






"""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 					lowercase__				(					__snake_case   :							Dict=None					,	__snake_case   :							Optional[int]=None  ):


					'''simple docstring'''


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



@dataclass
class   lowerCamelCase				:







		'''simple docstring'''



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





def 					lowercase__				(					__snake_case   :							str  ):


					'''simple docstring'''


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




def 					lowercase__				(					__snake_case   :							Any  ):


					'''simple docstring'''


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



class   lowerCamelCase				:







		'''simple docstring'''



		def __init__(							self    ,		_UpperCamelCase       )     ->							str:
							UpperCAmelCase_   :				Union[str, Any]						=						args
							UpperCAmelCase_   :				List[str]						=						defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}       )

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



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							UpperCAmelCase_	,  UpperCAmelCase_   :				Any						=						plt.subplots()
							UpperCAmelCase_   :				List[Any]						=						'Time usage' if self.args.is_time else 'Memory usage'
							UpperCAmelCase_   :				List[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()       ):
												UpperCAmelCase_   :				Optional[int]						=						sorted(set(self.result_dict[model_name]['bsz']       )       )
												UpperCAmelCase_   :				List[str]						=						sorted(set(self.result_dict[model_name]['seq_len']       )       )
												UpperCAmelCase_   :				Any						=						self.result_dict[model_name]['result']

												((UpperCAmelCase_)	,  (UpperCAmelCase_))   :				int						=						(
												    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
												)

												UpperCAmelCase_   :				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:
																						UpperCAmelCase_   :				Dict						=						np.asarray(
																						    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]    ,		dtype=__lowerCamelCase    ,		)
																	else:
																						UpperCAmelCase_   :				Optional[int]						=						np.asarray(
																						    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]    ,		dtype=np.floataa    ,		)

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

																	UpperCAmelCase_   :				Any						=						np.asarray(__lowerCamelCase    ,		__lowerCamelCase       )[: len(__lowerCamelCase       )]
																	plt.scatter(
																	    __lowerCamelCase    ,		__lowerCamelCase    ,		label=f"{label_model_name} - {inner_loop_label}: {inner_loop_value}"       )
																	plt.plot(__lowerCamelCase    ,		__lowerCamelCase    ,		'--'       )

												title_str += f" {label_model_name} vs."

							UpperCAmelCase_   :				List[str]						=						title_str[:-4]
							UpperCAmelCase_   :				str						=						'Time in s' if self.args.is_time else 'Memory in MB'

							# plot
							plt.title(__lowerCamelCase       )
							plt.xlabel(__lowerCamelCase       )
							plt.ylabel(__lowerCamelCase       )
							plt.legend()

							if self.args.figure_png_file is not None:
												plt.savefig(self.args.figure_png_file       )
							else:
												plt.show()





def 					lowercase__				(					):


					'''simple docstring'''


					UpperCAmelCase_   :				Optional[int]						=						HfArgumentParser(UpperCamelCase__  )
					UpperCAmelCase_   :				str						=						parser.parse_args_into_dataclasses()[0]
					UpperCAmelCase_   :				Union[str, Any]						=						Plot(args=UpperCamelCase__  )
					plot.plot()


if __name__ == "__main__":
		main()














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


__UpperCAmelCase     							=						logging.get_logger(__name__)

__UpperCAmelCase     							=						{
    'microsoft/layoutlmv3-base': 'https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json',
}



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   Dict							=  '''layoutlmv3'''



