"""simple docstring"""






import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


lowerCAmelCase__  :  Any						     =	logging.get_logger(__name__)
class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = ["input_features"]







					def __init__(				self   :	Optional[int]    ,lowerCamelCase__   :	Any=80    ,lowerCamelCase__   :	Optional[Any]=16_000    ,lowerCamelCase__   :	List[Any]=160    ,lowerCamelCase__   :	int=30    ,lowerCamelCase__   :	int=400    ,lowerCamelCase__   :	Any=0.0    ,lowerCamelCase__   :	Union[str, Any]=False    ,**lowerCamelCase__   :	Optional[Any]    ,):
									super().__init__(
									    feature_size=lowerCamelCase__    ,sampling_rate=lowerCamelCase__    ,padding_value=lowerCamelCase__    ,return_attention_mask=lowerCamelCase__    ,**lowerCamelCase__    ,)
									UpperCAmelCase__             =							n_fft
									UpperCAmelCase__             =							hop_length
									UpperCAmelCase__             =							chunk_length
									UpperCAmelCase__             =							chunk_length * sampling_rate
									UpperCAmelCase__             =							self.n_samples // hop_length
									UpperCAmelCase__             =							sampling_rate
									UpperCAmelCase__             =							mel_filter_bank(
									    num_frequency_bins=1 + n_fft // 2    ,num_mel_filters=lowerCamelCase__    ,min_frequency=0.0    ,max_frequency=8_0_0_0.0    ,sampling_rate=lowerCamelCase__    ,norm='slaney'    ,mel_scale='slaney'    ,)







					def 		__lowerCAmelCase						(				self   :	Any    ,lowerCamelCase__   :	np.array			):
									UpperCAmelCase__             =							spectrogram(
									    lowerCamelCase__    ,window_function(self.n_fft    ,'hann'			)    ,frame_length=self.n_fft    ,hop_length=self.hop_length    ,power=2.0    ,mel_filters=self.mel_filters    ,log_mel='log10'    ,)
									UpperCAmelCase__             =							log_spec[:, :-1]
									UpperCAmelCase__             =							np.maximum(lowerCamelCase__    ,log_spec.max() - 8.0			)
									UpperCAmelCase__             =							(log_spec + 4.0) / 4.0
									return log_spec







					@staticmethod
					# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
					def 		__lowerCAmelCase						(				lowerCamelCase__   :	List[np.ndarray]    ,lowerCamelCase__   :	List[np.ndarray]    ,lowerCamelCase__   :	float = 0.0			):
									if attention_mask is not None:
													UpperCAmelCase__             =							np.array(lowerCamelCase__    ,np.intaa			)
													UpperCAmelCase__             =							[]

													for vector, length in zip(lowerCamelCase__    ,attention_mask.sum(-1			)			):
																	UpperCAmelCase__             =							(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7			)
																	if length < normed_slice.shape[0]:
																					UpperCAmelCase__             =							padding_value

																	normed_input_values.append(lowerCamelCase__			)
									else:
													UpperCAmelCase__             =							[(x - x.mean()) / np.sqrt(x.var() + 1e-7			) for x in input_values]

									return normed_input_values







					def __call__(				self   :	int    ,lowerCamelCase__   :	Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]    ,lowerCamelCase__   :	bool = True    ,lowerCamelCase__   :	Optional[int] = None    ,lowerCamelCase__   :	Optional[Union[str, TensorType]] = None    ,lowerCamelCase__   :	Optional[bool] = None    ,lowerCamelCase__   :	Optional[str] = "max_length"    ,lowerCamelCase__   :	Optional[int] = None    ,lowerCamelCase__   :	Optional[int] = None    ,lowerCamelCase__   :	Optional[bool] = None    ,**lowerCamelCase__   :	Dict    ,):

									if sampling_rate is not None:
													if sampling_rate != self.sampling_rate:
																	raise ValueError(
																	    f'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
																	    f''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
																	    f''' was sampled with {self.sampling_rate} and not {sampling_rate}.'''			)
									else:
													logger.warning(
													    'It is strongly recommended to pass the `sampling_rate` argument to this function. '
													    'Failing to do so can result in silent errors that might be hard to debug.'			)

