






'''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


_A     :      int   					={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 _lowercase     (    nn.Module		):




							def __init__(							self:    Optional[Any]      ,							UpperCamelCase__:    Optional[int]					):
														super().__init__()
														lowerCamelCase__ :     Union[str, Any]	  =	torchvision.models.resnetaaa(pretrained=UpperCamelCase__					)
														lowerCamelCase__ :     Optional[Any]	  =	list(model.children()					)[:-2]
														lowerCamelCase__ :     Any	  =	nn.Sequential(*UpperCamelCase__					)
														lowerCamelCase__ :     Union[str, Any]	  =	nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]					)




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



class _lowercase     (    _lowercase		):




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

														lowerCamelCase__ :     List[str]	  =	transforms




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




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

														lowerCamelCase__ :     List[Any]	  =	torch.zeros(self.n_classes					)
														lowerCamelCase__ :     Union[str, Any]	  =	1

														lowerCamelCase__ :     Union[str, Any]	  =	Image.open(os.path.join(self.data_dir      ,							self.data[index]["""img"""]					)					).convert("""RGB"""					)
														lowerCamelCase__ :     Tuple	  =	self.transforms(UpperCamelCase__					)

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




							def 		lowerCamelCase_    (							self:    Tuple					):
														lowerCamelCase__ :     Optional[int]	  =	Counter()
														for row in self.data:
																					label_freqs.update(row["""label"""]					)
														return label_freqs





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	List[str]:
							lowerCamelCase__ :     int	  =	[len(row["""sentence"""]				) for row in batch]
							lowerCamelCase__  ,		lowerCamelCase__ :     Tuple	  =	len(UpperCamelCase				), max(UpperCamelCase				)

							lowerCamelCase__ :     Union[str, Any]	  =	torch.zeros(UpperCamelCase    ,      UpperCamelCase    ,      dtype=torch.long				)
							lowerCamelCase__ :     Optional[int]	  =	torch.zeros(UpperCamelCase    ,      UpperCamelCase    ,      dtype=torch.long				)

							for i_batch, (input_row, length) in enumerate(zip(UpperCamelCase    ,      UpperCamelCase				)				):
														lowerCamelCase__ :     str	  =	input_row["""sentence"""]
														lowerCamelCase__ :     str	  =	1

							lowerCamelCase__ :     Dict	  =	torch.stack([row["""image"""] for row in batch]				)
							lowerCamelCase__ :     Tuple	  =	torch.stack([row["""label"""] for row in batch]				)
							lowerCamelCase__ :     List[Any]	  =	torch.stack([row["""image_start_token"""] for row in batch]				)
							lowerCamelCase__ :     List[str]	  =	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 			SCREAMING_SNAKE_CASE_	()				->	Optional[Any]:
							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 			SCREAMING_SNAKE_CASE_	()				->	str:
							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]    ,      ),
							    ]				)









'''simple docstring'''







import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase=None    ,      UpperCamelCase=None				)				->	Any:
							return field(default_factory=lambda: default    ,      metadata=UpperCamelCase				)

@dataclass
class _lowercase     :
							a    				=	field(
							    metadata={"""help""": """The csv file to plot."""}    ,				)
							a    				=	field(
							    default=_lowercase    ,				metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}    ,				)
							a    				=	field(
							    default=_lowercase    ,				metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}    ,				)
							a    				=	field(
							    default=_lowercase    ,				metadata={"""help""": """Disable logarithmic scale when plotting"""}    ,				)
							a    				=	field(
							    default=_lowercase    ,				metadata={
							        """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
							    }    ,				)
							a    				=	field(
							    default=_lowercase    ,				metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}    ,				)
							a    				=	list_field(
							    default=_lowercase    ,				metadata={"""help""": """List of model names that are used instead of the ones in the csv file."""}		)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Dict:
							try:
														int(UpperCamelCase				)
														return True
							except ValueError:
														return False





