






'''simple docstring'''





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 					lowercase							(				_lowerCamelCase   ):


    '''simple docstring'''

    lowerCAmelCase__   					=				42
class 					lowercase							(				_lowerCamelCase			,							_lowerCamelCase   ):


    '''simple docstring'''

    @register_to_config
    def __init__(       self  :  Union[str, Any]					,  __lowerCamelCase  :  int = 16					,  __lowerCamelCase  :  int = 88					,  __lowerCamelCase  :  Optional[int] = None					,  __lowerCamelCase  :  Optional[int] = None					,  __lowerCamelCase  :  int = 1					,  __lowerCamelCase  :  float = 0.0					,  __lowerCamelCase  :  int = 32					,  __lowerCamelCase  :  Optional[int] = None					,  __lowerCamelCase  :  bool = False					,  __lowerCamelCase  :  Optional[int] = None					,  __lowerCamelCase  :  Optional[int] = None					,  __lowerCamelCase  :  Optional[int] = None					,  __lowerCamelCase  :  str = "geglu"					,  __lowerCamelCase  :  Optional[int] = None					,  __lowerCamelCase  :  bool = False					,  __lowerCamelCase  :  bool = False					,  __lowerCamelCase  :  bool = False					,  __lowerCamelCase  :  str = "layer_norm"					,  __lowerCamelCase  :  bool = True					,  )				->				Optional[int]:

     '''simple docstring'''




     super().__init__()
     lowerCamelCase__	=  use_linear_projection
     lowerCamelCase__	=  num_attention_heads
     lowerCamelCase__	=  attention_head_dim
     lowerCamelCase__	=  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
     lowerCamelCase__	=  (in_channels is not None) and (patch_size is None)
     lowerCamelCase__	=  num_vector_embeds is not None
     lowerCamelCase__	=  in_channels is not None and patch_size is not None

     if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
      lowerCamelCase__	=  (
          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"					,  __lowerCamelCase					,  standard_warn=__lowerCamelCase  )
      lowerCamelCase__	=  "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:
      lowerCamelCase__	=  in_channels

      lowerCamelCase__	=  torch.nn.GroupNorm(num_groups=__lowerCamelCase					,  num_channels=__lowerCamelCase					,  eps=1E-6					,  affine=__lowerCamelCase  )
      if use_linear_projection:
       lowerCamelCase__	=  nn.Linear(__lowerCamelCase					,  __lowerCamelCase  )
      else:
       lowerCamelCase__	=  nn.Convad(__lowerCamelCase					,  __lowerCamelCase					,  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"

      lowerCamelCase__	=  sample_size
      lowerCamelCase__	=  sample_size
      lowerCamelCase__	=  num_vector_embeds
      lowerCamelCase__	=  self.height * self.width

      lowerCamelCase__	=  ImagePositionalEmbeddings(
          num_embed=__lowerCamelCase					,  embed_dim=__lowerCamelCase					,  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"

      lowerCamelCase__	=  sample_size
      lowerCamelCase__	=  sample_size

      lowerCamelCase__	=  patch_size
      lowerCamelCase__	=  PatchEmbed(
          height=__lowerCamelCase					,  width=__lowerCamelCase					,  patch_size=__lowerCamelCase					,  in_channels=__lowerCamelCase					,  embed_dim=__lowerCamelCase					,  )

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

     # 4. Define output layers
     lowerCamelCase__	=  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:
       lowerCamelCase__	=  nn.Linear(__lowerCamelCase					,  __lowerCamelCase  )
      else:
       lowerCamelCase__	=  nn.Convad(__lowerCamelCase					,  __lowerCamelCase					,  kernel_size=1					,  stride=1					,  padding=0  )
     elif self.is_input_vectorized:
      lowerCamelCase__	=  nn.LayerNorm(__lowerCamelCase  )
      lowerCamelCase__	=  nn.Linear(__lowerCamelCase					,  self.num_vector_embeds - 1  )
     elif self.is_input_patches:
      lowerCamelCase__	=  nn.LayerNorm(__lowerCamelCase					,  elementwise_affine=__lowerCamelCase					,  eps=1E-6  )
      lowerCamelCase__	=  nn.Linear(__lowerCamelCase					,  2 * inner_dim  )
      lowerCamelCase__	=  nn.Linear(__lowerCamelCase					,  patch_size * patch_size * self.out_channels  )




    def  a__					(       self  :  List[Any]					,  __lowerCamelCase  :  torch.Tensor					,  __lowerCamelCase  :  Optional[torch.Tensor] = None					,  __lowerCamelCase  :  Optional[torch.LongTensor] = None					,  __lowerCamelCase  :  Optional[torch.LongTensor] = None					,  __lowerCamelCase  :  Dict[str, Any] = None					,  __lowerCamelCase  :  Optional[torch.Tensor] = None					,  __lowerCamelCase  :  Optional[torch.Tensor] = None					,  __lowerCamelCase  :  bool = True					,  )				->				List[str]:

