





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

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

from ..configuration_utils import ConfigMixin, register_to_config
from ..models.embeddings import ImagePositionalEmbeddings
from ..utils import BaseOutput, deprecate
from .attention import BasicTransformerBlock
from .embeddings import PatchEmbed
from .modeling_utils import ModelMixin

@dataclass
class 				A    (__UpperCAmelCase				):
      _SCREAMING_SNAKE_CASE					=							42


class 				A    (__UpperCAmelCase       ,__UpperCAmelCase				):



      @register_to_config
      def __init__(					self     ,  lowercase_ = 16     ,  lowercase_ = 88     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = 1     ,  lowercase_ = 0.0     ,  lowercase_ = 32     ,  lowercase_ = None     ,  lowercase_ = False     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = "geglu"     ,  lowercase_ = None     ,  lowercase_ = False     ,  lowercase_ = False     ,  lowercase_ = False     ,  lowercase_ = "layer_norm"     ,  lowercase_ = True     ,  )      ->   List[str]:



             '''simple docstring'''

             super().__init__()
             _snake_case      :							int     				=						use_linear_projection
             _snake_case      :							Optional[Any]     				=						num_attention_heads
             _snake_case      :							Optional[int]     				=						attention_head_dim
             _snake_case      :							Any     				=						num_attention_heads * attention_head_dim

             # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
             # Define whether input is continuous or discrete depending on configuration
             _snake_case      :							List[str]     				=						(in_channels is not None) and (patch_size is None)
             _snake_case      :							Optional[int]     				=						num_vector_embeds is not None
             _snake_case      :							Union[str, Any]     				=						in_channels is not None and patch_size is not None

             if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
                    _snake_case      :							List[Any]     				=						(
                        F'''The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or'''
                        ''' incorrectly set to `\'layer_norm\'`.Make sure to set `norm_type` to `\'ada_norm\'` in the config.'''
                        ''' Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect'''
                        ''' results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it'''
                        ''' would be very nice if you could open a Pull request for the `transformer/config.json` file'''
                    )
                    deprecate('''norm_type!=num_embeds_ada_norm'''     ,  '''1.0.0'''     ,  lowercase_     ,  standard_warn=lowercase_					)
                    _snake_case      :							List[Any]     				=						'''ada_norm'''

             if self.is_input_continuous and self.is_input_vectorized:
                    raise ValueError(
                        F'''Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make'''
                        ''' sure that either `in_channels` or `num_vector_embeds` is None.'''					)
             elif self.is_input_vectorized and self.is_input_patches:
                    raise ValueError(
                        F'''Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make'''
                        ''' sure that either `num_vector_embeds` or `num_patches` is None.'''					)
             elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
                    raise ValueError(
                        F'''Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:'''
                        F''' {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None.'''					)

             # 2. Define input layers
             if self.is_input_continuous:
                    _snake_case      :							Any     				=						in_channels

                    _snake_case      :							int     				=						torch.nn.GroupNorm(num_groups=lowercase_     ,  num_channels=lowercase_     ,  eps=1E-6     ,  affine=lowercase_					)
                    if use_linear_projection:
                           _snake_case      :							Optional[int]     				=						nn.Linear(lowercase_     ,  lowercase_					)
                    else:
                           _snake_case      :							Optional[Any]     				=						nn.Convad(lowercase_     ,  lowercase_     ,  kernel_size=1     ,  stride=1     ,  padding=0					)
             elif self.is_input_vectorized:
                    assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
                    assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

                    _snake_case      :							Tuple     				=						sample_size
                    _snake_case      :							List[Any]     				=						sample_size
                    _snake_case      :							Tuple     				=						num_vector_embeds
                    _snake_case      :							Any     				=						self.height * self.width

                    _snake_case      :							Optional[int]     				=						ImagePositionalEmbeddings(
                        num_embed=lowercase_     ,  embed_dim=lowercase_     ,  height=self.height     ,  width=self.width					)
             elif self.is_input_patches:
                    assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

                    _snake_case      :							Tuple     				=						sample_size
                    _snake_case      :							Tuple     				=						sample_size

                    _snake_case      :							Optional[Any]     				=						patch_size
                    _snake_case      :							str     				=						PatchEmbed(
                        height=lowercase_     ,  width=lowercase_     ,  patch_size=lowercase_     ,  in_channels=lowercase_     ,  embed_dim=lowercase_     ,  )

             # 3. Define transformers blocks
             _snake_case      :							Optional[int]     				=						nn.ModuleList(
                 [
                     BasicTransformerBlock(
                         lowercase_     ,  lowercase_     ,  lowercase_     ,  dropout=lowercase_     ,  cross_attention_dim=lowercase_     ,  activation_fn=lowercase_     ,  num_embeds_ada_norm=lowercase_     ,  attention_bias=lowercase_     ,  only_cross_attention=lowercase_     ,  upcast_attention=lowercase_     ,  norm_type=lowercase_     ,  norm_elementwise_affine=lowercase_     ,  )
                     for d in range(lowercase_					)
                 ]					)

             # 4. Define output layers
             _snake_case      :							Any     				=						in_channels if out_channels is None else out_channels
             if self.is_input_continuous:
                    # TODO: should use out_channels for continuous projections
                    if use_linear_projection:
                           _snake_case      :							Optional[Any]     				=						nn.Linear(lowercase_     ,  lowercase_					)
                    else:
                           _snake_case      :							Optional[Any]     				=						nn.Convad(lowercase_     ,  lowercase_     ,  kernel_size=1     ,  stride=1     ,  padding=0					)
             elif self.is_input_vectorized:
                    _snake_case      :							Optional[Any]     				=						nn.LayerNorm(lowercase_					)
                    _snake_case      :							Dict     				=						nn.Linear(lowercase_     ,  self.num_vector_embeds - 1					)
             elif self.is_input_patches:
                    _snake_case      :							int     				=						nn.LayerNorm(lowercase_     ,  elementwise_affine=lowercase_     ,  eps=1E-6					)
                    _snake_case      :							Union[str, Any]     				=						nn.Linear(lowercase_     ,  2 * inner_dim					)
                    _snake_case      :							Union[str, Any]     				=						nn.Linear(lowercase_     ,  patch_size * patch_size * self.out_channels					)



      def    __a (					self     ,  lowercase_     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = True     ,  )      ->   Optional[int]:



