


"""simple docstring"""







from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


a__       :							Any        =  logging.get_logger(__name__)




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	snake_case__       :						Any					      =    ["pixel_values"]




	def __init__(				self    :       Optional[int]    ,    UpperCAmelCase__    :       bool = True    ,    UpperCAmelCase__    :       Dict[str, int] = None    ,    UpperCAmelCase__    :       PILImageResampling = PILImageResampling.BICUBIC    ,    UpperCAmelCase__    :       bool = True    ,    UpperCAmelCase__    :       Dict[str, int] = None    ,    UpperCAmelCase__    :       bool = True    ,    UpperCAmelCase__    :       Union[int, float] = 1 / 2_5_5    ,    UpperCAmelCase__    :       bool = True    ,    UpperCAmelCase__    :       Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN    ,    UpperCAmelCase__    :       Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD    ,    **UpperCAmelCase__    :       List[Any]    ,    ) ->			None:
						super().__init__(**UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       size if size is not None else {"shortest_edge": 2_2_4}
						__SCREAMING_SNAKE_CASE		     =       get_size_dict(UpperCAmelCase__    ,    default_to_square=UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
						__SCREAMING_SNAKE_CASE		     =       get_size_dict(UpperCAmelCase__    ,    param_name="crop_size"     )

						__SCREAMING_SNAKE_CASE		     =       do_resize
						__SCREAMING_SNAKE_CASE		     =       size
						__SCREAMING_SNAKE_CASE		     =       resample
						__SCREAMING_SNAKE_CASE		     =       do_center_crop
						__SCREAMING_SNAKE_CASE		     =       crop_size
						__SCREAMING_SNAKE_CASE		     =       do_rescale
						__SCREAMING_SNAKE_CASE		     =       rescale_factor
						__SCREAMING_SNAKE_CASE		     =       do_normalize
						__SCREAMING_SNAKE_CASE		     =       image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
						__SCREAMING_SNAKE_CASE		     =       image_std if image_std is not None else IMAGENET_DEFAULT_STD




