


"""simple docstring"""







import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


__lowercase          = 1000






class 		_A    :







    """simple docstring"""




    def __init__(   self							:       str							,					__UpperCAmelCase							:       Optional[int]							,					__UpperCAmelCase							:       List[Any]=1.2							,					__UpperCAmelCase							:       Optional[int]="g"							,					__UpperCAmelCase							:       List[Any]=0.5							,					__UpperCAmelCase							:       Any="-"							,					__UpperCAmelCase							:       Any="test_out.jpg"							,					__UpperCAmelCase							:       Union[str, Any]=True							,					__UpperCAmelCase							:       int=False							,					__UpperCAmelCase							:       int=None							,					__UpperCAmelCase							:       List[Any]=None							,					__UpperCAmelCase							:       Dict=0.7							,					):
      if isinstance(__UpperCAmelCase							,					torch.Tensor):
        a		:						Tuple 				=     img.numpy().astype("np.uint8")
      if isinstance(__UpperCAmelCase							,					__UpperCAmelCase):
        a		:						str 				=     img_tensorize(__UpperCAmelCase)
      assert isinstance(__UpperCAmelCase							,					np.ndarray)

      a				,						a		:						str 				=     img.shape[1], img.shape[0]
      a		:						Optional[Any] 				=     mplfigure.Figure(frameon=__UpperCAmelCase)
      a		:						Dict 				=     fig.get_dpi()
      a		:						Union[str, Any] 				=     (width * scale + 1e-2) / dpi
      a		:						Optional[int] 				=     (height * scale + 1e-2) / dpi
      fig.set_size_inches(__UpperCAmelCase							,					__UpperCAmelCase)
      a		:						List[str] 				=     fig.add_axes([0.0, 0.0, 1.0, 1.0])
      ax.axis("off")
      ax.set_xlim(0.0							,					__UpperCAmelCase)
      ax.set_ylim(__UpperCAmelCase)

      a		:						int 				=     saveas
      a		:						Optional[int] 				=     rgb
      a		:						List[Any] 				=     pynb
      a		:						str 				=     img
      a		:						Any 				=     edgecolor
      a		:						Optional[int] 				=     0.5
      a		:						int 				=     linestyle
      a		:						Union[str, Any] 				=     int(np.sqrt(min(__UpperCAmelCase							,					__UpperCAmelCase)) * scale // 3)
      a		:						Any 				=     width
      a		:						Optional[Any] 				=     height
      a		:						Optional[int] 				=     scale
      a		:						Tuple 				=     fig
      a		:						Union[str, Any] 				=     ax
      a		:						List[Any] 				=     pad
      a		:						str 				=     idaobj
      a		:						Tuple 				=     idaattr
      a		:						List[str] 				=     FigureCanvasAgg(__UpperCAmelCase)






    def       __snake_case				(   self							:       Any							,					__UpperCAmelCase							:       Optional[Any]							,					__UpperCAmelCase							:       str=None):
      if color is None:
        a		:						Optional[Any] 				=     self.edgecolor
      ((a)				,						(a)				,						(a)				,						(a))		:						str 				=     box
      a		:						Union[str, Any] 				=     xa - xa
      a		:						Union[str, Any] 				=     ya - ya
      self.ax.add_patch(
          mpl.patches.Rectangle(
              (xa, ya)							,					__UpperCAmelCase							,					__UpperCAmelCase							,					fill=__UpperCAmelCase							,					edgecolor=__UpperCAmelCase							,					linewidth=self.font_size // 3							,					alpha=self.alpha							,					linestyle=self.linestyle							,					))






    def       __snake_case				(   self							:       Union[str, Any]							,					__UpperCAmelCase							:       str							,					__UpperCAmelCase							:       str=None							,					__UpperCAmelCase							:       Dict=None							,					__UpperCAmelCase							:       Optional[int]=None							,					__UpperCAmelCase							:       List[Any]=None):
      if len(boxes.shape) > 2:
        a		:						List[Any] 				=     boxes[0]
      if len(obj_ids.shape) > 1:
        a		:						List[Any] 				=     obj_ids[0]
      if len(obj_scores.shape) > 1:
        a		:						Optional[Any] 				=     obj_scores[0]
      if len(attr_ids.shape) > 1:
        a		:						Tuple 				=     attr_ids[0]
      if len(attr_scores.shape) > 1:
        a		:						int 				=     attr_scores[0]
      if isinstance(__UpperCAmelCase							,					torch.Tensor):
        a		:						Tuple 				=     boxes.numpy()
      if isinstance(__UpperCAmelCase							,					__UpperCAmelCase):
        a		:						Optional[Any] 				=     np.array(__UpperCAmelCase)
      assert isinstance(__UpperCAmelCase							,					np.ndarray)
      a		:						str 				=     np.prod(boxes[:, 2:] - boxes[:, :2]							,					axis=1)
      a		:						int 				=     np.argsort(-areas).tolist()
      a		:						Any 				=     boxes[sorted_idxs] if boxes is not None else None
      a		:						Optional[int] 				=     obj_ids[sorted_idxs] if obj_ids is not None else None
      a		:						int 				=     obj_scores[sorted_idxs] if obj_scores is not None else None
      a		:						Optional[int] 				=     attr_ids[sorted_idxs] if attr_ids is not None else None
      a		:						Tuple 				=     attr_scores[sorted_idxs] if attr_scores is not None else None

