






'''simple docstring'''







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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_A     :      Any   					=logging.get_logger(__name__)


if is_vision_available():
			import PIL

class _lowercase     (    _UpperCAmelCase		):
		a    				=	["pixel_values"]




		def __init__(							self:    Union[str, Any]      ,							UpperCamelCase__:    List[Any] = True      ,							UpperCamelCase__:    str = None      ,							UpperCamelCase__:    Any = PILImageResampling.BICUBIC      ,							UpperCamelCase__:    Tuple = True      ,							UpperCamelCase__:    List[str] = None      ,							UpperCamelCase__:    int = True      ,							UpperCamelCase__:    Any = 1 / 255      ,							UpperCamelCase__:    Dict = True      ,							UpperCamelCase__:    Dict = None      ,							UpperCamelCase__:    List[Any] = None      ,							UpperCamelCase__:    List[str] = True      ,							**UpperCamelCase__:    Tuple      ,							):
									super().__init__(**_UpperCAmelCase					)
									lowerCamelCase__ :     str	  =	size if size is not None else {'''shortest_edge''': 224}
									lowerCamelCase__ :     List[Any]	  =	get_size_dict(_UpperCAmelCase      ,							default_to_square=_UpperCAmelCase					)
									lowerCamelCase__ :     List[str]	  =	crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
									lowerCamelCase__ :     Dict	  =	get_size_dict(_UpperCAmelCase      ,							default_to_square=_UpperCAmelCase      ,							param_name="""crop_size"""					)

									lowerCamelCase__ :     Optional[Any]	  =	do_resize
									lowerCamelCase__ :     Any	  =	size
									lowerCamelCase__ :     Optional[Any]	  =	resample
									lowerCamelCase__ :     List[Any]	  =	do_center_crop
									lowerCamelCase__ :     Optional[Any]	  =	crop_size
									lowerCamelCase__ :     Optional[Any]	  =	do_rescale
									lowerCamelCase__ :     Any	  =	rescale_factor
									lowerCamelCase__ :     List[Any]	  =	do_normalize
									lowerCamelCase__ :     str	  =	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
									lowerCamelCase__ :     Dict	  =	image_std if image_std is not None else OPENAI_CLIP_STD
									lowerCamelCase__ :     str	  =	do_convert_rgb




		def 		lowerCamelCase_    (							self:    Optional[Any]      ,							UpperCamelCase__:    List[str]      ,							UpperCamelCase__:    Tuple      ,							UpperCamelCase__:    Union[str, Any] = PILImageResampling.BICUBIC      ,							UpperCamelCase__:    Dict = None      ,							**UpperCamelCase__:    Optional[Any]      ,							):
									lowerCamelCase__ :     Tuple	  =	get_size_dict(_UpperCAmelCase      ,							default_to_square=_UpperCAmelCase					)
									if "shortest_edge" not in size:
																raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''					)
									lowerCamelCase__ :     Dict	  =	get_resize_output_image_size(_UpperCAmelCase      ,							size=size["""shortest_edge"""]      ,							default_to_square=_UpperCAmelCase					)
									return resize(_UpperCAmelCase      ,							size=_UpperCAmelCase      ,							resample=_UpperCAmelCase      ,							data_format=_UpperCAmelCase      ,							**_UpperCAmelCase					)




		def 		lowerCamelCase_    (							self:    str      ,							UpperCamelCase__:    Optional[Any]      ,							UpperCamelCase__:    Tuple      ,							UpperCamelCase__:    Optional[Any] = None      ,							**UpperCamelCase__:    Tuple      ,							):
									lowerCamelCase__ :     Dict	  =	get_size_dict(_UpperCAmelCase					)
									if "height" not in size or "width" not in size:
																raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''					)
									return center_crop(_UpperCAmelCase      ,							size=(size["""height"""], size["""width"""])      ,							data_format=_UpperCAmelCase      ,							**_UpperCAmelCase					)




		def 		lowerCamelCase_    (							self:    Any      ,							UpperCamelCase__:    Union[str, Any]      ,							UpperCamelCase__:    Union[str, Any]      ,							UpperCamelCase__:    int = None      ,							**UpperCamelCase__:    Optional[int]      ,							):
									return rescale(_UpperCAmelCase      ,							scale=_UpperCAmelCase      ,							data_format=_UpperCAmelCase      ,							**_UpperCAmelCase					)