	def 						UpperCAmelCase_   (				self    :       str    ,    UpperCAmelCase__    :       np.ndarray    ,    UpperCAmelCase__    :       Dict[str, int]    ,    UpperCAmelCase__    :       PILImageResampling = PILImageResampling.BICUBIC    ,    UpperCAmelCase__    :       Optional[Union[str, ChannelDimension]] = None    ,    **UpperCAmelCase__    :       Optional[int]    ,    ) ->			np.ndarray:
						__SCREAMING_SNAKE_CASE		     =       get_size_dict(UpperCAmelCase__    ,    default_to_square=UpperCAmelCase__     )
						# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
						if "shortest_edge" in size:
											__SCREAMING_SNAKE_CASE		     =       int((2_5_6 / 2_2_4) * size["shortest_edge"]     )
											__SCREAMING_SNAKE_CASE		     =       get_resize_output_image_size(UpperCAmelCase__    ,    size=UpperCAmelCase__    ,    default_to_square=UpperCAmelCase__     )
											__SCREAMING_SNAKE_CASE		     =       {"height": output_size[0], "width": output_size[1]}
						if "height" not in size_dict or "width" not in size_dict:
											raise ValueError(
											    F"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"""     )
						return resize(
						    UpperCAmelCase__    ,    size=(size_dict["height"], size_dict["width"])    ,    resample=UpperCAmelCase__    ,    data_format=UpperCAmelCase__    ,    **UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Tuple    ,    UpperCAmelCase__    :       np.ndarray    ,    UpperCAmelCase__    :       Dict[str, int]    ,    UpperCAmelCase__    :       Optional[Union[str, ChannelDimension]] = None    ,    **UpperCAmelCase__    :       Tuple    ,    ) ->			np.ndarray:
						__SCREAMING_SNAKE_CASE		     =       get_size_dict(UpperCAmelCase__     )
						if "height" not in size or "width" not in size:
											raise ValueError(F"""Size dict must have keys 'height' and 'width'. Got {size.keys()}"""     )
						return center_crop(UpperCAmelCase__    ,    size=(size["height"], size["width"])    ,    data_format=UpperCAmelCase__    ,    **UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Dict    ,    UpperCAmelCase__    :       np.ndarray    ,    UpperCAmelCase__    :       Union[int, float]    ,    UpperCAmelCase__    :       Optional[Union[str, ChannelDimension]] = None    ,    **UpperCAmelCase__    :       Union[str, Any]    ,    ) ->			np.ndarray:
						return rescale(UpperCAmelCase__    ,    scale=UpperCAmelCase__    ,    data_format=UpperCAmelCase__    ,    **UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       List[Any]    ,    UpperCAmelCase__    :       np.ndarray    ,    UpperCAmelCase__    :       Union[float, List[float]]    ,    UpperCAmelCase__    :       Union[float, List[float]]    ,    UpperCAmelCase__    :       Optional[Union[str, ChannelDimension]] = None    ,    **UpperCAmelCase__    :       int    ,    ) ->			np.ndarray:
						return normalize(UpperCAmelCase__    ,    mean=UpperCAmelCase__    ,    std=UpperCAmelCase__    ,    data_format=UpperCAmelCase__    ,    **UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       int    ,    UpperCAmelCase__    :       ImageInput    ,    UpperCAmelCase__    :       Optional[bool] = None    ,    UpperCAmelCase__    :       Optional[Dict[str, int]] = None    ,    UpperCAmelCase__    :       PILImageResampling = None    ,    UpperCAmelCase__    :       Optional[bool] = None    ,    UpperCAmelCase__    :       Optional[Dict[str, int]] = None    ,    UpperCAmelCase__    :       Optional[bool] = None    ,    UpperCAmelCase__    :       Optional[float] = None    ,    UpperCAmelCase__    :       Optional[bool] = None    ,    UpperCAmelCase__    :       Optional[Union[float, Iterable[float]]] = None    ,    UpperCAmelCase__    :       Optional[Union[float, Iterable[float]]] = None    ,    UpperCAmelCase__    :       Optional[TensorType] = None    ,    UpperCAmelCase__    :       ChannelDimension = ChannelDimension.FIRST    ,    **UpperCAmelCase__    :       Union[str, Any]    ,    ) ->			BatchFeature:
						__SCREAMING_SNAKE_CASE		     =       do_resize if do_resize is not None else self.do_resize
						__SCREAMING_SNAKE_CASE		     =       resample if resample is not None else self.resample
						__SCREAMING_SNAKE_CASE		     =       do_center_crop if do_center_crop is not None else self.do_center_crop
						__SCREAMING_SNAKE_CASE		     =       do_rescale if do_rescale is not None else self.do_rescale
						__SCREAMING_SNAKE_CASE		     =       rescale_factor if rescale_factor is not None else self.rescale_factor
						__SCREAMING_SNAKE_CASE		     =       do_normalize if do_normalize is not None else self.do_normalize
						__SCREAMING_SNAKE_CASE		     =       image_mean if image_mean is not None else self.image_mean
						__SCREAMING_SNAKE_CASE		     =       image_std if image_std is not None else self.image_std

						__SCREAMING_SNAKE_CASE		     =       size if size is not None else self.size
						__SCREAMING_SNAKE_CASE		     =       get_size_dict(UpperCAmelCase__    ,    default_to_square=UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       crop_size if crop_size is not None else self.crop_size
						__SCREAMING_SNAKE_CASE		     =       get_size_dict(UpperCAmelCase__    ,    param_name="crop_size"     )

						__SCREAMING_SNAKE_CASE		     =       make_list_of_images(UpperCAmelCase__     )

						if not valid_images(UpperCAmelCase__     ):
											raise ValueError(
											    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
											    "torch.Tensor, tf.Tensor or jax.ndarray."     )

						if do_resize and size is None:
											raise ValueError("Size must be specified if do_resize is True."     )

						if do_center_crop and crop_size is None:
											raise ValueError("Crop size must be specified if do_center_crop is True."     )

						if do_rescale and rescale_factor is None:
											raise ValueError("Rescale factor must be specified if do_rescale is True."     )

						if do_normalize and (image_mean is None or image_std is None):
											raise ValueError("Image mean and std must be specified if do_normalize is True."     )

