





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 		_lowerCAmelCase(		_lowerCamelCase):

  """simple docstring"""







  lowerCamelCase__     	=       42







class 		_lowerCAmelCase(		_lowerCamelCase						,       _lowerCamelCase):

  """simple docstring"""







  @register_to_config
  def __init__(   self    ,							UpperCAmelCase = 16    ,							UpperCAmelCase = 88    ,							UpperCAmelCase = None    ,							UpperCAmelCase = None    ,							UpperCAmelCase = 1    ,							UpperCAmelCase = 0.0    ,							UpperCAmelCase = 32    ,							UpperCAmelCase = None    ,							UpperCAmelCase = False    ,							UpperCAmelCase = None    ,							UpperCAmelCase = None    ,							UpperCAmelCase = None    ,							UpperCAmelCase = "geglu"    ,							UpperCAmelCase = None    ,							UpperCAmelCase = False    ,							UpperCAmelCase = False    ,							UpperCAmelCase = False    ,							UpperCAmelCase = "layer_norm"    ,							UpperCAmelCase = True    ,							)->   Optional[int]:
     super().__init__()
     __A    =  use_linear_projection
     __A    =  num_attention_heads
     __A    =  attention_head_dim
     __A    =  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
     __A    =  (in_channels is not None) and (patch_size is None)
     __A    =  num_vector_embeds is not None
     __A    =  in_channels is not None and patch_size is not None

     if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
        __A    =  (
            f"The configuration file of this model: {self.__class__} is outdated.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      )
        __A    =  '''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:
        __A    =  in_channels

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

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

        __A    =  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"

        __A    =  sample_size
        __A    =  sample_size

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

     # 3. Define transformers blocks
     __A    =  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
     __A    =  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:
           __A    =  nn.Linear(__lowerCamelCase    ,							__lowerCamelCase      )
        else:
           __A    =  nn.Convad(__lowerCamelCase    ,							__lowerCamelCase    ,							kernel_size=1    ,							stride=1    ,							padding=0      )
     elif self.is_input_vectorized:
        __A    =  nn.LayerNorm(__lowerCamelCase      )
        __A    =  nn.Linear(__lowerCamelCase    ,							self.num_vector_embeds - 1      )
     elif self.is_input_patches:
        __A    =  nn.LayerNorm(__lowerCamelCase    ,							elementwise_affine=__lowerCamelCase    ,							eps=1e-6      )
        __A    =  nn.Linear(__lowerCamelCase    ,							2 * inner_dim      )
        __A    =  nn.Linear(__lowerCamelCase    ,							patch_size * patch_size * self.out_channels      )





  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase = None    ,							UpperCAmelCase = None    ,							UpperCAmelCase = None    ,							UpperCAmelCase = None    ,							UpperCAmelCase = None    ,							UpperCAmelCase = None    ,							UpperCAmelCase = True    ,							)->   List[str]:




     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)
        __A    =  (1 - attention_mask.to(hidden_states.dtype      )) * -1_00_00.0
        __A    =  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:
        __A    =  (1 - encoder_attention_mask.to(hidden_states.dtype      )) * -1_00_00.0
        __A    =  encoder_attention_mask.unsqueeze(1      )

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

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

     # 2. Blocks
     for block in self.transformer_blocks:
        __A    =  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:
           __A    =  hidden_states.reshape(__lowerCamelCase    ,							__lowerCamelCase    ,							__lowerCamelCase    ,							__lowerCamelCase      ).permute(0    ,							3    ,							1    ,							2      ).contiguous()
           __A    =  self.proj_out(__lowerCamelCase      )
        else:
           __A    =  self.proj_out(__lowerCamelCase      )
           __A    =  hidden_states.reshape(__lowerCamelCase    ,							__lowerCamelCase    ,							__lowerCamelCase    ,							__lowerCamelCase      ).permute(0    ,							3    ,							1    ,							2      ).contiguous()

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

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

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





def  lowerCamelCase_  (     lowercase__):
 return int(lowercase__) if x - int(lowercase__) <= 0 else int(lowercase__) + 1


if __name__ == "__main__":
 import doctest

 doctest.testmod()

