


'''simple docstring'''



import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import ASTForAudioClassification, ASTModel
	from transformers.models.audio_spectrogram_transformer.modeling_audio_spectrogram_transformer import (
	    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
	)


if is_torchaudio_available():
	import torchaudio

	from transformers import ASTFeatureExtractor






class  UpperCamelCase__:




				def __init__(	self    :				Dict   ,					lowerCAmelCase    :				List[str]   ,					lowerCAmelCase    :				Union[str, Any]=13   ,					lowerCAmelCase    :				Optional[int]=2   ,					lowerCAmelCase    :				Tuple=24   ,					lowerCAmelCase    :				Union[str, Any]=16   ,					lowerCAmelCase    :				List[Any]=True   ,					lowerCAmelCase    :				int=True   ,					lowerCAmelCase    :				int=32   ,					lowerCAmelCase    :				Optional[Any]=5   ,					lowerCAmelCase    :				List[str]=4   ,					lowerCAmelCase    :				Optional[Any]=37   ,					lowerCAmelCase    :				Any="gelu"   ,					lowerCAmelCase    :				Dict=0.1   ,					lowerCAmelCase    :				int=0.1   ,					lowerCAmelCase    :				List[str]=10   ,					lowerCAmelCase    :				List[str]=0.02   ,					lowerCAmelCase    :				List[str]=None   ,					lowerCAmelCase    :				Union[str, Any]=2   ,					lowerCAmelCase    :				int=2   ,					)->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			parent
										UpperCAmelCase         =			batch_size
										UpperCAmelCase         =			patch_size
										UpperCAmelCase         =			max_length
										UpperCAmelCase         =			num_mel_bins
										UpperCAmelCase         =			is_training
										UpperCAmelCase         =			use_labels
										UpperCAmelCase         =			hidden_size
										UpperCAmelCase         =			num_hidden_layers
										UpperCAmelCase         =			num_attention_heads
										UpperCAmelCase         =			intermediate_size
										UpperCAmelCase         =			hidden_act
										UpperCAmelCase         =			hidden_dropout_prob
										UpperCAmelCase         =			attention_probs_dropout_prob
										UpperCAmelCase         =			type_sequence_label_size
										UpperCAmelCase         =			initializer_range
										UpperCAmelCase         =			scope
										UpperCAmelCase         =			frequency_stride
										UpperCAmelCase         =			time_stride

										# in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
										UpperCAmelCase         =			(self.num_mel_bins - self.patch_size) // self.frequency_stride + 1
										UpperCAmelCase         =			(self.max_length - self.patch_size) // self.time_stride + 1
										UpperCAmelCase         =			frequency_out_dimension * time_out_dimension
										UpperCAmelCase         =			num_patches + 2




				def   a__(	self    :				List[Any]   )->  List[str]:

										"""simple docstring"""




										UpperCAmelCase         =			floats_tensor([self.batch_size, self.max_length, self.num_mel_bins]   )

										UpperCAmelCase         =			None
										if self.use_labels:
																UpperCAmelCase         =			ids_tensor([self.batch_size]   ,					self.type_sequence_label_size   )

										UpperCAmelCase         =			self.get_config()

										return config, input_values, labels




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

										"""simple docstring"""




										return ASTConfig(
										    patch_size=self.patch_size   ,					max_length=self.max_length   ,					num_mel_bins=self.num_mel_bins   ,					hidden_size=self.hidden_size   ,					num_hidden_layers=self.num_hidden_layers   ,					num_attention_heads=self.num_attention_heads   ,					intermediate_size=self.intermediate_size   ,					hidden_act=self.hidden_act   ,					hidden_dropout_prob=self.hidden_dropout_prob   ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,					is_decoder=lowerCAmelCase   ,					initializer_range=self.initializer_range   ,					frequency_stride=self.frequency_stride   ,					time_stride=self.time_stride   ,					)




				def   a__(	self    :				int   ,					lowerCAmelCase    :				Union[str, Any]   ,					lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				int   )->  Dict:

										"""simple docstring"""




										UpperCAmelCase         =			ASTModel(config=lowerCAmelCase   )
										model.to(lowerCAmelCase   )
										model.eval()
										UpperCAmelCase         =			model(lowerCAmelCase   )
										self.parent.assertEqual(result.last_hidden_state.shape   ,					(self.batch_size, self.seq_length, self.hidden_size)   )





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

										"""simple docstring"""




										UpperCAmelCase         =			self.prepare_config_and_inputs()
										(
										    (
										    UpperCAmelCase
										)       ,       (
										    UpperCAmelCase
										)       ,       (
										    UpperCAmelCase
										)       ,       
										)         =			config_and_inputs
										UpperCAmelCase         =			{'''input_values''': input_values}
										return config, inputs_dict







