




import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
lowercase_		=							logging.get_logger(__name__)

lowercase_		=							{
    '''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''',
    '''mask_emb''': '''masked_spec_embed''',
}







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase	)  ->str:






					"""simple docstring"""






					for attribute in key.split('''.'''	):
										__magic_name__ :							Union[str, Any]		       =							getattr(_UpperCamelCase,     _UpperCamelCase	)

					if weight_type is not None:
										__magic_name__ :							Dict		       =							getattr(_UpperCamelCase,     _UpperCamelCase	).shape
					else:
										__magic_name__ :							List[Any]		       =							hf_pointer.shape

					assert hf_shape == value.shape, (
					    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":
										__magic_name__ :							Union[str, Any]		       =							value
					elif weight_type == "weight_g":
										__magic_name__ :							List[Any]		       =							value
					elif weight_type == "weight_v":
										__magic_name__ :							Optional[int]		       =							value
					elif weight_type == "bias":
										__magic_name__ :							Union[str, Any]		       =							value
					else:
										__magic_name__ :							Optional[Any]		       =							value

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







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase	)  ->int:






					"""simple docstring"""






					__magic_name__ :							Optional[int]		       =							[]
					__magic_name__ :							Dict		       =							fairseq_model.state_dict()

					__magic_name__ :							Tuple		       =							hf_model.feature_extractor

					for name, value in fairseq_dict.items():
										__magic_name__ :							Optional[int]		       =							False
										if "conv_layers" in name:
															load_conv_layer(
															    _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase,     hf_model.config.feat_extract_norm == '''group''',     )
															__magic_name__ :							List[Any]		       =							True
										else:
															for key, mapped_key in MAPPING.items():
																				__magic_name__ :							List[Any]		       =							mapped_key

																				if key in name:
																									__magic_name__ :							Union[str, Any]		       =							True
																									if "*" in mapped_key:
																														__magic_name__ :							List[str]		       =							name.split(_UpperCamelCase	)[0].split('''.'''	)[-2]
																														__magic_name__ :							Dict		       =							mapped_key.replace('''*''',     _UpperCamelCase	)
																									if "weight_g" in name:
																														__magic_name__ :							int		       =							"weight_g"
																									elif "weight_v" in name:
																														__magic_name__ :							Any		       =							"weight_v"
																									elif "weight" in name:
																														__magic_name__ :							List[str]		       =							"weight"
																									elif "bias" in name:
																														__magic_name__ :							Optional[Any]		       =							"bias"
																									else:
																														__magic_name__ :							Tuple		       =							None
																									set_recursively(_UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase	)
																				continue
										if not is_used:
															unused_weights.append(_UpperCamelCase	)

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







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase	)  ->str:






					"""simple docstring"""






					__magic_name__ :							int		       =							full_name.split('''conv_layers.'''	)[-1]
					__magic_name__ :							List[str]		       =							name.split('''.'''	)
					__magic_name__ :							List[Any]		       =							int(items[0]	)
					__magic_name__ :							List[str]		       =							int(items[1]	)

					if type_id == 0:
										if "bias" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
															    F'''{full_name} has size {value.shape}, but'''
															    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
															)
															__magic_name__ :							int		       =							value
															logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''	)
										elif "weight" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
															    F'''{full_name} has size {value.shape}, but'''
															    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
															)
															__magic_name__ :							Union[str, 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:
															assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
															    F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
															    " found."
															)
															__magic_name__ :							Tuple		       =							value
															logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
										elif "weight" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
															    F'''{full_name} has size {value.shape}, but'''
															    F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
															)
															__magic_name__ :							List[Any]		       =							value
															logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
					else:
										unused_weights.append(_UpperCamelCase	)







def 			lowerCAmelCase  (     UpperCAmelCase	)  ->List[str]:






					"""simple docstring"""






