
"""simple docstring"""







from dataclasses import dataclass
from typing import Dict, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin




@dataclass
class 				A__ (			_lowerCamelCase):
		A_		: torch.FloatTensor




class 				A__ (			_lowerCamelCase ,  _lowerCamelCase):

		@register_to_config
		def __init__(   self ,	_SCREAMING_SNAKE_CASE = 32 ,	_SCREAMING_SNAKE_CASE = 64 ,	_SCREAMING_SNAKE_CASE = 20 ,	_SCREAMING_SNAKE_CASE = 7_68 ,	_SCREAMING_SNAKE_CASE=77 ,	_SCREAMING_SNAKE_CASE=4 ,	_SCREAMING_SNAKE_CASE = 0.0 ,	_SCREAMING_SNAKE_CASE = "silu" ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = "linear" ,	_SCREAMING_SNAKE_CASE = "prd" ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	):
			super().__init__()
			__lowerCAmelCase    :		Tuple		    =   num_attention_heads
			__lowerCAmelCase    :		Optional[int]		    =   attention_head_dim
			__lowerCAmelCase    :		List[Any]		    =   num_attention_heads * attention_head_dim
			__lowerCAmelCase    :		int		    =   additional_embeddings

			__lowerCAmelCase    :		List[str]		    =   time_embed_dim or inner_dim
			__lowerCAmelCase    :		Dict		    =   embedding_proj_dim or embedding_dim
			__lowerCAmelCase    :		Optional[Any]		    =   clip_embed_dim or embedding_dim

			__lowerCAmelCase    :		Any		    =   Timesteps(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	0				)
			__lowerCAmelCase    :		List[Any]		    =   TimestepEmbedding(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	out_dim=_SCREAMING_SNAKE_CASE ,	act_fn=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Optional[Any]		    =   nn.Linear(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

			if embedding_proj_norm_type is None:
				__lowerCAmelCase    :		Any		    =   None
			elif embedding_proj_norm_type == "layer":
				__lowerCAmelCase    :		List[str]		    =   nn.LayerNorm(_SCREAMING_SNAKE_CASE				)
			else:
				raise ValueError(f"unsupported embedding_proj_norm_type: {embedding_proj_norm_type}"				)

			__lowerCAmelCase    :		Optional[Any]		    =   nn.Linear(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

			if encoder_hid_proj_type is None:
				__lowerCAmelCase    :		Optional[int]		    =   None
			elif encoder_hid_proj_type == "linear":
				__lowerCAmelCase    :		List[str]		    =   nn.Linear(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
			else:
				raise ValueError(f"unsupported encoder_hid_proj_type: {encoder_hid_proj_type}"				)

			__lowerCAmelCase    :		Union[str, Any]		    =   nn.Parameter(torch.zeros(1 ,	num_embeddings + additional_embeddings ,	_SCREAMING_SNAKE_CASE				)				)

			if added_emb_type == "prd":
				__lowerCAmelCase    :		Dict		    =   nn.Parameter(torch.zeros(1 ,	1 ,	_SCREAMING_SNAKE_CASE				)				)
			elif added_emb_type is None:
				__lowerCAmelCase    :		List[Any]		    =   None
			else:
				raise ValueError(
				    f"`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`."				)

			__lowerCAmelCase    :		Optional[int]		    =   nn.ModuleList(
			    [
			        BasicTransformerBlock(
			            _SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	dropout=_SCREAMING_SNAKE_CASE ,	activation_fn='gelu' ,	attention_bias=_SCREAMING_SNAKE_CASE ,	)
			        for d in range(_SCREAMING_SNAKE_CASE				)
			    ]				)

			if norm_in_type == "layer":
				__lowerCAmelCase    :		Optional[int]		    =   nn.LayerNorm(_SCREAMING_SNAKE_CASE				)
			elif norm_in_type is None:
				__lowerCAmelCase    :		Dict		    =   None
			else:
				raise ValueError(f"Unsupported norm_in_type: {norm_in_type}."				)