@require_torch
class  UpperCamelCase__(     lowerCAmelCase		,       lowerCAmelCase		,       unittest.TestCase ):
				__magic_name__       :   List[Any]							= (
				    (
				        ASTModel,
				        ASTForAudioClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				__magic_name__       :   Any							= (
				    {"audio-classification": ASTForAudioClassification, "feature-extraction": ASTModel}
				    if is_torch_available()
				    else {}
				)
				__magic_name__       :   List[Any]							= False
				__magic_name__       :   Any							= False
				__magic_name__       :   Any							= False
				__magic_name__       :   Any							= False




				def   a__(	self    :				List[str]   ,					lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				int   ,					lowerCAmelCase    :				Union[str, Any]   ,					lowerCAmelCase    :				Union[str, Any]   ,					lowerCAmelCase    :				Dict   )->  Optional[int]:

										"""simple docstring"""




										if pipeline_test_casse_name == "AudioClassificationPipelineTests":
																return True

										return False




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

										"""simple docstring"""




										UpperCAmelCase         =			ASTModelTester(self   )
										UpperCAmelCase         =			ConfigTester(self   ,					config_class=lowerCAmelCase   ,					has_text_modality=lowerCAmelCase   ,					hidden_size=37   )




				def   a__(	self    :				Optional[int]   )->  Any:

										"""simple docstring"""




										self.config_tester.run_common_tests()




				@unittest.skip(reason='''AST does not use inputs_embeds'''   )
				def   a__(	self    :				Optional[int]   )->  int:

										"""simple docstring"""




										pass




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

										"""simple docstring"""




										UpperCAmelCase       ,       UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																UpperCAmelCase         =			model_class(lowerCAmelCase   )
																self.assertIsInstance(model.get_input_embeddings()   ,					(nn.Module)   )
																UpperCAmelCase         =			model.get_output_embeddings()
																self.assertTrue(x is None or isinstance(lowerCAmelCase   ,					nn.Linear   )   )




				def   a__(	self    :				str   )->  int:

										"""simple docstring"""




										UpperCAmelCase       ,       UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																UpperCAmelCase         =			model_class(lowerCAmelCase   )
																UpperCAmelCase         =			inspect.signature(model.forward   )
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																UpperCAmelCase         =			[*signature.parameters.keys()]

																UpperCAmelCase         =			['''input_values''']
																self.assertListEqual(arg_names[:1]   ,					lowerCAmelCase   )




				def   a__(	self    :				Tuple   )->  List[str]:

										"""simple docstring"""




										UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase   )





				@slow
				def   a__(	self    :				Optional[Any]   )->  Dict:

										"""simple docstring"""




										for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																UpperCAmelCase         =			ASTModel.from_pretrained(lowerCAmelCase   )
																self.assertIsNotNone(lowerCAmelCase   )







def    lowerCamelCase__   ( ):




						'''simple docstring'''






						UpperCAmelCase         =			hf_hub_download(
						    repo_id='''nielsr/audio-spectogram-transformer-checkpoint'''   ,     filename='''sample_audio.flac'''   ,     repo_type='''dataset'''  )

						UpperCAmelCase       ,       UpperCAmelCase         =			torchaudio.load(A  )

						return audio, sampling_rate






@require_torch
@require_torchaudio
class  UpperCamelCase__(     unittest.TestCase ):




				@cached_property
				def   a__(	self    :				Any   )->  Tuple:

										"""simple docstring"""




										return (
										    ASTFeatureExtractor.from_pretrained('''MIT/ast-finetuned-audioset-10-10-0.4593'''   )
										    if is_torchaudio_available()
										    else None
										)





				@slow
				def   a__(	self    :				List[str]   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			self.default_feature_extractor
										UpperCAmelCase         =			ASTForAudioClassification.from_pretrained('''MIT/ast-finetuned-audioset-10-10-0.4593'''   ).to(lowerCAmelCase   )

										UpperCAmelCase         =			self.default_feature_extractor
										UpperCAmelCase       ,       UpperCAmelCase         =			prepare_audio()
										UpperCAmelCase         =			audio.squeeze().numpy()
										UpperCAmelCase         =			feature_extractor(lowerCAmelCase   ,					sampling_rate=lowerCAmelCase   ,					return_tensors='''pt'''   ).to(lowerCAmelCase   )

										# forward pass
										with torch.no_grad():
																UpperCAmelCase         =			model(**lowerCAmelCase   )

										# verify the logits
										UpperCAmelCase         =			torch.Size((1, 527)   )
										self.assertEqual(outputs.logits.shape   ,					lowerCAmelCase   )

										UpperCAmelCase         =			torch.tensor([-0.8760, -7.0042, -8.6602]   ).to(lowerCAmelCase   )

										self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,					lowerCAmelCase   ,					atol=1E-4   )   )