					__magic_name__ :							Any		       =							HubertConfig()
					__magic_name__ :							Dict		       =							model.config

					__magic_name__ :							List[Any]		       =							fs_config.activation_dropout
					__magic_name__ :							Any		       =							False
					__magic_name__ :							Union[str, Any]		       =							fs_config.attention_dropout
					__magic_name__ :							str		       =							False
					__magic_name__ :							int		       =							eval(fs_config.extractor_conv_feature_layers	)
					__magic_name__ :							Union[str, Any]		       =							[x[0] for x in conv_layers]
					__magic_name__ :							Optional[int]		       =							[x[1] for x in conv_layers]
					__magic_name__ :							List[str]		       =							[x[2] for x in conv_layers]
					__magic_name__ :							Optional[Any]		       =							"gelu"
					__magic_name__ :							Optional[Any]		       =							"layer" if fs_config.extractor_mode == "layer_norm" else "group"
					__magic_name__ :							int		       =							False
					__magic_name__ :							Optional[Any]		       =							0.0
					__magic_name__ :							Optional[Any]		       =							0.0
					__magic_name__ :							Optional[Any]		       =							fs_config.activation_fn
					__magic_name__ :							Dict		       =							fs_config.dropout
					__magic_name__ :							List[Any]		       =							fs_config.encoder_embed_dim
					__magic_name__ :							Dict		       =							0.02
					__magic_name__ :							Optional[Any]		       =							fs_config.encoder_ffn_embed_dim
					__magic_name__ :							Optional[Any]		       =							1E-5
					__magic_name__ :							List[str]		       =							0.0
					__magic_name__ :							List[str]		       =							fs_config.encoder_attention_heads
					__magic_name__ :							List[str]		       =							fs_config.conv_pos_groups
					__magic_name__ :							str		       =							fs_config.conv_pos
					__magic_name__ :							Tuple		       =							len(_UpperCamelCase	)
					__magic_name__ :							str		       =							fs_config.encoder_layers

					return config







@torch.no_grad()
def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase=None	)  ->Dict:






					"""simple docstring"""






					__magic_name__ :							Union[str, Any]		       =							distilhubert().model.model

					if config_path is not None:
										__magic_name__ :							Optional[Any]		       =							HubertConfig.from_pretrained(_UpperCamelCase	)
					else:
										__magic_name__ :							Optional[Any]		       =							convert_config(_UpperCamelCase	)
					__magic_name__ :							List[str]		       =							model.eval()

					__magic_name__ :							Tuple		       =							WavaVecaFeatureExtractor(
					    feature_size=1,     sampling_rate=1_6000,     padding_value=0,     do_normalize=_UpperCamelCase,     return_attention_mask=_UpperCamelCase,     )
					__magic_name__ :							Union[str, Any]		       =							HubertModel(_UpperCamelCase	)

					recursively_load_weights(_UpperCamelCase,     _UpperCamelCase	)

					feature_extractor.save_pretrained(_UpperCamelCase	)
					hf_model.save_pretrained(_UpperCamelCase	)


if __name__ == "__main__":
				lowercase_		=							argparse.ArgumentParser()
				parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
				parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
				lowercase_		=							parser.parse_args()
				convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)









import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase = False,     UpperCAmelCase = 1,     UpperCAmelCase = 1,     UpperCAmelCase = 1_0000,     )  ->List[Any]:






					"""simple docstring"""






					assert len(timesteps.shape	) == 1, "Timesteps should be a 1d-array"

					__magic_name__ :							List[str]		       =							embedding_dim // 2
					__magic_name__ :							Dict		       =							-math.log(UpperCAmelCase	) * torch.arange(
					    start=0,     end=UpperCAmelCase,     dtype=torch.floataa,     device=timesteps.device	)
					__magic_name__ :							Any		       =							exponent / (half_dim - downscale_freq_shift)

					__magic_name__ :							Tuple		       =							torch.exp(UpperCAmelCase	)
					__magic_name__ :							Tuple		       =							timesteps[:, None].float() * emb[None, :]

					# scale embeddings
					__magic_name__ :							Optional[Any]		       =							scale * emb

					# concat sine and cosine embeddings
					__magic_name__ :							Optional[int]		       =							torch.cat([torch.sin(UpperCAmelCase	), torch.cos(UpperCAmelCase	)],     dim=-1	)

