
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_			(				snake_case				: Optional[Any]=None			,   snake_case				: Optional[Any]=None			)   ->     Optional[Any]:



						'''simple docstring'''

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






@dataclass
class 							SCREAMING_SNAKE_CASE__     :

							"""simple docstring"""






							_snake_case  		=	field(
							    metadata={'help': 'The csv file to plot.'} ,		)
							_snake_case  		=	field(
							    default=SCREAMING_SNAKE_CASE_ ,		metadata={'help': 'Whether to plot along batch size or sequence length. Defaults to sequence length.'} ,		)
							_snake_case  		=	field(
							    default=SCREAMING_SNAKE_CASE_ ,		metadata={'help': 'Whether the csv file has time results or memory results. Defaults to memory results.'} ,		)
							_snake_case  		=	field(
							    default=SCREAMING_SNAKE_CASE_ ,		metadata={'help': 'Disable logarithmic scale when plotting'} ,		)
							_snake_case  		=	field(
							    default=SCREAMING_SNAKE_CASE_ ,		metadata={
							        'help': 'Whether the csv file has training results or inference results. Defaults to inference results.'
							    } ,		)
							_snake_case  		=	field(
							    default=SCREAMING_SNAKE_CASE_ ,		metadata={'help': 'Filename under which the plot will be saved. If unused no plot is saved.'} ,		)
							_snake_case  		=	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_			(				snake_case				: List[Any]			)   ->     str:



						'''simple docstring'''

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




def 						A_			(				snake_case				: List[str]			)   ->     int:



						'''simple docstring'''

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






class 							SCREAMING_SNAKE_CASE__     :

							"""simple docstring"""






							def __init__(	self  ,						SCREAMING_SNAKE_CASE_      )-> 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(SCREAMING_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      A__		(	self      )-> int:





													'''simple docstring'''






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

																									__UpperCamelCase  	=      np.asarray(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )[: len(SCREAMING_SNAKE_CASE_      )]
																									plt.scatter(
																									    SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						label=F"{label_model_name} - {inner_loop_label}: {inner_loop_value}"      )
																									plt.plot(SCREAMING_SNAKE_CASE_  ,						SCREAMING_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(SCREAMING_SNAKE_CASE_      )
													plt.xlabel(SCREAMING_SNAKE_CASE_      )
													plt.ylabel(SCREAMING_SNAKE_CASE_      )
													plt.legend()

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





def 						A_			(				)   ->     Dict:



						'''simple docstring'''

						__UpperCamelCase  	=      HfArgumentParser(snake_case			)
						__UpperCamelCase  	=      parser.parse_args_into_dataclasses()[0]
						__UpperCamelCase  	=      Plot(args=snake_case			)
						plot.plot()


if __name__ == "__main__":
				main()








"""simple docstring"""







import argparse
import json
import re
import string
import sys
from collections import Counter
def _A		(		lowercase						):







    """simple docstring"""

    def remove_articles(lowercase						):
        return re.sub(R'''\b(a|an|the)\b'''			,       ''' '''			,       lowercase						)

    def white_space_fix(lowercase						):
        return " ".join(text.split()						)

    def remove_punc(lowercase						):
        a											=set(string.punctuation						)
        return "".join(ch for ch in text if ch not in exclude						)

    def lower(lowercase						):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(lowercase						)						)						)						)
def _A		(		lowercase			,       lowercase						):







    """simple docstring"""
    a											=normalize_answer(lowercase						).split()
    a											=normalize_answer(lowercase						).split()
    a											=Counter(lowercase						) & Counter(lowercase						)
    a											=sum(common.values()						)
    if num_same == 0:
        return 0
    a											=1.0 * num_same / len(lowercase						)
    a											=1.0 * num_same / len(lowercase						)
    a											=(2 * precision * recall) / (precision + recall)
    return fa
def _A		(		lowercase			,       lowercase						):







    """simple docstring"""
    return normalize_answer(lowercase						) == normalize_answer(lowercase						)
def _A		(		lowercase			,       lowercase			,       lowercase						):







    """simple docstring"""
    a											=[]
    for ground_truth in ground_truths:
        a											=metric_fn(lowercase			,       lowercase						)
        scores_for_ground_truths.append(lowercase						)
    return max(lowercase						)
def _A		(		lowercase			,       lowercase						):







    """simple docstring"""
    a											=a											=a											=0
    for article in dataset:
        for paragraph in article["paragraphs"]:
            for qa in paragraph["qas"]:
                total += 1
                if qa["id"] not in predictions:
                    a											='''Unanswered question ''' + qa['''id'''] + ''' will receive score 0.'''
                    print(lowercase			,       file=sys.stderr						)
                    continue
                a											=[x['''text'''] for x in qa['''answers''']]
                a											=predictions[qa['''id''']]
                exact_match += metric_max_over_ground_truths(lowercase			,       lowercase			,       lowercase						)
                fa += metric_max_over_ground_truths(lowercase			,       lowercase			,       lowercase						)

    a											=100.0 * exact_match / total
    a											=100.0 * fa / total

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


if __name__ == "__main__":
       lowerCamelCase_  :  List[Any]     		=    """1.1"""
       lowerCamelCase_  :  Optional[Any]     		=    argparse.ArgumentParser(description="""Evaluation for SQuAD """ + expected_version)
       parser.add_argument("""dataset_file""", help="""Dataset file""")
       parser.add_argument("""prediction_file""", help="""Prediction File""")
       lowerCamelCase_  :  Optional[Any]     		=    parser.parse_args()
       with open(args.dataset_file) as dataset_file:
              lowerCamelCase_  :  List[str]     		=    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,
                     )
              lowerCamelCase_  :  Dict     		=    dataset_json["""data"""]
       with open(args.prediction_file) as prediction_file:
              lowerCamelCase_  :  Optional[Any]     		=    json.load(prediction_file)
       print(json.dumps(evaluate(dataset, predictions)))