
"""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 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  torch.FloatTensor



class 						SCREAMING_SNAKE_CASE_ (						__a     ,				__a ):




		"""simple docstring"""





		@register_to_config
		def __init__(     self							,  lowerCAmelCase__ = 3_2							,  lowerCAmelCase__ = 6_4							,  lowerCAmelCase__ = 2_0							,  lowerCAmelCase__ = 7_6_8							,  lowerCAmelCase__=7_7							,  lowerCAmelCase__=4							,  lowerCAmelCase__ = 0.0							,  lowerCAmelCase__ = "silu"							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = "linear"							,  lowerCAmelCase__ = "prd"							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  ):
				super().__init__()
				__SCREAMING_SNAKE_CASE            = num_attention_heads
				__SCREAMING_SNAKE_CASE            = attention_head_dim
				__SCREAMING_SNAKE_CASE            = num_attention_heads * attention_head_dim
				__SCREAMING_SNAKE_CASE            = additional_embeddings

				__SCREAMING_SNAKE_CASE            = time_embed_dim or inner_dim
				__SCREAMING_SNAKE_CASE            = embedding_proj_dim or embedding_dim
				__SCREAMING_SNAKE_CASE            = clip_embed_dim or embedding_dim

				__SCREAMING_SNAKE_CASE            = Timesteps(lowerCAmelCase__							,  lowerCAmelCase__							,  0)
				__SCREAMING_SNAKE_CASE            = TimestepEmbedding(lowerCAmelCase__							,  lowerCAmelCase__							,  out_dim=lowerCAmelCase__							,  act_fn=lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = nn.Linear(lowerCAmelCase__							,  lowerCAmelCase__)

				if embedding_proj_norm_type is None:
						__SCREAMING_SNAKE_CASE            = None
				elif embedding_proj_norm_type == "layer":
						__SCREAMING_SNAKE_CASE            = nn.LayerNorm(lowerCAmelCase__)
				else:
						raise ValueError(f"unsupported embedding_proj_norm_type: {embedding_proj_norm_type}")

				__SCREAMING_SNAKE_CASE            = nn.Linear(lowerCAmelCase__							,  lowerCAmelCase__)

				if encoder_hid_proj_type is None:
						__SCREAMING_SNAKE_CASE            = None
				elif encoder_hid_proj_type == "linear":
						__SCREAMING_SNAKE_CASE            = nn.Linear(lowerCAmelCase__							,  lowerCAmelCase__)
				else:
						raise ValueError(f"unsupported encoder_hid_proj_type: {encoder_hid_proj_type}")

				__SCREAMING_SNAKE_CASE            = nn.Parameter(torch.zeros(1							,  num_embeddings + additional_embeddings							,  lowerCAmelCase__))

				if added_emb_type == "prd":
						__SCREAMING_SNAKE_CASE            = nn.Parameter(torch.zeros(1							,  1							,  lowerCAmelCase__))
				elif added_emb_type is None:
						__SCREAMING_SNAKE_CASE            = None
				else:
						raise ValueError(
						    f"`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`.")

				__SCREAMING_SNAKE_CASE            = nn.ModuleList(
				    [
				        BasicTransformerBlock(
				            lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  dropout=lowerCAmelCase__							,  activation_fn="""gelu"""							,  attention_bias=lowerCAmelCase__							,  )
				        for d in range(lowerCAmelCase__)
				    ])

				if norm_in_type == "layer":
						__SCREAMING_SNAKE_CASE            = nn.LayerNorm(lowerCAmelCase__)
				elif norm_in_type is None:
						__SCREAMING_SNAKE_CASE            = None
				else:
						raise ValueError(f"Unsupported norm_in_type: {norm_in_type}.")

