






'''simple docstring'''







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
_A     :      int   					=logging.get_logger(__name__)

_A     :      Union[str, Any]   					={
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''adapter_layer''': '''encoder.layers.*.adapter_layer''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
    '''pooling_layer.linear''': '''projector''',
    '''pooling_layer.projection''': '''classifier''',
}
_A     :      Dict   					=[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''projector''',
    '''classifier''',
]





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Any:
							lowerCamelCase__ :     Any	  =	{}
							with open(UpperCamelCase    ,      """r"""				) as file:
														for line_number, line in enumerate(UpperCamelCase				):
																					lowerCamelCase__ :     Dict	  =	line.strip()
																					if line:
																												lowerCamelCase__ :     str	  =	line.split()
																												lowerCamelCase__ :     Optional[Any]	  =	line_number
																												lowerCamelCase__ :     str	  =	words[0]
																												lowerCamelCase__ :     Optional[Any]	  =	value
							return result





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)				->	Tuple:
							for attribute in key.split("""."""				):
														lowerCamelCase__ :     Any	  =	getattr(UpperCamelCase    ,      UpperCamelCase				)

							lowerCamelCase__ :     Tuple	  =	None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(UpperCamelCase				):
																					lowerCamelCase__ :     List[str]	  =	PARAM_MAPPING[full_name.split("""."""				)[-1]]
																					lowerCamelCase__ :     List[str]	  =	"""param"""

							if weight_type is not None and weight_type != "param":
														lowerCamelCase__ :     Optional[Any]	  =	getattr(UpperCamelCase    ,      UpperCamelCase				).shape
							elif weight_type is not None and weight_type == "param":
														lowerCamelCase__ :     Dict	  =	hf_pointer
														for attribute in hf_param_name.split("""."""				):
																					lowerCamelCase__ :     List[Any]	  =	getattr(UpperCamelCase    ,      UpperCamelCase				)
														lowerCamelCase__ :     List[str]	  =	shape_pointer.shape

														# let's reduce dimension
														lowerCamelCase__ :     Any	  =	value[0]
							else:
														lowerCamelCase__ :     List[str]	  =	hf_pointer.shape

							if hf_shape != value.shape:
														raise ValueError(
														    f'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
														    f''' {value.shape} for {full_name}'''				)

							if weight_type == "weight":
														lowerCamelCase__ :     Optional[int]	  =	value
							elif weight_type == "weight_g":
														lowerCamelCase__ :     Dict	  =	value
							elif weight_type == "weight_v":
														lowerCamelCase__ :     List[str]	  =	value
							elif weight_type == "bias":
														lowerCamelCase__ :     Tuple	  =	value
							elif weight_type == "param":
														for attribute in hf_param_name.split("""."""				):
																					lowerCamelCase__ :     Tuple	  =	getattr(UpperCamelCase    ,      UpperCamelCase				)
														lowerCamelCase__ :     List[str]	  =	value
							else:
														lowerCamelCase__ :     Dict	  =	value

							logger.info(f'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''				)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)				->	List[Any]:
							lowerCamelCase__ :     List[str]	  =	None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(UpperCamelCase				):
																					lowerCamelCase__ :     Dict	  =	PARAM_MAPPING[full_name.split("""."""				)[-1]]
																					lowerCamelCase__ :     Optional[int]	  =	"""param"""

							if weight_type is not None and weight_type != "param":
														lowerCamelCase__ :     Optional[int]	  =	""".""".join([key, weight_type]				)
							elif weight_type is not None and weight_type == "param":
														lowerCamelCase__ :     Any	  =	""".""".join([key, hf_param_name]				)
							else:
														lowerCamelCase__ :     List[str]	  =	key

							lowerCamelCase__ :     List[Any]	  =	value if """lm_head""" in full_key else value[0]


_A     :      Union[str, Any]   					={
    '''W_a''': '''linear_1.weight''',
    '''W_b''': '''linear_2.weight''',
    '''b_a''': '''linear_1.bias''',
    '''b_b''': '''linear_2.bias''',
    '''ln_W''': '''norm.weight''',
    '''ln_b''': '''norm.bias''',
}





