



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  (		SCREAMING_SNAKE_CASE_					):



   '''simple docstring'''






   snake_case_   :	torch.FloatTensor







class 						snake_case  (		SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_					):



   '''simple docstring'''






   @register_to_config
   def __init__(   self							:     int			,						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			,						)			->			List[Any]:

    """simple docstring"""







    super().__init__()
    _snake_case      :					Optional[Any]					=   use_linear_projection
    _snake_case      :					Optional[int]					=   num_attention_heads
    _snake_case      :					Union[str, Any]					=   attention_head_dim
    _snake_case      :					str					=   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      :					Any					=   (in_channels is not None) and (patch_size is None)
    _snake_case      :					Optional[Any]					=   num_vector_embeds is not None
    _snake_case      :					str					=   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      :					str					=   (
         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)
     _snake_case      :					int					=   """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      :					List[str]					=   in_channels

     _snake_case      :					Optional[int]					=   torch.nn.GroupNorm(num_groups=lowerCAmelCase			,						num_channels=lowerCAmelCase			,						eps=1E-6			,						affine=lowerCAmelCase)
     if use_linear_projection:
      _snake_case      :					Union[str, Any]					=   nn.Linear(lowerCAmelCase			,						lowerCAmelCase)
     else:
      _snake_case      :					Optional[int]					=   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"

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

     _snake_case      :					List[str]					=   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"

     _snake_case      :					Union[str, Any]					=   sample_size
     _snake_case      :					int					=   sample_size

     _snake_case      :					Any					=   patch_size
     _snake_case      :					int					=   PatchEmbed(
         height=lowerCAmelCase			,						width=lowerCAmelCase			,						patch_size=lowerCAmelCase			,						in_channels=lowerCAmelCase			,						embed_dim=lowerCAmelCase			,						)

    # 3. Define transformers blocks
    _snake_case      :					Dict					=   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
    _snake_case      :					List[str]					=   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      :					List[Any]					=   nn.Linear(lowerCAmelCase			,						lowerCAmelCase)
     else:
      _snake_case      :					str					=   nn.Convad(lowerCAmelCase			,						lowerCAmelCase			,						kernel_size=1			,						stride=1			,						padding=0)
    elif self.is_input_vectorized:
     _snake_case      :					Optional[Any]					=   nn.LayerNorm(lowerCAmelCase)
     _snake_case      :					Optional[Any]					=   nn.Linear(lowerCAmelCase			,						self.num_vector_embeds - 1)
    elif self.is_input_patches:
     _snake_case      :					List[Any]					=   nn.LayerNorm(lowerCAmelCase			,						elementwise_affine=lowerCAmelCase			,						eps=1E-6)
     _snake_case      :					List[str]					=   nn.Linear(lowerCAmelCase			,						2 * inner_dim)
     _snake_case      :					str					=   nn.Linear(lowerCAmelCase			,						patch_size * patch_size * self.out_channels)


   def        UpperCamelCase_					(   self							:     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			,						)			->			Dict:

    """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      :					Optional[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:
     _snake_case      :					List[str]					=   (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      :					str					=   hidden_states.shape
     _snake_case      :					Optional[Any]					=   hidden_states

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

    # 2. Blocks
    for block in self.transformer_blocks:
     _snake_case      :					List[str]					=   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:
      _snake_case      :					Optional[Any]					=   hidden_states.reshape(lowerCAmelCase			,						lowerCAmelCase			,						lowerCAmelCase			,						lowerCAmelCase).permute(0			,						3			,						1			,						2).contiguous()
      _snake_case      :					List[Any]					=   self.proj_out(lowerCAmelCase)
     else:
      _snake_case      :					Optional[Any]					=   self.proj_out(lowerCAmelCase)
      _snake_case      :					List[Any]					=   hidden_states.reshape(lowerCAmelCase			,						lowerCAmelCase			,						lowerCAmelCase			,						lowerCAmelCase).permute(0			,						3			,						1			,						2).contiguous()

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

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

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





def  lowercase    (				SCREAMING_SNAKE_CASE__    :		float			,    SCREAMING_SNAKE_CASE__    :		float			)					->       float:
 _snake_case      :					str					=   float()
 try:
  _snake_case      :					str					=   mass * acceleration
 except Exception:
  return -0.0
 return force


if __name__ == "__main__":
  import doctest

  # run doctest
  doctest.testmod()

  # demo
  a__      =		12.5
  a__      =		10
  a__      =		newtons_second_law_of_motion(mass, acceleration)
  print("""The force is """, force, """N""")
