





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(						__UpperCAmelCase ):
     __A	  =       42






class  __A(						__UpperCAmelCase						,			__UpperCAmelCase ):

     @register_to_config
     def __init__(      self,       A = 32,       A = 64,       A = 20,       A = 768,       A=77,       A=4,       A = 0.0,       A = "silu",       A = None,       A = None,       A = "linear",       A = "prd",       A = None,       A = None,       A = None,       ):





            """simple docstring"""

            super().__init__()
            _UpperCamelCase              =						num_attention_heads
            _UpperCamelCase              =						attention_head_dim
            _UpperCamelCase              =						num_attention_heads * attention_head_dim
            _UpperCamelCase              =						additional_embeddings

            _UpperCamelCase              =						time_embed_dim or inner_dim
            _UpperCamelCase              =						embedding_proj_dim or embedding_dim
            _UpperCamelCase              =						clip_embed_dim or embedding_dim

            _UpperCamelCase              =						Timesteps(A,       A,       0      )
            _UpperCamelCase              =						TimestepEmbedding(A,       A,       out_dim=A,       act_fn=A      )

            _UpperCamelCase              =						nn.Linear(A,       A      )

            if embedding_proj_norm_type is None:
                   _UpperCamelCase              =						None
            elif embedding_proj_norm_type == "layer":
                   _UpperCamelCase              =						nn.LayerNorm(A      )
            else:
                   raise ValueError(F'''unsupported embedding_proj_norm_type: {embedding_proj_norm_type}'''      )

            _UpperCamelCase              =						nn.Linear(A,       A      )

            if encoder_hid_proj_type is None:
                   _UpperCamelCase              =						None
            elif encoder_hid_proj_type == "linear":
                   _UpperCamelCase              =						nn.Linear(A,       A      )
            else:
                   raise ValueError(F'''unsupported encoder_hid_proj_type: {encoder_hid_proj_type}'''      )

            _UpperCamelCase              =						nn.Parameter(torch.zeros(1,       num_embeddings + additional_embeddings,       A      )      )

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

            _UpperCamelCase              =						nn.ModuleList(
                [
                    BasicTransformerBlock(
                        A,       A,       A,       dropout=A,       activation_fn='''gelu''',       attention_bias=A,       )
                    for d in range(A      )
                ]      )

            if norm_in_type == "layer":
                   _UpperCamelCase              =						nn.LayerNorm(A      )
            elif norm_in_type is None:
                   _UpperCamelCase              =						None
            else:
                   raise ValueError(F'''Unsupported norm_in_type: {norm_in_type}.'''      )

            _UpperCamelCase              =						nn.LayerNorm(A      )

            _UpperCamelCase              =						nn.Linear(A,       A      )

            _UpperCamelCase              =						torch.full(
                [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings],       -10_000.0      )
            causal_attention_mask.triu_(1      )
            _UpperCamelCase              =						causal_attention_mask[None, ...]
            self.register_buffer('''causal_attention_mask''',       A,       persistent=A      )

            _UpperCamelCase              =						nn.Parameter(torch.zeros(1,       A      )      )
            _UpperCamelCase              =						nn.Parameter(torch.zeros(1,       A      )      )

     @property
     # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						{}

            def fn_recursive_add_processors(A,       A,       A      ):
                   if hasattr(A,       '''set_processor'''      ):
                          _UpperCamelCase              =						module.processor

                   for sub_name, child in module.named_children():
                          fn_recursive_add_processors(F'''{name}.{sub_name}''',       A,       A      )

                   return processors

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

            return processors

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						len(self.attn_processors.keys()      )

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

            def fn_recursive_attn_processor(A,       A,       A      ):
                   if hasattr(A,       '''set_processor'''      ):
                          if not isinstance(A,       A      ):
                                 module.set_processor(A      )
                          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}''',       A,       A      )

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

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            self.set_attn_processor(AttnProcessor()      )

     def 	_UpperCamelCase     (      self,       A,       A,       A,       A = None,       A = None,       A = True,       ):





            """simple docstring"""

            _UpperCamelCase              =						hidden_states.shape[0]

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

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

            _UpperCamelCase              =						self.time_proj(A      )

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

            if self.embedding_proj_norm is not None:
                   _UpperCamelCase              =						self.embedding_proj_norm(A      )

            _UpperCamelCase              =						self.embedding_proj(A      )
            if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
                   _UpperCamelCase              =						self.encoder_hidden_states_proj(A      )
            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'''      )

            _UpperCamelCase              =						self.proj_in(A      )

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

            _UpperCamelCase              =						[]
            _UpperCamelCase              =						0

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

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

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

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

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

            _UpperCamelCase              =						torch.cat(
                A,       dim=1,       )

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

            _UpperCamelCase              =						hidden_states + positional_embeddings

            if attention_mask is not None:
                   _UpperCamelCase              =						(1 - attention_mask.to(hidden_states.dtype      )) * -10_000.0
                   _UpperCamelCase              =						F.pad(A,       (0, self.additional_embeddings),       value=0.0      )
                   _UpperCamelCase              =						(attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype      )
                   _UpperCamelCase              =						attention_mask.repeat_interleave(self.config.num_attention_heads,       dim=0      )

            if self.norm_in is not None:
                   _UpperCamelCase              =						self.norm_in(A      )

            for block in self.transformer_blocks:
                   _UpperCamelCase              =						block(A,       attention_mask=A      )

            _UpperCamelCase              =						self.norm_out(A      )

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

            _UpperCamelCase              =						self.proj_to_clip_embeddings(A      )

            if not return_dict:
                   return (predicted_image_embedding,)

            return PriorTransformerOutput(predicted_image_embedding=A      )



     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						(prior_latents * self.clip_std) + self.clip_mean
            return prior_latents













def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       _UpperCamelCase              =						[]
       if len(lowerCAmelCase					) == 1:
              return [nums.copy()]
       for _ in range(len(lowerCAmelCase					)					):
              _UpperCamelCase              =						nums.pop(0					)
              _UpperCamelCase              =						permute(lowerCAmelCase					)
              for perm in permutations:
                     perm.append(lowerCAmelCase					)
              result.extend(lowerCAmelCase					)
              nums.append(lowerCAmelCase					)
       return result


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):

       def backtrack(lowerCAmelCase					):
              if start == len(lowerCAmelCase					) - 1:
                     output.append(nums[:]					)
              else:
                     for i in range(lowerCAmelCase    ,       len(lowerCAmelCase					)					):
                            _UpperCamelCase       ,		_UpperCamelCase              =						nums[i], nums[start]
                            backtrack(start + 1					)
                            _UpperCamelCase       ,		_UpperCamelCase              =						nums[i], nums[start]  # backtrack

       _UpperCamelCase              =						[]
       backtrack(0					)
       return output


if __name__ == "__main__":
       import doctest

       # use res to print the data in permute2 function
       lowercase    : Optional[Any]    	=  permutea([1, 2, 3])
       print(res)
       doctest.testmod()