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase=None    ,      UpperCamelCase=None				)				->	Optional[int]:
							lowerCamelCase__ :     List[str]	  =	False
							for key, mapped_key in MAPPING.items():
														lowerCamelCase__ :     Optional[Any]	  =	"""wav2vec2.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
														if key in name or key.split("""w2v_model."""				)[-1] == name.split("""."""				)[0]:
																					lowerCamelCase__ :     Dict	  =	True
																					if "*" in mapped_key:
																												lowerCamelCase__ :     int	  =	name.split(UpperCamelCase				)[0].split("""."""				)[-2]
																												lowerCamelCase__ :     List[str]	  =	mapped_key.replace("""*"""    ,      UpperCamelCase				)
																					if "weight_g" in name:
																												lowerCamelCase__ :     Optional[Any]	  =	"""weight_g"""
																					elif "weight_v" in name:
																												lowerCamelCase__ :     str	  =	"""weight_v"""
																					elif "bias" in name:
																												lowerCamelCase__ :     int	  =	"""bias"""
																					elif "weight" in name:
																												# TODO: don't match quantizer.weight_proj
																												lowerCamelCase__ :     Dict	  =	"""weight"""
																					else:
																												lowerCamelCase__ :     Optional[Any]	  =	None
																					if hf_dict is not None:
																												rename_dict(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
																					else:
																												set_recursively(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
																					return is_used
							return is_used





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)				->	List[Any]:
							lowerCamelCase__ :     List[Any]	  =	[]
							lowerCamelCase__ :     Dict	  =	fairseq_model.state_dict()

							lowerCamelCase__ :     Union[str, Any]	  =	hf_model.wavaveca.feature_extractor

							for name, value in fairseq_dict.items():
														lowerCamelCase__ :     Any	  =	False
														if "conv_layers" in name:
																					load_conv_layer(
																					    UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      hf_model.config.feat_extract_norm == """group"""    ,      )
																					lowerCamelCase__ :     Optional[int]	  =	True
														else:
																					lowerCamelCase__ :     Optional[Any]	  =	load_wavaveca_layer(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
														if not is_used:
																					unused_weights.append(UpperCamelCase				)

							logger.warning(f'''Unused weights: {unused_weights}'''				)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)				->	Any:
							lowerCamelCase__ :     List[str]	  =	full_name.split("""conv_layers."""				)[-1]
							lowerCamelCase__ :     List[str]	  =	name.split("""."""				)
							lowerCamelCase__ :     Tuple	  =	int(items[0]				)
							lowerCamelCase__ :     int	  =	int(items[1]				)

							if type_id == 0:
														if "bias" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																												raise ValueError(
																												    f'''{full_name} has size {value.shape}, but'''
																												    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''				)
																					lowerCamelCase__ :     Dict	  =	value
																					logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''				)
														elif "weight" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																												raise ValueError(
																												    f'''{full_name} has size {value.shape}, but'''
																												    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''				)
																					lowerCamelCase__ :     Any	  =	value
																					logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''				)
							elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
														if "bias" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																												raise ValueError(
																												    f'''{full_name} has size {value.shape}, but'''
																												    f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''				)
																					lowerCamelCase__ :     List[Any]	  =	value
																					logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
														elif "weight" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																												raise ValueError(
																												    f'''{full_name} has size {value.shape}, but'''
																												    f''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''				)
																					lowerCamelCase__ :     Union[str, Any]	  =	value
																					logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
							else:
														unused_weights.append(UpperCamelCase				)





@torch.no_grad()
def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase=None    ,      UpperCamelCase=None    ,      UpperCamelCase=True    ,      UpperCamelCase=False				)				->	List[str]:
							if config_path is not None:
														lowerCamelCase__ :     Dict	  =	WavaVecaConfig.from_pretrained(UpperCamelCase				)
							else:
														lowerCamelCase__ :     Optional[Any]	  =	WavaVecaConfig()

							if is_seq_class:
														lowerCamelCase__ :     List[Any]	  =	read_txt_into_dict(UpperCamelCase				)
														lowerCamelCase__ :     Optional[int]	  =	idalabel
														lowerCamelCase__ :     List[Any]	  =	WavaVecaForSequenceClassification(UpperCamelCase				)
														lowerCamelCase__ :     int	  =	WavaVecaFeatureExtractor(
														    feature_size=1    ,      sampling_rate=16000    ,      padding_value=0    ,      do_normalize=UpperCamelCase    ,      return_attention_mask=UpperCamelCase    ,      )
														feature_extractor.save_pretrained(UpperCamelCase				)

							elif is_finetuned:
														if dict_path:
																					lowerCamelCase__ :     Optional[int]	  =	Dictionary.load(UpperCamelCase				)

