


import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
							from tokenizers import Tokenizer
							from tokenizers.models import WordPiece

class   A__							(						unittest.TestCase     ):




						def 							__UpperCamelCase (				self    :					Dict ,			_a    :					List[Any]     )							->   Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =list(tokenizer_class.max_model_input_sizes.keys()     )
										for model_name in sa_models[:1]:
														_SCREAMING_SNAKE_CASE	     =tokenizer_class.from_pretrained(_a     )
														self.assertIsNotNone(_a     )
														self.assertIsInstance(_a ,			_a     )
														self.assertIsInstance(_a ,			_a     )

														for special_tok in tokenizer.all_special_tokens:
																		self.assertIsInstance(_a ,			_a     )
																		_SCREAMING_SNAKE_CASE	     =tokenizer.convert_tokens_to_ids(_a     )
																		self.assertIsInstance(_a ,			_a     )




						def 							__UpperCamelCase (				self    :					Any ,			_a    :					BatchEncoding ,			_a    :					Optional[Callable] = None     )							->   Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =pickle.dumps(_a     )
										self.assertIsNotNone(_a     )

										_SCREAMING_SNAKE_CASE	     =pickle.loads(_a     )

										# Ensure is_fast is correctly restored
										self.assertEqual(be_restored.is_fast ,			be_original.is_fast     )

										# Ensure encodings are potentially correctly restored
										if be_original.is_fast:
														self.assertIsNotNone(be_restored.encodings     )
										else:
														self.assertIsNone(be_restored.encodings     )

										# Ensure the keys are the same
										for original_v, restored_v in zip(be_original.values() ,			be_restored.values()     ):
														if equal_op:
																		self.assertTrue(equal_op(_a ,			_a     )     )
														else:
																		self.assertEqual(_a ,			_a     )




						@slow
						def 							__UpperCamelCase (				self    :					str     )							->   int:


										"""simple docstring"""





										self.check_tokenizer_from_pretrained(_a     )




						def 							__UpperCamelCase (				self    :					Optional[int]     )							->   List[str]:


										"""simple docstring"""





										self.assertEqual(TensorType('''tf'''     ) ,			TensorType.TENSORFLOW     )
										self.assertEqual(TensorType('''pt'''     ) ,			TensorType.PYTORCH     )
										self.assertEqual(TensorType('''np'''     ) ,			TensorType.NUMPY     )




						@require_tokenizers
						def 							__UpperCamelCase (				self    :					Optional[int]     )							->   Any:


										"""simple docstring"""





										import numpy as np

										_SCREAMING_SNAKE_CASE	     =BertTokenizer.from_pretrained('''bert-base-cased'''     )
										_SCREAMING_SNAKE_CASE	     =BertTokenizerFast.from_pretrained('''bert-base-cased'''     )

										# Python no tensor
										with self.subTest('''BatchEncoding (Python, return_tensors=None)'''     ):
														self.assert_dump_and_restore(tokenizer_p('''Small example to encode'''     )     )

										with self.subTest('''BatchEncoding (Python, return_tensors=NUMPY)'''     ):
														self.assert_dump_and_restore(
														    tokenizer_p('''Small example to encode''' ,			return_tensors=TensorType.NUMPY     ) ,			np.array_equal     )

										with self.subTest('''BatchEncoding (Rust, return_tensors=None)'''     ):
														self.assert_dump_and_restore(tokenizer_r('''Small example to encode'''     )     )

										with self.subTest('''BatchEncoding (Rust, return_tensors=NUMPY)'''     ):
														self.assert_dump_and_restore(
														    tokenizer_r('''Small example to encode''' ,			return_tensors=TensorType.NUMPY     ) ,			np.array_equal     )




						@require_tf
						@require_tokenizers
						def 							__UpperCamelCase (				self    :					int     )							->   List[str]:


										"""simple docstring"""





										import tensorflow as tf

										def tf_array_equals(_a    :					Optional[Any] ,			_a    :					Any     ):
														return tf.reduce_all(tf.equal(_a ,			_a     )     )

