

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__			(       _a=None				,      _a=None):
	return field(default_factory=lambda: default				,      metadata=_a)






@dataclass
class _UpperCamelCase				:
			'''simple docstring'''







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

def 			lowerCamelCase__			(       _a):
	try:
		int(_a)
		return True
	except ValueError:
		return False

def 			lowerCamelCase__			(       _a):
	try:
		float(_a)
		return True
	except ValueError:
		return False






class _UpperCamelCase				:
			'''simple docstring'''







			def __init__(      self				:			Tuple   ,   a				:			Optional[int]						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Tuple					  =							args
				SCREAMING_SNAKE_CASE :   int					  =							defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}						)

				with open(self.args.csv_file   ,   newline=""						) as csv_file:
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							csv.DictReader(a						)
					for row in reader:
						SCREAMING_SNAKE_CASE :   Optional[Any]					  =							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
							SCREAMING_SNAKE_CASE :   Tuple					  =							int(row["result"]						)
						elif can_convert_to_float(row["result"]						):
							# value is not None
							SCREAMING_SNAKE_CASE :   str					  =							float(row["result"]						)






			def 				__UpperCamelCase			(      self				:			Tuple						)				->							List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   List[str]					  =							plt.subplots()
				SCREAMING_SNAKE_CASE :   Tuple					  =							"Time usage" if self.args.is_time else "Memory usage"
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							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()						):
					SCREAMING_SNAKE_CASE :   str					  =							sorted(set(self.result_dict[model_name]["bsz"]						)						)
					SCREAMING_SNAKE_CASE :   Any					  =							sorted(set(self.result_dict[model_name]["seq_len"]						)						)
					SCREAMING_SNAKE_CASE :   Any					  =							self.result_dict[model_name]["result"]

					((SCREAMING_SNAKE_CASE)  ,(SCREAMING_SNAKE_CASE)) :   str					  =							(
					    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
					)

					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							(
					    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:
							SCREAMING_SNAKE_CASE :   List[str]					  =							np.asarray(
							    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]   ,   dtype=a   ,   )
						else:
							SCREAMING_SNAKE_CASE :   Tuple					  =							np.asarray(
							    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]   ,   dtype=np.floataa   ,   )

						((SCREAMING_SNAKE_CASE)  ,(SCREAMING_SNAKE_CASE)) :   Union[str, Any]					  =							(
						    ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
						)

						SCREAMING_SNAKE_CASE :   str					  =							np.asarray(a   ,   a						)[: len(a						)]
						plt.scatter(
						    a   ,   a   ,   label=F"{label_model_name} - {inner_loop_label}: {inner_loop_value}"						)
						plt.plot(a   ,   a   ,   "--"						)

					title_str += F" {label_model_name} vs."

				SCREAMING_SNAKE_CASE :   List[Any]					  =							title_str[:-4]
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							"Time in s" if self.args.is_time else "Memory in MB"

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

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

def 			lowerCamelCase__			(       ):
	SCREAMING_SNAKE_CASE :   Any					  =							HfArgumentParser(_a)
	SCREAMING_SNAKE_CASE :   List[Any]					  =							parser.parse_args_into_dataclasses()[0]
	SCREAMING_SNAKE_CASE :   Any					  =							Plot(args=_a)
	plot.plot()


if __name__ == "__main__":
			main()

"""simple docstring"""


import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin







class 					lowerCAmelCase_					(   _lowercase		,       unittest.TestCase     ):



						'''simple docstring'''


						_lowerCamelCase:     List[str]   =     BioGptTokenizer
						_lowerCamelCase:     Tuple   =     False


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Optional[int]:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							A     = [
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    'w</w>',
							    'r</w>',
							    't</w>',
							    'lo',
							    'low',
							    'er</w>',
							    'low</w>',
							    'lowest</w>',
							    'newer</w>',
							    'wider</w>',
							    '<unk>',
							]
							A     = dict(zip(A_	,range(len(A_   )   )   )   )
							A     = ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

							A     = os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['vocab_file']   )
							A     = os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['merges_file']   )
							with open(self.vocab_file	,'w'   ) as fp:
								fp.write(json.dumps(A_   )   )
							with open(self.merges_file	,'w'   ) as fp:
								fp.write('\n'.join(A_   )   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict	,A_  :   Tuple   )  -> int:
							A     = 'lower newer'
							A     = 'lower newer'
							return input_text, output_text


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> Any:
							A     = BioGptTokenizer(self.vocab_file	,self.merges_file   )

							A     = 'lower'
							A     = ['low', 'er</w>']
							A     = tokenizer.tokenize(A_   )
							self.assertListEqual(A_	,A_   )

							A     = tokens + ['<unk>']
							A     = [14, 15, 20]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(A_   )	,A_   )




						@slow
						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]   )  -> Optional[Any]:
							A     = BioGptTokenizer.from_pretrained('microsoft/biogpt'   )

							A     = tokenizer.encode('sequence builders'	,add_special_tokens=A_   )
							A     = tokenizer.encode('multi-sequence build'	,add_special_tokens=A_   )

							A     = tokenizer.build_inputs_with_special_tokens(A_   )
							A     = tokenizer.build_inputs_with_special_tokens(A_	,A_   )

							self.assertTrue(encoded_sentence == [2] + text   )
							self.assertTrue(encoded_pair == [2] + text + [2] + text_a   )