		def 		lowerCamelCase_    (							self:    Optional[int]      ,							UpperCamelCase__:    Dict      ,							UpperCamelCase__:    Tuple      ,							UpperCamelCase__:    Dict      ,							UpperCamelCase__:    Any = None      ,							**UpperCamelCase__:    Union[str, Any]      ,							):
									return normalize(_UpperCAmelCase      ,							mean=_UpperCAmelCase      ,							std=_UpperCAmelCase      ,							data_format=_UpperCAmelCase      ,							**_UpperCAmelCase					)




		def 		lowerCamelCase_    (							self:    Optional[int]      ,							UpperCamelCase__:    Dict      ,							UpperCamelCase__:    Tuple = None      ,							UpperCamelCase__:    Tuple = None      ,							UpperCamelCase__:    Union[str, Any] = None      ,							UpperCamelCase__:    Any = None      ,							UpperCamelCase__:    Any = None      ,							UpperCamelCase__:    Union[str, Any] = None      ,							UpperCamelCase__:    List[str] = None      ,							UpperCamelCase__:    str = None      ,							UpperCamelCase__:    Any = None      ,							UpperCamelCase__:    Optional[Any] = None      ,							UpperCamelCase__:    List[Any] = None      ,							UpperCamelCase__:    Optional[Any] = None      ,							UpperCamelCase__:    int = ChannelDimension.FIRST      ,							**UpperCamelCase__:    List[Any]      ,							):
									lowerCamelCase__ :     List[Any]	  =	do_resize if do_resize is not None else self.do_resize
									lowerCamelCase__ :     Optional[int]	  =	size if size is not None else self.size
									lowerCamelCase__ :     Optional[int]	  =	get_size_dict(_UpperCAmelCase      ,							param_name="""size"""      ,							default_to_square=_UpperCAmelCase					)
									lowerCamelCase__ :     str	  =	resample if resample is not None else self.resample
									lowerCamelCase__ :     Tuple	  =	do_center_crop if do_center_crop is not None else self.do_center_crop
									lowerCamelCase__ :     List[str]	  =	crop_size if crop_size is not None else self.crop_size
									lowerCamelCase__ :     Dict	  =	get_size_dict(_UpperCAmelCase      ,							param_name="""crop_size"""      ,							default_to_square=_UpperCAmelCase					)
									lowerCamelCase__ :     Optional[Any]	  =	do_rescale if do_rescale is not None else self.do_rescale
									lowerCamelCase__ :     int	  =	rescale_factor if rescale_factor is not None else self.rescale_factor
									lowerCamelCase__ :     str	  =	do_normalize if do_normalize is not None else self.do_normalize
									lowerCamelCase__ :     List[Any]	  =	image_mean if image_mean is not None else self.image_mean
									lowerCamelCase__ :     int	  =	image_std if image_std is not None else self.image_std
									lowerCamelCase__ :     int	  =	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

									lowerCamelCase__ :     Tuple	  =	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."""					)

									# PIL RGBA images are converted to RGB
									if do_convert_rgb:
																lowerCamelCase__ :     Union[str, Any]	  =	[convert_to_rgb(_UpperCAmelCase					) for image in images]

									# All transformations expect numpy arrays.
									lowerCamelCase__ :     Optional[int]	  =	[to_numpy_array(_UpperCAmelCase					) for image in images]

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

									if do_center_crop:
																lowerCamelCase__ :     Optional[Any]	  =	[self.center_crop(image=_UpperCAmelCase      ,							size=_UpperCAmelCase					) for image in images]

									if do_rescale:
																lowerCamelCase__ :     Any	  =	[self.rescale(image=_UpperCAmelCase      ,							scale=_UpperCAmelCase					) for image in images]

									if do_normalize:
																lowerCamelCase__ :     Optional[Any]	  =	[self.normalize(image=_UpperCAmelCase      ,							mean=_UpperCAmelCase      ,							std=_UpperCAmelCase					) for image in images]

									lowerCamelCase__ :     Tuple	  =	[to_channel_dimension_format(_UpperCAmelCase      ,							_UpperCAmelCase					) for image in images]

									lowerCamelCase__ :     Dict	  =	{'''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 			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()

