





'''simple docstring'''

from random import shuffle

import tensorflow as tf
from numpy import array







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = int(lowerCAmelCase_							)
				assert noofclusters < len(lowerCAmelCase_							)

				# Find out the dimensionality
				lowercase         = len(vectors[0]							)

				# Will help select random centroids from among the available vectors
				lowercase         = 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.

				lowercase         = tf.Graph()

				with graph.as_default():
								# SESSION OF COMPUTATION

								lowercase         = 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
								lowercase         = [
								    tf.Variable(vectors[vector_indices[i]]							) for i in range(lowerCAmelCase_							)
								]
								##These nodes will assign the centroid Variables the appropriate
								##values
								lowercase         = tf.placeholder("float64"		,							[dim]							)
								lowercase         = []
								for centroid in centroids:
												cent_assigns.append(tf.assign(lowerCAmelCase_		,							lowerCAmelCase_							)							)

								##Variables for cluster assignments of individual vectors(initialized
								##to 0 at first)
								lowercase         = [tf.Variable(0							) for i in range(len(lowerCAmelCase_							)							)]
								##These nodes will assign an assignment Variable the appropriate
								##value
								lowercase         = tf.placeholder("int32"							)
								lowercase         = []
								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
								lowercase         = 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
								lowercase         = tf.reduce_mean(lowerCAmelCase_		,							0							)

								##Node for computing Euclidean distances
								# Placeholders for input
								lowercase         = tf.placeholder("float"		,							[dim]							)
								lowercase         = tf.placeholder("float"		,							[dim]							)
								lowercase         = 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
								lowercase         = tf.placeholder("float"		,							[noofclusters]							)
								lowercase         = 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.
								lowercase         = 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.
								lowercase         = 100
								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_							)							):
																lowercase         = 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.
																lowercase         = [
																    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
																lowercase         = 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
																lowercase         = [
																    vectors[i]
																    for i in range(len(lowerCAmelCase_							)							)
																    if sess.run(assignments[i]							) == cluster_n
																]
																# Compute new centroid location
																lowercase         = 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
								lowercase         = sess.run(lowerCAmelCase_							)
								lowercase         = sess.run(lowerCAmelCase_							)
								return centroids, assignments











'''simple docstring'''

import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


__lowerCamelCase : Any											=			{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}

class       UpperCAmelCase (						nn.Module  ):




		def __init__(self      :	Tuple							,			A__      :	Any				)					-> str:
						super().__init__()
						lowercase         = torchvision.models.resnetaaa(pretrained=A__				)
						lowercase         = list(model.children()				)[:-2]
						lowercase         = nn.Sequential(*A__				)
						lowercase         = nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]				)





		def 				UpperCAmelCase__   (self      :	List[str]							,			A__      :	Optional[Any]				)					-> Optional[int]:
						# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
						lowercase         = self.pool(self.model(A__				)				)
						lowercase         = torch.flatten(A__							,			start_dim=2				)
						lowercase         = out.transpose(1							,			2				).contiguous()
						return out  # BxNx2048




class       UpperCAmelCase (						_lowercase  ):




		def __init__(self      :	int							,			A__      :	Optional[int]							,			A__      :	Optional[Any]							,			A__      :	List[Any]							,			A__      :	Tuple							,			A__      :	Optional[int]				)					-> Union[str, Any]:
						lowercase         = [json.loads(A__				) for l in open(A__				)]
						lowercase         = os.path.dirname(A__				)
						lowercase         = tokenizer
						lowercase         = labels
						lowercase         = len(A__				)
						lowercase         = max_seq_length

						lowercase         = transforms




		def __len__(self      :	List[str]				)					-> Dict:
						return len(self.data				)




		def __getitem__(self      :	int							,			A__      :	Union[str, Any]				)					-> List[str]:
						lowercase         = torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]							,			add_special_tokens=A__				)				)
						lowercase   ,							lowercase   ,							lowercase         = sentence[0], sentence[1:-1], sentence[-1]
						lowercase         = sentence[: self.max_seq_length]

						lowercase         = torch.zeros(self.n_classes				)
						lowercase         = 1

						lowercase         = Image.open(os.path.join(self.data_dir							,			self.data[index]["img"]				)				).convert("RGB"				)
						lowercase         = self.transforms(A__				)

						return {
						    "image_start_token": start_token,
						    "image_end_token": end_token,
						    "sentence": sentence,
						    "image": image,
						    "label": label,
						}





		def 				UpperCAmelCase__   (self      :	str				)					-> str:
						lowercase         = Counter()
						for row in self.data:
										label_freqs.update(row["label"]				)
						return label_freqs







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = [len(row["sentence"]							) for row in batch]
				lowercase   ,							lowercase         = len(lowerCAmelCase_							), max(lowerCAmelCase_							)

				lowercase         = torch.zeros(lowerCAmelCase_		,							lowerCAmelCase_		,							dtype=torch.long							)
				lowercase         = torch.zeros(lowerCAmelCase_		,							lowerCAmelCase_		,							dtype=torch.long							)

				for i_batch, (input_row, length) in enumerate(zip(lowerCAmelCase_		,							lowerCAmelCase_							)							):
								lowercase         = input_row["sentence"]
								lowercase         = 1

				lowercase         = torch.stack([row["image"] for row in batch]							)
				lowercase         = torch.stack([row["label"] for row in batch]							)
				lowercase         = torch.stack([row["image_start_token"] for row in batch]							)
				lowercase         = torch.stack([row["image_end_token"] for row in batch]							)

				return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






				return [
				    "Crime",
				    "Drama",
				    "Thriller",
				    "Action",
				    "Comedy",
				    "Romance",
				    "Documentary",
				    "Short",
				    "Mystery",
				    "History",
				    "Family",
				    "Adventure",
				    "Fantasy",
				    "Sci-Fi",
				    "Western",
				    "Horror",
				    "Sport",
				    "War",
				    "Music",
				    "Musical",
				    "Animation",
				    "Biography",
				    "Film-Noir",
				]







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






				return transforms.Compose(
				    [
				        transforms.Resize(256							),
				        transforms.CenterCrop(224							),
				        transforms.ToTensor(),
				        transforms.Normalize(
				            mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17]		,							std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69]		,							),
				    ]							)




