





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__    (     __a=None ,			__a=None ):
	return field(default_factory=lambda: default ,			metadata=__a )
@dataclass
class SCREAMING_SNAKE_CASE_    :
						__magic_name__:      str			   =							field(
						    metadata={"help": "The csv file to plot."}							,					)
						__magic_name__:      bool			   =							field(
						    default=snake_case_							,					metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}							,					)
						__magic_name__:      bool			   =							field(
						    default=snake_case_							,					metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}							,					)
						__magic_name__:      bool			   =							field(
						    default=snake_case_							,					metadata={"help": "Disable logarithmic scale when plotting"}							,					)
						__magic_name__:      bool			   =							field(
						    default=snake_case_							,					metadata={
						        "help": "Whether the csv file has training results or inference results. Defaults to inference results."
						    }							,					)
						__magic_name__:      Optional[str]			   =							field(
						    default=snake_case_							,					metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}							,					)
						__magic_name__:      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 				SCREAMING_SNAKE_CASE__    (     __a ):
	try:
		int(__a )
		return True
	except ValueError:
		return False


def 				SCREAMING_SNAKE_CASE__    (     __a ):
	try:
		float(__a )
		return True
	except ValueError:
		return False
class SCREAMING_SNAKE_CASE_    :






						def __init__(   self   :   Dict		, _A   :   List[Any]						)				->     str:







							"""simple docstring"""


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

							with open(self.args.csv_file		, newline=''						) as csv_file:
								snake_case_		:      Union[str, Any]           =						csv.DictReader(_A						)
								for row in reader:
									snake_case_		:      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
										snake_case_		:      Dict           =						int(row['result']						)
									elif can_convert_to_float(row['result']						):
										# value is not None
										snake_case_		:      Optional[int]           =						float(row['result']						)






						def UpperCAmelCase_					(   self   :   Any						)				->     Optional[Any]:







							"""simple docstring"""


							snake_case_    ,snake_case_		:      Any           =						plt.subplots()
							snake_case_		:      Tuple           =						'Time usage' if self.args.is_time else 'Memory usage'
							snake_case_		:      Tuple           =						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()						):
								snake_case_		:      Tuple           =						sorted(set(self.result_dict[model_name]['bsz']						)						)
								snake_case_		:      Dict           =						sorted(set(self.result_dict[model_name]['seq_len']						)						)
								snake_case_		:      Optional[int]           =						self.result_dict[model_name]['result']

								((snake_case_)    ,(snake_case_))		:      Optional[int]           =						(
								    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
								)

								snake_case_		:      List[str]           =						(
								    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:
										snake_case_		:      int           =						np.asarray(
										    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]		, dtype=_A		, )
									else:
										snake_case_		:      str           =						np.asarray(
										    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]		, dtype=np.floataa		, )

									((snake_case_)    ,(snake_case_))		:      Union[str, Any]           =						(
									    ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
									)

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

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

							snake_case_		:      Optional[int]           =						title_str[:-4]
							snake_case_		:      Tuple           =						'Time in s' if self.args.is_time else 'Memory in MB'

							# plot
							plt.title(_A						)
							plt.xlabel(_A						)
							plt.ylabel(_A						)
							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__    (     ):
	snake_case_		:      Dict           =						HfArgumentParser(__a )
	snake_case_		:      List[str]           =						parser.parse_args_into_dataclasses()[0]
	snake_case_		:      int           =						Plot(args=__a )
	plot.plot()


if __name__ == "__main__":
						main()



from typing import Dict, List, Optional, Union

import numpy as np

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


if is_vision_available():
				import PIL


__lowerCamelCase								=					logging.get_logger(__name__)
class    _UpperCamelCase(   SCREAMING_SNAKE_CASE				):
						__A:						Union[str, Any]				   =      ["""pixel_values"""]

