


'''simple docstring'''






import warnings

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.metrics import pairwise_distances

warnings.filterwarnings("""ignore""")

_A      =     """K-MEANS-CLUST/ """


def A_					(				__SCREAMING_SNAKE_CASE :					int	,   __SCREAMING_SNAKE_CASE :					Tuple	,   __SCREAMING_SNAKE_CASE :					List[str]=None       )     ->  Optional[Any]:
					if seed is not None:  # useful for obtaining consistent results
										np.random.seed(__SCREAMING_SNAKE_CASE       )
					__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = data.shape[0]  # number of data points

					# Pick K indices from range [0, N).
					__SCREAMING_SNAKE_CASE					:							Optional[int]			   = np.random.randint(0	,   __SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE       )

					# Keep centroids as dense format, as many entries will be nonzero due to averaging.
					# As long as at least one document in a cluster contains a word,
					# it will carry a nonzero weight in the TF-IDF vector of the centroid.
					__SCREAMING_SNAKE_CASE					:							Tuple			   = data[rand_indices, :]

					return centroids


def A_					(				__SCREAMING_SNAKE_CASE :					List[str]	,   __SCREAMING_SNAKE_CASE :					List[str]       )     ->  Optional[Any]:
					return pairwise_distances(__SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE	,   metric='''euclidean'''       )


def A_					(				__SCREAMING_SNAKE_CASE :					Union[str, Any]	,   __SCREAMING_SNAKE_CASE :					int       )     ->  int:
					# Compute distances between each data point and the set of centroids:
					# Fill in the blank (RHS only)
					__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = centroid_pairwise_dist(__SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE       )

					# Compute cluster assignments for each data point:
					# Fill in the blank (RHS only)
					__SCREAMING_SNAKE_CASE					:							Dict			   = np.argmin(__SCREAMING_SNAKE_CASE	,   axis=1       )

					return cluster_assignment


def A_					(				__SCREAMING_SNAKE_CASE :					Optional[Any]	,   __SCREAMING_SNAKE_CASE :					int	,   __SCREAMING_SNAKE_CASE :					List[Any]       )     ->  Tuple:
					__SCREAMING_SNAKE_CASE					:							str			   = []
					for i in range(__SCREAMING_SNAKE_CASE       ):
										# Select all data points that belong to cluster i. Fill in the blank (RHS only)
										__SCREAMING_SNAKE_CASE					:							Tuple			   = data[cluster_assignment == i]
										# Compute the mean of the data points. Fill in the blank (RHS only)
										__SCREAMING_SNAKE_CASE					:							Any			   = member_data_points.mean(axis=0       )
										new_centroids.append(__SCREAMING_SNAKE_CASE       )
					__SCREAMING_SNAKE_CASE					:							List[Any]			   = np.array(__SCREAMING_SNAKE_CASE       )

					return new_centroids


def A_					(				__SCREAMING_SNAKE_CASE :					str	,   __SCREAMING_SNAKE_CASE :					Union[str, Any]	,   __SCREAMING_SNAKE_CASE :					Union[str, Any]	,   __SCREAMING_SNAKE_CASE :					str       )     ->  List[Any]:
					__SCREAMING_SNAKE_CASE					:							Tuple			   = 0.0
					for i in range(__SCREAMING_SNAKE_CASE       ):
										# Select all data points that belong to cluster i. Fill in the blank (RHS only)
										__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = data[cluster_assignment == i, :]

										if member_data_points.shape[0] > 0:  # check if i-th cluster is non-empty
															# Compute distances from centroid to data points (RHS only)
															__SCREAMING_SNAKE_CASE					:							int			   = pairwise_distances(
															    __SCREAMING_SNAKE_CASE	,   [centroids[i]]	,   metric='''euclidean'''       )
															__SCREAMING_SNAKE_CASE					:							int			   = distances**2
															heterogeneity += np.sum(__SCREAMING_SNAKE_CASE       )

					return heterogeneity


def A_					(				__SCREAMING_SNAKE_CASE :					str	,   __SCREAMING_SNAKE_CASE :					Any       )     ->  int:
					plt.figure(figsize=(7, 4)       )
					plt.plot(__SCREAMING_SNAKE_CASE	,   linewidth=4       )
					plt.xlabel('''# Iterations'''       )
					plt.ylabel('''Heterogeneity'''       )
					plt.title(f"""Heterogeneity of clustering over time, K={k:d}"""       )
					plt.rcParams.update({'''font.size''': 16}       )
					plt.show()


def A_					(				__SCREAMING_SNAKE_CASE :					str	,   __SCREAMING_SNAKE_CASE :					str	,   __SCREAMING_SNAKE_CASE :					Union[str, Any]	,   __SCREAMING_SNAKE_CASE :					Tuple=5_00	,   __SCREAMING_SNAKE_CASE :					int=None	,   __SCREAMING_SNAKE_CASE :					Optional[int]=False       )     ->  Dict:
					__SCREAMING_SNAKE_CASE					:							Optional[int]			   = initial_centroids[:]
					__SCREAMING_SNAKE_CASE					:							List[str]			   = None

					for itr in range(__SCREAMING_SNAKE_CASE       ):
										if verbose:
															print(__SCREAMING_SNAKE_CASE	,   end=''''''       )

