






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 		_UpperCamelCase      (			UpperCamelCase_				:    Dict=None			,				UpperCamelCase_				:    Optional[Any]=None						)     ->      Tuple:

						"""simple docstring"""



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


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







def 		_UpperCamelCase      (			UpperCamelCase_				:    Tuple						)     ->      Tuple:

						"""simple docstring"""



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







def 		_UpperCamelCase      (			UpperCamelCase_				:    List[Any]						)     ->      Union[str, Any]:

						"""simple docstring"""



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


class 			__SCREAMING_SNAKE_CASE    :






						def __init__(		self   ,  _UpperCamelCase      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  args
												lowerCAmelCase__			  =  defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}      )

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







						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												lowerCAmelCase__  ,				lowerCAmelCase__			  =  plt.subplots()
												lowerCAmelCase__			  =  'Time usage' if self.args.is_time else 'Memory usage'
												lowerCAmelCase__			  =  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()      ):
																		lowerCAmelCase__			  =  sorted(set(self.result_dict[model_name]['bsz']      )      )
																		lowerCAmelCase__			  =  sorted(set(self.result_dict[model_name]['seq_len']      )      )
																		lowerCAmelCase__			  =  self.result_dict[model_name]['result']

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

																		lowerCAmelCase__			  =  (
																		    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:
																														lowerCAmelCase__			  =  np.asarray(
																														    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]   ,  dtype=_UpperCamelCase   ,  )
																								else:
																														lowerCAmelCase__			  =  np.asarray(
																														    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]   ,  dtype=np.floataa   ,  )

																								((lowerCAmelCase__)  ,				(lowerCAmelCase__))			  =  (
																								    ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
																								)

																								lowerCAmelCase__			  =  np.asarray(_UpperCamelCase   ,  _UpperCamelCase      )[: len(_UpperCamelCase      )]
																								plt.scatter(
																								    _UpperCamelCase   ,  _UpperCamelCase   ,  label=F"{label_model_name} - {inner_loop_label}: {inner_loop_value}"      )
																								plt.plot(_UpperCamelCase   ,  _UpperCamelCase   ,  '--'      )

																		title_str += F" {label_model_name} vs."

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

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

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







def 		_UpperCamelCase      (			)     ->      Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  HfArgumentParser(UpperCamelCase_						)
						lowerCAmelCase__			  =  parser.parse_args_into_dataclasses()[0]
						lowerCAmelCase__			  =  Plot(args=UpperCamelCase_						)
						plot.plot()


if __name__ == "__main__":
					main()













import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


__snake_case			:			Union[str, Any]			   =   logging.get_logger(__name__)


@dataclass
class 			__SCREAMING_SNAKE_CASE    :
						_SCREAMING_SNAKE_CASE		:				str
						_SCREAMING_SNAKE_CASE		:				str
						_SCREAMING_SNAKE_CASE		:				Optional[str]            =    None
						_SCREAMING_SNAKE_CASE		:				Optional[str]            =    None






						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												return json.dumps(dataclasses.asdict(self      )   ,  indent=2      ) + "\n"




@dataclass(frozen=__lowercase)
class 			__SCREAMING_SNAKE_CASE    :
						_SCREAMING_SNAKE_CASE		:				List[int]
						_SCREAMING_SNAKE_CASE		:				Optional[List[int]]            =    None
						_SCREAMING_SNAKE_CASE		:				Optional[List[int]]            =    None
						_SCREAMING_SNAKE_CASE		:				Optional[Union[int, float]]            =    None






						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												return json.dumps(dataclasses.asdict(self      )      ) + "\n"




class 			__SCREAMING_SNAKE_CASE    :






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase      ):






												"""simple docstring"""
												raise NotImplementedError()






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase      ):






												"""simple docstring"""
												raise NotImplementedError()






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase      ):






												"""simple docstring"""
												raise NotImplementedError()






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase      ):






												"""simple docstring"""
												raise NotImplementedError()






						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												raise NotImplementedError()






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase      ):






												"""simple docstring"""
												if len(self.get_labels()      ) > 1:
																		lowerCAmelCase__			  =  self.get_labels()[int(example.label      )]
												return example







						@classmethod
						def 					UpperCamelCase__					(		cls   ,  _UpperCamelCase   ,  _UpperCamelCase=None      ):






												"""simple docstring"""
												with open(_UpperCamelCase   ,  'r'   ,  encoding='utf-8-sig'      ) as f:
																		return list(csv.reader(_UpperCamelCase   ,  delimiter='\t'   ,  quotechar=_UpperCamelCase      )      )




class 			__SCREAMING_SNAKE_CASE    (      __lowercase):






						def __init__(		self   ,  _UpperCamelCase=None   ,  _UpperCamelCase=None   ,  _UpperCamelCase="classification"   ,  _UpperCamelCase=False      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  [] if labels is None else labels
												lowerCAmelCase__			  =  [] if examples is None else examples
												lowerCAmelCase__			  =  mode
												lowerCAmelCase__			  =  verbose






						def __len__(		self      ):