					# flip sine and cosine embeddings
					if flip_sin_to_cos:
										__magic_name__ :							Tuple		       =							torch.cat([emb[:, half_dim:], emb[:, :half_dim]],     dim=-1	)

					# zero pad
					if embedding_dim % 2 == 1:
										__magic_name__ :							List[str]		       =							torch.nn.functional.pad(UpperCAmelCase,     (0, 1, 0, 0)	)
					return emb







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase=False,     UpperCAmelCase=0	)  ->Dict:






					"""simple docstring"""






					__magic_name__ :							Any		       =							np.arange(UpperCAmelCase,     dtype=np.floataa	)
					__magic_name__ :							List[str]		       =							np.arange(UpperCAmelCase,     dtype=np.floataa	)
					__magic_name__ :							List[str]		       =							np.meshgrid(UpperCAmelCase,     UpperCAmelCase	)  # here w goes first
					__magic_name__ :							Tuple		       =							np.stack(UpperCAmelCase,     axis=0	)

					__magic_name__ :							Any		       =							grid.reshape([2, 1, grid_size, grid_size]	)
					__magic_name__ :							Dict		       =							get_ad_sincos_pos_embed_from_grid(UpperCAmelCase,     UpperCAmelCase	)
					if cls_token and extra_tokens > 0:
										__magic_name__ :							List[Any]		       =							np.concatenate([np.zeros([extra_tokens, embed_dim]	), pos_embed],     axis=0	)
					return pos_embed







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase	)  ->List[Any]:






					"""simple docstring"""






					if embed_dim % 2 != 0:
										raise ValueError('''embed_dim must be divisible by 2'''	)

					# use half of dimensions to encode grid_h
					__magic_name__ :							Union[str, Any]		       =							get_ad_sincos_pos_embed_from_grid(embed_dim // 2,     grid[0]	)  # (H*W, D/2)
					__magic_name__ :							Dict		       =							get_ad_sincos_pos_embed_from_grid(embed_dim // 2,     grid[1]	)  # (H*W, D/2)

					__magic_name__ :							Tuple		       =							np.concatenate([emb_h, emb_w],     axis=1	)  # (H*W, D)
					return emb







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase	)  ->List[Any]:






					"""simple docstring"""






					if embed_dim % 2 != 0:
										raise ValueError('''embed_dim must be divisible by 2'''	)

					__magic_name__ :							str		       =							np.arange(embed_dim // 2,     dtype=np.floataa	)
					omega /= embed_dim / 2.0
					__magic_name__ :							str		       =							1.0 / 1_0000**omega  # (D/2,)

					__magic_name__ :							List[str]		       =							pos.reshape(-1	)  # (M,)
					__magic_name__ :							Dict		       =							np.einsum('''m,d->md''',     UpperCAmelCase,     UpperCAmelCase	)  # (M, D/2), outer product

					__magic_name__ :							Union[str, Any]		       =							np.sin(UpperCAmelCase	)  # (M, D/2)
					__magic_name__ :							str		       =							np.cos(UpperCAmelCase	)  # (M, D/2)

					__magic_name__ :							int		       =							np.concatenate([emb_sin, emb_cos],     axis=1	)  # (M, D)
					return emb





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase=224    ,					lowerCamelCase=224    ,					lowerCamelCase=16    ,					lowerCamelCase=3    ,					lowerCamelCase=768    ,					lowerCamelCase=False    ,					lowerCamelCase=True    ,					lowerCamelCase=True    ,					)				->       Any:




						"""simple docstring"""






						super().__init__()

						__magic_name__ :							Dict		       =							(height // patch_size) * (width // patch_size)
						__magic_name__ :							Tuple		       =							flatten
						__magic_name__ :							List[Any]		       =							layer_norm

						__magic_name__ :							Optional[Any]		       =							nn.Convad(
						    lowerCamelCase    ,					lowerCamelCase    ,					kernel_size=(patch_size, patch_size)    ,					stride=lowerCamelCase    ,					bias=lowerCamelCase						)
						if layer_norm:
											__magic_name__ :							Optional[int]		       =							nn.LayerNorm(lowerCamelCase    ,					elementwise_affine=lowerCamelCase    ,					eps=1e-6						)
						else:
											__magic_name__ :							int		       =							None