						def __init__(					self		:			str	,						_lowerCamelCase		:			bool = True	,						_lowerCamelCase		:			Dict[str, int] = None	,						_lowerCamelCase		:			PILImageResampling = PIL.Image.NEAREST	,						_lowerCamelCase		:			bool = False	,						_lowerCamelCase		:			Dict[str, int] = None	,						_lowerCamelCase		:			Union[int, float] = 1 / 2_55	,						_lowerCamelCase		:			bool = False	,						_lowerCamelCase		:			bool = True	,						_lowerCamelCase		:			bool = True	,						_lowerCamelCase		:			Optional[Union[float, List[float]]] = None	,						_lowerCamelCase		:			Optional[Union[float, List[float]]] = None	,						_lowerCamelCase		:			bool = True	,						**_lowerCamelCase		:			Tuple	,						):
													super().__init__(**_lowerCamelCase			)
													_UpperCAmelCase  :			int	       =		size if size is not None else {"height": 3_46, "width": 3_46}
													_UpperCAmelCase  :			Dict	       =		get_size_dict(_lowerCamelCase			)
													_UpperCAmelCase  :			List[str]	       =		crop_size if crop_size is not None else {"height": 2_89, "width": 2_89}
													_UpperCAmelCase  :			int	       =		get_size_dict(_lowerCamelCase	,						param_name="crop_size"			)

													_UpperCAmelCase  :			Dict	       =		do_resize
													_UpperCAmelCase  :			Tuple	       =		size
													_UpperCAmelCase  :			Union[str, Any]	       =		resample
													_UpperCAmelCase  :			List[Any]	       =		do_center_crop
													_UpperCAmelCase  :			List[str]	       =		crop_size
													_UpperCAmelCase  :			int	       =		do_rescale
													_UpperCAmelCase  :			str	       =		rescale_factor
													_UpperCAmelCase  :			int	       =		rescale_offset
													_UpperCAmelCase  :			Any	       =		do_normalize
													_UpperCAmelCase  :			Optional[int]	       =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													_UpperCAmelCase  :			List[str]	       =		image_std if image_std is not None else IMAGENET_STANDARD_STD
													_UpperCAmelCase  :			Dict	       =		include_top

						def 						a__ (					self		:			Optional[Any]	,						_lowerCamelCase		:			np.ndarray	,						_lowerCamelCase		:			Dict[str, int]	,						_lowerCamelCase		:			PILImageResampling = PIL.Image.NEAREST	,						_lowerCamelCase		:			Optional[Union[str, ChannelDimension]] = None	,						**_lowerCamelCase		:			Tuple	,						):
													_UpperCAmelCase  :			Optional[int]	       =		get_size_dict(_lowerCamelCase			)
													if "height" not in size or "width" not in size:
																				raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""			)
													return resize(
													    _lowerCamelCase	,						size=(size["height"], size["width"])	,						resample=_lowerCamelCase	,						data_format=_lowerCamelCase	,						**_lowerCamelCase			)

						def 						a__ (					self		:			Optional[Any]	,						_lowerCamelCase		:			np.ndarray	,						_lowerCamelCase		:			Dict[str, int]	,						_lowerCamelCase		:			Optional[Union[str, ChannelDimension]] = None	,						**_lowerCamelCase		:			List[str]	,						):
													_UpperCAmelCase  :			Optional[Any]	       =		get_size_dict(_lowerCamelCase			)
													if "height" not in size or "width" not in size:
																				raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""			)
													return center_crop(_lowerCamelCase	,						size=(size["height"], size["width"])	,						data_format=_lowerCamelCase	,						**_lowerCamelCase			)

						def 						a__ (					self		:			Optional[Any]	,						_lowerCamelCase		:			np.ndarray	,						_lowerCamelCase		:			Union[int, float]	,						_lowerCamelCase		:			bool = True	,						_lowerCamelCase		:			Optional[Union[str, ChannelDimension]] = None	,						**_lowerCamelCase		:			Tuple	,						):
													if offset:
																				_UpperCAmelCase  :			Any	       =		(image - 1_27.5) * scale
																				if data_format is not None:
																											_UpperCAmelCase  :			Union[str, Any]	       =		to_channel_dimension_format(_lowerCamelCase	,						_lowerCamelCase			)
																				_UpperCAmelCase  :			Optional[Any]	       =		rescaled_image.astype(np.floataa			)
													else:
																				_UpperCAmelCase  :			List[Any]	       =		rescale(_lowerCamelCase	,						scale=_lowerCamelCase	,						data_format=_lowerCamelCase	,						**_lowerCamelCase			)
													return rescaled_image

						def 						a__ (					self		:			Union[str, Any]	,						_lowerCamelCase		:			np.ndarray	,						_lowerCamelCase		:			Union[float, List[float]]	,						_lowerCamelCase		:			Union[float, List[float]]	,						_lowerCamelCase		:			Optional[Union[str, ChannelDimension]] = None	,						**_lowerCamelCase		:			Union[str, Any]	,						):
													return normalize(_lowerCamelCase	,						mean=_lowerCamelCase	,						std=_lowerCamelCase	,						data_format=_lowerCamelCase	,						**_lowerCamelCase			)






