarization'
            _lowerCamelCase    	=					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )

            _lowerCamelCase    	=					BART_TINY
            _lowerCamelCase    	=					self.get_auto_remove_tmp_dir()

            args_d.update(
                data_dir=a__					,     model_name_or_path=a__					,     output_dir=a__					,     tokenizer_name=a__					,     train_batch_size=2					,     eval_batch_size=2					,     do_predict=a__					,     task=a__					,     src_lang='en_XX'					,     tgt_lang='ro_RO'					,     freeze_encoder=a__					,     freeze_embeds=a__					,     )

            # emulate finetune.py
            _lowerCamelCase    	=					argparse.ArgumentParser()
            _lowerCamelCase    	=					pl.Trainer.add_argparse_args(a__   )
            _lowerCamelCase    	=					SummarizationModule.add_model_specific_args(a__					,     os.getcwd()   )
            _lowerCamelCase    	=					{'--help': True}

            # --help test
            with pytest.raises(a__   ) as excinfo:
                   with CaptureStdout() as cs:
                          _lowerCamelCase    	=					parser.parse_args(a__   )
                   assert False, "--help is expected to sys.exit"
            assert excinfo.type == SystemExit
            _lowerCamelCase    	=					lightning_base.arg_to_scheduler_metavar
            assert expected in cs.out, "--help is expected to list the supported schedulers"

            # --lr_scheduler=non_existing_scheduler test
            _lowerCamelCase    	=					'non_existing_scheduler'
            _lowerCamelCase    	=					{F'--lr_scheduler={unsupported_param}'}
            with pytest.raises(a__   ) as excinfo:
                   with CaptureStderr() as cs:
                          _lowerCamelCase    	=					parser.parse_args(a__   )
                   assert False, "invalid argument is expected to sys.exit"
            assert excinfo.type == SystemExit
            _lowerCamelCase    	=					F'invalid choice: \'{unsupported_param}\''
            assert expected in cs.err, F'should have bailed on invalid choice of scheduler {unsupported_param}'

            # --lr_scheduler=existing_scheduler test
            _lowerCamelCase    	=					'cosine'
            _lowerCamelCase    	=					args_d.copy()
            _lowerCamelCase    	=					supported_param
            _lowerCamelCase    	=					argparse.Namespace(**a__   )
            _lowerCamelCase    	=					main(a__   )
            assert (
                getattr(model.hparams					,     'lr_scheduler'   ) == supported_param
            ), F'lr_scheduler={supported_param} shouldn\'t fail'













"""simple docstring"""


import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Tuple						,  snake_case   :					List[Any]		)->		str:
       _lowerCamelCase    	=					UNetaDModel(**snake_case		)
       _lowerCamelCase    	=					checkpoint['all_modules.0.W'].data
       _lowerCamelCase    	=					checkpoint['all_modules.0.W'].data
       _lowerCamelCase    	=					checkpoint['all_modules.1.weight'].data
       _lowerCamelCase    	=					checkpoint['all_modules.1.bias'].data

       _lowerCamelCase    	=					checkpoint['all_modules.2.weight'].data
       _lowerCamelCase    	=					checkpoint['all_modules.2.bias'].data

       _lowerCamelCase    	=					checkpoint['all_modules.3.weight'].data
       _lowerCamelCase    	=					checkpoint['all_modules.3.bias'].data

       _lowerCamelCase    	=					checkpoint[list(checkpoint.keys()		)[-4]].data
       _lowerCamelCase    	=					checkpoint[list(checkpoint.keys()		)[-3]].data
       _lowerCamelCase    	=					checkpoint[list(checkpoint.keys()		)[-2]].data
       _lowerCamelCase    	=					checkpoint[list(checkpoint.keys()		)[-1]].data

       _lowerCamelCase    	=					4

       def set_attention_weights(snake_case   :					List[Any]						,  snake_case   :					Any						,  snake_case   :					List[Any]		):
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_0.W'].data.T
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_1.W'].data.T
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_2.W'].data.T

              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_0.b'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_1.b'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_2.b'].data

              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_3.W'].data.T
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_3.b'].data