									UpperCAmelCase__             =							isinstance(lowerCamelCase__    ,np.ndarray			) and len(raw_speech.shape			) > 1
									if is_batched_numpy and len(raw_speech.shape			) > 2:
													raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''			)
									UpperCAmelCase__             =							is_batched_numpy or (
									    isinstance(lowerCamelCase__    ,(list, tuple)			) and (isinstance(raw_speech[0]    ,(np.ndarray, tuple, list)			))
									)

									if is_batched:
													UpperCAmelCase__             =							[np.asarray([speech]    ,dtype=np.floataa			).T for speech in raw_speech]
									elif not is_batched and not isinstance(lowerCamelCase__    ,np.ndarray			):
													UpperCAmelCase__             =							np.asarray(lowerCamelCase__    ,dtype=np.floataa			)
									elif isinstance(lowerCamelCase__    ,np.ndarray			) and raw_speech.dtype is np.dtype(np.floataa			):
													UpperCAmelCase__             =							raw_speech.astype(np.floataa			)

									# always return batch
									if not is_batched:
													UpperCAmelCase__             =							[np.asarray([raw_speech]			).T]

									UpperCAmelCase__             =							BatchFeature({'input_features': raw_speech}			)

									# convert into correct format for padding

									UpperCAmelCase__             =							self.pad(
									    lowerCamelCase__    ,padding=lowerCamelCase__    ,max_length=max_length if max_length else self.n_samples    ,truncation=lowerCamelCase__    ,pad_to_multiple_of=lowerCamelCase__    ,return_attention_mask=return_attention_mask or do_normalize    ,)

									# zero-mean and unit-variance normalization
									if do_normalize:
													UpperCAmelCase__             =							self.zero_mean_unit_var_norm(
													    padded_inputs['input_features']    ,attention_mask=padded_inputs['attention_mask']    ,padding_value=self.padding_value    ,)
													UpperCAmelCase__             =							np.stack(padded_inputs['input_features']    ,axis=0			)

									# make sure list is in array format
									UpperCAmelCase__             =							padded_inputs.get('input_features'			).transpose(2    ,0    ,1			)

									UpperCAmelCase__             =							[self._np_extract_fbank_features(lowerCamelCase__			) for waveform in input_features[0]]

									if isinstance(input_features[0]    ,lowerCamelCase__			):
													UpperCAmelCase__             =							[np.asarray(lowerCamelCase__    ,dtype=np.floataa			) for feature in input_features]
									else:
													UpperCAmelCase__             =							input_features

									if return_attention_mask:
													# rescale from sample (48000) to feature (3000)
													UpperCAmelCase__             =							padded_inputs['attention_mask'][:, :: self.hop_length]

									if return_tensors is not None:
													UpperCAmelCase__             =							padded_inputs.convert_to_tensors(lowerCamelCase__			)

									return padded_inputs







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									UpperCAmelCase__             =							copy.deepcopy(self.__dict__			)
									UpperCAmelCase__             =							self.__class__.__name__
									if "mel_filters" in output:
													del output["mel_filters"]
									return output




"""simple docstring"""






import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

lowerCAmelCase__  :  Optional[int]						     =	[
    # (stable-diffusion, HF Diffusers)
    ('time_embed.0.weight', 'time_embedding.linear_1.weight'),
    ('time_embed.0.bias', 'time_embedding.linear_1.bias'),
    ('time_embed.2.weight', 'time_embedding.linear_2.weight'),
    ('time_embed.2.bias', 'time_embedding.linear_2.bias'),
    ('input_blocks.0.0.weight', 'conv_in.weight'),
    ('input_blocks.0.0.bias', 'conv_in.bias'),
    ('out.0.weight', 'conv_norm_out.weight'),
    ('out.0.bias', 'conv_norm_out.bias'),
    ('out.2.weight', 'conv_out.weight'),
    ('out.2.bias', 'conv_out.bias'),
]

lowerCAmelCase__  :  str						     =	[
    # (stable-diffusion, HF Diffusers)
    ('in_layers.0', 'norm1'),
    ('in_layers.2', 'conv1'),
    ('out_layers.0', 'norm2'),
    ('out_layers.3', 'conv2'),
    ('emb_layers.1', 'time_emb_proj'),
    ('skip_connection', 'conv_shortcut'),
]

lowerCAmelCase__  :  int						     =	[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
						# loop over downblocks/upblocks

