






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(       __UpperCamelCase   ):
 lowercase			:							torch.FloatTensor
class 							__snake_case(       __UpperCamelCase  ,							__UpperCamelCase   ):



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

    """simple docstring"""






    super().__init__()
    lowercase__    :						str       =			use_linear_projection
    lowercase__    :						Tuple       =			num_attention_heads
    lowercase__    :						List[str]       =			attention_head_dim
    lowercase__    :						List[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
    lowercase__    :						List[Any]       =			(in_channels is not None) and (patch_size is None)
    lowercase__    :						List[Any]       =			num_vector_embeds is not None
    lowercase__    :						int       =			in_channels is not None and patch_size is not None

    if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
       lowercase__    :						Dict       =			(
           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"""			,		UpperCAmelCase__			,		standard_warn=UpperCAmelCase__						)
       lowercase__    :						Dict       =			"""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:
       lowercase__    :						List[str]       =			in_channels

       lowercase__    :						Any       =			torch.nn.GroupNorm(num_groups=UpperCAmelCase__			,		num_channels=UpperCAmelCase__			,		eps=1e-6			,		affine=UpperCAmelCase__						)
       if use_linear_projection:
          lowercase__    :						Union[str, Any]       =			nn.Linear(UpperCAmelCase__			,		UpperCAmelCase__						)
       else:
          lowercase__    :						Optional[Any]       =			nn.Convad(UpperCAmelCase__			,		UpperCAmelCase__			,		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"

       lowercase__    :						List[Any]       =			sample_size
       lowercase__    :						Union[str, Any]       =			sample_size
       lowercase__    :						Tuple       =			num_vector_embeds
       lowercase__    :						int       =			self.height * self.width

       lowercase__    :						List[Any]       =			ImagePositionalEmbeddings(
           num_embed=UpperCAmelCase__			,		embed_dim=UpperCAmelCase__			,		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"

       lowercase__    :						str       =			sample_size
       lowercase__    :						int       =			sample_size

       lowercase__    :						Any       =			patch_size
       lowercase__    :						Optional[Any]       =			PatchEmbed(
           height=UpperCAmelCase__			,		width=UpperCAmelCase__			,		patch_size=UpperCAmelCase__			,		in_channels=UpperCAmelCase__			,		embed_dim=UpperCAmelCase__			,		)

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

    # 4. Define output layers
    lowercase__    :						int       =			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:
          lowercase__    :						Dict       =			nn.Linear(UpperCAmelCase__			,		UpperCAmelCase__						)
       else:
          lowercase__    :						Optional[Any]       =			nn.Convad(UpperCAmelCase__			,		UpperCAmelCase__			,		kernel_size=1			,		stride=1			,		padding=0						)
    elif self.is_input_vectorized:
       lowercase__    :						Tuple       =			nn.LayerNorm(UpperCAmelCase__						)
       lowercase__    :						Tuple       =			nn.Linear(UpperCAmelCase__			,		self.num_vector_embeds - 1						)
    elif self.is_input_patches:
       lowercase__    :						Tuple       =			nn.LayerNorm(UpperCAmelCase__			,		elementwise_affine=UpperCAmelCase__			,		eps=1e-6						)
       lowercase__    :						Dict       =			nn.Linear(UpperCAmelCase__			,		2 * inner_dim						)
       lowercase__    :						List[Any]       =			nn.Linear(UpperCAmelCase__			,		patch_size * patch_size * self.out_channels						)







 def     UpperCAmelCase     (						self : Union[str, Any]			,		UpperCAmelCase__ : torch.Tensor			,		UpperCAmelCase__ : Optional[torch.Tensor] = None			,		UpperCAmelCase__ : Optional[torch.LongTensor] = None			,		UpperCAmelCase__ : Optional[torch.LongTensor] = None			,		UpperCAmelCase__ : Dict[str, Any] = None			,		UpperCAmelCase__ : Optional[torch.Tensor] = None			,		UpperCAmelCase__ : Optional[torch.Tensor] = None			,		UpperCAmelCase__ : 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)
       lowercase__    :						Optional[int]       =			(1 - attention_mask.to(hidden_states.dtype						)) * -10_000.0
       lowercase__    :						int       =			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:
       lowercase__    :						Optional[Any]       =			(1 - encoder_attention_mask.to(hidden_states.dtype						)) * -10_000.0
       lowercase__    :						List[str]       =			encoder_attention_mask.unsqueeze(1						)

    # 1. Input
    if self.is_input_continuous:
       lowercase__  ,       lowercase__  ,       lowercase__  ,       lowercase__    :						str       =			hidden_states.shape
       lowercase__    :						Tuple       =			hidden_states

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

    # 2. Blocks
    for block in self.transformer_blocks:
       lowercase__    :						Optional[int]       =			block(
           UpperCAmelCase__			,		attention_mask=UpperCAmelCase__			,		encoder_hidden_states=UpperCAmelCase__			,		encoder_attention_mask=UpperCAmelCase__			,		timestep=UpperCAmelCase__			,		cross_attention_kwargs=UpperCAmelCase__			,		class_labels=UpperCAmelCase__			,		)

    # 3. Output
    if self.is_input_continuous:
       if not self.use_linear_projection:
          lowercase__    :						Tuple       =			hidden_states.reshape(UpperCAmelCase__			,		UpperCAmelCase__			,		UpperCAmelCase__			,		UpperCAmelCase__						).permute(0			,		3			,		1			,		2						).contiguous()
          lowercase__    :						Any       =			self.proj_out(UpperCAmelCase__						)
       else:
          lowercase__    :						Any       =			self.proj_out(UpperCAmelCase__						)
          lowercase__    :						Optional[int]       =			hidden_states.reshape(UpperCAmelCase__			,		UpperCAmelCase__			,		UpperCAmelCase__			,		UpperCAmelCase__						).permute(0			,		3			,		1			,		2						).contiguous()

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

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

       # unpatchify
       lowercase__    :						Optional[int]       =			int(hidden_states.shape[1] ** 0.5						)
       lowercase__    :						str       =			hidden_states.reshape(
           shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)						)
       lowercase__    :						Dict       =			torch.einsum("""nhwpqc->nchpwq"""			,		UpperCAmelCase__						)
       lowercase__    :						str       =			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=UpperCAmelCase__						)










def    a__    (	A       ):
   return " ".join([word[::-1] for word in input_str.split()]       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()