              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_0.weight'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_0.bias'].data

       def set_resnet_weights(snake_case   :					Tuple						,  snake_case   :					Optional[int]						,  snake_case   :					Tuple		):
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Conv_0.weight'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Conv_0.bias'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_0.weight'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_0.bias'].data

              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Conv_1.weight'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Conv_1.bias'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_1.weight'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_1.bias'].data

              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Dense_0.weight'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Dense_0.bias'].data

              if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
                     _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Conv_2.weight'].data
                     _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Conv_2.bias'].data

       for i, block in enumerate(new_model_architecture.downsample_blocks		):
              _lowerCamelCase    	=					hasattr(snake_case						,  'attentions'		)
              for j in range(len(block.resnets		)		):
                     set_resnet_weights(block.resnets[j]						,  snake_case						,  snake_case		)
                     module_index += 1
                     if has_attentions:
                            set_attention_weights(block.attentions[j]						,  snake_case						,  snake_case		)
                            module_index += 1

              if hasattr(snake_case						,  'downsamplers'		) and block.downsamplers is not None:
                     set_resnet_weights(block.resnet_down						,  snake_case						,  snake_case		)
                     module_index += 1
                     _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.Conv_0.weight'].data
                     _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.Conv_0.bias'].data
                     module_index += 1

       set_resnet_weights(new_model_architecture.mid_block.resnets[0]						,  snake_case						,  snake_case		)
       module_index += 1
       set_attention_weights(new_model_architecture.mid_block.attentions[0]						,  snake_case						,  snake_case		)
       module_index += 1
       set_resnet_weights(new_model_architecture.mid_block.resnets[1]						,  snake_case						,  snake_case		)
       module_index += 1

       for i, block in enumerate(new_model_architecture.up_blocks		):
              _lowerCamelCase    	=					hasattr(snake_case						,  'attentions'		)
              for j in range(len(block.resnets		)		):
                     set_resnet_weights(block.resnets[j]						,  snake_case						,  snake_case		)
                     module_index += 1
              if has_attentions:
                     set_attention_weights(
                         block.attentions[0]						,  snake_case						,  snake_case		)  # why can there only be a single attention layer for up?
                     module_index += 1

              if hasattr(snake_case						,  'resnet_up'		) and block.resnet_up is not None:
                     _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.weight'].data
                     _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.bias'].data
                     module_index += 1
                     _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.weight'].data
                     _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.bias'].data
                     module_index += 1
                     set_resnet_weights(block.resnet_up						,  snake_case						,  snake_case		)
                     module_index += 1

       _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.weight'].data
       _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.bias'].data
       module_index += 1
       _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.weight'].data
       _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.bias'].data

       return new_model_architecture.state_dict()


if __name__ == "__main__":
      A_     :      Any								=argparse.ArgumentParser()

      parser.add_argument(
          """--checkpoint_path""",
          default="""/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin""",
          type=str,
          required=False,
          help="""Path to the checkpoint to convert.""",
      )

      parser.add_argument(
          """--config_file""",
          default="""/Users/arthurzucker/Work/diffusers/ArthurZ/config.json""",
          type=str,
          required=False,
          help="""The config json file corresponding to the architecture.""",
      )

      parser.add_argument(
          """--dump_path""",
          default="""/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt""",
          type=str,
          required=False,
          help="""Path to the output model.""",
      )

      A_     :      int								=parser.parse_args()

      A_     :      Dict								=torch.load(args.checkpoint_path, map_location="""cpu""")

      with open(args.config_file) as f:
            A_     :      List[str]								=json.loads(f.read())

      A_     :      str								=convert_ncsnpp_checkpoint(
          checkpoint,
          config,
      )

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

      A_     :      str								=UNetaDModel(**config)
      model.load_state_dict(converted_checkpoint)

      try:
            A_     :      List[Any]								=ScoreSdeVeScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

            A_     :      Optional[Any]								=ScoreSdeVePipeline(unet=model, scheduler=scheduler)
            pipe.save_pretrained(args.dump_path)
      except:  # noqa: E722
            model.save_pretrained(args.dump_path)