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	int:
							try:
														float(UpperCamelCase				)
														return True
							except ValueError:
														return False

class _lowercase     :




							def __init__(							self:    Tuple      ,							UpperCamelCase__:    str					):
														lowerCamelCase__ :     int	  =	args
														lowerCamelCase__ :     Optional[int]	  =	defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}					)

														with open(self.args.csv_file      ,							newline=""""""					) as csv_file:
																					lowerCamelCase__ :     str	  =	csv.DictReader(UpperCamelCase__					)
																					for row in reader:
																												lowerCamelCase__ :     Optional[int]	  =	row["""model"""]
																												self.result_dict[model_name]["bsz"].append(int(row["""batch_size"""]					)					)
																												self.result_dict[model_name]["seq_len"].append(int(row["""sequence_length"""]					)					)
																												if can_convert_to_int(row["""result"""]					):
																																			# value is not None
																																			lowerCamelCase__ :     Tuple	  =	int(row["""result"""]					)
																												elif can_convert_to_float(row["""result"""]					):
																																			# value is not None
																																			lowerCamelCase__ :     Any	  =	float(row["""result"""]					)




							def 		lowerCamelCase_    (							self:    str					):
														lowerCamelCase__  ,		lowerCamelCase__ :     Tuple	  =	plt.subplots()
														lowerCamelCase__ :     Any	  =	"""Time usage""" if self.args.is_time else """Memory usage"""
														lowerCamelCase__ :     List[str]	  =	title_str + """ for training""" if self.args.is_train else title_str + """ for inference"""

														if not self.args.no_log_scale:
																					# set logarithm scales
																					ax.set_xscale("""log"""					)
																					ax.set_yscale("""log"""					)

														for axis in [ax.xaxis, ax.yaxis]:
																					axis.set_major_formatter(ScalarFormatter()					)

														for model_name_idx, model_name in enumerate(self.result_dict.keys()					):
																					lowerCamelCase__ :     Any	  =	sorted(set(self.result_dict[model_name]["""bsz"""]					)					)
																					lowerCamelCase__ :     int	  =	sorted(set(self.result_dict[model_name]["""seq_len"""]					)					)
																					lowerCamelCase__ :     Any	  =	self.result_dict[model_name]["""result"""]

																					((lowerCamelCase__)  ,		(lowerCamelCase__)) :     Dict	  =	(
																					    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
																					)

																					lowerCamelCase__ :     Any	  =	(
																					    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
																					)

																					for inner_loop_value in inner_loop_array:
																												if self.args.plot_along_batch:
																																			lowerCamelCase__ :     int	  =	np.asarray(
																																			    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]      ,							dtype=UpperCamelCase__      ,							)
																												else:
																																			lowerCamelCase__ :     List[Any]	  =	np.asarray(
																																			    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]      ,							dtype=np.floataa      ,							)

																												((lowerCamelCase__)  ,		(lowerCamelCase__)) :     List[str]	  =	(
																												    ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
																												)

																												lowerCamelCase__ :     int	  =	np.asarray(UpperCamelCase__      ,							UpperCamelCase__					)[: len(UpperCamelCase__					)]
																												plt.scatter(
																												    UpperCamelCase__      ,							UpperCamelCase__      ,							label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''					)
																												plt.plot(UpperCamelCase__      ,							UpperCamelCase__      ,							"""--"""					)

																					title_str += F''' {label_model_name} vs.'''

														lowerCamelCase__ :     Any	  =	title_str[:-4]
														lowerCamelCase__ :     Optional[int]	  =	"""Time in s""" if self.args.is_time else """Memory in MB"""

														# plot
														plt.title(UpperCamelCase__					)
														plt.xlabel(UpperCamelCase__					)
														plt.ylabel(UpperCamelCase__					)
														plt.legend()

														if self.args.figure_png_file is not None:
																					plt.savefig(self.args.figure_png_file					)
														else:
																					plt.show()





def 			SCREAMING_SNAKE_CASE_	()				->	str:
							lowerCamelCase__ :     str	  =	HfArgumentParser(UpperCamelCase				)
							lowerCamelCase__ :     str	  =	parser.parse_args_into_dataclasses()[0]
							lowerCamelCase__ :     Any	  =	Plot(args=UpperCamelCase				)
							plot.plot()


if __name__ == "__main__":
			main()