						# All transformations expect numpy arrays.
						__SCREAMING_SNAKE_CASE		     =       [to_numpy_array(UpperCAmelCase__     ) for image in images]

						if do_resize:
											__SCREAMING_SNAKE_CASE		     =       [self.resize(UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__     ) for image in images]

						if do_center_crop:
											__SCREAMING_SNAKE_CASE		     =       [self.center_crop(UpperCAmelCase__    ,    UpperCAmelCase__     ) for image in images]

						if do_rescale:
											__SCREAMING_SNAKE_CASE		     =       [self.rescale(UpperCAmelCase__    ,    UpperCAmelCase__     ) for image in images]

						if do_normalize:
											__SCREAMING_SNAKE_CASE		     =       [self.normalize(UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__     ) for image in images]

						__SCREAMING_SNAKE_CASE		     =       [to_channel_dimension_format(UpperCAmelCase__    ,    UpperCAmelCase__     ) for image in images]

						__SCREAMING_SNAKE_CASE		     =       {"pixel_values": images}
						return BatchFeature(data=UpperCAmelCase__    ,    tensor_type=UpperCAmelCase__     )




"""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      UpperCAmelCase__ (lowerCAmelCase_=None						,		lowerCAmelCase_=None     ):




					'''simple docstring'''


					return field(default_factory=lambda: default						,		metadata=lowerCAmelCase_     )




@dataclass
class 				UpperCamelCase_			:

	"""simple docstring"""





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




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					try:
										int(lowerCAmelCase_     )
										return True
					except ValueError:
										return False




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					try:
										float(lowerCAmelCase_     )
										return True
					except ValueError:
										return False




class 				UpperCamelCase_			:

	"""simple docstring"""





	def __init__(				self    :       str    ,    UpperCAmelCase__    :       Dict     ) ->			Optional[Any]:
						__SCREAMING_SNAKE_CASE		     =       args
						__SCREAMING_SNAKE_CASE		     =       defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}     )

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




	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			Tuple:
						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       plt.subplots()
						__SCREAMING_SNAKE_CASE		     =       "Time usage" if self.args.is_time else "Memory usage"
						__SCREAMING_SNAKE_CASE		     =       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()     ):
											__SCREAMING_SNAKE_CASE		     =       sorted(set(self.result_dict[model_name]["bsz"]     )     )
											__SCREAMING_SNAKE_CASE		     =       sorted(set(self.result_dict[model_name]["seq_len"]     )     )
											__SCREAMING_SNAKE_CASE		     =       self.result_dict[model_name]["result"]

											((__SCREAMING_SNAKE_CASE)     ,						(__SCREAMING_SNAKE_CASE))		     =       (
											    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
											)

											__SCREAMING_SNAKE_CASE		     =       (
											    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:
																					__SCREAMING_SNAKE_CASE		     =       np.asarray(
																					    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]    ,    dtype=UpperCAmelCase__    ,    )
																else:
																					__SCREAMING_SNAKE_CASE		     =       np.asarray(
																					    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]    ,    dtype=np.floataa    ,    )

																((__SCREAMING_SNAKE_CASE)     ,						(__SCREAMING_SNAKE_CASE))		     =       (
																    ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
																)

																__SCREAMING_SNAKE_CASE		     =       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."""

						__SCREAMING_SNAKE_CASE		     =       title_str[:-4]
						__SCREAMING_SNAKE_CASE		     =       "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      UpperCAmelCase__ ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       HfArgumentParser(lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE		     =       parser.parse_args_into_dataclasses()[0]
					__SCREAMING_SNAKE_CASE		     =       Plot(args=lowerCAmelCase_     )
					plot.plot()


if __name__ == "__main__":
		main()