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					lowerCamelCase__ :     Any	  =	target_dict.pad_index
																					lowerCamelCase__ :     Dict	  =	target_dict.bos_index
																					lowerCamelCase__ :     Optional[Any]	  =	target_dict.eos_index
																					lowerCamelCase__ :     Tuple	  =	len(target_dict.symbols				)
																					lowerCamelCase__ :     Tuple	  =	os.path.join(UpperCamelCase    ,      """vocab.json"""				)
																					if not os.path.isdir(UpperCamelCase				):
																												logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(UpperCamelCase				)				)
																												return
																					os.makedirs(UpperCamelCase    ,      exist_ok=UpperCamelCase				)
																					lowerCamelCase__ :     Tuple	  =	target_dict.indices

																					# fairseq has the <pad> and <s> switched
																					lowerCamelCase__ :     List[str]	  =	0
																					lowerCamelCase__ :     Union[str, Any]	  =	1
																					with open(UpperCamelCase    ,      """w"""    ,      encoding="""utf-8"""				) as vocab_handle:
																												json.dump(UpperCamelCase    ,      UpperCamelCase				)
																					lowerCamelCase__ :     List[str]	  =	WavaVecaCTCTokenizer(
																					    UpperCamelCase    ,      unk_token=target_dict.unk_word    ,      pad_token=target_dict.pad_word    ,      bos_token=target_dict.bos_word    ,      eos_token=target_dict.eos_word    ,      word_delimiter_token="""|"""    ,      do_lower_case=UpperCamelCase    ,      )
																					lowerCamelCase__ :     Any	  =	True if config.feat_extract_norm == """layer""" else False
																					lowerCamelCase__ :     str	  =	WavaVecaFeatureExtractor(
																					    feature_size=1    ,      sampling_rate=16000    ,      padding_value=0    ,      do_normalize=UpperCamelCase    ,      return_attention_mask=UpperCamelCase    ,      )
																					lowerCamelCase__ :     Dict	  =	WavaVecaProcessor(feature_extractor=UpperCamelCase    ,      tokenizer=UpperCamelCase				)
																					processor.save_pretrained(UpperCamelCase				)

														lowerCamelCase__ :     Union[str, Any]	  =	WavaVecaForCTC(UpperCamelCase				)
							else:
														lowerCamelCase__ :     Any	  =	WavaVecaForPreTraining(UpperCamelCase				)

							if is_finetuned or is_seq_class:
														lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__ :     Optional[Any]	  =	fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]    ,      arg_overrides={"""data""": """/""".join(dict_path.split("""/"""				)[:-1]				)}				)
							else:
														lowerCamelCase__ :     Any	  =	argparse.Namespace(task="""audio_pretraining"""				)
														lowerCamelCase__ :     Optional[Any]	  =	fairseq.tasks.setup_task(UpperCamelCase				)

														lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__ :     str	  =	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    ,      task=UpperCamelCase				)

							lowerCamelCase__ :     int	  =	model[0].eval()

							recursively_load_weights(UpperCamelCase    ,      UpperCamelCase    ,      not is_finetuned				)

							hf_wavavec.save_pretrained(UpperCamelCase				)


if __name__ == "__main__":
			_A     :      Dict   					=argparse.ArgumentParser()
			parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
			parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
			parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
			parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
			parser.add_argument(
			    '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
			)
			parser.add_argument(
			    '''--is_seq_class''',
			    action='''store_true''',
			    help='''Whether the model to convert is a fine-tuned sequence classification model or not''',
			)
			_A     :      int   					=parser.parse_args()

			_A     :      Union[str, Any]   					=not args.not_finetuned and not args.is_seq_class
			convert_wavaveca_checkpoint(
			    args.checkpoint_path,
			    args.pytorch_dump_folder_path,
			    args.config_path,
			    args.dict_path,
			    is_finetuned,
			    args.is_seq_class,
			)









'''simple docstring'''







import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_A     :      str   					={
    '''sample_size''': 32,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': 1_000,
    '''block_out_channels''': [32, 64],
    '''attention_head_dim''': 8,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

