
'''simple docstring'''



import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
	pass

if is_speech_available():
	from transformers import EncodecFeatureExtractor, MusicgenProcessor


__A          =    random.Random()





def  _A		(							lowercase__		,		lowercase__=1.0		,		lowercase__=None		,		lowercase__=None	):
						if rng is None:
												lowercase__							 =       global_rng

						lowercase__							 =       []
						for batch_idx in range(shape[0]	):
												values.append([]	)
												for _ in range(shape[1]	):
																		values[-1].append(rng.random() * scale	)

						return values







@require_torch
@require_sentencepiece
class 			A							(       unittest.TestCase				):



			def A__ (					self       )			->		List[str]:




									'''simple docstring'''



									lowercase__							 =       """facebook/musicgen-small"""
									lowercase__							 =       tempfile.mkdtemp()



			def A__ (					self       ,     **lowerCamelCase__       )			->		Tuple:




									'''simple docstring'''



									return TaTokenizer.from_pretrained(self.checkpoint       ,     **lowerCamelCase__       )



			def A__ (					self       ,     **lowerCamelCase__       )			->		Tuple:




									'''simple docstring'''



									return EncodecFeatureExtractor.from_pretrained(self.checkpoint       ,     **lowerCamelCase__       )



			def A__ (					self       )			->		Optional[int]:




									'''simple docstring'''



									shutil.rmtree(self.tmpdirname       )



			def A__ (					self       )			->		Union[str, Any]:




									'''simple docstring'''



									lowercase__							 =       self.get_tokenizer()
									lowercase__							 =       self.get_feature_extractor()

									lowercase__							 =       MusicgenProcessor(tokenizer=lowerCamelCase__       ,     feature_extractor=lowerCamelCase__       )

									processor.save_pretrained(self.tmpdirname       )
									lowercase__							 =       MusicgenProcessor.from_pretrained(self.tmpdirname       )

									self.assertEqual(processor.tokenizer.get_vocab()       ,     tokenizer.get_vocab()       )
									self.assertIsInstance(processor.tokenizer       ,     lowerCamelCase__       )

									self.assertEqual(processor.feature_extractor.to_json_string()       ,     feature_extractor.to_json_string()       )
									self.assertIsInstance(processor.feature_extractor       ,     lowerCamelCase__       )



			def A__ (					self       )			->		Any:




									'''simple docstring'''



									lowercase__							 =       MusicgenProcessor(tokenizer=self.get_tokenizer()       ,     feature_extractor=self.get_feature_extractor()       )
									processor.save_pretrained(self.tmpdirname       )

									lowercase__							 =       self.get_tokenizer(bos_token="""(BOS)"""       ,     eos_token="""(EOS)"""       )
									lowercase__							 =       self.get_feature_extractor(do_normalize=lowerCamelCase__       ,     padding_value=1.0       )

									lowercase__							 =       MusicgenProcessor.from_pretrained(
									    self.tmpdirname       ,     bos_token="""(BOS)"""       ,     eos_token="""(EOS)"""       ,     do_normalize=lowerCamelCase__       ,     padding_value=1.0       )

									self.assertEqual(processor.tokenizer.get_vocab()       ,     tokenizer_add_kwargs.get_vocab()       )
									self.assertIsInstance(processor.tokenizer       ,     lowerCamelCase__       )

									self.assertEqual(processor.feature_extractor.to_json_string()       ,     feature_extractor_add_kwargs.to_json_string()       )
									self.assertIsInstance(processor.feature_extractor       ,     lowerCamelCase__       )



			def A__ (					self       )			->		Union[str, Any]:




									'''simple docstring'''



									lowercase__							 =       self.get_feature_extractor()
									lowercase__							 =       self.get_tokenizer()

									lowercase__							 =       MusicgenProcessor(tokenizer=lowerCamelCase__       ,     feature_extractor=lowerCamelCase__       )

									lowercase__							 =       floats_list((3, 1_000)       )

									lowercase__							 =       feature_extractor(lowerCamelCase__       ,     return_tensors="""np"""       )
									lowercase__							 =       processor(lowerCamelCase__       ,     return_tensors="""np"""       )

									for key in input_feat_extract.keys():
															self.assertAlmostEqual(input_feat_extract[key].sum()       ,     input_processor[key].sum()       ,     delta=1e-2       )



			def A__ (					self       )			->		Dict:




									'''simple docstring'''



									lowercase__							 =       self.get_feature_extractor()
									lowercase__							 =       self.get_tokenizer()

									lowercase__							 =       MusicgenProcessor(tokenizer=lowerCamelCase__       ,     feature_extractor=lowerCamelCase__       )

									lowercase__							 =       """This is a test string"""

									lowercase__							 =       processor(text=lowerCamelCase__       )

									lowercase__							 =       tokenizer(lowerCamelCase__       )

									for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key]       ,     encoded_processor[key]       )



			def A__ (					self       )			->		str:




									'''simple docstring'''



									lowercase__							 =       self.get_feature_extractor()
									lowercase__							 =       self.get_tokenizer()

									lowercase__							 =       MusicgenProcessor(tokenizer=lowerCamelCase__       ,     feature_extractor=lowerCamelCase__       )

									lowercase__							 =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									lowercase__							 =       processor.batch_decode(sequences=lowerCamelCase__       )
									lowercase__							 =       tokenizer.batch_decode(lowerCamelCase__       )

									self.assertListEqual(lowerCamelCase__       ,     lowerCamelCase__       )



			def A__ (					self       )			->		Tuple:




									'''simple docstring'''



									lowercase__							 =       self.get_feature_extractor()
									lowercase__							 =       self.get_tokenizer()

									lowercase__							 =       MusicgenProcessor(tokenizer=lowerCamelCase__       ,     feature_extractor=lowerCamelCase__       )

									self.assertListEqual(
									    processor.model_input_names       ,     feature_extractor.model_input_names       ,     msg="""`processor` and `feature_extractor` model input names do not match"""       ,     )



			def A__ (					self       )			->		Dict:




									'''simple docstring'''



									lowercase__							 =       self.get_feature_extractor(padding_side="""left"""       )
									lowercase__							 =       self.get_tokenizer()

									lowercase__							 =       MusicgenProcessor(tokenizer=lowerCamelCase__       ,     feature_extractor=lowerCamelCase__       )

									lowercase__							 =       [floats_list((1, x)       )[0] for x in range(5       ,     20       ,     5       )]
									lowercase__							 =       processor(lowerCamelCase__       ).padding_mask

									lowercase__							 =       np.asarray(floats_list((3, 20)       )       )[:, None, :]
									lowercase__							 =       processor.batch_decode(lowerCamelCase__       ,     padding_mask=lowerCamelCase__       )

									self.assertIsInstance(lowerCamelCase__       ,     lowerCamelCase__       )

									for audio in decoded_audios:
															self.assertIsInstance(lowerCamelCase__       ,     np.ndarray       )

									self.assertTrue(decoded_audios[0].shape == (1, 10)       )
									self.assertTrue(decoded_audios[1].shape == (1, 15)       )
									self.assertTrue(decoded_audios[2].shape == (1, 20)       )








'''simple docstring'''



import argparse
import json

import torch

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





def  _A		(							lowercase__		,		lowercase__=1	):
						if n_shave_prefix_segments >= 0:
												return ".".join(path.split("""."""	)[n_shave_prefix_segments:]	)
						else:
												return ".".join(path.split("""."""	)[:n_shave_prefix_segments]	)





def  _A		(							lowercase__		,		lowercase__=0	):
						lowercase__							 =       []
						for old_item in old_list:
												lowercase__							 =       old_item
												lowercase__							 =       new_item.replace("""block."""		,		"""resnets."""	)
												lowercase__							 =       new_item.replace("""conv_shorcut"""		,		"""conv1"""	)
												lowercase__							 =       new_item.replace("""in_shortcut"""		,		"""conv_shortcut"""	)
												lowercase__							 =       new_item.replace("""temb_proj"""		,		"""time_emb_proj"""	)

												lowercase__							 =       shave_segments(lowercase__		,		n_shave_prefix_segments=lowercase__	)

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

						return mapping





def  _A		(							lowercase__		,		lowercase__=0		,		lowercase__=False	):
						lowercase__							 =       []
						for old_item in old_list:
												lowercase__							 =       old_item