										# 1. Make cluster assignments using nearest centroids
										__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = assign_clusters(__SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE       )

										# 2. Compute a new centroid for each of the k clusters, averaging all data
										#    points assigned to that cluster.
										__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = revise_centroids(__SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE       )

										# Check for convergence: if none of the assignments changed, stop
										if (
										    prev_cluster_assignment is not None
										    and (prev_cluster_assignment == cluster_assignment).all()
										):
															break

										# Print number of new assignments
										if prev_cluster_assignment is not None:
															__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = np.sum(prev_cluster_assignment != cluster_assignment       )
															if verbose:
																				print(
																				    f"""    {num_changed:5d} elements changed their cluster assignment."""       )

        # Record heterogeneity convergence metric
										if record_heterogeneity is not None:
															# YOUR CODE HERE
															__SCREAMING_SNAKE_CASE					:							str			   = compute_heterogeneity(__SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE       )
															record_heterogeneity.append(__SCREAMING_SNAKE_CASE       )

										__SCREAMING_SNAKE_CASE					:							str			   = cluster_assignment[:]

					return centroids, cluster_assignment


# Mock test below
if False:  # change to true to run this test case.
						from sklearn import datasets as ds

						_A      =     ds.load_iris()
						_A      =     3
						_A      =     []
						_A      =     get_initial_centroids(dataset["""data"""], k, seed=0)
						_A      =     kmeans(
						    dataset["""data"""],
						    k,
						    initial_centroids,
						    maxiter=4_00,
						    record_heterogeneity=heterogeneity,
						    verbose=True,
						)
						plot_heterogeneity(heterogeneity, k)


