


'''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


_lowerCAmelCase	:		List[str]		  =						1000



class 			lowerCAmelCase					:



	def __init__(       self			,       snake_case__			,       snake_case__=1.2			,       snake_case__="g"			,       snake_case__=0.5			,       snake_case__="-"			,       snake_case__="test_out.jpg"			,       snake_case__=True			,       snake_case__=False			,       snake_case__=None			,       snake_case__=None			,       snake_case__=0.7			,       ):
		if isinstance(__UpperCAmelCase			,       torch.Tensor     ):
			lowerCAmelCase       :     Tuple   				=			img.numpy().astype('np.uint8'     )
		if isinstance(__UpperCAmelCase			,       __UpperCAmelCase     ):
			lowerCAmelCase       :     str   				=			img_tensorize(__UpperCAmelCase     )
		assert isinstance(__UpperCAmelCase			,       np.ndarray     )

		lowerCAmelCase       :     str   				=			img.shape[1], img.shape[0]
		lowerCAmelCase       :     Optional[Any]   				=			mplfigure.Figure(frameon=__UpperCAmelCase     )
		lowerCAmelCase       :     Dict   				=			fig.get_dpi()
		lowerCAmelCase       :     Union[str, Any]   				=			(width * scale + 1e-2) / dpi
		lowerCAmelCase       :     Optional[int]   				=			(height * scale + 1e-2) / dpi
		fig.set_size_inches(__UpperCAmelCase			,       __UpperCAmelCase     )
		lowerCAmelCase       :     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     )

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



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

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



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

		lowerCAmelCase       :     Dict   				=			(ya - ya) / np.sqrt(self.height * self.width     )
		lowerCAmelCase       :     int   				=			self._change_color_brightness(__UpperCAmelCase			,       brightness_factor=0.7     )
		lowerCAmelCase       :     Optional[int]   				=			np.clip((height_ratio - 0.0_2) / 0.0_8 + 1			,       1.2			,       2     )
		font_size *= 0.7_5 * self.font_size

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



	def 					lowercase  (       self			,       snake_case__			,       snake_case__			,       snake_case__="g"			,       snake_case__="left"			,       ):
		lowerCAmelCase       :     int   				=			0
		lowerCAmelCase       :     Optional[Any]   				=			self.font_size
		lowerCAmelCase       :     Any   				=			np.maximum(list(mplc.to_rgb(__UpperCAmelCase     )     )			,       0.2     )
		lowerCAmelCase       :     Tuple   				=			max(0.8			,       np.max(__UpperCAmelCase     )     )
		lowerCAmelCase       :     Union[str, Any]   				=			{
		    "facecolor": "black",
		    "alpha": self.alpha,
		    "pad": self.pad,
		    "edgecolor": "none",
		}
		lowerCAmelCase       :     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 					lowercase  (       self			,       snake_case__=None     ):
		if saveas is None:
			lowerCAmelCase       :     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 					lowercase  (       self			,       snake_case__			,       snake_case__			,       snake_case__			,       snake_case__     ):
		lowerCAmelCase       :     int   				=			[self.idaobj[i] for i in classes]
		lowerCAmelCase       :     Any   				=			[self.idaattr[i] for i in attr_classes]
		lowerCAmelCase       :     Dict   				=			[
		    f"{label} {score:.2f} {attr} {attr_score:.2f}"
		    for label, score, attr, attr_score in zip(__UpperCAmelCase			,       __UpperCAmelCase			,       __UpperCAmelCase			,       __UpperCAmelCase     )
		]
		return labels



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



	def 					lowercase  (       self			,       snake_case__=255     ):
		lowerCAmelCase       :     str   				=			np.random.randint(0			,       len(_COLORS     )     )
		lowerCAmelCase       :     List[Any]   				=			_COLORS[idx] * maximum
		if not self.rgb:
			lowerCAmelCase       :     Tuple   				=			ret[::-1]
		return ret



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

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

		try:
			import numexpr as ne  # fuse them with numexpr

			lowerCAmelCase       :     Optional[Any]   				=			ne.evaluate('img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'     )
		except ImportError:
			lowerCAmelCase       :     str   				=			alpha.astype('float32'     ) / 2_5_5.0
			lowerCAmelCase       :     Dict   				=			img * (1 - alpha) + rgb * alpha

		return visualized_image.astype('uint8'     )



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


_lowerCAmelCase	:		Tuple		  =						(
    np.array(
        [
            0.0_0_0,
            0.4_4_7,
            0.7_4_1,
            0.8_5_0,
            0.3_2_5,
            0.0_9_8,
            0.9_2_9,
            0.6_9_4,
            0.1_2_5,
            0.4_9_4,
            0.1_8_4,
            0.5_5_6,
            0.4_6_6,
            0.6_7_4,
            0.1_8_8,
            0.3_0_1,
            0.7_4_5,
            0.9_3_3,
            0.6_3_5,
            0.0_7_8,
            0.1_8_4,
            0.3_0_0,
            0.3_0_0,
            0.3_0_0,
            0.6_0_0,
            0.6_0_0,
            0.6_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.7_4_9,
            0.7_4_9,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.3_3_3,
            0.0_0_0,
            0.3_3_3,
            0.6_6_7,
            0.0_0_0,
            0.3_3_3,
            1.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.3_3_3,
            0.0_0_0,
            0.6_6_7,
            0.6_6_7,
            0.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.5_0_0,
            0.0_0_0,
            0.6_6_7,
            0.5_0_0,
            0.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.3_3_3,
            0.0_0_0,
            0.5_0_0,
            0.3_3_3,
            0.3_3_3,
            0.5_0_0,
            0.3_3_3,
            0.6_6_7,
            0.5_0_0,
            0.3_3_3,
            1.0_0_0,
            0.5_0_0,
            0.6_6_7,
            0.0_0_0,
            0.5_0_0,
            0.6_6_7,
            0.3_3_3,
            0.5_0_0,
            0.6_6_7,
            0.6_6_7,
            0.5_0_0,
            0.6_6_7,
            1.0_0_0,
            0.5_0_0,
            1.0_0_0,
            0.0_0_0,
            0.5_0_0,
            1.0_0_0,
            0.3_3_3,
            0.5_0_0,
            1.0_0_0,
            0.6_6_7,
            0.5_0_0,
            1.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.3_3_3,
            1.0_0_0,
            0.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.3_3_3,
            1.0_0_0,
            0.3_3_3,
            0.6_6_7,
            1.0_0_0,
            0.3_3_3,
            1.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.3_3_3,
            1.0_0_0,
            0.6_6_7,
            0.6_6_7,
            1.0_0_0,
            0.6_6_7,
            1.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.3_3_3,
            1.0_0_0,
            1.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_6_7,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_6_7,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_4_3,
            0.1_4_3,
            0.1_4_3,
            0.8_5_7,
            0.8_5_7,
            0.8_5_7,
            1.0_0_0,
            1.0_0_0,
            1.0_0_0,
        ]
    )
    .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)






