
"""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						(   a_ :							Optional[int]=None		,		a_ :							Optional[Any]=None     )->							int:




		'''simple docstring'''






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




@dataclass
class 				lowercase__:
	'''simple docstring'''

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





def __A						(   a_ :							Tuple     )->							int:




		'''simple docstring'''






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





def __A						(   a_ :							Optional[Any]     )->							Union[str, Any]:




		'''simple docstring'''






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




class 				lowercase__:
	'''simple docstring'''

	def __init__(						self    :Union[str, Any]		, lowerCamelCase_    :Union[str, Any]					)							->			Tuple:







			'''simple docstring'''




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

			with open(self.args.csv_file		, newline=''''''					) as csv_file:
					SCREAMING_SNAKE_CASE       :     Tuple				= csv.DictReader(_A					)
					for row in reader:
							SCREAMING_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
									SCREAMING_SNAKE_CASE       :     Tuple				= int(row['''result''']					)
							elif can_convert_to_float(row['''result''']					):
									# value is not None
									SCREAMING_SNAKE_CASE       :     int				= float(row['''result''']					)







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







			'''simple docstring'''




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

					((SCREAMING_SNAKE_CASE),					(SCREAMING_SNAKE_CASE))       :     Union[str, Any]				= (
					    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
					)

					SCREAMING_SNAKE_CASE       :     Optional[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:
									SCREAMING_SNAKE_CASE       :     List[str]				= np.asarray(
									    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]		, dtype=_A		, )
							else:
									SCREAMING_SNAKE_CASE       :     Optional[Any]				= 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))       :     Dict				= (
							    ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
							)

							SCREAMING_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."

			SCREAMING_SNAKE_CASE       :     Optional[int]				= title_str[:-4]
			SCREAMING_SNAKE_CASE       :     Dict				= '''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 __A						(   )->							Dict:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Tuple				= HfArgumentParser(UpperCAmelCase__     )
		SCREAMING_SNAKE_CASE       :     Dict				= parser.parse_args_into_dataclasses()[0]
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= Plot(args=UpperCAmelCase__     )
		plot.plot()


if __name__ == "__main__":
					main()







"""simple docstring"""






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
					from ...processing_utils import ProcessorMixin
					from ...utils import TensorType


lowerCamelCase__ :       Dict						     =			logging.get_logger(__name__)

lowerCamelCase__ :       Dict						     =			{
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  """layoutlmv3"""





	def __init__(						self    :str		, lowerCamelCase_    :Optional[Any]=5_02_65		, lowerCamelCase_    :Dict=7_68		, lowerCamelCase_    :Union[str, Any]=12		, lowerCamelCase_    :Optional[Any]=12		, lowerCamelCase_    :Union[str, Any]=30_72		, lowerCamelCase_    :Any="gelu"		, lowerCamelCase_    :Union[str, Any]=0.1		, lowerCamelCase_    :str=0.1		, lowerCamelCase_    :Any=5_12		, lowerCamelCase_    :int=2		, lowerCamelCase_    :Optional[Any]=0.0_2		, lowerCamelCase_    :Optional[int]=1E-5		, lowerCamelCase_    :Dict=1		, lowerCamelCase_    :int=0		, lowerCamelCase_    :Tuple=2		, lowerCamelCase_    :List[str]=10_24		, lowerCamelCase_    :Tuple=1_28		, lowerCamelCase_    :Any=1_28		, lowerCamelCase_    :Optional[Any]=True		, lowerCamelCase_    :str=32		, lowerCamelCase_    :int=1_28		, lowerCamelCase_    :int=64		, lowerCamelCase_    :List[Any]=2_56		, lowerCamelCase_    :Any=True		, lowerCamelCase_    :str=True		, lowerCamelCase_    :Union[str, Any]=True		, lowerCamelCase_    :List[str]=2_24		, lowerCamelCase_    :Dict=3		, lowerCamelCase_    :Union[str, Any]=16		, lowerCamelCase_    :Any=None		, **lowerCamelCase_    :Optional[Any]		, )							->			int:







			'''simple docstring'''




			super().__init__(
			    vocab_size=lowerCamelCase_		, hidden_size=lowerCamelCase_		, num_hidden_layers=lowerCamelCase_		, num_attention_heads=lowerCamelCase_		, intermediate_size=lowerCamelCase_		, hidden_act=lowerCamelCase_		, hidden_dropout_prob=lowerCamelCase_		, attention_probs_dropout_prob=lowerCamelCase_		, max_position_embeddings=lowerCamelCase_		, type_vocab_size=lowerCamelCase_		, initializer_range=lowerCamelCase_		, layer_norm_eps=lowerCamelCase_		, pad_token_id=lowerCamelCase_		, bos_token_id=lowerCamelCase_		, eos_token_id=lowerCamelCase_		, **lowerCamelCase_		, )
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= max_ad_position_embeddings
			SCREAMING_SNAKE_CASE       :     List[Any]				= coordinate_size
			SCREAMING_SNAKE_CASE       :     Tuple				= shape_size
			SCREAMING_SNAKE_CASE       :     Optional[int]				= has_relative_attention_bias
			SCREAMING_SNAKE_CASE       :     List[Any]				= rel_pos_bins
			SCREAMING_SNAKE_CASE       :     int				= max_rel_pos
			SCREAMING_SNAKE_CASE       :     Any				= has_spatial_attention_bias
			SCREAMING_SNAKE_CASE       :     List[Any]				= rel_ad_pos_bins
			SCREAMING_SNAKE_CASE       :     Dict				= max_rel_ad_pos
			SCREAMING_SNAKE_CASE       :     Optional[int]				= text_embed
			SCREAMING_SNAKE_CASE       :     Any				= visual_embed
			SCREAMING_SNAKE_CASE       :     Any				= input_size
			SCREAMING_SNAKE_CASE       :     Tuple				= num_channels
			SCREAMING_SNAKE_CASE       :     List[str]				= patch_size
			SCREAMING_SNAKE_CASE       :     str				= classifier_dropout





class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  version.parse("""1.12"""   )





	@property
	def   __lowerCAmelCase (						self    :List[Any]					)							->			Mapping[str, Mapping[int, str]]:







			'''simple docstring'''




			if self.task in ["question-answering", "sequence-classification"]:
					return OrderedDict(
					    [
					        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
					        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
					        ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
					        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
					    ]					)
			else:
					return OrderedDict(
					    [
					        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
					        ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
					        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
					        ('''pixel_values''', {0: '''batch''', 1: '''num_channels'''}),
					    ]					)





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







			'''simple docstring'''




			return 1E-5





	@property
	def   __lowerCAmelCase (						self    :Tuple					)							->			int:







			'''simple docstring'''




			return 12







	def   __lowerCAmelCase (						self    :List[Any]		, lowerCamelCase_    :"ProcessorMixin"		, lowerCamelCase_    :int = -1		, lowerCamelCase_    :int = -1		, lowerCamelCase_    :bool = False		, lowerCamelCase_    :Optional["TensorType"] = None		, lowerCamelCase_    :int = 3		, lowerCamelCase_    :int = 40		, lowerCamelCase_    :int = 40		, )							->			Mapping[str, Any]:







			'''simple docstring'''




			setattr(processor.image_processor		, '''apply_ocr'''		, lowerCamelCase_					)

			# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
			SCREAMING_SNAKE_CASE       :     Dict				= compute_effective_axis_dimension(
			    lowerCamelCase_		, fixed_dimension=OnnxConfig.default_fixed_batch		, num_token_to_add=0					)
			# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= processor.tokenizer.num_special_tokens_to_add(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Any				= compute_effective_axis_dimension(
			    lowerCamelCase_		, fixed_dimension=OnnxConfig.default_fixed_sequence		, num_token_to_add=lowerCamelCase_					)
			# Generate dummy inputs according to compute batch and sequence
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= [[''' '''.join([processor.tokenizer.unk_token]					) * seq_length]] * batch_size

			# Generate dummy bounding boxes
			SCREAMING_SNAKE_CASE       :     int				= [[[48, 84, 73, 1_28]]] * batch_size

			# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
			# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
			SCREAMING_SNAKE_CASE       :     List[Any]				= self._generate_dummy_images(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     int				= dict(
			    processor(
			        lowerCamelCase_		, text=lowerCamelCase_		, boxes=lowerCamelCase_		, return_tensors=lowerCamelCase_		, )					)

			return inputs





