





"""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__  (      __a=None				,	__a=None				):
	return field(default_factory=lambda: default				,	metadata=lowerCAmelCase__				)
@dataclass
class SCREAMING_SNAKE_CASE_  :
						__magic_name__:     str	 =    field(
						    metadata={"help": "The csv file to plot."}    ,			)
						__magic_name__:     bool	 =    field(
						    default=__SCREAMING_SNAKE_CASE    ,			metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}    ,			)
						__magic_name__:     bool	 =    field(
						    default=__SCREAMING_SNAKE_CASE    ,			metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}    ,			)
						__magic_name__:     bool	 =    field(
						    default=__SCREAMING_SNAKE_CASE    ,			metadata={"help": "Disable logarithmic scale when plotting"}    ,			)
						__magic_name__:     bool	 =    field(
						    default=__SCREAMING_SNAKE_CASE    ,			metadata={
						        "help": "Whether the csv file has training results or inference results. Defaults to inference results."
						    }    ,			)
						__magic_name__:     Optional[str]	 =    field(
						    default=__SCREAMING_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=__SCREAMING_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(lowerCAmelCase__				)
		return True
	except ValueError:
		return False


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






						def __init__( self : Tuple						,				_A : Dict		)							->      Any:







							"""simple docstring"""


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

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






						def UpperCAmelCase_			( self : Dict		)							->      Any:







							"""simple docstring"""


							snake_case_  ,snake_case_						:     int     =		plt.subplots()
							snake_case_						:     Dict     =		'Time usage' if self.args.is_time else 'Memory usage'
							snake_case_						:     Union[str, Any]     =		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_						:     int     =		sorted(set(self.result_dict[model_name]['bsz']		)		)
								snake_case_						:     List[Any]     =		sorted(set(self.result_dict[model_name]['seq_len']		)		)
								snake_case_						:     Any     =		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_						:     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_						:     Optional[Any]     =		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_						:     Union[str, Any]     =		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_))						:     Optional[Any]     =		(
									    ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
									)

									snake_case_						:     int     =		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_						:     List[str]     =		title_str[:-4]
							snake_case_						:     List[Any]     =		'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_						:     int     =		HfArgumentParser(lowerCAmelCase__				)
	snake_case_						:     Union[str, Any]     =		parser.parse_args_into_dataclasses()[0]
	snake_case_						:     Tuple     =		Plot(args=lowerCAmelCase__				)
	plot.plot()


if __name__ == "__main__":
						main()








import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE_  (   snake_case_   ):
						__magic_name__:     Optional[Any]	 =    ["image_processor", "tokenizer"]
						__magic_name__:     Optional[Any]	 =    "LayoutLMv3ImageProcessor"
						__magic_name__:     str	 =    ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")






						def __init__( self : int						,				_A : List[str]=None						,				_A : Dict=None						,				**_A : int		)							->      List[str]:







							"""simple docstring"""


							snake_case_						:     Any     =		None
							if "feature_extractor" in kwargs:
								warnings.warn(
								    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
								    ' instead.'						,				_A						,				)
								snake_case_						:     Any     =		kwargs.pop('feature_extractor'		)

							snake_case_						:     Optional[int]     =		image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
								raise ValueError('You need to specify an `image_processor`.'		)
							if tokenizer is None:
								raise ValueError('You need to specify a `tokenizer`.'		)

							super().__init__(_A						,				_A		)






						def __call__( self : List[str]						,				_A : Optional[Any]						,				_A : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						,				_A : Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None						,				_A : Union[List[List[int]], List[List[List[int]]]] = None						,				_A : Optional[Union[List[int], List[List[int]]]] = None						,				_A : bool = True						,				_A : Union[bool, str, PaddingStrategy] = False						,				_A : Union[bool, str, TruncationStrategy] = None						,				_A : Optional[int] = None						,				_A : int = 0						,				_A : Optional[int] = None						,				_A : Optional[bool] = None						,				_A : Optional[bool] = None						,				_A : bool = False						,				_A : bool = False						,				_A : bool = False						,				_A : bool = False						,				_A : bool = True						,				_A : Optional[Union[str, TensorType]] = None						,				**_A : str						,				)							->      BatchEncoding:







							"""simple docstring"""


							if self.image_processor.apply_ocr and (boxes is not None):
								raise ValueError(
								    'You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'		)

							if self.image_processor.apply_ocr and (word_labels is not None):
								raise ValueError(
								    'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'		)

							# first, apply the image processor
							snake_case_						:     str     =		self.image_processor(images=_A						,				return_tensors=_A		)

							# second, apply the tokenizer
							if text is not None and self.image_processor.apply_ocr and text_pair is None:
								if isinstance(_A						,				_A		):
									snake_case_						:     List[Any]     =		[text]  # add batch dimension (as the image processor always adds a batch dimension)
								snake_case_						:     str     =		features['words']

							snake_case_						:     Optional[int]     =		self.tokenizer(
							    text=text if text is not None else features['words']						,				text_pair=text_pair if text_pair is not None else None						,				boxes=boxes if boxes is not None else features['boxes']						,				word_labels=_A						,				add_special_tokens=_A						,				padding=_A						,				truncation=_A						,				max_length=_A						,				stride=_A						,				pad_to_multiple_of=_A						,				return_token_type_ids=_A						,				return_attention_mask=_A						,				return_overflowing_tokens=_A						,				return_special_tokens_mask=_A						,				return_offsets_mapping=_A						,				return_length=_A						,				verbose=_A						,				return_tensors=_A						,				**_A						,				)

							# add pixel values
							snake_case_						:     List[str]     =		features.pop('pixel_values'		)
							if return_overflowing_tokens is True:
								snake_case_						:     Dict     =		self.get_overflowing_images(_A						,				encoded_inputs['overflow_to_sample_mapping']		)
							snake_case_						:     Optional[Any]     =		images

							return encoded_inputs






						def UpperCAmelCase_			( self : Dict						,				_A : Tuple						,				_A : Dict		)							->      Union[str, Any]:







							"""simple docstring"""


							snake_case_						:     List[str]     =		[]
							for sample_idx in overflow_to_sample_mapping:
								images_with_overflow.append(images[sample_idx]		)

							if len(_A		) != len(_A		):
								raise ValueError(
								    'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
								    F""" {len(_A		)} and {len(_A		)}"""		)

							return images_with_overflow






						def UpperCAmelCase_			( self : Optional[Any]						,				*_A : Optional[Any]						,				**_A : List[Any]		)							->      List[str]:







							"""simple docstring"""


							return self.tokenizer.batch_decode(*_A						,				**_A		)






						def UpperCAmelCase_			( self : Union[str, Any]						,				*_A : Dict						,				**_A : str		)							->      Any:







							"""simple docstring"""


							return self.tokenizer.decode(*_A						,				**_A		)






						@property
						def UpperCAmelCase_			( self : Optional[int]		)							->      int:







							"""simple docstring"""


							return ["input_ids", "bbox", "attention_mask", "pixel_values"]






						@property
						def UpperCAmelCase_			( self : Any		)							->      Any:







							"""simple docstring"""


							warnings.warn(
							    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'						,				_A						,				)
							return self.image_processor_class






						@property
						def UpperCAmelCase_			( self : List[Any]		)							->      int:







							"""simple docstring"""


							warnings.warn(
							    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'						,				_A						,				)
							return self.image_processor