												"""simple docstring"""
												return len(self.examples      )






						def __getitem__(		self   ,  _UpperCamelCase      ):






												"""simple docstring"""
												if isinstance(_UpperCamelCase   ,  _UpperCamelCase      ):
																		return SingleSentenceClassificationProcessor(labels=self.labels   ,  examples=self.examples[idx]      )
												return self.examples[idx]






						@classmethod
						def 					UpperCamelCase__					(		cls   ,  _UpperCamelCase   ,  _UpperCamelCase=""   ,  _UpperCamelCase=0   ,  _UpperCamelCase=1   ,  _UpperCamelCase=None   ,  _UpperCamelCase=False   ,  **_UpperCamelCase      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  cls(**_UpperCamelCase      )
												processor.add_examples_from_csv(
												    _UpperCamelCase   ,  split_name=_UpperCamelCase   ,  column_label=_UpperCamelCase   ,  column_text=_UpperCamelCase   ,  column_id=_UpperCamelCase   ,  skip_first_row=_UpperCamelCase   ,  overwrite_labels=_UpperCamelCase   ,  overwrite_examples=_UpperCamelCase   ,  )
												return processor






						@classmethod
						def 					UpperCamelCase__					(		cls   ,  _UpperCamelCase   ,  _UpperCamelCase=None   ,  **_UpperCamelCase      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  cls(**_UpperCamelCase      )
												processor.add_examples(_UpperCamelCase   ,  labels=_UpperCamelCase      )
												return processor






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase=""   ,  _UpperCamelCase=0   ,  _UpperCamelCase=1   ,  _UpperCamelCase=None   ,  _UpperCamelCase=False   ,  _UpperCamelCase=False   ,  _UpperCamelCase=False   ,  ):






												"""simple docstring"""
												lowerCAmelCase__			  =  self._read_tsv(_UpperCamelCase      )
												if skip_first_row:
																		lowerCAmelCase__			  =  lines[1:]
												lowerCAmelCase__			  =  []
												lowerCAmelCase__			  =  []
												lowerCAmelCase__			  =  []
												for i, line in enumerate(_UpperCamelCase      ):
																		texts.append(line[column_text]      )
																		labels.append(line[column_label]      )
																		if column_id is not None:
																								ids.append(line[column_id]      )
																		else:
																								lowerCAmelCase__			  =  F"{split_name}-{i}" if split_name else str(_UpperCamelCase      )
																								ids.append(_UpperCamelCase      )

												return self.add_examples(
												    _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  overwrite_labels=_UpperCamelCase   ,  overwrite_examples=_UpperCamelCase      )






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase=None   ,  _UpperCamelCase=None   ,  _UpperCamelCase=False   ,  _UpperCamelCase=False      ):






												"""simple docstring"""
												if labels is not None and len(_UpperCamelCase      ) != len(_UpperCamelCase      ):
																		raise ValueError(
																		    F"Text and labels have mismatched lengths {len(_UpperCamelCase      )} and {len(_UpperCamelCase      )}"      )
												if ids is not None and len(_UpperCamelCase      ) != len(_UpperCamelCase      ):
																		raise ValueError(F"Text and ids have mismatched lengths {len(_UpperCamelCase      )} and {len(_UpperCamelCase      )}"      )
												if ids is None:
																		lowerCAmelCase__			  =  [None] * len(_UpperCamelCase      )
												if labels is None:
																		lowerCAmelCase__			  =  [None] * len(_UpperCamelCase      )
												lowerCAmelCase__			  =  []
												lowerCAmelCase__			  =  set()
												for text_or_text_and_label, label, guid in zip(_UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase      ):
																		if isinstance(_UpperCamelCase   ,  (tuple, list)      ) and label is None:
																								lowerCAmelCase__  ,				lowerCAmelCase__			  =  text_or_text_and_label
																		else:
																								lowerCAmelCase__			  =  text_or_text_and_label
																		added_labels.add(_UpperCamelCase      )
																		examples.append(InputExample(guid=_UpperCamelCase   ,  text_a=_UpperCamelCase   ,  text_b=_UpperCamelCase   ,  label=_UpperCamelCase      )      )

												# Update examples
												if overwrite_examples:
																		lowerCAmelCase__			  =  examples
												else:
																		self.examples.extend(_UpperCamelCase      )