						for j in range(2):
												# loop over resnets/attentions for downblocks
												lowerCAmelCase__  :  Dict						     =	F"""down_blocks.{i}.resnets.{j}."""
												lowerCAmelCase__  :  Union[str, Any]						     =	F"""input_blocks.{3*i + j + 1}.0."""
												unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

												if i < 3:
																		# no attention layers in down_blocks.3
																		lowerCAmelCase__  :  Tuple						     =	F"""down_blocks.{i}.attentions.{j}."""
																		lowerCAmelCase__  :  int						     =	F"""input_blocks.{3*i + j + 1}.1."""
																		unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

						for j in range(3):
												# loop over resnets/attentions for upblocks
												lowerCAmelCase__  :  Tuple						     =	F"""up_blocks.{i}.resnets.{j}."""
												lowerCAmelCase__  :  int						     =	F"""output_blocks.{3*i + j}.0."""
												unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

												if i > 0:
																		# no attention layers in up_blocks.0
																		lowerCAmelCase__  :  Any						     =	F"""up_blocks.{i}.attentions.{j}."""
																		lowerCAmelCase__  :  List[str]						     =	F"""output_blocks.{3*i + j}.1."""
																		unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

						if i < 3:
												# no downsample in down_blocks.3
												lowerCAmelCase__  :  List[str]						     =	F"""down_blocks.{i}.downsamplers.0.conv."""
												lowerCAmelCase__  :  Union[str, Any]						     =	F"""input_blocks.{3*(i+1)}.0.op."""
												unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

												# no upsample in up_blocks.3
												lowerCAmelCase__  :  Dict						     =	F"""up_blocks.{i}.upsamplers.0."""
												lowerCAmelCase__  :  List[Any]						     =	F"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
												unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

lowerCAmelCase__  :  str						     =	'mid_block.attentions.0.'
lowerCAmelCase__  :  Union[str, Any]						     =	'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
						lowerCAmelCase__  :  int						     =	F"""mid_block.resnets.{j}."""
						lowerCAmelCase__  :  List[str]						     =	F"""middle_block.{2*j}."""
						unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))



def   a_						(					lowerCamelCase       ):
				# buyer beware: this is a *brittle* function,
				# and correct output requires that all of these pieces interact in
				# the exact order in which I have arranged them.
				UpperCAmelCase__             =							{k: k for k in unet_state_dict.keys()}
				for sd_name, hf_name in unet_conversion_map:
								UpperCAmelCase__             =							sd_name
				for k, v in mapping.items():
								if "resnets" in k:
												for sd_part, hf_part in unet_conversion_map_resnet:
																UpperCAmelCase__             =							v.replace(lowerCamelCase					,			lowerCamelCase       )
												UpperCAmelCase__             =							v
				for k, v in mapping.items():
								for sd_part, hf_part in unet_conversion_map_layer:
												UpperCAmelCase__             =							v.replace(lowerCamelCase					,			lowerCamelCase       )
								UpperCAmelCase__             =							v
				UpperCAmelCase__             =							{v: unet_state_dict[k] for k, v in mapping.items()}
				return new_state_dict


# ================#
# VAE Conversion #
# ================#

lowerCAmelCase__  :  str						     =	[
    # (stable-diffusion, HF Diffusers)
    ('nin_shortcut', 'conv_shortcut'),
    ('norm_out', 'conv_norm_out'),
    ('mid.attn_1.', 'mid_block.attentions.0.'),
]

for i in range(4):
						# down_blocks have two resnets
						for j in range(2):
												lowerCAmelCase__  :  List[str]						     =	F"""encoder.down_blocks.{i}.resnets.{j}."""
												lowerCAmelCase__  :  List[Any]						     =	F"""encoder.down.{i}.block.{j}."""
												vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

						if i < 3:
												lowerCAmelCase__  :  Dict						     =	F"""down_blocks.{i}.downsamplers.0."""
												lowerCAmelCase__  :  str						     =	F"""down.{i}.downsample."""
												vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

												lowerCAmelCase__  :  int						     =	F"""up_blocks.{i}.upsamplers.0."""
												lowerCAmelCase__  :  str						     =	F"""up.{3-i}.upsample."""
												vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