def A_					(				__SCREAMING_SNAKE_CASE :					List[Any]	,   __SCREAMING_SNAKE_CASE :					Union[str, Any]	,   __SCREAMING_SNAKE_CASE :					Optional[Any]=None       )     ->  List[str]:
					# Fill missing values with given rules
					if fill_missing_report:
										__SCREAMING_SNAKE_CASE					:							int			   = df.fillna(value=__SCREAMING_SNAKE_CASE       )
					__SCREAMING_SNAKE_CASE					:							Any			   = 1
					__SCREAMING_SNAKE_CASE					:							Any			   = df.select_dtypes(np.number       ).columns
					__SCREAMING_SNAKE_CASE					:							List[Any]			   = (
					    df.groupby(['''Cluster''']       )[  # construct report dataframe
					        numeric_cols
					    ]  # group by cluster number
					    .agg(
					        [
					            ('''sum''', np.sum),
					            ('''mean_with_zeros''', lambda __SCREAMING_SNAKE_CASE       : np.mean(np.nan_to_num(__SCREAMING_SNAKE_CASE       )       )),
					            ('''mean_without_zeros''', lambda __SCREAMING_SNAKE_CASE       : x.replace(0	,   np.NaN       ).mean()),
					            (
					                '''mean_25-75''',
					                lambda __SCREAMING_SNAKE_CASE       : np.mean(
					                    np.nan_to_num(
					                        sorted(__SCREAMING_SNAKE_CASE       )[
					                            round(len(__SCREAMING_SNAKE_CASE       ) * 25 / 1_00       ) : round(len(__SCREAMING_SNAKE_CASE       ) * 75 / 1_00       )
					                        ]       )       ),
					            ),
					            ('''mean_with_na''', np.mean),
					            ('''min''', lambda __SCREAMING_SNAKE_CASE       : x.min()),
					            ('''5%''', lambda __SCREAMING_SNAKE_CASE       : x.quantile(0.05       )),
					            ('''25%''', lambda __SCREAMING_SNAKE_CASE       : x.quantile(0.25       )),
					            ('''50%''', lambda __SCREAMING_SNAKE_CASE       : x.quantile(0.50       )),
					            ('''75%''', lambda __SCREAMING_SNAKE_CASE       : x.quantile(0.75       )),
					            ('''95%''', lambda __SCREAMING_SNAKE_CASE       : x.quantile(0.95       )),
					            ('''max''', lambda __SCREAMING_SNAKE_CASE       : x.max()),
					            ('''count''', lambda __SCREAMING_SNAKE_CASE       : x.count()),
					            ('''stdev''', lambda __SCREAMING_SNAKE_CASE       : x.std()),
					            ('''mode''', lambda __SCREAMING_SNAKE_CASE       : x.mode()[0]),
					            ('''median''', lambda __SCREAMING_SNAKE_CASE       : x.median()),
					            ('''# > 0''', lambda __SCREAMING_SNAKE_CASE       : (x > 0).sum()),
					        ]       )
					    .T.reset_index()
					    .rename(index=__SCREAMING_SNAKE_CASE	,   columns={'''level_0''': '''Features''', '''level_1''': '''Type'''}       )
					)  # rename columns
					# calculate the size of cluster(count of clientID's)
					__SCREAMING_SNAKE_CASE					:							str			   = report[
					    (report["Features"] == "dummy") & (report["Type"] == "count")
					].copy()  # avoid SettingWithCopyWarning
					__SCREAMING_SNAKE_CASE					:							int			   = (
					    "ClusterSize"  # rename created cluster df to match report column names
					)
					__SCREAMING_SNAKE_CASE					:							List[str]			   = "# of Customers"
					__SCREAMING_SNAKE_CASE					:							int			   = pd.DataFrame(
					    clustersize.iloc[:, 2:].values
					    / clustersize.iloc[:, 2:].values.sum()       )
					__SCREAMING_SNAKE_CASE					:							Dict			   = "% of Customers"  # rename created cluster df to match report column names
					__SCREAMING_SNAKE_CASE					:							Any			   = "ClusterProportion"
					__SCREAMING_SNAKE_CASE					:							int			   = clusterproportion.columns.tolist()
					__SCREAMING_SNAKE_CASE					:							int			   = cols[-2:] + cols[:-2]
					__SCREAMING_SNAKE_CASE					:							Optional[int]			   = clusterproportion[cols]  # rearrange columns to match report
					__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = report.columns
					__SCREAMING_SNAKE_CASE					:							int			   = pd.DataFrame(
					    abs(
					        report[report['''Type'''] == '''count'''].iloc[:, 2:].values
					        - clustersize.iloc[:, 2:].values       )       )  # generating df with count of nan values
					__SCREAMING_SNAKE_CASE					:							Optional[int]			   = 0
					__SCREAMING_SNAKE_CASE					:							Tuple			   = "# of nan"
					__SCREAMING_SNAKE_CASE					:							Any			   = report[
					    report["Type"] == "count"
					].Features.tolist()  # filling values in order to match report
					__SCREAMING_SNAKE_CASE					:							int			   = a.columns.tolist()
					__SCREAMING_SNAKE_CASE					:							Tuple			   = cols[-2:] + cols[:-2]
					__SCREAMING_SNAKE_CASE					:							List[str]			   = a[cols]  # rearrange columns to match report
					__SCREAMING_SNAKE_CASE					:							int			   = report.columns  # rename columns to match report
					__SCREAMING_SNAKE_CASE					:							List[Any]			   = report.drop(
					    report[report.Type == '''count'''].index       )  # drop count values except cluster size
					__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = pd.concat(
					    [report, a, clustersize, clusterproportion]	,   axis=0       )  # concat report with clustert size and nan values
					__SCREAMING_SNAKE_CASE					:							Any			   = report["Features"].isin(__SCREAMING_SNAKE_CASE       )
					__SCREAMING_SNAKE_CASE					:							Tuple			   = report.columns.tolist()
					__SCREAMING_SNAKE_CASE					:							str			   = cols[0:2] + cols[-1:] + cols[2:-1]
					__SCREAMING_SNAKE_CASE					:							int			   = report[cols]
					__SCREAMING_SNAKE_CASE					:							str			   = {
					    "ClusterSize": 9,
					    "ClusterProportion": 8,
					    "mean_with_zeros": 7,
					    "mean_with_na": 6,
					    "max": 5,
					    "50%": 4,
					    "min": 3,
					    "25%": 2,
					    "75%": 1,
					    "# of nan": 0,
					    "# > 0": -1,
					    "sum_with_na": -2,
					}
					__SCREAMING_SNAKE_CASE					:							int			   = (
					    report.assign(
					        Sortera=lambda __SCREAMING_SNAKE_CASE       : x.Type.map(__SCREAMING_SNAKE_CASE       )	,   Sortera=lambda __SCREAMING_SNAKE_CASE       : list(reversed(range(len(__SCREAMING_SNAKE_CASE       )       )       )       )	,   )
					    .sort_values(['''Sorter1''', '''Mark''', '''Sorter2''']	,   ascending=__SCREAMING_SNAKE_CASE       )
					    .drop(['''Sorter1''', '''Sorter2''']	,   axis=1       )
					)
					__SCREAMING_SNAKE_CASE					:							List[str]			   = ""
					__SCREAMING_SNAKE_CASE					:							Any			   = report.reset_index()
					__SCREAMING_SNAKE_CASE					:							Any			   = report.drop(columns=['''index''']       )
					return report


if __name__ == "__main__":
						import doctest

						doctest.testmod()






