



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


UpperCAmelCase_							:      Optional[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			,	UpperCAmelCase__		):
							super().__init__()
							A__							=       torchvision.models.resnetaaa(pretrained=UpperCAmelCase__		)
							A__							=       list(model.children()		)[:-2]
							A__							=       nn.Sequential(*UpperCAmelCase__		)
							A__							=       nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]		)



	def 			__A							(       self			,	UpperCAmelCase__		):
							# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
							A__							=       self.pool(self.model(UpperCAmelCase__		)		)
							A__							=       torch.flatten(UpperCAmelCase__			,	start_dim=2		)
							A__							=       out.transpose(1			,	2		).contiguous()
							return out  # BxNx2048






class      UpperCamelCase    (						_UpperCAmelCase     ):



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							A__							=       [json.loads(UpperCAmelCase__		) for l in open(UpperCAmelCase__		)]
							A__							=       os.path.dirname(UpperCAmelCase__		)
							A__							=       tokenizer
							A__							=       labels
							A__							=       len(UpperCAmelCase__		)
							A__							=       max_seq_length

							A__							=       transforms



	def __len__(       self		):
							return len(self.data		)



	def __getitem__(       self			,	UpperCAmelCase__		):
							A__							=       torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]			,	add_special_tokens=UpperCAmelCase__		)		)
							A__						,       A__						,       A__							=       sentence[0], sentence[1:-1], sentence[-1]
							A__							=       sentence[: self.max_seq_length]

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

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

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



	def 			__A							(       self		):
							A__							=       Counter()
							for row in self.data:
													label_freqs.update(row["label"]		)
							return label_freqs





def  UpperCamelCase						(       _A			:				Optional[int]    )->       Optional[int]:





						"""simple docstring"""





						A__							=       [len(row["sentence"]    ) for row in batch]
						A__						,       A__							=       len(_A    ), max(_A    )

						A__							=       torch.zeros(_A			,							_A			,							dtype=torch.long    )
						A__							=       torch.zeros(_A			,							_A			,							dtype=torch.long    )

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

						A__							=       torch.stack([row["image"] for row in batch]    )
						A__							=       torch.stack([row["label"] for row in batch]    )
						A__							=       torch.stack([row["image_start_token"] for row in batch]    )
						A__							=       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						(       )->       List[str]:





						"""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						(       )->       Any:





						"""simple docstring"""





						return transforms.Compose(
						    [
						        transforms.Resize(256    ),
						        transforms.CenterCrop(224    ),
						        transforms.ToTensor(),
						        transforms.Normalize(
						            mean=[0.4677_7044, 0.4453_1429, 0.4066_1017]			,							std=[0.1222_1994, 0.1214_5835, 0.1438_0469]			,							),
						    ]    )










from manim import *






class      UpperCamelCase    (						_UpperCAmelCase     ):



	def 			__A							(       self		):
							A__							=       Rectangle(height=0.5			,	width=0.5		)
							A__							=       Rectangle(height=0.46			,	width=0.46		).set_stroke(width=0		)

							A__							=       [mem.copy() for i in range(6		)]
							A__							=       [mem.copy() for i in range(6		)]
							A__							=       VGroup(*UpperCAmelCase__		).arrange(UpperCAmelCase__			,	buff=0		)
							A__							=       VGroup(*UpperCAmelCase__		).arrange(UpperCAmelCase__			,	buff=0		)
							A__							=       VGroup(UpperCAmelCase__			,	UpperCAmelCase__		).arrange(UpperCAmelCase__			,	buff=0		)
							A__							=       Text("CPU"			,	font_size=24		)
							A__							=       Group(UpperCAmelCase__			,	UpperCAmelCase__		).arrange(UpperCAmelCase__			,	buff=0.5			,	aligned_edge=UpperCAmelCase__		)
							cpu.move_to([-2.5, -0.5, 0]		)
							self.add(UpperCAmelCase__		)

