

'''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  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Dict=None    ,				SCREAMING_SNAKE_CASE :				int=None  ):
		return field(default_factory=lambda: default    ,				metadata=_lowerCamelCase  )



@dataclass
class     lowercase_		:

		'''simple docstring'''





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




def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Dict  ):
		try:
				int(_lowerCamelCase  )
				return True
		except ValueError:
				return False


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Optional[Any]  ):
		try:
				float(_lowerCamelCase  )
				return True
		except ValueError:
				return False



class     lowercase_		:

		'''simple docstring'''





		def __init__(       self				,	a_       )							->			Any:


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

				with open(self.args.csv_file				,	newline=''       ) as csv_file:
						UpperCAmelCase  							=		csv.DictReader(snake_case_       )
						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   snake_case_     (       self       )							->			Dict:


				"""simple docstring"""
				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)  							=		(
						    (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=snake_case_				,	)
								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)  							=		(
								    ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
								)

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

						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(snake_case_       )
				plt.xlabel(snake_case_       )
				plt.ylabel(snake_case_       )
				plt.legend()

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




def  lowerCamelCase__						(  ):
		UpperCAmelCase  							=		HfArgumentParser(_lowerCamelCase  )
		UpperCAmelCase  							=		parser.parse_args_into_dataclasses()[0]
		UpperCAmelCase  							=		Plot(args=_lowerCamelCase  )
		plot.plot()


if __name__ == "__main__":
		main()





'''simple docstring'''




import argparse
import json
import re
import string
import sys
from collections import Counter


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				List[str]  ):

  def remove_articles(SCREAMING_SNAKE_CASE :				Union[str, Any]  ):
    return re.sub(R'\b(a|an|the)\b'    ,				' '    ,				SCREAMING_SNAKE_CASE  )

  def white_space_fix(SCREAMING_SNAKE_CASE :				Union[str, Any]  ):
    return " ".join(text.split()  )

  def remove_punc(SCREAMING_SNAKE_CASE :				List[Any]  ):
    UpperCAmelCase  							=		set(string.punctuation  )
    return "".join(ch for ch in text if ch not in exclude  )

  def lower(SCREAMING_SNAKE_CASE :				Any  ):
    return text.lower()

  return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE  )  )  )  )


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Tuple    ,				SCREAMING_SNAKE_CASE :				Dict  ):
  UpperCAmelCase  							=		normalize_answer(SCREAMING_SNAKE_CASE  ).split()
  UpperCAmelCase  							=		normalize_answer(SCREAMING_SNAKE_CASE  ).split()
  UpperCAmelCase  							=		Counter(SCREAMING_SNAKE_CASE  ) & Counter(SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		sum(common.values()  )
  if num_same == 0:
    return 0
  UpperCAmelCase  							=		1.0 * num_same / len(SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		1.0 * num_same / len(SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		(2 * precision * recall) / (precision + recall)
  return fa


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				str    ,				SCREAMING_SNAKE_CASE :				int  ):
  return normalize_answer(SCREAMING_SNAKE_CASE  ) == normalize_answer(SCREAMING_SNAKE_CASE  )


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Dict    ,				SCREAMING_SNAKE_CASE :				Tuple    ,				SCREAMING_SNAKE_CASE :				Dict  ):
  UpperCAmelCase  							=		[]
  for ground_truth in ground_truths:
    UpperCAmelCase  							=		metric_fn(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  )
    scores_for_ground_truths.append(SCREAMING_SNAKE_CASE  )
  return max(SCREAMING_SNAKE_CASE  )


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Optional[Any]    ,				SCREAMING_SNAKE_CASE :				Optional[int]  ):
  UpperCAmelCase  							=		UpperCAmelCase  							=		UpperCAmelCase  							=		0
  for article in dataset:
    for paragraph in article["paragraphs"]:
      for qa in paragraph["qas"]:
        total += 1
        if qa["id"] not in predictions:
          UpperCAmelCase  							=		'Unanswered question ' + qa['id'] + ' will receive score 0.'
          print(SCREAMING_SNAKE_CASE    ,				file=sys.stderr  )
          continue
        UpperCAmelCase  							=		[x['text'] for x in qa['answers']]
        UpperCAmelCase  							=		predictions[qa['id']]
        exact_match += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  )
        fa += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  )

  UpperCAmelCase  							=		100.0 * exact_match / total
  UpperCAmelCase  							=		100.0 * fa / total

  return {"exact_match": exact_match, "f1": fa}


if __name__ == "__main__":
  _a			:	Tuple								=  '1.1'
  _a			:	int								=  argparse.ArgumentParser(description='Evaluation for SQuAD ' + expected_version)
  parser.add_argument('dataset_file', help='Dataset file')
  parser.add_argument('prediction_file', help='Prediction File')
  _a			:	List[Any]								=  parser.parse_args()
  with open(args.dataset_file) as dataset_file:
    _a			:	Optional[int]								=  json.load(dataset_file)
    if dataset_json["version"] != expected_version:
      print(
          'Evaluation expects v-' + expected_version + ', but got dataset with v-' + dataset_json['version'],
          file=sys.stderr,
      )
    _a			:	Optional[Any]								=  dataset_json['data']
  with open(args.prediction_file) as prediction_file:
    _a			:	Optional[Any]								=  json.load(prediction_file)
  print(json.dumps(evaluate(dataset, predictions)))