		def __init__(							self    ,		_UpperCamelCase=5_0_2_6_5    ,		_UpperCamelCase=7_6_8    ,		_UpperCamelCase=1_2    ,		_UpperCamelCase=1_2    ,		_UpperCamelCase=3_0_7_2    ,		_UpperCamelCase="gelu"    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=5_1_2    ,		_UpperCamelCase=2    ,		_UpperCamelCase=0.02    ,		_UpperCamelCase=1E-5    ,		_UpperCamelCase=1    ,		_UpperCamelCase=0    ,		_UpperCamelCase=2    ,		_UpperCamelCase=1_0_2_4    ,		_UpperCamelCase=1_2_8    ,		_UpperCamelCase=1_2_8    ,		_UpperCamelCase=True    ,		_UpperCamelCase=3_2    ,		_UpperCamelCase=1_2_8    ,		_UpperCamelCase=6_4    ,		_UpperCamelCase=2_5_6    ,		_UpperCamelCase=True    ,		_UpperCamelCase=True    ,		_UpperCamelCase=True    ,		_UpperCamelCase=2_2_4    ,		_UpperCamelCase=3    ,		_UpperCamelCase=1_6    ,		_UpperCamelCase=None    ,		**_UpperCamelCase    ,		)     ->							Optional[Any]:
							super().__init__(
							    vocab_size=_UpperCamelCase    ,		hidden_size=_UpperCamelCase    ,		num_hidden_layers=_UpperCamelCase    ,		num_attention_heads=_UpperCamelCase    ,		intermediate_size=_UpperCamelCase    ,		hidden_act=_UpperCamelCase    ,		hidden_dropout_prob=_UpperCamelCase    ,		attention_probs_dropout_prob=_UpperCamelCase    ,		max_position_embeddings=_UpperCamelCase    ,		type_vocab_size=_UpperCamelCase    ,		initializer_range=_UpperCamelCase    ,		layer_norm_eps=_UpperCamelCase    ,		pad_token_id=_UpperCamelCase    ,		bos_token_id=_UpperCamelCase    ,		eos_token_id=_UpperCamelCase    ,		**_UpperCamelCase    ,		)
							UpperCAmelCase_   :				str						=						max_ad_position_embeddings
							UpperCAmelCase_   :				Union[str, Any]						=						coordinate_size
							UpperCAmelCase_   :				Union[str, Any]						=						shape_size
							UpperCAmelCase_   :				str						=						has_relative_attention_bias
							UpperCAmelCase_   :				Tuple						=						rel_pos_bins
							UpperCAmelCase_   :				Dict						=						max_rel_pos
							UpperCAmelCase_   :				Any						=						has_spatial_attention_bias
							UpperCAmelCase_   :				Optional[Any]						=						rel_ad_pos_bins
							UpperCAmelCase_   :				List[str]						=						max_rel_ad_pos
							UpperCAmelCase_   :				List[str]						=						text_embed
							UpperCAmelCase_   :				Dict						=						visual_embed
							UpperCAmelCase_   :				Optional[int]						=						input_size
							UpperCAmelCase_   :				List[Any]						=						num_channels
							UpperCAmelCase_   :				List[Any]						=						patch_size
							UpperCAmelCase_   :				Union[str, Any]						=						classifier_dropout





class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   Tuple							=  version.parse('''1.12'''      )



		@property
		def  __UpperCAmelCase      (							self       )     ->							Mapping[str, Mapping[int, str]]:
							# The order of inputs is different for question answering and sequence classification
							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  __UpperCAmelCase      (							self       )     ->							float:
							return 1E-5



		@property
		def  __UpperCAmelCase      (							self       )     ->							int:
							return 1_2



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = -1    ,		_UpperCamelCase = -1    ,		_UpperCamelCase = False    ,		_UpperCamelCase = None    ,		_UpperCamelCase = 3    ,		_UpperCamelCase = 4_0    ,		_UpperCamelCase = 4_0    ,		)     ->							Mapping[str, Any]:
							setattr(processor.image_processor    ,		'apply_ocr'    ,		_UpperCamelCase       )

							# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
							UpperCAmelCase_   :				Optional[int]						=						compute_effective_axis_dimension(
							    _UpperCamelCase    ,		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
							UpperCAmelCase_   :				Any						=						processor.tokenizer.num_special_tokens_to_add(_UpperCamelCase       )
							UpperCAmelCase_   :				Any						=						compute_effective_axis_dimension(
							    _UpperCamelCase    ,		fixed_dimension=OnnxConfig.default_fixed_sequence    ,		num_token_to_add=_UpperCamelCase       )
							# Generate dummy inputs according to compute batch and sequence
							UpperCAmelCase_   :				Tuple						=						[[' '.join([processor.tokenizer.unk_token]       ) * seq_length]] * batch_size

							# Generate dummy bounding boxes
							UpperCAmelCase_   :				List[str]						=						[[[4_8, 8_4, 7_3, 1_2_8]]] * 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)
							UpperCAmelCase_   :				str						=						self._generate_dummy_images(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )

							UpperCAmelCase_   :				List[str]						=						dict(
							    processor(
							        _UpperCamelCase    ,		text=_UpperCamelCase    ,		boxes=_UpperCamelCase    ,		return_tensors=_UpperCamelCase    ,		)       )

							return inputs