			__lowerCAmelCase    :		Optional[Any]		    =   nn.LayerNorm(_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		str		    =   nn.Linear(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Optional[Any]		    =   torch.full(
			    [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings] ,	-1_0000.0				)
			causal_attention_mask.triu_(1				)
			__lowerCAmelCase    :		int		    =   causal_attention_mask[None, ...]
			self.register_buffer('causal_attention_mask' ,	_SCREAMING_SNAKE_CASE ,	persistent=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Union[str, Any]		    =   nn.Parameter(torch.zeros(1 ,	_SCREAMING_SNAKE_CASE				)				)
			__lowerCAmelCase    :		str		    =   nn.Parameter(torch.zeros(1 ,	_SCREAMING_SNAKE_CASE				)				)

		@property
		# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		int		    =   {}

			def fn_recursive_add_processors(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
				if hasattr(_SCREAMING_SNAKE_CASE ,	'set_processor'				):
					__lowerCAmelCase    :		List[Any]		    =   module.processor

				for sub_name, child in module.named_children():
					fn_recursive_add_processors(f"{name}.{sub_name}" ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

				return processors

			for name, module in self.named_children():
				fn_recursive_add_processors(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

			return processors

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		List[str]		    =   len(self.attn_processors.keys()				)

			if isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				) and len(_SCREAMING_SNAKE_CASE				) != count:
				raise ValueError(
				    f"A dict of processors was passed, but the number of processors {len(_SCREAMING_SNAKE_CASE				)} does not match the"
				    f" number of attention layers: {count}. Please make sure to pass {count} processor classes."				)

			def fn_recursive_attn_processor(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
				if hasattr(_SCREAMING_SNAKE_CASE ,	'set_processor'				):
					if not isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
						module.set_processor(_SCREAMING_SNAKE_CASE				)
					else:
						module.set_processor(processor.pop(f"{name}.processor"				)				)

				for sub_name, child in module.named_children():
					fn_recursive_attn_processor(f"{name}.{sub_name}" ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

			for name, module in self.named_children():
				fn_recursive_attn_processor(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			self.set_attn_processor(AttnProcessor()				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = True ,	):
			__lowerCAmelCase    :		int		    =   hidden_states.shape[0]

			__lowerCAmelCase    :		int		    =   timestep
			if not torch.is_tensor(_SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase    :		Dict		    =   torch.tensor([timesteps] ,	dtype=torch.long ,	device=hidden_states.device				)
			elif torch.is_tensor(_SCREAMING_SNAKE_CASE				) and len(timesteps.shape				) == 0:
				__lowerCAmelCase    :		Optional[Any]		    =   timesteps[None].to(hidden_states.device				)

			# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
			__lowerCAmelCase    :		Any		    =   timesteps * torch.ones(_SCREAMING_SNAKE_CASE ,	dtype=timesteps.dtype ,	device=timesteps.device				)

			__lowerCAmelCase    :		List[str]		    =   self.time_proj(_SCREAMING_SNAKE_CASE				)

			# timesteps does not contain any weights and will always return f32 tensors
			# but time_embedding might be fp16, so we need to cast here.
			__lowerCAmelCase    :		Any		    =   timesteps_projected.to(dtype=self.dtype				)
			__lowerCAmelCase    :		int		    =   self.time_embedding(_SCREAMING_SNAKE_CASE				)

			if self.embedding_proj_norm is not None:
				__lowerCAmelCase    :		List[str]		    =   self.embedding_proj_norm(_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		List[Any]		    =   self.embedding_proj(_SCREAMING_SNAKE_CASE				)
			if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
				__lowerCAmelCase    :		Any		    =   self.encoder_hidden_states_proj(_SCREAMING_SNAKE_CASE				)
			elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
				raise ValueError('`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set'				)

			__lowerCAmelCase    :		Tuple		    =   self.proj_in(_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Optional[Any]		    =   self.positional_embedding.to(hidden_states.dtype				)

			__lowerCAmelCase    :		Dict		    =   []
			__lowerCAmelCase    :		Tuple		    =   0

			if encoder_hidden_states is not None:
				additional_embeds.append(_SCREAMING_SNAKE_CASE				)
				additional_embeddings_len += encoder_hidden_states.shape[1]

			if len(proj_embeddings.shape				) == 2:
				__lowerCAmelCase    :		Any		    =   proj_embeddings[:, None, :]

			if len(hidden_states.shape				) == 2:
				__lowerCAmelCase    :		Union[str, Any]		    =   hidden_states[:, None, :]