'''simple docstring'''



import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel


def    lowerCamelCase__   ( A     :    Optional[Any]   ,     A     :    Tuple=1  ):




						'''simple docstring'''






						if n_shave_prefix_segments >= 0:
												return ".".join(path.split('''.'''  )[n_shave_prefix_segments:]  )
						else:
												return ".".join(path.split('''.'''  )[:n_shave_prefix_segments]  )


def    lowerCamelCase__   ( A     :    int   ,     A     :    Optional[Any]=0  ):




						'''simple docstring'''






						UpperCAmelCase         =			[]
						for old_item in old_list:
												UpperCAmelCase         =			old_item.replace('''in_layers.0'''   ,     '''norm1'''  )
												UpperCAmelCase         =			new_item.replace('''in_layers.2'''   ,     '''conv1'''  )

												UpperCAmelCase         =			new_item.replace('''out_layers.0'''   ,     '''norm2'''  )
												UpperCAmelCase         =			new_item.replace('''out_layers.3'''   ,     '''conv2'''  )

												UpperCAmelCase         =			new_item.replace('''emb_layers.1'''   ,     '''time_emb_proj'''  )
												UpperCAmelCase         =			new_item.replace('''skip_connection'''   ,     '''conv_shortcut'''  )

												UpperCAmelCase         =			shave_segments(A   ,     n_shave_prefix_segments=A  )

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

						return mapping


def    lowerCamelCase__   ( A     :    Any   ,     A     :    int=0  ):




						'''simple docstring'''






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

												UpperCAmelCase         =			new_item.replace('''norm.weight'''   ,     '''group_norm.weight'''  )
												UpperCAmelCase         =			new_item.replace('''norm.bias'''   ,     '''group_norm.bias'''  )

												UpperCAmelCase         =			new_item.replace('''proj_out.weight'''   ,     '''proj_attn.weight'''  )
												UpperCAmelCase         =			new_item.replace('''proj_out.bias'''   ,     '''proj_attn.bias'''  )

												UpperCAmelCase         =			shave_segments(A   ,     n_shave_prefix_segments=A  )

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

						return mapping


def    lowerCamelCase__   ( A     :    Tuple   ,     A     :    Union[str, Any]   ,     A     :    int   ,     A     :    Dict=None   ,     A     :    Optional[int]=None   ,     A     :    Optional[Any]=None  ):




						'''simple docstring'''






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

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

																		UpperCAmelCase         =			old_tensor.shape[0] // config['''num_head_channels'''] // 3

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

																		UpperCAmelCase         =			query.reshape(A  )
																		UpperCAmelCase         =			key.reshape(A  )
																		UpperCAmelCase         =			value.reshape(A  )

						for path in paths:
												UpperCAmelCase         =			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
												UpperCAmelCase         =			new_path.replace('''middle_block.0'''   ,     '''mid_block.resnets.0'''  )
												UpperCAmelCase         =			new_path.replace('''middle_block.1'''   ,     '''mid_block.attentions.0'''  )
												UpperCAmelCase         =			new_path.replace('''middle_block.2'''   ,     '''mid_block.resnets.1'''  )

												if additional_replacements is not None:
																		for replacement in additional_replacements:
																								UpperCAmelCase         =			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:
																		UpperCAmelCase         =			old_checkpoint[path['''old''']][:, :, 0]
												else:
																		UpperCAmelCase         =			old_checkpoint[path['''old''']]


def    lowerCamelCase__   ( A     :    Union[str, Any]   ,     A     :    Dict  ):




						'''simple docstring'''






						UpperCAmelCase         =			{}

						UpperCAmelCase         =			checkpoint['''time_embed.0.weight''']
						UpperCAmelCase         =			checkpoint['''time_embed.0.bias''']
						UpperCAmelCase         =			checkpoint['''time_embed.2.weight''']
						UpperCAmelCase         =			checkpoint['''time_embed.2.bias''']

						UpperCAmelCase         =			checkpoint['''input_blocks.0.0.weight''']
						UpperCAmelCase         =			checkpoint['''input_blocks.0.0.bias''']

						UpperCAmelCase         =			checkpoint['''out.0.weight''']
						UpperCAmelCase         =			checkpoint['''out.0.bias''']
						UpperCAmelCase         =			checkpoint['''out.2.weight''']
						UpperCAmelCase         =			checkpoint['''out.2.bias''']

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

						# Retrieves the keys for the middle blocks only
						UpperCAmelCase         =			len({'''.'''.join(layer.split('''.'''  )[:2]  ) for layer in checkpoint if '''middle_block''' in layer}  )
						UpperCAmelCase         =			{
						    layer_id: [key for key in checkpoint if f"""middle_block.{layer_id}""" in key]
						    for layer_id in range(A  )
						}

