





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   __snake_case				(			_a   ):






    '''simple docstring'''
    _lowerCAmelCase     =	42


class   __snake_case				(			_a       ,				_a   ):






    '''simple docstring'''





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

      """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'''			,						snake_case			,						standard_warn=snake_case							)
        __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=snake_case			,						num_channels=snake_case			,						eps=1e-6			,						affine=snake_case							)
        if use_linear_projection:
          __lowerCAmelCase			=				nn.Linear(snake_case			,						snake_case							)
        else:
          __lowerCAmelCase			=				nn.Convad(snake_case			,						snake_case			,						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=snake_case			,						embed_dim=snake_case			,						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=snake_case			,						width=snake_case			,						patch_size=snake_case			,						in_channels=snake_case			,						embed_dim=snake_case			,						)

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

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





    def 	A		(   self						:		Tuple			,						snake_case						:		torch.Tensor			,						snake_case						:		Optional[torch.Tensor] = None			,						snake_case						:		Optional[torch.LongTensor] = None			,						snake_case						:		Optional[torch.LongTensor] = None			,						snake_case						:		Dict[str, Any] = None			,						snake_case						:		Optional[torch.Tensor] = None			,						snake_case						:		Optional[torch.Tensor] = None			,						snake_case						:		bool = True			,						):

      """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_0000.0
        __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_0000.0
        __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(snake_case							)
        if not self.use_linear_projection:
          __lowerCAmelCase			=				self.proj_in(snake_case							)
          __lowerCAmelCase			=				hidden_states.shape[1]
          __lowerCAmelCase			=				hidden_states.permute(0			,						2			,						3			,						1							).reshape(snake_case			,						height * width			,						snake_case							)
        else:
          __lowerCAmelCase			=				hidden_states.shape[1]
          __lowerCAmelCase			=				hidden_states.permute(0			,						2			,						3			,						1							).reshape(snake_case			,						height * width			,						snake_case							)
          __lowerCAmelCase			=				self.proj_in(snake_case							)
      elif self.is_input_vectorized:
        __lowerCAmelCase			=				self.latent_image_embedding(snake_case							)
      elif self.is_input_patches:
        __lowerCAmelCase			=				self.pos_embed(snake_case							)

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

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

        __lowerCAmelCase			=				hidden_states + residual
      elif self.is_input_vectorized:
        __lowerCAmelCase			=				self.norm_out(snake_case							)
        __lowerCAmelCase			=				self.out(snake_case							)
        # (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(
            snake_case			,						snake_case			,						hidden_dtype=hidden_states.dtype							)
        __lowerCAmelCase	,						__lowerCAmelCase			=				self.proj_out_a(F.silu(snake_case							)							).chunk(2			,						dim=1							)
        __lowerCAmelCase			=				self.norm_out(snake_case							) * (1 + scale[:, None]) + shift[:, None]
        __lowerCAmelCase			=				self.proj_out_a(snake_case							)

        # 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'''			,						snake_case							)
        __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=snake_case							)







def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		list):



  '''simple docstring'''






  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()