										_SCREAMING_SNAKE_CASE	     =BertTokenizer.from_pretrained('''bert-base-cased'''     )
										_SCREAMING_SNAKE_CASE	     =BertTokenizerFast.from_pretrained('''bert-base-cased'''     )

										with self.subTest('''BatchEncoding (Python, return_tensors=TENSORFLOW)'''     ):
														self.assert_dump_and_restore(
														    tokenizer_p('''Small example to encode''' ,			return_tensors=TensorType.TENSORFLOW     ) ,			_a     )

										with self.subTest('''BatchEncoding (Rust, return_tensors=TENSORFLOW)'''     ):
														self.assert_dump_and_restore(
														    tokenizer_r('''Small example to encode''' ,			return_tensors=TensorType.TENSORFLOW     ) ,			_a     )




						@require_torch
						@require_tokenizers
						def 							__UpperCamelCase (				self    :					List[str]     )							->   List[Any]:


										"""simple docstring"""





										import torch

										_SCREAMING_SNAKE_CASE	     =BertTokenizer.from_pretrained('''bert-base-cased'''     )
										_SCREAMING_SNAKE_CASE	     =BertTokenizerFast.from_pretrained('''bert-base-cased'''     )

										with self.subTest('''BatchEncoding (Python, return_tensors=PYTORCH)'''     ):
														self.assert_dump_and_restore(
														    tokenizer_p('''Small example to encode''' ,			return_tensors=TensorType.PYTORCH     ) ,			torch.equal     )

										with self.subTest('''BatchEncoding (Rust, return_tensors=PYTORCH)'''     ):
														self.assert_dump_and_restore(
														    tokenizer_r('''Small example to encode''' ,			return_tensors=TensorType.PYTORCH     ) ,			torch.equal     )




						@require_tokenizers
						def 							__UpperCamelCase (				self    :					Any     )							->   List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =BertTokenizer.from_pretrained('''bert-base-cased'''     )
										_SCREAMING_SNAKE_CASE	     =BertTokenizerFast.from_pretrained('''bert-base-cased'''     )

										with self.subTest('''Python Tokenizer'''     ):
														self.assertFalse(tokenizer_p('''Small example to_encode'''     ).is_fast     )

										with self.subTest('''Rust Tokenizer'''     ):
														self.assertTrue(tokenizer_r('''Small example to_encode'''     ).is_fast     )




						@require_tokenizers
						def 							__UpperCamelCase (				self    :					Any     )							->   Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =BertTokenizerFast.from_pretrained('''bert-base-cased'''     )
										_SCREAMING_SNAKE_CASE	     =tokenizer_r(['''Test''', '''\xad''', '''test'''] ,			is_split_into_words=_a     )

										self.assertEqual(encoded.word_to_tokens(0     ) ,			TokenSpan(start=1 ,			end=2     )     )
										self.assertEqual(encoded.word_to_tokens(1     ) ,			_a     )
										self.assertEqual(encoded.word_to_tokens(2     ) ,			TokenSpan(start=2 ,			end=3     )     )




						def 							__UpperCamelCase (				self    :					Optional[Any]     )							->   Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}     )
										_SCREAMING_SNAKE_CASE	     =batch.convert_to_tensors(tensor_type='''np'''     )
										self.assertEqual(tensor_batch['''inputs'''].shape ,			(2, 3)     )
										self.assertEqual(tensor_batch['''labels'''].shape ,			(2,)     )
										# test converting the converted
										with CaptureStderr() as cs:
														_SCREAMING_SNAKE_CASE	     =batch.convert_to_tensors(tensor_type='''np'''     )
										self.assertFalse(len(cs.err     ) ,			msg=f"should have no warning, but got {cs.err}"     )

										_SCREAMING_SNAKE_CASE	     =BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}     )
										_SCREAMING_SNAKE_CASE	     =batch.convert_to_tensors(tensor_type='''np''' ,			prepend_batch_axis=_a     )
										self.assertEqual(tensor_batch['''inputs'''].shape ,			(1, 3)     )
										self.assertEqual(tensor_batch['''labels'''].shape ,			(1,)     )