							A__							=       [mem.copy() for i in range(4		)]
							A__							=       VGroup(*UpperCAmelCase__		).arrange(UpperCAmelCase__			,	buff=0		)
							A__							=       Text("GPU"			,	font_size=24		)
							A__							=       Group(UpperCAmelCase__			,	UpperCAmelCase__		).arrange(UpperCAmelCase__			,	buff=0.5			,	aligned_edge=UpperCAmelCase__		)
							gpu.move_to([-1, -1, 0]		)
							self.add(UpperCAmelCase__		)

							A__							=       [mem.copy() for i in range(6		)]
							A__							=       VGroup(*UpperCAmelCase__		).arrange(UpperCAmelCase__			,	buff=0		)

							A__							=       Text("Model"			,	font_size=24		)
							A__							=       Group(UpperCAmelCase__			,	UpperCAmelCase__		).arrange(UpperCAmelCase__			,	buff=0.5			,	aligned_edge=UpperCAmelCase__		)
							model.move_to([3, -1.0, 0]		)
							self.add(UpperCAmelCase__		)

							A__							=       []
							for i, rect in enumerate(UpperCAmelCase__		):
													rect.set_stroke(UpperCAmelCase__		)
													# target = fill.copy().set_fill(YELLOW, opacity=0.7)
													# target.move_to(rect)
													# self.add(target)

													A__							=       Rectangle(height=0.46 / 4			,	width=0.46 / 3		).set_stroke(width=0.0		).set_fill(UpperCAmelCase__			,	opacity=0.7		)

													if i == 0:
																			cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT		)			,	buff=0.02			,	direction=UpperCAmelCase__		)
																			cpu_target.set_x(cpu_target.get_x() + 0.1		)
													elif i == 3:
																			cpu_target.next_to(cpu_targs[0]			,	direction=UpperCAmelCase__			,	buff=0.0		)
													else:
																			cpu_target.next_to(cpu_targs[i - 1]			,	direction=UpperCAmelCase__			,	buff=0.0		)
													self.add(UpperCAmelCase__		)
													cpu_targs.append(UpperCAmelCase__		)

							A__							=       [mem.copy() for i in range(6		)]
							A__							=       VGroup(*UpperCAmelCase__		).arrange(UpperCAmelCase__			,	buff=0		)

							A__							=       Text("Loaded Checkpoint"			,	font_size=24		)
							A__							=       Group(UpperCAmelCase__			,	UpperCAmelCase__		).arrange(UpperCAmelCase__			,	aligned_edge=UpperCAmelCase__			,	buff=0.4		)
							checkpoint.move_to([3, 0.5, 0]		)

							A__							=       Square(side_length=2.2		)
							key.move_to([-5, 2, 0]		)

							A__							=       MarkupText(
							    F"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"""			,	font_size=18			,	)

							key_text.move_to([-5, 2.4, 0]		)

							self.add(UpperCAmelCase__			,	UpperCAmelCase__		)

							A__							=       MarkupText(
							    F"""<span fgcolor='{BLUE}'>●</span> Checkpoint"""			,	font_size=18			,	)

							blue_text.next_to(UpperCAmelCase__			,	DOWN * 2.4			,	aligned_edge=key_text.get_left()		)

							A__							=       MarkupText(
							    F"""Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>."""			,	font_size=24			,	)
							step_a.move_to([2, 2, 0]		)
							self.play(Write(UpperCAmelCase__		)			,	Write(UpperCAmelCase__		)		)

							self.play(Write(UpperCAmelCase__			,	run_time=1		)			,	Create(UpperCAmelCase__			,	run_time=1		)		)

							A__							=       []
							A__							=       []
							for i, rect in enumerate(UpperCAmelCase__		):
													A__							=       fill.copy().set_fill(UpperCAmelCase__			,	opacity=0.7		)
													target.move_to(UpperCAmelCase__		)
													first_animations.append(GrowFromCenter(UpperCAmelCase__			,	run_time=1		)		)

													A__							=       target.copy()
													cpu_target.generate_target()
													if i < 5:
																			cpu_target.target.move_to(cpu_left_col_base[i + 1]		)
													else:
																			cpu_target.target.move_to(cpu_right_col_base[i - 5]		)
													second_animations.append(MoveToTarget(UpperCAmelCase__			,	run_time=1.5		)		)

							self.play(*UpperCAmelCase__		)
							self.play(*UpperCAmelCase__		)
							self.wait()