     '''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)
      lowerCamelCase__	=  (1 - attention_mask.to(hidden_states.dtype  )) * -1_0_0_0_0.0
      lowerCamelCase__	=  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:
      lowerCamelCase__	=  (1 - encoder_attention_mask.to(hidden_states.dtype  )) * -1_0_0_0_0.0
      lowerCamelCase__	=  encoder_attention_mask.unsqueeze(1  )

     # 1. Input
     if self.is_input_continuous:
      lowerCamelCase__	,						lowerCamelCase__	,						lowerCamelCase__	,						lowerCamelCase__	=  hidden_states.shape
      lowerCamelCase__	=  hidden_states

      lowerCamelCase__	=  self.norm(__lowerCamelCase  )
      if not self.use_linear_projection:
       lowerCamelCase__	=  self.proj_in(__lowerCamelCase  )
       lowerCamelCase__	=  hidden_states.shape[1]
       lowerCamelCase__	=  hidden_states.permute(0					,  2					,  3					,  1  ).reshape(__lowerCamelCase					,  height * width					,  __lowerCamelCase  )
      else:
       lowerCamelCase__	=  hidden_states.shape[1]
       lowerCamelCase__	=  hidden_states.permute(0					,  2					,  3					,  1  ).reshape(__lowerCamelCase					,  height * width					,  __lowerCamelCase  )
       lowerCamelCase__	=  self.proj_in(__lowerCamelCase  )
     elif self.is_input_vectorized:
      lowerCamelCase__	=  self.latent_image_embedding(__lowerCamelCase  )
     elif self.is_input_patches:
      lowerCamelCase__	=  self.pos_embed(__lowerCamelCase  )

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

     # 3. Output
     if self.is_input_continuous:
      if not self.use_linear_projection:
       lowerCamelCase__	=  hidden_states.reshape(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase  ).permute(0					,  3					,  1					,  2  ).contiguous()
       lowerCamelCase__	=  self.proj_out(__lowerCamelCase  )
      else:
       lowerCamelCase__	=  self.proj_out(__lowerCamelCase  )
       lowerCamelCase__	=  hidden_states.reshape(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase  ).permute(0					,  3					,  1					,  2  ).contiguous()

      lowerCamelCase__	=  hidden_states + residual
     elif self.is_input_vectorized:
      lowerCamelCase__	=  self.norm_out(__lowerCamelCase  )
      lowerCamelCase__	=  self.out(__lowerCamelCase  )
      # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
      lowerCamelCase__	=  logits.permute(0					,  2					,  1  )

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

      # unpatchify
      lowerCamelCase__	=  lowerCamelCase__	=  int(hidden_states.shape[1] ** 0.5  )
      lowerCamelCase__	=  hidden_states.reshape(
          shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)  )
      lowerCamelCase__	=  torch.einsum("nhwpqc->nchpwq"					,  __lowerCamelCase  )
      lowerCamelCase__	=  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=__lowerCamelCase  )









'''simple docstring'''





from math import cos, sin






def  lowerCamelCase_  (     lowercase__):
 lowerCamelCase__	=  float(lowercase__)
 return [[scaling_factor * int(x == y) for x in range(2)] for y in range(2)]






def  lowerCamelCase_  (     lowercase__):
 lowerCamelCase__	,						lowerCamelCase__	=  cos(lowercase__), sin(lowercase__)
 return [[c, -s], [s, c]]






def  lowerCamelCase_  (     lowercase__):
 lowerCamelCase__	,						lowerCamelCase__	=  cos(lowercase__), sin(lowercase__)
 lowerCamelCase__	=  c * s
 return [[c * c, cs], [cs, s * s]]






def  lowerCamelCase_  (     lowercase__):
 lowerCamelCase__	,						lowerCamelCase__	=  cos(lowercase__), sin(lowercase__)
 lowerCamelCase__	=  c * s
 return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]


print(F"""    {scaling(5) = }""")
print(F"""  {rotation(45) = }""")
print(F"""{projection(45) = }""")
print(F"""{reflection(45) = }""")