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

						for i in range(1   ,     A  ):
												UpperCAmelCase         =			(i - 1) // (config['''num_res_blocks'''] + 1)
												UpperCAmelCase         =			(i - 1) % (config['''num_res_blocks'''] + 1)

												UpperCAmelCase         =			[key for key in input_blocks[i] if f"""input_blocks.{i}.0""" in key]
												UpperCAmelCase         =			[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:
																		UpperCAmelCase         =			checkpoint[
																		    f"""input_blocks.{i}.0.op.weight"""
																		]
																		UpperCAmelCase         =			checkpoint[
																		    f"""input_blocks.{i}.0.op.bias"""
																		]
																		continue

												UpperCAmelCase         =			renew_resnet_paths(A  )
												UpperCAmelCase         =			{'''old''': f"""input_blocks.{i}.0""", '''new''': f"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
												UpperCAmelCase         =			{'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
												assign_to_checkpoint(
												    A   ,     A   ,     A   ,     additional_replacements=[meta_path, resnet_op]   ,     config=A  )

												if len(A  ):
																		UpperCAmelCase         =			renew_attention_paths(A  )
																		UpperCAmelCase         =			{
																		    '''old''': f"""input_blocks.{i}.1""",
																		    '''new''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
																		}
																		UpperCAmelCase         =			{
																		    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(
																		    A   ,     A   ,     A   ,     additional_replacements=[meta_path]   ,     attention_paths_to_split=A   ,     config=A   ,     )

						UpperCAmelCase         =			middle_blocks[0]
						UpperCAmelCase         =			middle_blocks[1]
						UpperCAmelCase         =			middle_blocks[2]

						UpperCAmelCase         =			renew_resnet_paths(A  )
						assign_to_checkpoint(A   ,     A   ,     A   ,     config=A  )

						UpperCAmelCase         =			renew_resnet_paths(A  )
						assign_to_checkpoint(A   ,     A   ,     A   ,     config=A  )

						UpperCAmelCase         =			renew_attention_paths(A  )
						UpperCAmelCase         =			{
						    '''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(
						    A   ,     A   ,     A   ,     attention_paths_to_split=A   ,     config=A  )

						for i in range(A  ):
												UpperCAmelCase         =			i // (config['''num_res_blocks'''] + 1)
												UpperCAmelCase         =			i % (config['''num_res_blocks'''] + 1)
												UpperCAmelCase         =			[shave_segments(A   ,     2  ) for name in output_blocks[i]]
												UpperCAmelCase         =			{}

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

												if len(A  ) > 1:
																		UpperCAmelCase         =			[key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
																		UpperCAmelCase         =			[key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]

																		UpperCAmelCase         =			renew_resnet_paths(A  )
																		UpperCAmelCase         =			renew_resnet_paths(A  )

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

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

																								# Clear attentions as they have been attributed above.
																								if len(A  ) == 2:
																														UpperCAmelCase         =			[]

																		if len(A  ):
																								UpperCAmelCase         =			renew_attention_paths(A  )
																								UpperCAmelCase         =			{
																								    '''old''': f"""output_blocks.{i}.1""",
																								    '''new''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
																								}
																								UpperCAmelCase         =			{
																								    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(
																								    A   ,     A   ,     A   ,     additional_replacements=[meta_path]   ,     attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions  ) else None   ,     config=A   ,     )
												else:
																		UpperCAmelCase         =			renew_resnet_paths(A   ,     n_shave_prefix_segments=1  )
																		for path in resnet_0_paths:
																								UpperCAmelCase         =			'''.'''.join(['''output_blocks''', str(A  ), path['''old''']]  )
																								UpperCAmelCase         =			'''.'''.join(['''up_blocks''', str(A  ), '''resnets''', str(A  ), path['''new''']]  )

																								UpperCAmelCase         =			checkpoint[old_path]

						return new_checkpoint


if __name__ == "__main__":
	_lowercase    :    Union[str, Any]			     =  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.""")

	_lowercase    :    Dict			     =  parser.parse_args()

	_lowercase    :    List[Any]			     =  torch.load(args.checkpoint_path)

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

	_lowercase    :    Union[str, Any]			     =  convert_ldm_checkpoint(checkpoint, config)

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

	_lowercase    :    Any			     =  UNetaDModel(**config)
	model.load_state_dict(converted_checkpoint)

	try:
		_lowercase    :    Tuple			     =  DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
		_lowercase    :    Optional[Any]			     =  VQModel.from_pretrained("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

		_lowercase    :    Optional[Any]			     =  LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
		pipe.save_pretrained(args.dump_path)
	except:  # noqa: E722
		model.save_pretrained(args.dump_path)