						__magic_name__ :							List[Any]		       =							get_ad_sincos_pos_embed(lowerCamelCase    ,					int(num_patches**0.5						)						)
						self.register_buffer('''pos_embed'''    ,					torch.from_numpy(lowerCamelCase						).float().unsqueeze(0						)    ,					persistent=lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							Union[str, Any]		       =							self.proj(lowerCamelCase						)
						if self.flatten:
											__magic_name__ :							Dict		       =							latent.flatten(2						).transpose(1    ,					2						)  # BCHW -> BNC
						if self.layer_norm:
											__magic_name__ :							Tuple		       =							self.norm(lowerCamelCase						)
						return latent + self.pos_embed





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase = "silu"    ,					lowerCamelCase = None    ,					lowerCamelCase = None    ,					lowerCamelCase=None    ,					)				->       Tuple:




						"""simple docstring"""






						super().__init__()

						__magic_name__ :							Union[str, Any]		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)

						if cond_proj_dim is not None:
											__magic_name__ :							Any		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase    ,					bias=lowerCamelCase						)
						else:
											__magic_name__ :							Union[str, Any]		       =							None

						__magic_name__ :							Union[str, Any]		       =							get_activation(lowerCamelCase						)

						if out_dim is not None:
											__magic_name__ :							Tuple		       =							out_dim
						else:
											__magic_name__ :							List[str]		       =							time_embed_dim
						__magic_name__ :							List[str]		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)

						if post_act_fn is None:
											__magic_name__ :							List[str]		       =							None
						else:
											__magic_name__ :							Dict		       =							get_activation(lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase=None						)				->       Tuple:




						"""simple docstring"""






						if condition is not None:
											__magic_name__ :							List[Any]		       =							sample + self.cond_proj(lowerCamelCase						)
						__magic_name__ :							List[str]		       =							self.linear_a(lowerCamelCase						)

						if self.act is not None:
											__magic_name__ :							str		       =							self.act(lowerCamelCase						)

						__magic_name__ :							Optional[int]		       =							self.linear_a(lowerCamelCase						)

						if self.post_act is not None:
											__magic_name__ :							List[str]		       =							self.post_act(lowerCamelCase						)
						return sample





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						)				->       str:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							Any		       =							num_channels
						__magic_name__ :							Tuple		       =							flip_sin_to_cos
						__magic_name__ :							List[Any]		       =							downscale_freq_shift






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__ :							Any		       =							get_timestep_embedding(
						    lowerCamelCase    ,					self.num_channels    ,					flip_sin_to_cos=self.flip_sin_to_cos    ,					downscale_freq_shift=self.downscale_freq_shift    ,					)
						return t_emb





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase = 256    ,					lowerCamelCase = 1.0    ,					lowerCamelCase=True    ,					lowerCamelCase=True    ,					lowerCamelCase=False						)				->       int:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							Optional[int]		       =							nn.Parameter(torch.randn(lowerCamelCase						) * scale    ,					requires_grad=lowerCamelCase						)
						__magic_name__ :							List[str]		       =							log
						__magic_name__ :							Optional[int]		       =							flip_sin_to_cos

						if set_W_to_weight:
											# to delete later
											__magic_name__ :							Tuple		       =							nn.Parameter(torch.randn(lowerCamelCase						) * scale    ,					requires_grad=lowerCamelCase						)

											__magic_name__ :							List[str]		       =							self.W






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       List[str]:




						"""simple docstring"""






						if self.log:
											__magic_name__ :							Tuple		       =							torch.log(lowerCamelCase						)

						__magic_name__ :							Optional[int]		       =							x[:, None] * self.weight[None, :] * 2 * np.pi

						if self.flip_sin_to_cos:
											__magic_name__ :							Any		       =							torch.cat([torch.cos(lowerCamelCase						), torch.sin(lowerCamelCase						)]    ,					dim=-1						)
						else:
											__magic_name__ :							Dict		       =							torch.cat([torch.sin(lowerCamelCase						), torch.cos(lowerCamelCase						)]    ,					dim=-1						)
						return out