      a		:						Dict 				=     [self._random_color(maximum=1) for _ in range(len(__UpperCAmelCase))]
      a		:						Optional[Any] 				=     [assigned_colors[idx] for idx in sorted_idxs]
      if obj_ids is not None:
        a		:						Union[str, Any] 				=     self._create_text_labels_attr(__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase)
        for i in range(len(__UpperCAmelCase)):
          a		:						Optional[Any] 				=     assigned_colors[i]
          self.add_box(boxes[i]							,					__UpperCAmelCase)
          self.draw_labels(labels[i]							,					boxes[i]							,					__UpperCAmelCase)






    def       __snake_case				(   self							:       str							,					__UpperCAmelCase							:       int							,					__UpperCAmelCase							:       Optional[Any]							,					__UpperCAmelCase							:       int):
      a				,						a				,						a				,						a		:						int 				=     box
      a		:						Tuple 				=     (xa, ya)
      a		:						Optional[int] 				=     (ya - ya) * (xa - xa)
      a		:						Optional[int] 				=     _SMALL_OBJ * self.scale
      if instance_area < small or ya - ya < 40 * self.scale:
        if ya >= self.height - 5:
          a		:						Optional[int] 				=     (xa, ya)
        else:
          a		:						Optional[Any] 				=     (xa, ya)

      a		:						Dict 				=     (ya - ya) / np.sqrt(self.height * self.width)
      a		:						int 				=     self._change_color_brightness(__UpperCAmelCase							,					brightness_factor=0.7)
      a		:						Optional[int] 				=     np.clip((height_ratio - 0.02) / 0.08 + 1							,					1.2							,					2)
      font_size *= 0.75 * self.font_size

      self.draw_text(
          text=__UpperCAmelCase							,					position=__UpperCAmelCase							,					color=__UpperCAmelCase							,					)






    def       __snake_case				(   self							:       Any							,					__UpperCAmelCase							:       str							,					__UpperCAmelCase							:       List[Any]							,					__UpperCAmelCase							:       Optional[int]="g"							,					__UpperCAmelCase							:       Optional[Any]="left"							,					):
      a		:						int 				=     0
      a		:						Optional[Any] 				=     self.font_size
      a		:						Any 				=     np.maximum(list(mplc.to_rgb(__UpperCAmelCase))							,					0.2)
      a		:						Tuple 				=     max(0.8							,					np.max(__UpperCAmelCase))
      a		:						Union[str, Any] 				=     {
          "facecolor": "black",
          "alpha": self.alpha,
          "pad": self.pad,
          "edgecolor": "none",
      }
      a				,						a		:						Optional[int] 				=     position
      self.ax.text(
          __UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					size=font_size * self.scale							,					family="sans-serif"							,					bbox=__UpperCAmelCase							,					verticalalignment="top"							,					horizontalalignment=__UpperCAmelCase							,					color=__UpperCAmelCase							,					zorder=10							,					rotation=__UpperCAmelCase							,					)






    def       __snake_case				(   self							:       Any							,					__UpperCAmelCase							:       int=None):
      if saveas is None:
        a		:						Tuple 				=     self.saveas
      if saveas.lower().endswith(".jpg") or saveas.lower().endswith(".png"):
        cva.imwrite(
            __UpperCAmelCase							,					self._get_buffer()[:, :, ::-1]							,					)
      else:
        self.fig.savefig(__UpperCAmelCase)






    def       __snake_case				(   self							:       Tuple							,					__UpperCAmelCase							:       str							,					__UpperCAmelCase							:       str							,					__UpperCAmelCase							:       Tuple							,					__UpperCAmelCase							:       Optional[int]):
      a		:						int 				=     [self.idaobj[i] for i in classes]
      a		:						Any 				=     [self.idaattr[i] for i in attr_classes]
      a		:						Dict 				=     [
          f'''{label} {score:.2f} {attr} {attr_score:.2f}'''
          for label, score, attr, attr_score in zip(__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase)
      ]
      return labels






