"""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_						( lowerCamelCase=None ,      lowerCamelCase=None	):
				return field(default_factory=lambda: default ,      metadata=lowerCamelCase	)
@dataclass
class     snake_case	:




					"""simple docstring"""

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






def   a_						( lowerCamelCase	):
				try:
								int(lowerCamelCase	)
								return True
				except ValueError:
								return False



def   a_						( lowerCamelCase	):
				try:
								float(lowerCamelCase	)
								return True
				except ValueError:
								return False
class     snake_case	:




					"""simple docstring"""

					def __init__(    self       :     List[str]   ,lowerCamelCase__       :     str      ):
									UpperCAmelCase__					=							args
									UpperCAmelCase__					=							defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}      )

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







					def 		__lowerCAmelCase						(    self       :     Any      ):
									UpperCAmelCase__				,     UpperCAmelCase__					=							plt.subplots()
									UpperCAmelCase__					=							'Time usage' if self.args.is_time else 'Memory usage'
									UpperCAmelCase__					=							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__					=							sorted(set(self.result_dict[model_name]['bsz']      )      )
													UpperCAmelCase__					=							sorted(set(self.result_dict[model_name]['seq_len']      )      )
													UpperCAmelCase__					=							self.result_dict[model_name]['result']

													((UpperCAmelCase__)				,     (UpperCAmelCase__))					=							(
													    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
													)

													UpperCAmelCase__					=							(
													    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__					=							np.asarray(
																					    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]   ,dtype=lowerCamelCase__   ,)
																	else:
																					UpperCAmelCase__					=							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__))					=							(
																	    ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
																	)

																	UpperCAmelCase__					=							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__					=							title_str[:-4]
									UpperCAmelCase__					=							'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_						( ):
				UpperCAmelCase__					=							HfArgumentParser(lowerCamelCase	)
				UpperCAmelCase__					=							parser.parse_args_into_dataclasses()[0]
				UpperCAmelCase__					=							Plot(args=lowerCamelCase	)
				plot.plot()


if __name__ == "__main__":
						main()




"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowerCAmelCase__  :  str											=     logging.get_logger(__name__)

lowerCAmelCase__  :  Union[str, Any]											=     {
    'uclanlp/plbart-base': 'https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}
class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					snake_case__       =			"plbart"
					snake_case__       =			["past_key_values"]
					snake_case__       =			{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







					def __init__(    self       :     str   ,lowerCamelCase__       :     List[Any]=50_005   ,lowerCamelCase__       :     Optional[Any]=1_024   ,lowerCamelCase__       :     List[str]=6   ,lowerCamelCase__       :     Tuple=3_072   ,lowerCamelCase__       :     Optional[int]=12   ,lowerCamelCase__       :     Optional[int]=6   ,lowerCamelCase__       :     Optional[int]=3_072   ,lowerCamelCase__       :     Any=12   ,lowerCamelCase__       :     str=0.0   ,lowerCamelCase__       :     str=0.0   ,lowerCamelCase__       :     Union[str, Any]=True   ,lowerCamelCase__       :     str=True   ,lowerCamelCase__       :     Optional[int]="gelu"   ,lowerCamelCase__       :     Optional[Any]=768   ,lowerCamelCase__       :     int=0.1   ,lowerCamelCase__       :     int=0.1   ,lowerCamelCase__       :     Union[str, Any]=0.0   ,lowerCamelCase__       :     List[str]=0.0_2   ,lowerCamelCase__       :     Optional[int]=0.0   ,lowerCamelCase__       :     Dict=True   ,lowerCamelCase__       :     List[Any]=1   ,lowerCamelCase__       :     List[str]=0   ,lowerCamelCase__       :     Any=2   ,lowerCamelCase__       :     Any=2   ,**lowerCamelCase__       :     Dict   ,):
									UpperCAmelCase__					=							vocab_size
									UpperCAmelCase__					=							max_position_embeddings
									UpperCAmelCase__					=							d_model
									UpperCAmelCase__					=							encoder_ffn_dim
									UpperCAmelCase__					=							encoder_layers
									UpperCAmelCase__					=							encoder_attention_heads
									UpperCAmelCase__					=							decoder_ffn_dim
									UpperCAmelCase__					=							decoder_layers
									UpperCAmelCase__					=							decoder_attention_heads
									UpperCAmelCase__					=							dropout
									UpperCAmelCase__					=							attention_dropout
									UpperCAmelCase__					=							activation_dropout
									UpperCAmelCase__					=							activation_function
									UpperCAmelCase__					=							init_std
									UpperCAmelCase__					=							encoder_layerdrop
									UpperCAmelCase__					=							decoder_layerdrop
									UpperCAmelCase__					=							classifier_dropout
									UpperCAmelCase__					=							use_cache
									UpperCAmelCase__					=							encoder_layers
									UpperCAmelCase__					=							scale_embedding  # scale factor will be sqrt(d_model) if True
									super().__init__(
									    pad_token_id=lowerCamelCase__   ,bos_token_id=lowerCamelCase__   ,eos_token_id=lowerCamelCase__   ,is_encoder_decoder=lowerCamelCase__   ,forced_eos_token_id=lowerCamelCase__   ,**lowerCamelCase__   ,)






class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					@property
					def 		__lowerCAmelCase						(    self       :     int      ):
									return OrderedDict(
									    [
									        ('input_ids', {0: 'batch', 1: 'sequence'}),
									        ('attention_mask', {0: 'batch', 1: 'sequence'}),
									    ]      )







					@property
					def 		__lowerCAmelCase						(    self       :     Any      ):
									if self.use_past:
													return OrderedDict(
													    [
													        ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
													        ('past_keys', {0: 'batch', 2: 'sequence'}),
													        ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
													    ]      )
									else:
													return OrderedDict(
													    [
													        ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
													        ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
													    ]      )



