
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 jax
import jax.numpy as jnp
from bigbird_flax import FlaxBigBirdForNaturalQuestions
from datasets import load_from_disk

from transformers import BigBirdTokenizerFast


lowercase__								=						{0: '''null''', 1: '''short''', 2: '''long''', 3: '''yes''', 4: '''no'''}
lowercase__								=						set(''''''.join(['''‘''', '''’''', '''´''', '''`''', '''.''', ''',''', '''-''', '''"''']))


def 					UpperCamelCase						(	snake_case						,  snake_case=0						,  snake_case=None ):




   '''simple docstring'''

   return [" ".join(x.split(''' ''' )[begin:end] ) for x in answers if len(x.split(''' ''' ) ) > 1]


def 					UpperCamelCase						(	snake_case						,  snake_case=False ):




   '''simple docstring'''

   if make_sub_answers:
      # if answers are longer than one word, make sure a predictions is correct if it coresponds to the complete 1: or :-1 sub word
      # *e.g.* if the correct answer contains a prefix such as "the", or "a"
      SCREAMING_SNAKE_CASE__      :					Tuple							    =			(
          given_answers + get_sub_answers(snake_case						,  begin=1 ) + get_sub_answers(snake_case						,  end=-1 )
      )
   SCREAMING_SNAKE_CASE__      :					Any							    =			[]
   for answer in given_answers:
      SCREAMING_SNAKE_CASE__      :					int							    =			answer.replace('''_'''						,  ''' ''' ).lower()
      SCREAMING_SNAKE_CASE__      :					Tuple							    =			''''''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ''' ''' for c in alias )
      answers.append(''' '''.join(alias.split() ).strip() )
   return set(snake_case )


def 					UpperCamelCase						(	snake_case						,  snake_case						,  snake_case=1						,  snake_case=1_00 ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					List[Any]							    =			jax.lax.top_k(snake_case						,  snake_case )
   SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			jax.lax.top_k(snake_case						,  snake_case )

   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			best_end_idx[:, None] - best_start_idx[None, :]
   SCREAMING_SNAKE_CASE__      :					Dict							    =			jnp.logical_or(widths < 0						,  widths > max_size )
   SCREAMING_SNAKE_CASE__      :					Any							    =			(best_end_scores[:, None] + best_start_scores[None, :]) - (1e8 * mask)
   SCREAMING_SNAKE_CASE__      :					str							    =			jnp.argmax(snake_case ).item()

   return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k]


def 					UpperCamelCase						(	snake_case ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Any							    =			sample['''question''']['''text''']
   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			sample['''document''']['''tokens''']['''token''']
   SCREAMING_SNAKE_CASE__      :					Tuple							    =			sample['''document''']['''tokens''']['''is_html''']
   SCREAMING_SNAKE_CASE__      :					Tuple							    =			sample['''annotations''']['''long_answer''']
   SCREAMING_SNAKE_CASE__      :					str							    =			sample['''annotations''']['''short_answers''']

   SCREAMING_SNAKE_CASE__      :					List[str]							    =			''' '''.join([context[i] for i in range(len(snake_case ) ) if not is_html[i]] )

   # 0 - No ; 1 - Yes
   for answer in sample["annotations"]["yes_no_answer"]:
      if answer == 0 or answer == 1:
         return {
             "question": question,
             "context": context_string,
             "short": [],
             "long": [],
             "category": "no" if answer == 0 else "yes",
         }

   SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			[]
   for s in short_answers:
      short_targets.extend(s['''text'''] )
   SCREAMING_SNAKE_CASE__      :					Dict							    =			list(set(snake_case ) )

   SCREAMING_SNAKE_CASE__      :					Any							    =			[]
   for s in long_answers:
      if s["start_token"] == -1:
         continue
      SCREAMING_SNAKE_CASE__      :					List[Any]							    =			context[s['''start_token'''] : s['''end_token''']]
      SCREAMING_SNAKE_CASE__      :					Dict							    =			is_html[s['''start_token'''] : s['''end_token''']]
      SCREAMING_SNAKE_CASE__      :					List[str]							    =			''' '''.join([answer[i] for i in range(len(snake_case ) ) if not html[i]] )
      if new_answer not in long_targets:
         long_targets.append(snake_case )

