



from random import shuffle

import tensorflow as tf
from numpy import array







def       lowerCAmelCase_     (lowerCAmelCase__:						Optional[int]						,		lowerCAmelCase__:						str   ):





			"""simple docstring"""


			UpperCAmelCase_:						Optional[int]					=			int(lowerCAmelCase__   )
			assert noofclusters < len(lowerCAmelCase__   )

			# Find out the dimensionality
			UpperCAmelCase_:						Dict					=			len(vectors[0]   )

			# Will help select random centroids from among the available vectors
			UpperCAmelCase_:						str					=			list(range(len(lowerCAmelCase__   )   )   )
			shuffle(lowerCAmelCase__   )

			# GRAPH OF COMPUTATION
			# We initialize a new graph and set it as the default during each run
			# of this algorithm. This ensures that as this function is called
			# multiple times, the default graph doesn't keep getting crowded with
			# unused ops and Variables from previous function calls.

			UpperCAmelCase_:						Union[str, Any]					=			tf.Graph()

			with graph.as_default():
						# SESSION OF COMPUTATION

						UpperCAmelCase_:						int					=			tf.Session()

						##CONSTRUCTING THE ELEMENTS OF COMPUTATION

						##First lets ensure we have a Variable vector for each centroid,
						##initialized to one of the vectors from the available data points
						UpperCAmelCase_:						Tuple					=			[
						    tf.Variable(vectors[vector_indices[i]]   ) for i in range(lowerCAmelCase__   )
						]
						##These nodes will assign the centroid Variables the appropriate
						##values
						UpperCAmelCase_:						Tuple					=			tf.placeholder("""float64"""						,		[dim]   )
						UpperCAmelCase_:						Optional[Any]					=			[]
						for centroid in centroids:
									cent_assigns.append(tf.assign(lowerCAmelCase__						,		lowerCAmelCase__   )   )

						##Variables for cluster assignments of individual vectors(initialized
						##to 0 at first)
						UpperCAmelCase_:						Union[str, Any]					=			[tf.Variable(0   ) for i in range(len(lowerCAmelCase__   )   )]
						##These nodes will assign an assignment Variable the appropriate
						##value
						UpperCAmelCase_:						int					=			tf.placeholder("""int32"""   )
						UpperCAmelCase_:						List[Any]					=			[]
						for assignment in assignments:
									cluster_assigns.append(tf.assign(lowerCAmelCase__						,		lowerCAmelCase__   )   )

						##Now lets construct the node that will compute the mean
						# The placeholder for the input
						UpperCAmelCase_:						str					=			tf.placeholder("""float"""						,		[None, dim]   )
						# The Node/op takes the input and computes a mean along the 0th
						# dimension, i.e. the list of input vectors
						UpperCAmelCase_:						List[str]					=			tf.reduce_mean(lowerCAmelCase__						,		0   )

						##Node for computing Euclidean distances
						# Placeholders for input
						UpperCAmelCase_:						int					=			tf.placeholder("""float"""						,		[dim]   )
						UpperCAmelCase_:						Optional[int]					=			tf.placeholder("""float"""						,		[dim]   )
						UpperCAmelCase_:						Tuple					=			tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(lowerCAmelCase__						,		lowerCAmelCase__   )						,		2   )   )   )

						##This node will figure out which cluster to assign a vector to,
						##based on Euclidean distances of the vector from the centroids.
						# Placeholder for input
						UpperCAmelCase_:						int					=			tf.placeholder("""float"""						,		[noofclusters]   )
						UpperCAmelCase_:						Optional[int]					=			tf.argmin(lowerCAmelCase__						,		0   )

						##INITIALIZING STATE VARIABLES

						##This will help initialization of all Variables defined with respect
						##to the graph. The Variable-initializer should be defined after
						##all the Variables have been constructed, so that each of them
						##will be included in the initialization.
						UpperCAmelCase_:						List[Any]					=			tf.initialize_all_variables()

						# Initialize all variables
						sess.run(lowerCAmelCase__   )

						##CLUSTERING ITERATIONS

						# Now perform the Expectation-Maximization steps of K-Means clustering
						# iterations. To keep things simple, we will only do a set number of
						# iterations, instead of using a Stopping Criterion.
						UpperCAmelCase_:						Union[str, Any]					=			1_0_0
						for _ in range(lowerCAmelCase__   ):
									##EXPECTATION STEP
									##Based on the centroid locations till last iteration, compute
									##the _expected_ centroid assignments.
									# Iterate over each vector
									for vector_n in range(len(lowerCAmelCase__   )   ):
												UpperCAmelCase_:						Tuple					=			vectors[vector_n]
												# Compute Euclidean distance between this vector and each
												# centroid. Remember that this list cannot be named
												#'centroid_distances', since that is the input to the
												# cluster assignment node.
												UpperCAmelCase_:						Optional[int]					=			[
												    sess.run(lowerCAmelCase__						,		feed_dict={va: vect, va: sess.run(lowerCAmelCase__   )}   )
												    for centroid in centroids
												]
												# Now use the cluster assignment node, with the distances
												# as the input
												UpperCAmelCase_:						Union[str, Any]					=			sess.run(
												    lowerCAmelCase__						,		feed_dict={centroid_distances: distances}   )
												# Now assign the value to the appropriate state variable
												sess.run(
												    cluster_assigns[vector_n]						,		feed_dict={assignment_value: assignment}   )