    def       __snake_case				(   self							:       Dict							,					__UpperCAmelCase							:       Any							,					__UpperCAmelCase							:       List[Any]):
      a		:						Dict 				=     [self.idaobj[i] for i in classes]
      if scores is not None:
        if labels is None:
          a		:						Optional[int] 				=     ["{:.0f}%".format(s * 100) for s in scores]
        else:
          a		:						Optional[Any] 				=     ["{} {:.0f}%".format(__UpperCAmelCase							,					s * 100) for li, s in zip(__UpperCAmelCase							,					__UpperCAmelCase)]
      return labels






    def       __snake_case				(   self							:       Optional[Any]							,					__UpperCAmelCase							:       Union[str, Any]=255):
      a		:						str 				=     np.random.randint(0							,					len(_COLORS))
      a		:						List[Any] 				=     _COLORS[idx] * maximum
      if not self.rgb:
        a		:						Tuple 				=     ret[::-1]
      return ret






    def       __snake_case				(   self							:       List[Any]):
      if not self.pynb:
        a				,						a		:						Tuple 				=     self.canvas.print_to_buffer()
        if (width, height) != (self.width, self.height):
          a		:						Dict 				=     cva.resize(self.img							,					(width, height))
        else:
          a		:						str 				=     self.img
      else:
        a		:						str 				=     io.BytesIO()  # works for cairo backend
        self.canvas.print_rgba(__UpperCAmelCase)
        a				,						a		:						List[str] 				=     self.width, self.height
        a		:						List[str] 				=     buf.getvalue()
        a		:						Optional[Any] 				=     self.img

      a		:						List[Any] 				=     np.frombuffer(__UpperCAmelCase							,					dtype="uint8")
      a		:						List[str] 				=     buffer.reshape(__UpperCAmelCase							,					__UpperCAmelCase							,					4)
      a				,						a		:						Union[str, Any] 				=     np.split(__UpperCAmelCase							,					[3]							,					axis=2)

      try:
        import numexpr as ne  # fuse them with numexpr

        a		:						Optional[Any] 				=     ne.evaluate("img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)")
      except ImportError:
        a		:						str 				=     alpha.astype("float32") / 255.0
        a		:						Dict 				=     img * (1 - alpha) + rgb * alpha

      return visualized_image.astype("uint8")






    def       __snake_case				(   self							:       Union[str, Any]							,					__UpperCAmelCase							:       List[Any]							,					__UpperCAmelCase							:       List[Any]):
      assert brightness_factor >= -1.0 and brightness_factor <= 1.0
      a		:						str 				=     mplc.to_rgb(__UpperCAmelCase)
      a		:						int 				=     colorsys.rgb_to_hls(*mplc.to_rgb(__UpperCAmelCase))
      a		:						List[Any] 				=     polygon_color[1] + (brightness_factor * polygon_color[1])
      a		:						Optional[Any] 				=     0.0 if modified_lightness < 0.0 else modified_lightness
      a		:						Dict 				=     1.0 if modified_lightness > 1.0 else modified_lightness
      a		:						Optional[Any] 				=     colorsys.hls_to_rgb(polygon_color[0]							,					__UpperCAmelCase							,					polygon_color[2])
      return modified_color