   SCREAMING_SNAKE_CASE__      :					int							    =			'''long_short''' if len(short_targets + long_targets ) > 0 else '''null'''

   return {
       "question": question,
       "context": context_string,
       "short": short_targets,
       "long": long_targets,
       "category": category,
   }


def 					UpperCamelCase						(	):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					List[Any]							    =			load_from_disk('''natural-questions-validation''' )
   SCREAMING_SNAKE_CASE__      :					List[Any]							    =			dataset.map(snake_case ).remove_columns(['''annotations''', '''document''', '''id'''] )
   print(snake_case )

   SCREAMING_SNAKE_CASE__      :					Dict							    =			dataset.filter(lambda snake_case : (len(x['''question'''] ) + len(x['''context'''] )) < 4 * 40_96 )
   SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			short_validation_dataset.filter(lambda snake_case : x["category"] != "null" )
   short_validation_dataset

   SCREAMING_SNAKE_CASE__      :					List[Any]							    =			'''vasudevgupta/flax-bigbird-natural-questions'''
   SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			FlaxBigBirdForNaturalQuestions.from_pretrained(snake_case )
   SCREAMING_SNAKE_CASE__      :					List[str]							    =			BigBirdTokenizerFast.from_pretrained(snake_case )

   @jax.jit
   def forward(*snake_case						,  **snake_case ):
      SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					str							    =			model(*snake_case						,  **snake_case )
      return start_logits, end_logits, jnp.argmax(snake_case						,  axis=-1 )

   def evaluate(snake_case ):
      # encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
      SCREAMING_SNAKE_CASE__      :					Any							    =			tokenizer(
          example['''question''']						,  example['''context''']						,  return_tensors='''np'''						,  max_length=40_96						,  padding='''max_length'''						,  truncation=snake_case						,  )

      SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Any							    =			forward(**snake_case )

      SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			CATEGORY_MAPPING[category.item()]

      SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			example['''long'''] + example['''short''']
      if example["category"] in ["yes", "no", "null"]:
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			[example['''category''']]
      SCREAMING_SNAKE_CASE__      :					Tuple							    =			example['''category'''] != '''null'''
      # Now target can be: "yes", "no", "null", "list of long & short answers"

      if predicted_category in ["yes", "no", "null"]:
         SCREAMING_SNAKE_CASE__      :					str							    =			[predicted_category]
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			example['''output'''] == example['''targets''']
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			predicted_category != '''null'''
         return example

      SCREAMING_SNAKE_CASE__      :					Any							    =			38 if predicted_category == '''short''' else 10_24
      SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Tuple							    =			get_best_valid_start_end_idx(
          start_scores[0]						,  end_scores[0]						,  top_k=8						,  max_size=snake_case )

      SCREAMING_SNAKE_CASE__      :					Dict							    =			inputs['''input_ids'''][0].tolist()
      SCREAMING_SNAKE_CASE__      :					int							    =			[tokenizer.decode(input_ids[start_score : end_score + 1] )]

      SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			expand_to_aliases(example['''targets''']						,  make_sub_answers=snake_case )
      SCREAMING_SNAKE_CASE__      :					Any							    =			expand_to_aliases(example['''output'''] )

      # some preprocessing to both prediction and answer
      SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			{''''''.join(a.split() ) for a in answers}
      SCREAMING_SNAKE_CASE__      :					List[Any]							    =			{''''''.join(p.split() ) for p in predictions}
      SCREAMING_SNAKE_CASE__      :					int							    =			{s for s in predictions if s not in ['''``''', '''\'\'''', '''`''', '''\'''']}

      # if there is a common element, it's a exact match
      SCREAMING_SNAKE_CASE__      :					Tuple							    =			len(list(answers & predictions ) ) > 0
      SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			predicted_category != '''null''' and len(snake_case ) > 0

      return example

   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			short_validation_dataset.map(snake_case )

   SCREAMING_SNAKE_CASE__      :					int							    =			len(snake_case )
   SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			len(short_validation_dataset.filter(lambda snake_case : x["match"] == 1 ) )
   print('''EM score:'''						,  (matched / total) * 1_00						,  '''%''' )


if __name__ == "__main__":
       main()