'''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      SCREAMING_SNAKE_CASE_	( snake_case		):
			__a					:      torch.FloatTensor






class      SCREAMING_SNAKE_CASE_	( snake_case					,					snake_case		):




			@register_to_config
			def __init__(      self					,      lowercase = 1_6					,      lowercase = 8_8					,      lowercase = None					,      lowercase = None					,      lowercase = 1					,      lowercase = 0.0					,      lowercase = 3_2					,      lowercase = None					,      lowercase = False					,      lowercase = None					,      lowercase = None					,      lowercase = None					,      lowercase = "geglu"					,      lowercase = None					,      lowercase = False					,      lowercase = False					,      lowercase = False					,      lowercase = "layer_norm"					,      lowercase = True					,      )			->						Optional[int]:


								'''simple docstring'''






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

								if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
													__SCREAMING_SNAKE_CASE					:							Any			   = (
													    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'''					,      lowercase					,      standard_warn=lowercase       )
													__SCREAMING_SNAKE_CASE					:							str			   = '''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:
													__SCREAMING_SNAKE_CASE					:							List[Any]			   = in_channels

													__SCREAMING_SNAKE_CASE					:							Optional[int]			   = torch.nn.GroupNorm(num_groups=lowercase					,      num_channels=lowercase					,      eps=1e-6					,      affine=lowercase       )
													if use_linear_projection:
																		__SCREAMING_SNAKE_CASE					:							List[Any]			   = nn.Linear(lowercase					,      lowercase       )
													else:
																		__SCREAMING_SNAKE_CASE					:							Any			   = nn.Convad(lowercase					,      lowercase					,      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"

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

													__SCREAMING_SNAKE_CASE					:							int			   = ImagePositionalEmbeddings(
													    num_embed=lowercase					,      embed_dim=lowercase					,      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"

													__SCREAMING_SNAKE_CASE					:							Any			   = sample_size
													__SCREAMING_SNAKE_CASE					:							Any			   = sample_size

													__SCREAMING_SNAKE_CASE					:							int			   = patch_size
													__SCREAMING_SNAKE_CASE					:							Any			   = PatchEmbed(
													    height=lowercase					,      width=lowercase					,      patch_size=lowercase					,      in_channels=lowercase					,      embed_dim=lowercase					,      )

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

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




			def 			_snake_case   (      self					,      lowercase					,      lowercase = None					,      lowercase = None					,      lowercase = None					,      lowercase = None					,      lowercase = None					,      lowercase = None					,      lowercase = True					,      )			->						Optional[Any]:


								'''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)
													__SCREAMING_SNAKE_CASE					:							Optional[int]			   = (1 - attention_mask.to(hidden_states.dtype       )) * -1_0_0_0_0.0
													__SCREAMING_SNAKE_CASE					:							Dict			   = 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:
													__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = (1 - encoder_attention_mask.to(hidden_states.dtype       )) * -1_0_0_0_0.0
													__SCREAMING_SNAKE_CASE					:							List[Any]			   = encoder_attention_mask.unsqueeze(1       )

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

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

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

								# 3. Output
								if self.is_input_continuous:
													if not self.use_linear_projection:
																		__SCREAMING_SNAKE_CASE					:							Tuple			   = hidden_states.reshape(lowercase					,      lowercase					,      lowercase					,      lowercase       ).permute(0					,      3					,      1					,      2       ).contiguous()
																		__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = self.proj_out(lowercase       )
													else:
																		__SCREAMING_SNAKE_CASE					:							str			   = self.proj_out(lowercase       )
																		__SCREAMING_SNAKE_CASE					:							str			   = hidden_states.reshape(lowercase					,      lowercase					,      lowercase					,      lowercase       ).permute(0					,      3					,      1					,      2       ).contiguous()

													__SCREAMING_SNAKE_CASE					:							str			   = hidden_states + residual
								elif self.is_input_vectorized:
													__SCREAMING_SNAKE_CASE					:							Dict			   = self.norm_out(lowercase       )
													__SCREAMING_SNAKE_CASE					:							str			   = self.out(lowercase       )
													# (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
													__SCREAMING_SNAKE_CASE					:							Optional[int]			   = logits.permute(0					,      2					,      1       )

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

													# unpatchify
													__SCREAMING_SNAKE_CASE					:							List[str]			   = int(hidden_states.shape[1] ** 0.5       )
													__SCREAMING_SNAKE_CASE					:							int			   = hidden_states.reshape(
													    shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)       )
													__SCREAMING_SNAKE_CASE					:							int			   = torch.einsum('''nhwpqc->nchpwq'''					,      lowercase       )
													__SCREAMING_SNAKE_CASE					:							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=lowercase       )