__lowercase          = (
    np.array(
        [
            0.0_00,
            0.4_47,
            0.7_41,
            0.8_50,
            0.3_25,
            0.0_98,
            0.9_29,
            0.6_94,
            0.1_25,
            0.4_94,
            0.1_84,
            0.5_56,
            0.4_66,
            0.6_74,
            0.1_88,
            0.3_01,
            0.7_45,
            0.9_33,
            0.6_35,
            0.0_78,
            0.1_84,
            0.3_00,
            0.3_00,
            0.3_00,
            0.6_00,
            0.6_00,
            0.6_00,
            1.0_00,
            0.0_00,
            0.0_00,
            1.0_00,
            0.5_00,
            0.0_00,
            0.7_49,
            0.7_49,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            0.3_33,
            0.3_33,
            0.0_00,
            0.3_33,
            0.6_67,
            0.0_00,
            0.3_33,
            1.0_00,
            0.0_00,
            0.6_67,
            0.3_33,
            0.0_00,
            0.6_67,
            0.6_67,
            0.0_00,
            0.6_67,
            1.0_00,
            0.0_00,
            1.0_00,
            0.3_33,
            0.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.3_33,
            0.5_00,
            0.0_00,
            0.6_67,
            0.5_00,
            0.0_00,
            1.0_00,
            0.5_00,
            0.3_33,
            0.0_00,
            0.5_00,
            0.3_33,
            0.3_33,
            0.5_00,
            0.3_33,
            0.6_67,
            0.5_00,
            0.3_33,
            1.0_00,
            0.5_00,
            0.6_67,
            0.0_00,
            0.5_00,
            0.6_67,
            0.3_33,
            0.5_00,
            0.6_67,
            0.6_67,
            0.5_00,
            0.6_67,
            1.0_00,
            0.5_00,
            1.0_00,
            0.0_00,
            0.5_00,
            1.0_00,
            0.3_33,
            0.5_00,
            1.0_00,
            0.6_67,
            0.5_00,
            1.0_00,
            1.0_00,
            0.5_00,
            0.0_00,
            0.3_33,
            1.0_00,
            0.0_00,
            0.6_67,
            1.0_00,
            0.0_00,
            1.0_00,
            1.0_00,
            0.3_33,
            0.0_00,
            1.0_00,
            0.3_33,
            0.3_33,
            1.0_00,
            0.3_33,
            0.6_67,
            1.0_00,
            0.3_33,
            1.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            0.6_67,
            0.3_33,
            1.0_00,
            0.6_67,
            0.6_67,
            1.0_00,
            0.6_67,
            1.0_00,
            1.0_00,
            1.0_00,
            0.0_00,
            1.0_00,
            1.0_00,
            0.3_33,
            1.0_00,
            1.0_00,
            0.6_67,
            1.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_67,
            0.0_00,
            0.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_67,
            0.0_00,
            0.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_43,
            0.1_43,
            0.1_43,
            0.8_57,
            0.8_57,
            0.8_57,
            1.0_00,
            1.0_00,
            1.0_00,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)










"""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 		_A    (    _a    ):







    """simple docstring"""




    UpperCAmelCase			:				torch.FloatTensor






class 		_A    (    _a       ,_a    ):







    """simple docstring"""




    @register_to_config
    def __init__(   self							:       List[Any]							,					__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							,					):
      super().__init__()
      a		:						Optional[Any] 				=     use_linear_projection
      a		:						Any 				=     num_attention_heads
      a		:						Dict 				=     attention_head_dim
      a		:						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
      a		:						int 				=     (in_channels is not None) and (patch_size is None)
      a		:						str 				=     num_vector_embeds is not None
      a		:						Dict 				=     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		:						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)
        a		:						Tuple 				=     "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		:						Union[str, Any] 				=     in_channels

        a		:						Dict 				=     torch.nn.GroupNorm(num_groups=__UpperCAmelCase							,					num_channels=__UpperCAmelCase							,					eps=1e-6							,					affine=__UpperCAmelCase)
        if use_linear_projection:
          a		:						Tuple 				=     nn.Linear(__UpperCAmelCase							,					__UpperCAmelCase)
        else:
          a		:						str 				=     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"

        a		:						Dict 				=     sample_size
        a		:						List[str] 				=     sample_size
        a		:						Any 				=     num_vector_embeds
        a		:						Union[str, Any] 				=     self.height * self.width

        a		:						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"

        a		:						int 				=     sample_size
        a		:						Optional[int] 				=     sample_size

        a		:						Dict 				=     patch_size
        a		:						Tuple 				=     PatchEmbed(
            height=__UpperCAmelCase							,					width=__UpperCAmelCase							,					patch_size=__UpperCAmelCase							,					in_channels=__UpperCAmelCase							,					embed_dim=__UpperCAmelCase							,					)

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






    def       __snake_case				(   self							:       Dict							,					__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							,					):
      # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
      #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
      #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
      # expects mask of shape:
      #   [batch, key_tokens]
      # adds singleton query_tokens dimension:
      #   [batch,                    1, key_tokens]
      # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
      #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
      #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
      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		:						Optional[Any] 				=     (1 - attention_mask.to(hidden_states.dtype)) * -10_000.0
        a		:						Tuple 				=     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		:						List[Any] 				=     (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10_000.0
        a		:						List[Any] 				=     encoder_attention_mask.unsqueeze(1)

      # 1. Input
      if self.is_input_continuous:
        a				,						a				,						a				,						a		:						Any 				=     hidden_states.shape
        a		:						Optional[int] 				=     hidden_states

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

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

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

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

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