									##MAXIMIZATION STEP
									# Based on the expected state computed from the Expectation Step,
									# compute the locations of the centroids so as to maximize the
									# overall objective of minimizing within-cluster Sum-of-Squares
									for cluster_n in range(lowerCAmelCase__   ):
												# Collect all the vectors assigned to this cluster
												UpperCAmelCase_:						List[Any]					=			[
												    vectors[i]
												    for i in range(len(lowerCAmelCase__   )   )
												    if sess.run(assignments[i]   ) == cluster_n
												]
												# Compute new centroid location
												UpperCAmelCase_:						Tuple					=			sess.run(
												    lowerCAmelCase__						,		feed_dict={mean_input: array(lowerCAmelCase__   )}   )
												# Assign value to appropriate variable
												sess.run(
												    cent_assigns[cluster_n]						,		feed_dict={centroid_value: new_location}   )

        # Return centroids and assignments
						UpperCAmelCase_:						Any					=			sess.run(lowerCAmelCase__   )
						UpperCAmelCase_:						List[str]					=			sess.run(lowerCAmelCase__   )
						return centroids, assignments








import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


lowercase__		:Optional[int]   =    logging.get_logger(__name__)  # pylint: disable=invalid-name







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):




							def __init__(				self   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__   ,):
												super().__init__()
												self.register_modules(
												    vae=A__   ,text_encoder=A__   ,tokenizer=A__   ,unet=A__   ,scheduler=A__   ,safety_checker=A__   ,feature_extractor=A__   ,)




							def  A__      (				self   ,A__ = "auto"):
												if slice_size == "auto":
																	# half the attention head size is usually a good trade-off between
																	# speed and memory
																	lowercase							=						self.unet.config.attention_head_dim // 2
												self.unet.set_attention_slice(A__)




							def  A__      (				self):
												self.enable_attention_slicing(A__)





							@torch.no_grad()
							def __call__(				self   ,A__   ,A__ = 5_1_2   ,A__ = 5_1_2   ,A__ = 5_0   ,A__ = 7.5   ,A__ = None   ,A__ = 1   ,A__ = 0.0   ,A__ = None   ,A__ = None   ,A__ = "pil"   ,A__ = True   ,A__ = None   ,A__ = 1   ,A__ = None   ,**A__   ,):

												if isinstance(A__   ,A__):
																	lowercase							=						1
												elif isinstance(A__   ,A__):
																	lowercase							=						len(A__)
												else:
																	raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(A__)}')

												if height % 8 != 0 or width % 8 != 0:
																	raise ValueError(f'`height` and `width` have to be divisible by 8 but are {height} and {width}.')

												if (callback_steps is None) or (
												    callback_steps is not None and (not isinstance(A__   ,A__) or callback_steps <= 0)
												):
																	raise ValueError(
																	    f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
																	    f' {type(A__)}.')

												# get prompt text embeddings
												lowercase							=						self.tokenizer(
												    A__   ,padding='''max_length'''   ,max_length=self.tokenizer.model_max_length   ,return_tensors='''pt'''   ,)
												lowercase							=						text_inputs.input_ids

												if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																	lowercase							=						self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
																	logger.warning(
																	    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
																	    f' {self.tokenizer.model_max_length} tokens: {removed_text}')
																	lowercase							=						text_input_ids[:, : self.tokenizer.model_max_length]

												if text_embeddings is None:
																	lowercase							=						self.text_encoder(text_input_ids.to(self.device))[0]

												# duplicate text embeddings for each generation per prompt, using mps friendly method
												lowercase	,						lowercase	,						lowercase							=						text_embeddings.shape
												lowercase							=						text_embeddings.repeat(1   ,A__   ,1)
												lowercase							=						text_embeddings.view(bs_embed * num_images_per_prompt   ,A__   ,-1)

												# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
												# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
												# corresponds to doing no classifier free guidance.
												lowercase							=						guidance_scale > 1.0
												# get unconditional embeddings for classifier free guidance
												if do_classifier_free_guidance:
																	lowercase							=						42
																	if negative_prompt is None:
																						lowercase							=						['''''']
																	elif type(A__) is not type(A__):
																						raise TypeError(
																						    f'`negative_prompt` should be the same type to `prompt`, but got {type(A__)} !='
																						    f' {type(A__)}.')
																	elif isinstance(A__   ,A__):
																						lowercase							=						[negative_prompt]
																	elif batch_size != len(A__):
																						raise ValueError(
																						    f'`negative_prompt`: {negative_prompt} has batch size {len(A__)}, but `prompt`:'
																						    f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
																						    ''' the batch size of `prompt`.''')
																	else:
																						lowercase							=						negative_prompt

																	lowercase							=						text_input_ids.shape[-1]
																	lowercase							=						self.tokenizer(
																	    A__   ,padding='''max_length'''   ,max_length=A__   ,truncation=A__   ,return_tensors='''pt'''   ,)
																	lowercase							=						self.text_encoder(uncond_input.input_ids.to(self.device))[0]