_A     :      Union[str, Any]   					={
    '''sample_size''': 64,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 3,
    '''num_class_embeds''': 1_000,
    '''block_out_channels''': [192, 192 * 2, 192 * 3, 192 * 4],
    '''attention_head_dim''': 64,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

_A     :      Dict   					={
    '''sample_size''': 256,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': None,
    '''block_out_channels''': [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    '''attention_head_dim''': 64,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''default''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

_A     :      Dict   					={
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

_A     :      str   					={
    '''num_train_timesteps''': 201,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

_A     :      int   					={
    '''num_train_timesteps''': 151,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Dict:
							if isinstance(UpperCamelCase    ,      UpperCamelCase				):
														return v
							if v.lower() in ("yes", "true", "t", "y", "1"):
														return True
							elif v.lower() in ("no", "false", "f", "n", "0"):
														return False
							else:
														raise argparse.ArgumentTypeError("""boolean value expected"""				)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase=False				)				->	Any:
							lowerCamelCase__ :     Any	  =	checkpoint[f'''{old_prefix}.in_layers.0.weight''']
							lowerCamelCase__ :     int	  =	checkpoint[f'''{old_prefix}.in_layers.0.bias''']
							lowerCamelCase__ :     Any	  =	checkpoint[f'''{old_prefix}.in_layers.2.weight''']
							lowerCamelCase__ :     Any	  =	checkpoint[f'''{old_prefix}.in_layers.2.bias''']
							lowerCamelCase__ :     Optional[Any]	  =	checkpoint[f'''{old_prefix}.emb_layers.1.weight''']
							lowerCamelCase__ :     Optional[int]	  =	checkpoint[f'''{old_prefix}.emb_layers.1.bias''']
							lowerCamelCase__ :     Dict	  =	checkpoint[f'''{old_prefix}.out_layers.0.weight''']
							lowerCamelCase__ :     Tuple	  =	checkpoint[f'''{old_prefix}.out_layers.0.bias''']
							lowerCamelCase__ :     str	  =	checkpoint[f'''{old_prefix}.out_layers.3.weight''']
							lowerCamelCase__ :     int	  =	checkpoint[f'''{old_prefix}.out_layers.3.bias''']

							if has_skip:
														lowerCamelCase__ :     Tuple	  =	checkpoint[f'''{old_prefix}.skip_connection.weight''']
														lowerCamelCase__ :     List[Any]	  =	checkpoint[f'''{old_prefix}.skip_connection.bias''']

							return new_checkpoint





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase=None				)				->	str:
							lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__ :     Dict	  =	checkpoint[f'''{old_prefix}.qkv.weight'''].chunk(3    ,      dim=0				)
							lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__ :     str	  =	checkpoint[f'''{old_prefix}.qkv.bias'''].chunk(3    ,      dim=0				)

							lowerCamelCase__ :     Any	  =	checkpoint[f'''{old_prefix}.norm.weight''']
							lowerCamelCase__ :     Optional[int]	  =	checkpoint[f'''{old_prefix}.norm.bias''']

							lowerCamelCase__ :     List[Any]	  =	weight_q.squeeze(-1				).squeeze(-1				)
							lowerCamelCase__ :     List[Any]	  =	bias_q.squeeze(-1				).squeeze(-1				)
							lowerCamelCase__ :     Any	  =	weight_k.squeeze(-1				).squeeze(-1				)
							lowerCamelCase__ :     Optional[Any]	  =	bias_k.squeeze(-1				).squeeze(-1				)
							lowerCamelCase__ :     Dict	  =	weight_v.squeeze(-1				).squeeze(-1				)
							lowerCamelCase__ :     Union[str, Any]	  =	bias_v.squeeze(-1				).squeeze(-1				)

							lowerCamelCase__ :     Optional[Any]	  =	(
							    checkpoint[f'''{old_prefix}.proj_out.weight'''].squeeze(-1				).squeeze(-1				)
							)
							lowerCamelCase__ :     Dict	  =	checkpoint[f'''{old_prefix}.proj_out.bias'''].squeeze(-1				).squeeze(-1				)

							return new_checkpoint





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	List[Any]:
							lowerCamelCase__ :     str	  =	torch.load(UpperCamelCase    ,      map_location="""cpu"""				)
							lowerCamelCase__ :     Optional[int]	  =	{}