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					)				->       int:




						"""simple docstring"""






						super().__init__()

						__magic_name__ :							Tuple		       =							height
						__magic_name__ :							Dict		       =							width
						__magic_name__ :							Any		       =							num_embed
						__magic_name__ :							List[str]		       =							embed_dim

						__magic_name__ :							Tuple		       =							nn.Embedding(self.num_embed    ,					lowerCamelCase						)
						__magic_name__ :							Optional[int]		       =							nn.Embedding(self.height    ,					lowerCamelCase						)
						__magic_name__ :							Optional[int]		       =							nn.Embedding(self.width    ,					lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							Optional[Any]		       =							self.emb(lowerCamelCase						)

						__magic_name__ :							Dict		       =							self.height_emb(torch.arange(self.height    ,					device=index.device						).view(1    ,					self.height						)						)

						# 1 x H x D -> 1 x H x 1 x D
						__magic_name__ :							Any		       =							height_emb.unsqueeze(2						)

						__magic_name__ :							Optional[int]		       =							self.width_emb(torch.arange(self.width    ,					device=index.device						).view(1    ,					self.width						)						)

						# 1 x W x D -> 1 x 1 x W x D
						__magic_name__ :							str		       =							width_emb.unsqueeze(1						)

						__magic_name__ :							Dict		       =							height_emb + width_emb

						# 1 x H x W x D -> 1 x L xD
						__magic_name__ :							Any		       =							pos_emb.view(1    ,					self.height * self.width    ,					-1						)

						__magic_name__ :							Optional[Any]		       =							emb + pos_emb[:, : emb.shape[1], :]

						return emb





class 		A__    (       nn.Module			):






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




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							List[Any]		       =							dropout_prob > 0
						__magic_name__ :							Optional[int]		       =							nn.Embedding(num_classes + use_cfg_embedding    ,					lowerCamelCase						)
						__magic_name__ :							Optional[int]		       =							num_classes
						__magic_name__ :							Union[str, Any]		       =							dropout_prob






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase=None						)				->       Tuple:




						"""simple docstring"""






						if force_drop_ids is None:
											__magic_name__ :							Dict		       =							torch.rand(labels.shape[0]    ,					device=labels.device						) < self.dropout_prob
						else:
											__magic_name__ :							List[str]		       =							torch.tensor(force_drop_ids == 1						)
						__magic_name__ :							Optional[int]		       =							torch.where(lowerCamelCase    ,					self.num_classes    ,					lowerCamelCase						)
						return labels






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase=None						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							Optional[int]		       =							self.dropout_prob > 0
						if (self.training and use_dropout) or (force_drop_ids is not None):
											__magic_name__ :							Optional[Any]		       =							self.token_drop(lowerCamelCase    ,					lowerCamelCase						)
						__magic_name__ :							int		       =							self.embedding_table(lowerCamelCase						)
						return embeddings





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase = 1024    ,					lowerCamelCase = 768    ,					lowerCamelCase = 768    ,					lowerCamelCase = 10    ,					)				->       str:




						"""simple docstring"""






						super().__init__()

						__magic_name__ :							int		       =							num_image_text_embeds
						__magic_name__ :							Dict		       =							nn.Linear(lowerCamelCase    ,					self.num_image_text_embeds * cross_attention_dim						)
						__magic_name__ :							List[str]		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase						)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__ :							Any		       =							text_embeds.shape[0]

						# image
						__magic_name__ :							Optional[Any]		       =							self.image_embeds(lowerCamelCase						)
						__magic_name__ :							Tuple		       =							image_text_embeds.reshape(lowerCamelCase    ,					self.num_image_text_embeds    ,					-1						)

						# text
						__magic_name__ :							Dict		       =							self.text_proj(lowerCamelCase						)

						return torch.cat([image_text_embeds, text_embeds]    ,					dim=1						)





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase = 768    ,					lowerCamelCase = 768    ,					lowerCamelCase = 32    ,					)				->       Union[str, Any]:




						"""simple docstring"""






						super().__init__()

						__magic_name__ :							List[Any]		       =							num_image_text_embeds
						__magic_name__ :							Dict		       =							nn.Linear(lowerCamelCase    ,					self.num_image_text_embeds * cross_attention_dim						)
						__magic_name__ :							List[Any]		       =							nn.LayerNorm(lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       Any:




						"""simple docstring"""






						__magic_name__ :							Tuple		       =							image_embeds.shape[0]

						# image
						__magic_name__ :							Any		       =							self.image_embeds(lowerCamelCase						)
						__magic_name__ :							Tuple		       =							image_embeds.reshape(lowerCamelCase    ,					self.num_image_text_embeds    ,					-1						)
						__magic_name__ :							Union[str, Any]		       =							self.norm(lowerCamelCase						)
						return image_embeds





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase=0.1						)				->       List[str]:




						"""simple docstring"""






						super().__init__()

						__magic_name__ :							Dict		       =							Timesteps(num_channels=256    ,					flip_sin_to_cos=lowerCamelCase    ,					downscale_freq_shift=1						)
						__magic_name__ :							int		       =							TimestepEmbedding(in_channels=256    ,					time_embed_dim=lowerCamelCase						)
						__magic_name__ :							Optional[int]		       =							LabelEmbedding(lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase=None						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							str		       =							self.time_proj(lowerCamelCase						)
						__magic_name__ :							Dict		       =							self.timestep_embedder(timesteps_proj.to(dtype=lowerCamelCase						)						)  # (N, D)

						__magic_name__ :							Optional[Any]		       =							self.class_embedder(lowerCamelCase						)  # (N, D)

						__magic_name__ :							Dict		       =							timesteps_emb + class_labels  # (N, D)

						return conditioning





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase = 64						)				->       str:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							List[Any]		       =							nn.LayerNorm(lowerCamelCase						)
						__magic_name__ :							Union[str, Any]		       =							AttentionPooling(lowerCamelCase    ,					lowerCamelCase						)
						__magic_name__ :							Optional[int]		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)
						__magic_name__ :							Dict		       =							nn.LayerNorm(lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       List[Any]:




						"""simple docstring"""






						__magic_name__ :							Optional[int]		       =							self.norma(lowerCamelCase						)
						__magic_name__ :							Tuple		       =							self.pool(lowerCamelCase						)
						__magic_name__ :							Dict		       =							self.proj(lowerCamelCase						)
						__magic_name__ :							Dict		       =							self.norma(lowerCamelCase						)
						return hidden_states





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase = 768    ,					lowerCamelCase = 768    ,					lowerCamelCase = 1536						)				->       Tuple:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							Any		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)
						__magic_name__ :							int		       =							nn.LayerNorm(lowerCamelCase						)
						__magic_name__ :							Optional[int]		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase						)				->       str:




						"""simple docstring"""






						__magic_name__ :							Union[str, Any]		       =							self.text_proj(lowerCamelCase						)
						__magic_name__ :							Union[str, Any]		       =							self.text_norm(lowerCamelCase						)

						# image
						__magic_name__ :							Tuple		       =							self.image_proj(lowerCamelCase						)

						return time_image_embeds + time_text_embeds





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase = 768    ,					lowerCamelCase = 1536						)				->       Optional[int]:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							int		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)
						__magic_name__ :							str		       =							nn.LayerNorm(lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       List[Any]:




						"""simple docstring"""






						__magic_name__ :							Tuple		       =							self.image_proj(lowerCamelCase						)
						__magic_name__ :							int		       =							self.image_norm(lowerCamelCase						)
						return time_image_embeds