						@require_torch
						def 							__UpperCamelCase (				self    :					List[str]     )							->   Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}     )
										_SCREAMING_SNAKE_CASE	     =batch.convert_to_tensors(tensor_type='''pt'''     )
										self.assertEqual(tensor_batch['''inputs'''].shape ,			(2, 3)     )
										self.assertEqual(tensor_batch['''labels'''].shape ,			(2,)     )
										# test converting the converted
										with CaptureStderr() as cs:
														_SCREAMING_SNAKE_CASE	     =batch.convert_to_tensors(tensor_type='''pt'''     )
										self.assertFalse(len(cs.err     ) ,			msg=f"should have no warning, but got {cs.err}"     )

										_SCREAMING_SNAKE_CASE	     =BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}     )
										_SCREAMING_SNAKE_CASE	     =batch.convert_to_tensors(tensor_type='''pt''' ,			prepend_batch_axis=_a     )
										self.assertEqual(tensor_batch['''inputs'''].shape ,			(1, 3)     )
										self.assertEqual(tensor_batch['''labels'''].shape ,			(1,)     )




						@require_tf
						def 							__UpperCamelCase (				self    :					Any     )							->   Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}     )
										_SCREAMING_SNAKE_CASE	     =batch.convert_to_tensors(tensor_type='''tf'''     )
										self.assertEqual(tensor_batch['''inputs'''].shape ,			(2, 3)     )
										self.assertEqual(tensor_batch['''labels'''].shape ,			(2,)     )
										# test converting the converted
										with CaptureStderr() as cs:
														_SCREAMING_SNAKE_CASE	     =batch.convert_to_tensors(tensor_type='''tf'''     )
										self.assertFalse(len(cs.err     ) ,			msg=f"should have no warning, but got {cs.err}"     )

										_SCREAMING_SNAKE_CASE	     =BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}     )
										_SCREAMING_SNAKE_CASE	     =batch.convert_to_tensors(tensor_type='''tf''' ,			prepend_batch_axis=_a     )
										self.assertEqual(tensor_batch['''inputs'''].shape ,			(1, 3)     )
										self.assertEqual(tensor_batch['''labels'''].shape ,			(1,)     )




						@require_flax
						def 							__UpperCamelCase (				self    :					Union[str, Any]     )							->   Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}     )
										_SCREAMING_SNAKE_CASE	     =batch.convert_to_tensors(tensor_type='''jax'''     )
										self.assertEqual(tensor_batch['''inputs'''].shape ,			(2, 3)     )
										self.assertEqual(tensor_batch['''labels'''].shape ,			(2,)     )
										# test converting the converted
										with CaptureStderr() as cs:
														_SCREAMING_SNAKE_CASE	     =batch.convert_to_tensors(tensor_type='''jax'''     )
										self.assertFalse(len(cs.err     ) ,			msg=f"should have no warning, but got {cs.err}"     )

										_SCREAMING_SNAKE_CASE	     =BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}     )
										_SCREAMING_SNAKE_CASE	     =batch.convert_to_tensors(tensor_type='''jax''' ,			prepend_batch_axis=_a     )
										self.assertEqual(tensor_batch['''inputs'''].shape ,			(1, 3)     )
										self.assertEqual(tensor_batch['''labels'''].shape ,			(1,)     )




						def 							__UpperCamelCase (				self    :					Tuple     )							->   List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =[{'''input_ids''': np.array([0, 1, 2]     )}, {'''input_ids''': np.array([0, 1, 2, 3]     )}]
										_SCREAMING_SNAKE_CASE	     =BertTokenizer.from_pretrained('''bert-base-cased'''     )

										_SCREAMING_SNAKE_CASE	     =tokenizer.pad(_a ,			padding=_a     )
										self.assertTrue(isinstance(batch['''input_ids'''] ,			np.ndarray     )     )
										self.assertEqual(batch['''input_ids'''].tolist() ,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]     )
										_SCREAMING_SNAKE_CASE	     =tokenizer.pad(_a ,			padding=_a ,			return_tensors='''np'''     )
										self.assertTrue(isinstance(batch['''input_ids'''] ,			np.ndarray     )     )
										self.assertEqual(batch['''input_ids'''].tolist() ,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]     )




						@require_torch
						def 							__UpperCamelCase (				self    :					Tuple     )							->   Optional[Any]:


										"""simple docstring"""





										import torch

										_SCREAMING_SNAKE_CASE	     =[{'''input_ids''': torch.tensor([0, 1, 2]     )}, {'''input_ids''': torch.tensor([0, 1, 2, 3]     )}]
										_SCREAMING_SNAKE_CASE	     =BertTokenizer.from_pretrained('''bert-base-cased'''     )