							lowerCamelCase__ :     Optional[int]	  =	checkpoint["""time_embed.0.weight"""]
							lowerCamelCase__ :     List[Any]	  =	checkpoint["""time_embed.0.bias"""]
							lowerCamelCase__ :     int	  =	checkpoint["""time_embed.2.weight"""]
							lowerCamelCase__ :     Optional[Any]	  =	checkpoint["""time_embed.2.bias"""]

							if unet_config["num_class_embeds"] is not None:
														lowerCamelCase__ :     Optional[Any]	  =	checkpoint["""label_emb.weight"""]

							lowerCamelCase__ :     Tuple	  =	checkpoint["""input_blocks.0.0.weight"""]
							lowerCamelCase__ :     List[str]	  =	checkpoint["""input_blocks.0.0.bias"""]

							lowerCamelCase__ :     Optional[Any]	  =	unet_config["""down_block_types"""]
							lowerCamelCase__ :     Any	  =	unet_config["""layers_per_block"""]
							lowerCamelCase__ :     Any	  =	unet_config["""attention_head_dim"""]
							lowerCamelCase__ :     List[Any]	  =	unet_config["""block_out_channels"""]
							lowerCamelCase__ :     str	  =	1
							lowerCamelCase__ :     str	  =	channels_list[0]

							for i, layer_type in enumerate(UpperCamelCase				):
														lowerCamelCase__ :     List[Any]	  =	channels_list[i]
														lowerCamelCase__ :     List[Any]	  =	current_channels != prev_channels
														if layer_type == "ResnetDownsampleBlock2D":
																					for j in range(UpperCamelCase				):
																												lowerCamelCase__ :     int	  =	f'''down_blocks.{i}.resnets.{j}'''
																												lowerCamelCase__ :     Dict	  =	f'''input_blocks.{current_layer}.0'''
																												lowerCamelCase__ :     Tuple	  =	True if j == 0 and downsample_block_has_skip else False
																												lowerCamelCase__ :     List[Any]	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      has_skip=UpperCamelCase				)
																												current_layer += 1

														elif layer_type == "AttnDownBlock2D":
																					for j in range(UpperCamelCase				):
																												lowerCamelCase__ :     Tuple	  =	f'''down_blocks.{i}.resnets.{j}'''
																												lowerCamelCase__ :     Optional[Any]	  =	f'''input_blocks.{current_layer}.0'''
																												lowerCamelCase__ :     str	  =	True if j == 0 and downsample_block_has_skip else False
																												lowerCamelCase__ :     Union[str, Any]	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      has_skip=UpperCamelCase				)
																												lowerCamelCase__ :     Any	  =	f'''down_blocks.{i}.attentions.{j}'''
																												lowerCamelCase__ :     Dict	  =	f'''input_blocks.{current_layer}.1'''
																												lowerCamelCase__ :     Tuple	  =	convert_attention(
																												    UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
																												current_layer += 1

														if i != len(UpperCamelCase				) - 1:
																					lowerCamelCase__ :     Tuple	  =	f'''down_blocks.{i}.downsamplers.0'''
																					lowerCamelCase__ :     str	  =	f'''input_blocks.{current_layer}.0'''
																					lowerCamelCase__ :     Union[str, Any]	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
																					current_layer += 1

														lowerCamelCase__ :     Union[str, Any]	  =	current_channels

							# hardcoded the mid-block for now
							lowerCamelCase__ :     Any	  =	"""mid_block.resnets.0"""
							lowerCamelCase__ :     Optional[Any]	  =	"""middle_block.0"""
							lowerCamelCase__ :     int	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
							lowerCamelCase__ :     List[Any]	  =	"""mid_block.attentions.0"""
							lowerCamelCase__ :     Dict	  =	"""middle_block.1"""
							lowerCamelCase__ :     int	  =	convert_attention(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
							lowerCamelCase__ :     Any	  =	"""mid_block.resnets.1"""
							lowerCamelCase__ :     Tuple	  =	"""middle_block.2"""
							lowerCamelCase__ :     int	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)

							lowerCamelCase__ :     Union[str, Any]	  =	0
							lowerCamelCase__ :     Any	  =	unet_config["""up_block_types"""]