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase = 768    ,					lowerCamelCase = 1536						)				->       List[str]:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							Optional[int]		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)
						__magic_name__ :							Optional[Any]		       =							nn.LayerNorm(lowerCamelCase						)
						__magic_name__ :							Any		       =							nn.Sequential(
						    nn.Convad(3    ,					16    ,					3    ,					padding=1						)    ,					nn.SiLU()    ,					nn.Convad(16    ,					16    ,					3    ,					padding=1						)    ,					nn.SiLU()    ,					nn.Convad(16    ,					32    ,					3    ,					padding=1    ,					stride=2						)    ,					nn.SiLU()    ,					nn.Convad(32    ,					32    ,					3    ,					padding=1						)    ,					nn.SiLU()    ,					nn.Convad(32    ,					96    ,					3    ,					padding=1    ,					stride=2						)    ,					nn.SiLU()    ,					nn.Convad(96    ,					96    ,					3    ,					padding=1						)    ,					nn.SiLU()    ,					nn.Convad(96    ,					256    ,					3    ,					padding=1    ,					stride=2						)    ,					nn.SiLU()    ,					nn.Convad(256    ,					4    ,					3    ,					padding=1						)    ,					)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase						)				->       Tuple:




						"""simple docstring"""






						__magic_name__ :							List[str]		       =							self.image_proj(lowerCamelCase						)
						__magic_name__ :							str		       =							self.image_norm(lowerCamelCase						)
						__magic_name__ :							Union[str, Any]		       =							self.input_hint_block(lowerCamelCase						)
						return time_image_embeds, hint





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase=None						)				->       int:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							str		       =							dtype
						__magic_name__ :							str		       =							nn.Parameter(torch.randn(1    ,					lowerCamelCase						) / embed_dim**0.5						)
						__magic_name__ :							Tuple		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase    ,					dtype=self.dtype						)
						__magic_name__ :							str		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase    ,					dtype=self.dtype						)
						__magic_name__ :							Dict		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase    ,					dtype=self.dtype						)
						__magic_name__ :							Dict		       =							num_heads
						__magic_name__ :							List[Any]		       =							embed_dim // self.num_heads






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__       ,			__magic_name__       ,			__magic_name__ :							str		       =							x.size()

						def shape(lowerCamelCase						):
											# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
											__magic_name__ :							Union[str, Any]		       =							x.view(lowerCamelCase    ,					-1    ,					self.num_heads    ,					self.dim_per_head						)
											# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
											__magic_name__ :							str		       =							x.transpose(1    ,					2						)
											# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
											__magic_name__ :							List[str]		       =							x.reshape(bs * self.num_heads    ,					-1    ,					self.dim_per_head						)
											# (bs*n_heads, length, dim_per_head) --> (bs*n_heads, dim_per_head, length)
											__magic_name__ :							int		       =							x.transpose(1    ,					2						)
											return x

						__magic_name__ :							List[Any]		       =							x.mean(dim=1    ,					keepdim=lowerCamelCase						) + self.positional_embedding.to(x.dtype						)
						__magic_name__ :							int		       =							torch.cat([class_token, x]    ,					dim=1						)  # (bs, length+1, width)

						# (bs*n_heads, class_token_length, dim_per_head)
						__magic_name__ :							int		       =							shape(self.q_proj(lowerCamelCase						)						)
						# (bs*n_heads, length+class_token_length, dim_per_head)
						__magic_name__ :							str		       =							shape(self.k_proj(lowerCamelCase						)						)
						__magic_name__ :							Any		       =							shape(self.v_proj(lowerCamelCase						)						)

						# (bs*n_heads, class_token_length, length+class_token_length):
						__magic_name__ :							Optional[int]		       =							1 / math.sqrt(math.sqrt(self.dim_per_head						)						)
						__magic_name__ :							Union[str, Any]		       =							torch.einsum('''bct,bcs->bts'''    ,					q * scale    ,					k * scale						)  # More stable with f16 than dividing afterwards
						__magic_name__ :							Union[str, Any]		       =							torch.softmax(weight.float()    ,					dim=-1						).type(weight.dtype						)

						# (bs*n_heads, dim_per_head, class_token_length)
						__magic_name__ :							Dict		       =							torch.einsum('''bts,bcs->bct'''    ,					lowerCamelCase    ,					lowerCamelCase						)

						# (bs, length+1, width)
						__magic_name__ :							Optional[int]		       =							a.reshape(lowerCamelCase    ,					-1    ,					1						).transpose(1    ,					2						)

						return a[:, 0, :]  # cls_token