						# up_blocks have three resnets
						# also, up blocks in hf are numbered in reverse from sd
						for j in range(3):
												lowerCAmelCase__  :  Dict						     =	F"""decoder.up_blocks.{i}.resnets.{j}."""
												lowerCAmelCase__  :  Optional[int]						     =	F"""decoder.up.{3-i}.block.{j}."""
												vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
						lowerCAmelCase__  :  Any						     =	F"""mid_block.resnets.{i}."""
						lowerCAmelCase__  :  Any						     =	F"""mid.block_{i+1}."""
						vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


lowerCAmelCase__  :  str						     =	[
    # (stable-diffusion, HF Diffusers)
    ('norm.', 'group_norm.'),
    ('q.', 'query.'),
    ('k.', 'key.'),
    ('v.', 'value.'),
    ('proj_out.', 'proj_attn.'),
]



def   a_						(					lowerCamelCase       ):
				# convert HF linear weights to SD conv2d weights
				return w.reshape(*w.shape					,			1					,			1       )



def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							{k: k for k in vae_state_dict.keys()}
				for k, v in mapping.items():
								for sd_part, hf_part in vae_conversion_map:
												UpperCAmelCase__             =							v.replace(lowerCamelCase					,			lowerCamelCase       )
								UpperCAmelCase__             =							v
				for k, v in mapping.items():
								if "attentions" in k:
												for sd_part, hf_part in vae_conversion_map_attn:
																UpperCAmelCase__             =							v.replace(lowerCamelCase					,			lowerCamelCase       )
												UpperCAmelCase__             =							v
				UpperCAmelCase__             =							{v: vae_state_dict[k] for k, v in mapping.items()}
				UpperCAmelCase__             =							['q', 'k', 'v', 'proj_out']
				for k, v in new_state_dict.items():
								for weight_name in weights_to_convert:
												if f'''mid.attn_1.{weight_name}.weight''' in k:
																print(f'''Reshaping {k} for SD format'''       )
																UpperCAmelCase__             =							reshape_weight_for_sd(lowerCamelCase       )
				return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


lowerCAmelCase__  :  Optional[int]						     =	[
    # (stable-diffusion, HF Diffusers)
    ('resblocks.', 'text_model.encoder.layers.'),
    ('ln_1', 'layer_norm1'),
    ('ln_2', 'layer_norm2'),
    ('.c_fc.', '.fc1.'),
    ('.c_proj.', '.fc2.'),
    ('.attn', '.self_attn'),
    ('ln_final.', 'transformer.text_model.final_layer_norm.'),
    ('token_embedding.weight', 'transformer.text_model.embeddings.token_embedding.weight'),
    ('positional_embedding', 'transformer.text_model.embeddings.position_embedding.weight'),
]
lowerCAmelCase__  :  List[Any]						     =	{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
lowerCAmelCase__  :  int						     =	re.compile('|'.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
lowerCAmelCase__  :  Optional[int]						     =	{'q': 0, 'k': 1, 'v': 2}



def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							{}
				UpperCAmelCase__             =							{}
				UpperCAmelCase__             =							{}
				for k, v in text_enc_dict.items():
								if (
								    k.endswith('.self_attn.q_proj.weight'       )
								    or k.endswith('.self_attn.k_proj.weight'       )
								    or k.endswith('.self_attn.v_proj.weight'       )
								):
												UpperCAmelCase__             =							k[: -len('.q_proj.weight'       )]
												UpperCAmelCase__             =							k[-len('q_proj.weight'       )]
												if k_pre not in capture_qkv_weight:
																UpperCAmelCase__             =							[None, None, None]
												UpperCAmelCase__             =							v
												continue

								if (
								    k.endswith('.self_attn.q_proj.bias'       )
								    or k.endswith('.self_attn.k_proj.bias'       )
								    or k.endswith('.self_attn.v_proj.bias'       )
								):
												UpperCAmelCase__             =							k[: -len('.q_proj.bias'       )]
												UpperCAmelCase__             =							k[-len('q_proj.bias'       )]
												if k_pre not in capture_qkv_bias:
																UpperCAmelCase__             =							[None, None, None]
												UpperCAmelCase__             =							v
												continue