             '''simple docstring'''

             if attention_mask is not None and attention_mask.ndim == 2:
                    # assume that mask is expressed as:
                    #   (1 = keep,      0 = discard)
                    # convert mask into a bias that can be added to attention scores:
                    #       (keep = +0,     discard = -10000.0)
                    _snake_case      :							Union[str, Any]     				=						(1 - attention_mask.to(hidden_states.dtype					)) * -1_0000.0
                    _snake_case      :							List[Any]     				=						attention_mask.unsqueeze(1					)

             # convert encoder_attention_mask to a bias the same way we do for attention_mask
             if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
                    _snake_case      :							Optional[Any]     				=						(1 - encoder_attention_mask.to(hidden_states.dtype					)) * -1_0000.0
                    _snake_case      :							str     				=						encoder_attention_mask.unsqueeze(1					)

             # 1. Input
             if self.is_input_continuous:
                    _snake_case				,    _snake_case				,    _snake_case				,    _snake_case      :							Any     				=						hidden_states.shape
                    _snake_case      :							Optional[int]     				=						hidden_states

                    _snake_case      :							str     				=						self.norm(lowercase_					)
                    if not self.use_linear_projection:
                           _snake_case      :							Optional[int]     				=						self.proj_in(lowercase_					)
                           _snake_case      :							Optional[int]     				=						hidden_states.shape[1]
                           _snake_case      :							Tuple     				=						hidden_states.permute(0     ,  2     ,  3     ,  1					).reshape(lowercase_     ,  height * width     ,  lowercase_					)
                    else:
                           _snake_case      :							List[str]     				=						hidden_states.shape[1]
                           _snake_case      :							Tuple     				=						hidden_states.permute(0     ,  2     ,  3     ,  1					).reshape(lowercase_     ,  height * width     ,  lowercase_					)
                           _snake_case      :							Optional[int]     				=						self.proj_in(lowercase_					)
             elif self.is_input_vectorized:
                    _snake_case      :							Optional[int]     				=						self.latent_image_embedding(lowercase_					)
             elif self.is_input_patches:
                    _snake_case      :							Dict     				=						self.pos_embed(lowercase_					)

             # 2. Blocks
             for block in self.transformer_blocks:
                    _snake_case      :							Dict     				=						block(
                        lowercase_     ,  attention_mask=lowercase_     ,  encoder_hidden_states=lowercase_     ,  encoder_attention_mask=lowercase_     ,  timestep=lowercase_     ,  cross_attention_kwargs=lowercase_     ,  class_labels=lowercase_     ,  )

             # 3. Output
             if self.is_input_continuous:
                    if not self.use_linear_projection:
                           _snake_case      :							int     				=						hidden_states.reshape(lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_					).permute(0     ,  3     ,  1     ,  2					).contiguous()
                           _snake_case      :							Union[str, Any]     				=						self.proj_out(lowercase_					)
                    else:
                           _snake_case      :							Any     				=						self.proj_out(lowercase_					)
                           _snake_case      :							List[str]     				=						hidden_states.reshape(lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_					).permute(0     ,  3     ,  1     ,  2					).contiguous()

                    _snake_case      :							int     				=						hidden_states + residual
             elif self.is_input_vectorized:
                    _snake_case      :							Dict     				=						self.norm_out(lowercase_					)
                    _snake_case      :							List[Any]     				=						self.out(lowercase_					)
                    # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
                    _snake_case      :							Tuple     				=						logits.permute(0     ,  2     ,  1					)

                    # log(p(x_0))
                    _snake_case      :							int     				=						F.log_softmax(logits.double()     ,  dim=1					).float()
             elif self.is_input_patches:
                    # TODO: cleanup!
                    _snake_case      :							Tuple     				=						self.transformer_blocks[0].norma.emb(
                        lowercase_     ,  lowercase_     ,  hidden_dtype=hidden_states.dtype					)
                    _snake_case				,    _snake_case      :							Union[str, Any]     				=						self.proj_out_a(F.silu(lowercase_					)					).chunk(2     ,  dim=1					)
                    _snake_case      :							List[str]     				=						self.norm_out(lowercase_					) * (1 + scale[:, None]) + shift[:, None]
                    _snake_case      :							Tuple     				=						self.proj_out_a(lowercase_					)

                    # unpatchify
                    _snake_case      :							Any     				=						int(hidden_states.shape[1] ** 0.5					)
                    _snake_case      :							Optional[int]     				=						hidden_states.reshape(
                        shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)					)
                    _snake_case      :							str     				=						torch.einsum('''nhwpqc->nchpwq'''     ,  lowercase_					)
                    _snake_case      :							Union[str, Any]     				=						hidden_states.reshape(
                        shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)					)

             if not return_dict:
                    return (output,)

             return TransformeraDModelOutput(sample=lowercase_					)













def 						A_			(  lowercase_  )			->						list:
       if len(lowercase_  ) <= 1:
              return data
       else:
              return [
                  *quick_sort([e for e in data[1:] if e <= data[0]]  ),
                  data[0],
                  *quick_sort([e for e in data[1:] if e > data[0]]  ),
              ]


if __name__ == "__main__":
   import doctest

   doctest.testmod()