																	# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																	lowercase							=						uncond_embeddings.shape[1]
																	lowercase							=						uncond_embeddings.repeat(A__   ,A__   ,1)
																	lowercase							=						uncond_embeddings.view(batch_size * num_images_per_prompt   ,A__   ,-1)

																	# For classifier free guidance, we need to do two forward passes.
																	# Here we concatenate the unconditional and text embeddings into a single batch
																	# to avoid doing two forward passes
																	lowercase							=						torch.cat([uncond_embeddings, text_embeddings])

												# get the initial random noise unless the user supplied it

												# Unlike in other pipelines, latents need to be generated in the target device
												# for 1-to-1 results reproducibility with the CompVis implementation.
												# However this currently doesn't work in `mps`.
												lowercase							=						(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
												lowercase							=						(batch_size * num_images_per_prompt, self.unet.config.in_channels, 6_4, 6_4)
												lowercase							=						text_embeddings.dtype
												if latents is None:
																	if self.device.type == "mps":
																						# randn does not exist on mps
																						lowercase							=						torch.randn(
																						    A__   ,generator=A__   ,device='''cpu'''   ,dtype=A__).to(self.device)
																						lowercase							=						torch.randn(A__   ,generator=A__   ,device='''cpu'''   ,dtype=A__).to(
																						    self.device)
																	else:
																						lowercase							=						torch.randn(
																						    A__   ,generator=A__   ,device=self.device   ,dtype=A__)
																						lowercase							=						torch.randn(A__   ,generator=A__   ,device=self.device   ,dtype=A__)
												else:
																	if latents_reference.shape != latents_shape:
																						raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}')
																	lowercase							=						latents_reference.to(self.device)
																	lowercase							=						latents.to(self.device)

												# This is the key part of the pipeline where we
												# try to ensure that the generated images w/ the same seed
												# but different sizes actually result in similar images
												lowercase							=						(latents_shape[3] - latents_shape_reference[3]) // 2
												lowercase							=						(latents_shape[2] - latents_shape_reference[2]) // 2
												lowercase							=						latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
												lowercase							=						latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
												lowercase							=						0 if dx < 0 else dx
												lowercase							=						0 if dy < 0 else dy
												lowercase							=						max(-dx   ,0)
												lowercase							=						max(-dy   ,0)
												# import pdb
												# pdb.set_trace()
												lowercase							=						latents_reference[:, :, dy : dy + h, dx : dx + w]

												# set timesteps
												self.scheduler.set_timesteps(A__)

												# Some schedulers like PNDM have timesteps as arrays
												# It's more optimized to move all timesteps to correct device beforehand
												lowercase							=						self.scheduler.timesteps.to(self.device)

												# scale the initial noise by the standard deviation required by the scheduler
												lowercase							=						latents * self.scheduler.init_noise_sigma

												# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
												# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
												# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
												# and should be between [0, 1]
												lowercase							=						'''eta''' in set(inspect.signature(self.scheduler.step).parameters.keys())
												lowercase							=						{}
												if accepts_eta:
																	lowercase							=						eta

												for i, t in enumerate(self.progress_bar(A__)):
																	# expand the latents if we are doing classifier free guidance
																	lowercase							=						torch.cat([latents] * 2) if do_classifier_free_guidance else latents
																	lowercase							=						self.scheduler.scale_model_input(A__   ,A__)

																	# predict the noise residual
																	lowercase							=						self.unet(A__   ,A__   ,encoder_hidden_states=A__).sample

																	# perform guidance
																	if do_classifier_free_guidance:
																						lowercase	,						lowercase							=						noise_pred.chunk(2)
																						lowercase							=						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																	# compute the previous noisy sample x_t -> x_t-1
																	lowercase							=						self.scheduler.step(A__   ,A__   ,A__   ,**A__).prev_sample

																	# call the callback, if provided
																	if callback is not None and i % callback_steps == 0:
																						callback(A__   ,A__   ,A__)

												lowercase							=						1 / 0.18215 * latents
												lowercase							=						self.vae.decode(A__).sample

												lowercase							=						(image / 2 + 0.5).clamp(0   ,1)

												# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
												lowercase							=						image.cpu().permute(0   ,2   ,3   ,1).float().numpy()

												if self.safety_checker is not None:
																	lowercase							=						self.feature_extractor(self.numpy_to_pil(A__)   ,return_tensors='''pt''').to(
																	    self.device)
																	lowercase	,						lowercase							=						self.safety_checker(
																	    images=A__   ,clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype))
												else:
																	lowercase							=						None

												if output_type == "pil":
																	lowercase							=						self.numpy_to_pil(A__)

												if not return_dict:
																	return (image, has_nsfw_concept)

												return StableDiffusionPipelineOutput(images=A__   ,nsfw_content_detected=A__)




