


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__=None,      A__=None				):
			return field(default_factory=lambda: default,      metadata=A__				)







@dataclass
class 	__lowercase    :





							"""simple docstring"""







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







def 						a__			(				A__				):
			try:
						int(A__				)
						return True
			except ValueError:
						return False






def 						a__			(				A__				):
			try:
						float(A__				)
						return True
			except ValueError:
						return False







class 	__lowercase    :





							"""simple docstring"""







							def __init__(      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




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

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






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     int					=					plt.subplots()
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					'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_		:     List[Any]					=					sorted(set(self.result_dict[model_name]['bsz']       )       )
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					sorted(set(self.result_dict[model_name]['seq_len']       )       )
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.result_dict[model_name]['result']

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

													SCREAMING_SNAKE_CASE_		:     Dict					=					(
													    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_		:     Any					=					np.asarray(
																			    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]			,						dtype=lowerCAmelCase__			,						)
																else:
																			SCREAMING_SNAKE_CASE_		:     int					=					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_))		:     Tuple					=					(
																    ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
																)

																SCREAMING_SNAKE_CASE_		:     str					=					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.'''

										SCREAMING_SNAKE_CASE_		:     Any					=					title_str[:-4]
										SCREAMING_SNAKE_CASE_		:     List[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 						a__			(				):
			SCREAMING_SNAKE_CASE_		:     Any					=					HfArgumentParser(A__				)
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					parser.parse_args_into_dataclasses()[0]
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					Plot(args=A__				)
			plot.plot()


if __name__ == "__main__":
				main()












'''simple docstring'''




from ...configuration_utils import PretrainedConfig


lowercase__       : Any         =       {
    '''google/tapas-base-finetuned-sqa''': (
        '''https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-wtq''': (
        '''https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-wikisql-supervised''': (
        '''https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-tabfact''': (
        '''https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json'''
    ),
}


class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			'''tapas'''


     def __init__(		self		,      _UpperCAmelCase=3_0522		,      _UpperCAmelCase=768		,      _UpperCAmelCase=12		,      _UpperCAmelCase=12		,      _UpperCAmelCase=3072		,      _UpperCAmelCase="gelu"		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=1024		,      _UpperCAmelCase=[3, 256, 256, 2, 256, 256, 10]		,      _UpperCAmelCase=0.02		,      _UpperCAmelCase=1e-1_2		,      _UpperCAmelCase=0		,      _UpperCAmelCase=10.0		,      _UpperCAmelCase=0		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=None		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=False		,      _UpperCAmelCase=None		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=False		,      _UpperCAmelCase=False		,      _UpperCAmelCase="ratio"		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      _UpperCAmelCase=64		,      _UpperCAmelCase=32		,      _UpperCAmelCase=False		,      _UpperCAmelCase=True		,      _UpperCAmelCase=False		,      _UpperCAmelCase=False		,      _UpperCAmelCase=True		,      _UpperCAmelCase=False		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          super().__init__(pad_token_id=_UpperCAmelCase		,      **_UpperCAmelCase)

          # BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
          __A		:					Dict						=			vocab_size
          __A		:					Tuple						=			hidden_size
          __A		:					Any						=			num_hidden_layers
          __A		:					int						=			num_attention_heads
          __A		:					Tuple						=			hidden_act
          __A		:					Tuple						=			intermediate_size
          __A		:					List[Any]						=			hidden_dropout_prob
          __A		:					int						=			attention_probs_dropout_prob
          __A		:					List[str]						=			max_position_embeddings
          __A		:					Optional[int]						=			type_vocab_sizes
          __A		:					str						=			initializer_range
          __A		:					List[str]						=			layer_norm_eps

          # Fine-tuning task hyperparameters
          __A		:					List[str]						=			positive_label_weight
          __A		:					List[Any]						=			num_aggregation_labels
          __A		:					Optional[Any]						=			aggregation_loss_weight
          __A		:					Tuple						=			use_answer_as_supervision
          __A		:					List[str]						=			answer_loss_importance
          __A		:					Any						=			use_normalized_answer_loss
          __A		:					Any						=			huber_loss_delta
          __A		:					Union[str, Any]						=			temperature
          __A		:					Tuple						=			aggregation_temperature
          __A		:					Optional[Any]						=			use_gumbel_for_cells
          __A		:					List[str]						=			use_gumbel_for_aggregation
          __A		:					Tuple						=			average_approximation_function
          __A		:					List[str]						=			cell_selection_preference
          __A		:					Dict						=			answer_loss_cutoff
          __A		:					Union[str, Any]						=			max_num_rows
          __A		:					Optional[Any]						=			max_num_columns
          __A		:					int						=			average_logits_per_cell
          __A		:					Optional[Any]						=			select_one_column
          __A		:					int						=			allow_empty_column_selection
          __A		:					List[Any]						=			init_cell_selection_weights_to_zero
          __A		:					int						=			reset_position_index_per_cell
          __A		:					Union[str, Any]						=			disable_per_token_loss

          # Aggregation hyperparameters
          __A		:					Optional[Any]						=			aggregation_labels
          __A		:					List[str]						=			no_aggregation_label_index

          if isinstance(self.aggregation_labels		,      _UpperCAmelCase):
               __A		:					Optional[Any]						=			{int(_UpperCAmelCase): v for k, v in aggregation_labels.items()}