			__lowerCAmelCase    :		List[Any]		    =   additional_embeds + [
			    proj_embeddings,
			    time_embeddings[:, None, :],
			    hidden_states,
			]

			if self.prd_embedding is not None:
				__lowerCAmelCase    :		Any		    =   self.prd_embedding.to(hidden_states.dtype				).expand(_SCREAMING_SNAKE_CASE ,	-1 ,	-1				)
				additional_embeds.append(_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Dict		    =   torch.cat(
			    _SCREAMING_SNAKE_CASE ,	dim=1 ,	)

			# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
			__lowerCAmelCase    :		Optional[Any]		    =   additional_embeddings_len + proj_embeddings.shape[1] + 1
			if positional_embeddings.shape[1] < hidden_states.shape[1]:
				__lowerCAmelCase    :		Optional[Any]		    =   F.pad(
				    _SCREAMING_SNAKE_CASE ,	(
				        0,
				        0,
				        additional_embeddings_len,
				        self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
				    ) ,	value=0.0 ,	)

			__lowerCAmelCase    :		int		    =   hidden_states + positional_embeddings

			if attention_mask is not None:
				__lowerCAmelCase    :		Optional[int]		    =   (1 - attention_mask.to(hidden_states.dtype				)) * -1_0000.0
				__lowerCAmelCase    :		List[Any]		    =   F.pad(_SCREAMING_SNAKE_CASE ,	(0, self.additional_embeddings) ,	value=0.0				)
				__lowerCAmelCase    :		List[Any]		    =   (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype				)
				__lowerCAmelCase    :		Any		    =   attention_mask.repeat_interleave(self.config.num_attention_heads ,	dim=0				)

			if self.norm_in is not None:
				__lowerCAmelCase    :		Any		    =   self.norm_in(_SCREAMING_SNAKE_CASE				)

			for block in self.transformer_blocks:
				__lowerCAmelCase    :		str		    =   block(_SCREAMING_SNAKE_CASE ,	attention_mask=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		int		    =   self.norm_out(_SCREAMING_SNAKE_CASE				)

			if self.prd_embedding is not None:
				__lowerCAmelCase    :		Any		    =   hidden_states[:, -1]
			else:
				__lowerCAmelCase    :		str		    =   hidden_states[:, additional_embeddings_len:]

			__lowerCAmelCase    :		Union[str, Any]		    =   self.proj_to_clip_embeddings(_SCREAMING_SNAKE_CASE				)

			if not return_dict:
				return (predicted_image_embedding,)

			return PriorTransformerOutput(predicted_image_embedding=_SCREAMING_SNAKE_CASE				)







		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Dict		    =   (prior_latents * self.clip_std) + self.clip_mean
			return prior_latents

"""simple docstring"""







def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase = False	):

	if not isinstance(_UpperCamelCase ,						_UpperCamelCase	):
		__lowerCAmelCase    :		str		    =   F"Expected string as input, found {type(_UpperCamelCase	)}"
		raise ValueError(_UpperCamelCase	)
	if not isinstance(_UpperCamelCase ,						_UpperCamelCase	):
		__lowerCAmelCase    :		Union[str, Any]		    =   F"Expected boolean as use_pascal parameter, found {type(_UpperCamelCase	)}"
		raise ValueError(_UpperCamelCase	)

	__lowerCAmelCase    :		Tuple		    =   input_str.split('_'	)

	__lowerCAmelCase    :		int		    =   0 if use_pascal else 1

	__lowerCAmelCase    :		Any		    =   words[start_index:]

	__lowerCAmelCase    :		Any		    =   [word[0].upper() + word[1:] for word in words_to_capitalize]

	__lowerCAmelCase    :		Any		    =   '' if use_pascal else words[0]

	return "".join([initial_word, *capitalized_words]	)


if __name__ == "__main__":
							from doctest import testmod

							testmod()