						def 						a__ (					self		:			str	,						_lowerCamelCase		:			ImageInput	,						_lowerCamelCase		:			bool = None	,						_lowerCamelCase		:			Dict[str, int] = None	,						_lowerCamelCase		:			List[Any]=None	,						_lowerCamelCase		:			bool = None	,						_lowerCamelCase		:			Dict[str, int] = None	,						_lowerCamelCase		:			bool = None	,						_lowerCamelCase		:			float = None	,						_lowerCamelCase		:			bool = None	,						_lowerCamelCase		:			bool = None	,						_lowerCamelCase		:			Optional[Union[float, List[float]]] = None	,						_lowerCamelCase		:			Optional[Union[float, List[float]]] = None	,						_lowerCamelCase		:			bool = None	,						_lowerCamelCase		:			Optional[Union[str, TensorType]] = None	,						_lowerCamelCase		:			ChannelDimension = ChannelDimension.FIRST	,						**_lowerCamelCase		:			str	,						):
													_UpperCAmelCase  :			Optional[Any]	       =		do_resize if do_resize is not None else self.do_resize
													_UpperCAmelCase  :			Optional[int]	       =		resample if resample is not None else self.resample
													_UpperCAmelCase  :			Tuple	       =		do_center_crop if do_center_crop is not None else self.do_center_crop
													_UpperCAmelCase  :			str	       =		do_rescale if do_rescale is not None else self.do_rescale
													_UpperCAmelCase  :			str	       =		rescale_factor if rescale_factor is not None else self.rescale_factor
													_UpperCAmelCase  :			Tuple	       =		rescale_offset if rescale_offset is not None else self.rescale_offset
													_UpperCAmelCase  :			str	       =		do_normalize if do_normalize is not None else self.do_normalize
													_UpperCAmelCase  :			Dict	       =		image_mean if image_mean is not None else self.image_mean
													_UpperCAmelCase  :			Tuple	       =		image_std if image_std is not None else self.image_std
													_UpperCAmelCase  :			Tuple	       =		include_top if include_top is not None else self.include_top

													_UpperCAmelCase  :			Optional[int]	       =		size if size is not None else self.size
													_UpperCAmelCase  :			Union[str, Any]	       =		get_size_dict(_lowerCamelCase			)
													_UpperCAmelCase  :			int	       =		crop_size if crop_size is not None else self.crop_size
													_UpperCAmelCase  :			int	       =		get_size_dict(_lowerCamelCase	,						param_name="crop_size"			)

													_UpperCAmelCase  :			Union[str, Any]	       =		make_list_of_images(_lowerCamelCase			)

													if not valid_images(_lowerCamelCase			):
																				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 or resample is None:
																				raise ValueError("Size and resample 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.
													_UpperCAmelCase  :			Dict	       =		[to_numpy_array(_lowerCamelCase			) for image in images]

													if do_resize:
																				_UpperCAmelCase  :			Any	       =		[self.resize(image=_lowerCamelCase	,						size=_lowerCamelCase	,						resample=_lowerCamelCase			) for image in images]

													if do_center_crop:
																				_UpperCAmelCase  :			int	       =		[self.center_crop(image=_lowerCamelCase	,						size=_lowerCamelCase			) for image in images]

													if do_rescale:
																				_UpperCAmelCase  :			int	       =		[self.rescale(image=_lowerCamelCase	,						scale=_lowerCamelCase	,						offset=_lowerCamelCase			) for image in images]

													if do_normalize:
																				_UpperCAmelCase  :			str	       =		[self.normalize(image=_lowerCamelCase	,						mean=_lowerCamelCase	,						std=_lowerCamelCase			) for image in images]

													if include_top:
																				_UpperCAmelCase  :			int	       =		[self.normalize(image=_lowerCamelCase	,						mean=[0, 0, 0]	,						std=_lowerCamelCase			) for image in images]

													_UpperCAmelCase  :			str	       =		[to_channel_dimension_format(_lowerCamelCase	,						_lowerCamelCase			) for image in images]

													_UpperCAmelCase  :			Dict	       =		{"pixel_values": images}
													return BatchFeature(data=_lowerCamelCase	,						tensor_type=_lowerCamelCase			)