										_SCREAMING_SNAKE_CASE	     =tokenizer.pad(_a ,			padding=_a     )
										self.assertTrue(isinstance(batch['''input_ids'''] ,			torch.Tensor     )     )
										self.assertEqual(batch['''input_ids'''].tolist() ,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]     )
										_SCREAMING_SNAKE_CASE	     =tokenizer.pad(_a ,			padding=_a ,			return_tensors='''pt'''     )
										self.assertTrue(isinstance(batch['''input_ids'''] ,			torch.Tensor     )     )
										self.assertEqual(batch['''input_ids'''].tolist() ,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]     )




						@require_tf
						def 							__UpperCamelCase (				self    :					List[Any]     )							->   Any:


										"""simple docstring"""





										import tensorflow as tf

										_SCREAMING_SNAKE_CASE	     =[{'''input_ids''': tf.constant([0, 1, 2]     )}, {'''input_ids''': tf.constant([0, 1, 2, 3]     )}]
										_SCREAMING_SNAKE_CASE	     =BertTokenizer.from_pretrained('''bert-base-cased'''     )

										_SCREAMING_SNAKE_CASE	     =tokenizer.pad(_a ,			padding=_a     )
										self.assertTrue(isinstance(batch['''input_ids'''] ,			tf.Tensor     )     )
										self.assertEqual(batch['''input_ids'''].numpy().tolist() ,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]     )
										_SCREAMING_SNAKE_CASE	     =tokenizer.pad(_a ,			padding=_a ,			return_tensors='''tf'''     )
										self.assertTrue(isinstance(batch['''input_ids'''] ,			tf.Tensor     )     )
										self.assertEqual(batch['''input_ids'''].numpy().tolist() ,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]     )




						@require_tokenizers
						def 							__UpperCamelCase (				self    :					int     )							->   str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =Tokenizer(WordPiece(unk_token='''[UNK]'''     )     )
										PreTrainedTokenizerFast(tokenizer_object=_a     )




						@require_tokenizers
						def 							__UpperCamelCase (				self    :					Optional[Any]     )							->   Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =Tokenizer(WordPiece(unk_token='''[UNK]'''     )     )
										with tempfile.TemporaryDirectory() as tmpdirname:
														bert_tokenizer.save(os.path.join(_a ,			'''tokenizer.json'''     )     )
														PreTrainedTokenizerFast(tokenizer_file=os.path.join(_a ,			'''tokenizer.json'''     )     )



import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel







def   lowerCamelCase(      a__		,a__):
				_SCREAMING_SNAKE_CASE	     =UNetaDModel(**a__)
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.0.W'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.0.W'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.1.weight'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.1.bias'''].data

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

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

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

				_SCREAMING_SNAKE_CASE	     =4

				def set_attention_weights(a__		,a__		,a__):
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_0.b"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_1.b"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_2.b"].data

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_3.b"].data

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

				def set_resnet_weights(a__		,a__		,a__):
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_1.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_1.bias"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_1.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_1.bias"].data

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
								_SCREAMING_SNAKE_CASE	     =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:
												_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
												_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data

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

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

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

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

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

				_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.weight"].data
				_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.bias"].data
				module_index += 1
				_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.weight"].data
				_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.bias"].data

				return new_model_architecture.state_dict()


if __name__ == "__main__":
							snake_case_  :	Tuple							  =							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.''',
							)

							snake_case_  :	List[str]							  =							parser.parse_args()

							snake_case_  :	List[Any]							  =							torch.load(args.checkpoint_path, map_location='''cpu''')

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

							snake_case_  :	Optional[int]							  =							convert_ncsnpp_checkpoint(
							    checkpoint,
							    config,
							)

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

							snake_case_  :	Union[str, Any]							  =							UNetaDModel(**config)
							model.load_state_dict(converted_checkpoint)

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

														snake_case_  :	int							  =							ScoreSdeVePipeline(unet=model, scheduler=scheduler)
														pipe.save_pretrained(args.dump_path)
							except:  # noqa: E722
														model.save_pretrained(args.dump_path)