

"""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    a__   (   __SCREAMING_SNAKE_CASE=None ,			__SCREAMING_SNAKE_CASE=None			)     ->     str:
		return field(default_factory=lambda: default ,			metadata=__SCREAMING_SNAKE_CASE			)





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






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Any:
		try:
				int(__SCREAMING_SNAKE_CASE			)
				return True
		except ValueError:
				return False






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     List[Any]:
		try:
				float(__SCREAMING_SNAKE_CASE			)
				return True
		except ValueError:
				return False





class    snake_case     :

				def __init__(     self							:    Optional[Any]					,  UpperCamelCase__							:    List[Any])->       Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	List[Any]   				=      args
						__lowerCAmelCase:	int   				=      defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}})

						with open(self.args.csv_file					,  newline="") as csv_file:
								__lowerCAmelCase:	Union[str, Any]   				=      csv.DictReader(UpperCamelCase__)
								for row in reader:
										__lowerCAmelCase:	List[str]   				=      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:	List[str]   				=      int(row["result"])
										elif can_convert_to_float(row["result"]):
												# value is not None
												__lowerCAmelCase:	int   				=      float(row["result"])




				def 							lowercase_					(     self							:    int)->       Tuple:



						'''simple docstring'''


						__lowerCAmelCase				,			__lowerCAmelCase:	Union[str, Any]   				=      plt.subplots()
						__lowerCAmelCase:	List[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:	Tuple   				=      sorted(set(self.result_dict[model_name]["bsz"]))
								__lowerCAmelCase:	List[Any]   				=      sorted(set(self.result_dict[model_name]["seq_len"]))
								__lowerCAmelCase:	Optional[int]   				=      self.result_dict[model_name]["result"]

								((__lowerCAmelCase)				,			(__lowerCAmelCase)):	str   				=      (
								    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
								)

								__lowerCAmelCase:	int   				=      (
								    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:	Dict   				=      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)):	Union[str, Any]   				=      (
										    ("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:	int   				=      title_str[:-4]
						__lowerCAmelCase:	Dict   				=      "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    a__   (   )     ->     Optional[int]:
		__lowerCAmelCase:	Union[str, Any]   				=      HfArgumentParser(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Union[str, Any]   				=      parser.parse_args_into_dataclasses()[0]
		__lowerCAmelCase:	Any   				=      Plot(args=__SCREAMING_SNAKE_CASE			)
		plot.plot()


if __name__ == "__main__":
			main()





"""simple docstring"""


from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     np.ndarray:
		__lowerCAmelCase:	List[Any]   				=      cva.getAffineTransform(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
		return cva.warpAffine(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			(rows, cols)			)


if __name__ == "__main__":
			# read original image
			__A						       =      cva.imread(
			    str(Path(__file__).resolve().parent.parent / "image_data" / "lena.jpg")
			)
			# turn image in gray scale value
			__A						       =      cva.cvtColor(image, cva.COLOR_BGR2GRAY)
			# get image shape
			__A       ,    __A						       =      gray_img.shape

			# set different points to rotate image
			__A						       =      np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
			__A						       =      np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
			__A						       =      np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
			__A						       =      np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

			# add all rotated images in a list
			__A						       =      [
			    gray_img,
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			]

			# plot different image rotations
			__A						       =      plt.figure(1)
			__A						       =      ["Original", "Rotation 1", "Rotation 2", "Rotation 3"]
			for i, image in enumerate(images):
						plt.subplot(2, 2, i + 1), plt.imshow(image, "gray")
						plt.title(titles[i])
						plt.axis("off")
						plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
			plt.show()