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

												lowercase__							 =       new_item.replace("""proj_out"""		,		"""proj_attn"""	)
												lowercase__							 =       new_item.replace("""norm"""		,		"""group_norm"""	)

												lowercase__							 =       shave_segments(lowercase__		,		n_shave_prefix_segments=lowercase__	)
												mapping.append({"""old""": old_item, """new""": new_item}	)

						return mapping





def  _A		(							lowercase__		,		lowercase__		,		lowercase__		,		lowercase__=None		,		lowercase__=None		,		lowercase__=None	):
						assert isinstance(lowercase__		,		lowercase__	), "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():
																		lowercase__							 =       old_checkpoint[path]
																		lowercase__							 =       old_tensor.shape[0] // 3

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

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

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

																		lowercase__							 =       query.reshape(lowercase__	).squeeze()
																		lowercase__							 =       key.reshape(lowercase__	).squeeze()
																		lowercase__							 =       value.reshape(lowercase__	).squeeze()

						for path in paths:
												lowercase__							 =       path["""new"""]

												if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																		continue

												lowercase__							 =       new_path.replace("""down."""		,		"""down_blocks."""	)
												lowercase__							 =       new_path.replace("""up."""		,		"""up_blocks."""	)

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

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





def  _A		(							lowercase__		,		lowercase__	):
						lowercase__							 =       {}

						lowercase__							 =       checkpoint["""temb.dense.0.weight"""]
						lowercase__							 =       checkpoint["""temb.dense.0.bias"""]
						lowercase__							 =       checkpoint["""temb.dense.1.weight"""]
						lowercase__							 =       checkpoint["""temb.dense.1.bias"""]

						lowercase__							 =       checkpoint["""norm_out.weight"""]
						lowercase__							 =       checkpoint["""norm_out.bias"""]

						lowercase__							 =       checkpoint["""conv_in.weight"""]
						lowercase__							 =       checkpoint["""conv_in.bias"""]
						lowercase__							 =       checkpoint["""conv_out.weight"""]
						lowercase__							 =       checkpoint["""conv_out.bias"""]

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

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

						for i in range(lowercase__	):
												lowercase__							 =       (i - 1) // (config["""layers_per_block"""] + 1)

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

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

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

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

						lowercase__							 =       [key for key in checkpoint if """mid.block_1""" in key]
						lowercase__							 =       [key for key in checkpoint if """mid.block_2""" in key]
						lowercase__							 =       [key for key in checkpoint if """mid.attn_1""" in key]

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

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

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

						for i in range(lowercase__	):
												lowercase__							 =       num_up_blocks - 1 - i

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

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

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

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

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

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





def  _A		(							lowercase__		,		lowercase__	):
						lowercase__							 =       {}

						lowercase__							 =       checkpoint["""encoder.norm_out.weight"""]
						lowercase__							 =       checkpoint["""encoder.norm_out.bias"""]

						lowercase__							 =       checkpoint["""encoder.conv_in.weight"""]
						lowercase__							 =       checkpoint["""encoder.conv_in.bias"""]
						lowercase__							 =       checkpoint["""encoder.conv_out.weight"""]
						lowercase__							 =       checkpoint["""encoder.conv_out.bias"""]

						lowercase__							 =       checkpoint["""decoder.norm_out.weight"""]
						lowercase__							 =       checkpoint["""decoder.norm_out.bias"""]

						lowercase__							 =       checkpoint["""decoder.conv_in.weight"""]
						lowercase__							 =       checkpoint["""decoder.conv_in.bias"""]
						lowercase__							 =       checkpoint["""decoder.conv_out.weight"""]
						lowercase__							 =       checkpoint["""decoder.conv_out.bias"""]

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

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

						for i in range(lowercase__	):
												lowercase__							 =       (i - 1) // (config["""layers_per_block"""] + 1)

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

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

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

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

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

						lowercase__							 =       [key for key in checkpoint if """mid.block_1""" in key]
						lowercase__							 =       [key for key in checkpoint if """mid.block_2""" in key]
						lowercase__							 =       [key for key in checkpoint if """mid.attn_1""" in key]

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

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

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

						for i in range(lowercase__	):
												lowercase__							 =       num_up_blocks - 1 - i

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

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

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

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

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

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

						return new_checkpoint


if __name__ == "__main__":
				__A          =    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.")

				__A          =    parser.parse_args()
				__A          =    torch.load(args.checkpoint_path)

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

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

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

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

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

								__A          =    DDPMPipeline(unet=model, scheduler=scheduler)
								pipe.save_pretrained(args.dump_path)