								UpperCAmelCase__             =							textenc_pattern.sub(lambda lowerCamelCase       : protected[re.escape(m.group(0       )       )]					,			lowerCamelCase       )
								UpperCAmelCase__             =							v

				for k_pre, tensors in capture_qkv_weight.items():
								if None in tensors:
												raise Exception('CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'       )
								UpperCAmelCase__             =							textenc_pattern.sub(lambda lowerCamelCase       : protected[re.escape(m.group(0       )       )]					,			lowerCamelCase       )
								UpperCAmelCase__             =							torch.cat(lowerCamelCase       )

				for k_pre, tensors in capture_qkv_bias.items():
								if None in tensors:
												raise Exception('CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'       )
								UpperCAmelCase__             =							textenc_pattern.sub(lambda lowerCamelCase       : protected[re.escape(m.group(0       )       )]					,			lowerCamelCase       )
								UpperCAmelCase__             =							torch.cat(lowerCamelCase       )

				return new_state_dict



def   a_						(					lowerCamelCase       ):
				return text_enc_dict


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

						parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
						parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
						parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
						parser.add_argument(
						    '--use_safetensors', action='store_true', help='Save weights use safetensors, default is ckpt.'
						)

						lowerCAmelCase__  :  Optional[int]						     =	parser.parse_args()

						assert args.model_path is not None, "Must provide a model path!"

						assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

						# Path for safetensors
						lowerCAmelCase__  :  Tuple						     =	osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
						lowerCAmelCase__  :  List[str]						     =	osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
						lowerCAmelCase__  :  int						     =	osp.join(args.model_path, 'text_encoder', 'model.safetensors')

						# Load models from safetensors if it exists, if it doesn't pytorch
						if osp.exists(unet_path):
												lowerCAmelCase__  :  Union[str, Any]						     =	load_file(unet_path, device='cpu')
						else:
												lowerCAmelCase__  :  str						     =	osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
												lowerCAmelCase__  :  Dict						     =	torch.load(unet_path, map_location='cpu')

						if osp.exists(vae_path):
												lowerCAmelCase__  :  Optional[Any]						     =	load_file(vae_path, device='cpu')
						else:
												lowerCAmelCase__  :  Optional[int]						     =	osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
												lowerCAmelCase__  :  List[str]						     =	torch.load(vae_path, map_location='cpu')

						if osp.exists(text_enc_path):
												lowerCAmelCase__  :  Tuple						     =	load_file(text_enc_path, device='cpu')
						else:
												lowerCAmelCase__  :  Any						     =	osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
												lowerCAmelCase__  :  Any						     =	torch.load(text_enc_path, map_location='cpu')

						# Convert the UNet model
						lowerCAmelCase__  :  Any						     =	convert_unet_state_dict(unet_state_dict)
						lowerCAmelCase__  :  Dict						     =	{'model.diffusion_model.' + k: v for k, v in unet_state_dict.items()}

						# Convert the VAE model
						lowerCAmelCase__  :  List[Any]						     =	convert_vae_state_dict(vae_state_dict)
						lowerCAmelCase__  :  str						     =	{'first_stage_model.' + k: v for k, v in vae_state_dict.items()}

						# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
						lowerCAmelCase__  :  List[Any]						     =	'text_model.encoder.layers.22.layer_norm2.bias' in text_enc_dict

						if is_vaa_model:
												# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
												lowerCAmelCase__  :  Tuple						     =	{'transformer.' + k: v for k, v in text_enc_dict.items()}
												lowerCAmelCase__  :  List[str]						     =	convert_text_enc_state_dict_vaa(text_enc_dict)
												lowerCAmelCase__  :  str						     =	{'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
						else:
												lowerCAmelCase__  :  Optional[Any]						     =	convert_text_enc_state_dict(text_enc_dict)
												lowerCAmelCase__  :  Optional[Any]						     =	{'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}

						# Put together new checkpoint
						lowerCAmelCase__  :  List[Any]						     =	{**unet_state_dict, **vae_state_dict, **text_enc_dict}
						if args.half:
												lowerCAmelCase__  :  int						     =	{k: v.half() for k, v in state_dict.items()}

						if args.use_safetensors:
												save_file(state_dict, args.checkpoint_path)
						else:
												lowerCAmelCase__  :  List[str]						     =	{'state_dict': state_dict}
												torch.save(state_dict, args.checkpoint_path)