												# Update labels
												if overwrite_labels:
																		lowerCAmelCase__			  =  list(_UpperCamelCase      )
												else:
																		lowerCAmelCase__			  =  list(set(self.labels      ).union(_UpperCamelCase      )      )

												return self.examples







						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase=None   ,  _UpperCamelCase=False   ,  _UpperCamelCase=0   ,  _UpperCamelCase=True   ,  _UpperCamelCase=None   ,  ):






												"""simple docstring"""
												if max_length is None:
																		lowerCAmelCase__			  =  tokenizer.max_len

												lowerCAmelCase__			  =  {label: i for i, label in enumerate(self.labels      )}

												lowerCAmelCase__			  =  []
												for ex_index, example in enumerate(self.examples      ):
																		if ex_index % 1_00_00 == 0:
																								logger.info(F"Tokenizing example {ex_index}"      )

																		lowerCAmelCase__			  =  tokenizer.encode(
																		    example.text_a   ,  add_special_tokens=_UpperCamelCase   ,  max_length=min(_UpperCamelCase   ,  tokenizer.max_len      )   ,  )
																		all_input_ids.append(_UpperCamelCase      )

												lowerCAmelCase__			  =  max(len(_UpperCamelCase      ) for input_ids in all_input_ids      )

												lowerCAmelCase__			  =  []
												for ex_index, (input_ids, example) in enumerate(zip(_UpperCamelCase   ,  self.examples      )      ):
																		if ex_index % 1_00_00 == 0:
																								logger.info(F"Writing example {ex_index}/{len(self.examples      )}"      )
																		# The mask has 1 for real tokens and 0 for padding tokens. Only real
																		# tokens are attended to.
																		lowerCAmelCase__			  =  [1 if mask_padding_with_zero else 0] * len(_UpperCamelCase      )

																		# Zero-pad up to the sequence length.
																		lowerCAmelCase__			  =  batch_length - len(_UpperCamelCase      )
																		if pad_on_left:
																								lowerCAmelCase__			  =  ([pad_token] * padding_length) + input_ids
																								lowerCAmelCase__			  =  ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
																		else:
																								lowerCAmelCase__			  =  input_ids + ([pad_token] * padding_length)
																								lowerCAmelCase__			  =  attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

																		if len(_UpperCamelCase      ) != batch_length:
																								raise ValueError(F"Error with input length {len(_UpperCamelCase      )} vs {batch_length}"      )
																		if len(_UpperCamelCase      ) != batch_length:
																								raise ValueError(F"Error with input length {len(_UpperCamelCase      )} vs {batch_length}"      )

																		if self.mode == "classification":
																								lowerCAmelCase__			  =  label_map[example.label]
																		elif self.mode == "regression":
																								lowerCAmelCase__			  =  float(example.label      )
																		else:
																								raise ValueError(self.mode      )

																		if ex_index < 5 and self.verbose:
																								logger.info('*** Example ***'      )
																								logger.info(F"guid: {example.guid}"      )
																								logger.info(F"input_ids: {' '.join([str(_UpperCamelCase      ) for x in input_ids]      )}"      )
																								logger.info(F"attention_mask: {' '.join([str(_UpperCamelCase      ) for x in attention_mask]      )}"      )
																								logger.info(F"label: {example.label} (id = {label})"      )

																		features.append(InputFeatures(input_ids=_UpperCamelCase   ,  attention_mask=_UpperCamelCase   ,  label=_UpperCamelCase      )      )

												if return_tensors is None:
																		return features
												elif return_tensors == "tf":
																		if not is_tf_available():
																								raise RuntimeError('return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported'      )
																		import tensorflow as tf

																		def gen():
																								for ex in features:
																														yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

																		lowerCAmelCase__			  =  tf.data.Dataset.from_generator(
																		    _UpperCamelCase   ,  ({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)   ,  ({'input_ids': tf.TensorShape([None]      ), 'attention_mask': tf.TensorShape([None]      )}, tf.TensorShape([]      ))   ,  )
																		return dataset
												elif return_tensors == "pt":
																		if not is_torch_available():
																								raise RuntimeError('return_tensors set to \'pt\' but PyTorch can\'t be imported'      )
																		import torch
																		from torch.utils.data import TensorDataset

																		lowerCAmelCase__			  =  torch.tensor([f.input_ids for f in features]   ,  dtype=torch.long      )
																		lowerCAmelCase__			  =  torch.tensor([f.attention_mask for f in features]   ,  dtype=torch.long      )
																		if self.mode == "classification":
																								lowerCAmelCase__			  =  torch.tensor([f.label for f in features]   ,  dtype=torch.long      )
																		elif self.mode == "regression":
																								lowerCAmelCase__			  =  torch.tensor([f.label for f in features]   ,  dtype=torch.float      )

																		lowerCAmelCase__			  =  TensorDataset(_UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase      )
																		return dataset
												else:
																		raise ValueError('return_tensors should be one of \'tf\' or \'pt\''      )