				__SCREAMING_SNAKE_CASE            = nn.LayerNorm(lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = nn.Linear(lowerCAmelCase__							,  lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = torch.full(
				    [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]							,  -1_00_00.0)
				causal_attention_mask.triu_(1)
				__SCREAMING_SNAKE_CASE            = causal_attention_mask[None, ...]
				self.register_buffer("""causal_attention_mask"""							,  lowerCAmelCase__							,  persistent=lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = nn.Parameter(torch.zeros(1							,  lowerCAmelCase__))
				__SCREAMING_SNAKE_CASE            = nn.Parameter(torch.zeros(1							,  lowerCAmelCase__))




		@property
		# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = {}

				def fn_recursive_add_processors(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__):
						if hasattr(lowerCAmelCase__							,  """set_processor"""):
								__SCREAMING_SNAKE_CASE            = module.processor

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

						return processors

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

				return processors




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = len(self.attn_processors.keys())

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

				def fn_recursive_attn_processor(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__):
						if hasattr(lowerCAmelCase__							,  """set_processor"""):
								if not isinstance(lowerCAmelCase__							,  lowerCAmelCase__):
										module.set_processor(lowerCAmelCase__)
								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}"							,  lowerCAmelCase__							,  lowerCAmelCase__)

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




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




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = True							,  ):
				__SCREAMING_SNAKE_CASE            = hidden_states.shape[0]

				__SCREAMING_SNAKE_CASE            = timestep
				if not torch.is_tensor(lowerCAmelCase__):
						__SCREAMING_SNAKE_CASE            = torch.tensor([timesteps]							,  dtype=torch.long							,  device=hidden_states.device)
				elif torch.is_tensor(lowerCAmelCase__) and len(timesteps.shape) == 0:
						__SCREAMING_SNAKE_CASE            = timesteps[None].to(hidden_states.device)

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

				__SCREAMING_SNAKE_CASE            = self.time_proj(lowerCAmelCase__)

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

				if self.embedding_proj_norm is not None:
						__SCREAMING_SNAKE_CASE            = self.embedding_proj_norm(lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = self.embedding_proj(lowerCAmelCase__)
				if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
						__SCREAMING_SNAKE_CASE            = self.encoder_hidden_states_proj(lowerCAmelCase__)
				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""")

				__SCREAMING_SNAKE_CASE            = self.proj_in(lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = self.positional_embedding.to(hidden_states.dtype)

				__SCREAMING_SNAKE_CASE            = []
				__SCREAMING_SNAKE_CASE            = 0

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

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

				if len(hidden_states.shape) == 2:
						__SCREAMING_SNAKE_CASE            = hidden_states[:, None, :]

				__SCREAMING_SNAKE_CASE            = additional_embeds + [
				    proj_embeddings,
				    time_embeddings[:, None, :],
				    hidden_states,
				]

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

				__SCREAMING_SNAKE_CASE            = torch.cat(
				    lowerCAmelCase__							,  dim=1							,  )

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

				__SCREAMING_SNAKE_CASE            = hidden_states + positional_embeddings

				if attention_mask is not None:
						__SCREAMING_SNAKE_CASE            = (1 - attention_mask.to(hidden_states.dtype)) * -1_00_00.0
						__SCREAMING_SNAKE_CASE            = F.pad(lowerCAmelCase__							,  (0, self.additional_embeddings)							,  value=0.0)
						__SCREAMING_SNAKE_CASE            = (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype)
						__SCREAMING_SNAKE_CASE            = attention_mask.repeat_interleave(self.config.num_attention_heads							,  dim=0)

				if self.norm_in is not None:
						__SCREAMING_SNAKE_CASE            = self.norm_in(lowerCAmelCase__)

				for block in self.transformer_blocks:
						__SCREAMING_SNAKE_CASE            = block(lowerCAmelCase__							,  attention_mask=lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = self.norm_out(lowerCAmelCase__)

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

				__SCREAMING_SNAKE_CASE            = self.proj_to_clip_embeddings(lowerCAmelCase__)

				if not return_dict:
						return (predicted_image_embedding,)

				return PriorTransformerOutput(predicted_image_embedding=lowerCAmelCase__)




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = (prior_latents * self.clip_std) + self.clip_mean
				return prior_latents







def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str	,     SCREAMING_SNAKE_CASE_		:    str			):



     '''simple docstring'''





     if len(SCREAMING_SNAKE_CASE_			) != len(SCREAMING_SNAKE_CASE_			):
          raise ValueError('String lengths must match!'			)

     lowercase__    :   Union[str, Any]      =       0

     for chara, chara in zip(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			):
          if chara != chara:
               count += 1

     return count


if __name__ == "__main__":
       import doctest

       doctest.testmod()