							for i, layer_type in enumerate(UpperCamelCase				):
														if layer_type == "ResnetUpsampleBlock2D":
																					for j in range(layers_per_block + 1				):
																												lowerCamelCase__ :     int	  =	f'''up_blocks.{i}.resnets.{j}'''
																												lowerCamelCase__ :     Optional[Any]	  =	f'''output_blocks.{current_layer}.0'''
																												lowerCamelCase__ :     Any	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      has_skip=UpperCamelCase				)
																												current_layer += 1

																					if i != len(UpperCamelCase				) - 1:
																												lowerCamelCase__ :     Dict	  =	f'''up_blocks.{i}.upsamplers.0'''
																												lowerCamelCase__ :     List[str]	  =	f'''output_blocks.{current_layer-1}.1'''
																												lowerCamelCase__ :     Optional[Any]	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
														elif layer_type == "AttnUpBlock2D":
																					for j in range(layers_per_block + 1				):
																												lowerCamelCase__ :     str	  =	f'''up_blocks.{i}.resnets.{j}'''
																												lowerCamelCase__ :     List[Any]	  =	f'''output_blocks.{current_layer}.0'''
																												lowerCamelCase__ :     Optional[Any]	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      has_skip=UpperCamelCase				)
																												lowerCamelCase__ :     Optional[Any]	  =	f'''up_blocks.{i}.attentions.{j}'''
																												lowerCamelCase__ :     Any	  =	f'''output_blocks.{current_layer}.1'''
																												lowerCamelCase__ :     Optional[int]	  =	convert_attention(
																												    UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
																												current_layer += 1

																					if i != len(UpperCamelCase				) - 1:
																												lowerCamelCase__ :     Tuple	  =	f'''up_blocks.{i}.upsamplers.0'''
																												lowerCamelCase__ :     Tuple	  =	f'''output_blocks.{current_layer-1}.2'''
																												lowerCamelCase__ :     List[str]	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)

							lowerCamelCase__ :     Dict	  =	checkpoint["""out.0.weight"""]
							lowerCamelCase__ :     Dict	  =	checkpoint["""out.0.bias"""]
							lowerCamelCase__ :     Dict	  =	checkpoint["""out.2.weight"""]
							lowerCamelCase__ :     Tuple	  =	checkpoint["""out.2.bias"""]

							return new_checkpoint


if __name__ == "__main__":
			_A     :      Tuple   					=argparse.ArgumentParser()

			parser.add_argument('''--unet_path''', default=None, type=str, required=True, help='''Path to the unet.pt to convert.''')
			parser.add_argument(
			    '''--dump_path''', default=None, type=str, required=True, help='''Path to output the converted UNet model.'''
			)
			parser.add_argument('''--class_cond''', default=True, type=str, help='''Whether the model is class-conditional.''')

			_A     :      Tuple   					=parser.parse_args()
			_A     :      Optional[int]   					=strabool(args.class_cond)

			_A     :      List[str]   					=os.path.basename(args.unet_path)
			print(F'Checkpoint: {ckpt_name}')

			# Get U-Net config
			if "imagenet64" in ckpt_name:
						_A     :      int   					=IMAGENET_64_UNET_CONFIG
			elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
						_A     :      Tuple   					=LSUN_256_UNET_CONFIG
			elif "test" in ckpt_name:
						_A     :      Any   					=TEST_UNET_CONFIG
			else:
						raise ValueError(F'Checkpoint type {ckpt_name} is not currently supported.')

			if not args.class_cond:
						_A     :      str   					=None

			_A     :      Optional[int]   					=con_pt_to_diffuser(args.unet_path, unet_config)

			_A     :      Optional[int]   					=UNetaDModel(**unet_config)
			image_unet.load_state_dict(converted_unet_ckpt)

			# Get scheduler config
			if "cd" in ckpt_name or "test" in ckpt_name:
						_A     :      Tuple   					=CD_SCHEDULER_CONFIG
			elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
						_A     :      int   					=CT_IMAGENET_64_SCHEDULER_CONFIG
			elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
						_A     :      Union[str, Any]   					=CT_LSUN_256_SCHEDULER_CONFIG
			else:
						raise ValueError(F'Checkpoint type {ckpt_name} is not currently supported.')

			_A     :      str   					=CMStochasticIterativeScheduler(**scheduler_config)

			_A     :      Optional[Any]   					=ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
			consistency_model.save_pretrained(args.dump_path)

