





'''simple docstring'''






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
					from .tokenization_big_bird import BigBirdTokenizer
else:
					lowerCAmelCase       		=      None

lowerCAmelCase       		=      logging.get_logger(__name__)
lowerCAmelCase       		=      {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCAmelCase       		=      {
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    },
    """tokenizer_file""": {
        """google/bigbird-roberta-base""": (
            """https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"""
        ),
    },
}

lowerCAmelCase       		=      {
    """google/bigbird-roberta-base""": 40_96,
    """google/bigbird-roberta-large""": 40_96,
    """google/bigbird-base-trivia-itc""": 40_96,
}


lowerCAmelCase       		=      """▁"""
class 		lowerCamelCase  (      _A       ):
	snake_case_      =   VOCAB_FILES_NAMES
	snake_case_      =   PRETRAINED_VOCAB_FILES_MAP
	snake_case_      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case_      =   BigBirdTokenizer
	snake_case_      =   ["input_ids", "attention_mask"]
	snake_case_      =   []



	def __init__(   self		,      a_=None		,      a_=None		,      a_="<unk>"		,      a_="<s>"		,      a_="</s>"		,      a_="<pad>"		,      a_="[SEP]"		,      a_="[MASK]"		,      a_="[CLS]"		,      **a_		,      ):
			lowerCAmelCase     :    int						=   AddedToken(a_		,      lstrip=a_		,      rstrip=a_	) if isinstance(a_		,      a_	) else bos_token
			lowerCAmelCase     :    Optional[Any]						=   AddedToken(a_		,      lstrip=a_		,      rstrip=a_	) if isinstance(a_		,      a_	) else eos_token
			lowerCAmelCase     :    Optional[int]						=   AddedToken(a_		,      lstrip=a_		,      rstrip=a_	) if isinstance(a_		,      a_	) else unk_token
			lowerCAmelCase     :    Any						=   AddedToken(a_		,      lstrip=a_		,      rstrip=a_	) if isinstance(a_		,      a_	) else pad_token
			lowerCAmelCase     :    Optional[int]						=   AddedToken(a_		,      lstrip=a_		,      rstrip=a_	) if isinstance(a_		,      a_	) else cls_token
			lowerCAmelCase     :    Union[str, Any]						=   AddedToken(a_		,      lstrip=a_		,      rstrip=a_	) if isinstance(a_		,      a_	) else sep_token

			# Mask token behave like a normal word, i.e. include the space before it
			lowerCAmelCase     :    Dict						=   AddedToken(a_		,      lstrip=a_		,      rstrip=a_	) if isinstance(a_		,      a_	) else mask_token

			super().__init__(
			    a_		,      tokenizer_file=a_		,      bos_token=a_		,      eos_token=a_		,      unk_token=a_		,      sep_token=a_		,      pad_token=a_		,      cls_token=a_		,      mask_token=a_		,      **a_		,      )

			lowerCAmelCase     :    Union[str, Any]						=   vocab_file
			lowerCAmelCase     :    str						=   False if not self.vocab_file else True



	def   _lowerCamelCase    (   self		,      a_		,      a_ = None	):
			lowerCAmelCase     :    Union[str, Any]						=   [self.sep_token_id]
			lowerCAmelCase     :    Tuple						=   [self.cls_token_id]
			if token_ids_a is None:
					return cls + token_ids_a + sep
			return cls + token_ids_a + sep + token_ids_a + sep



	def   _lowerCamelCase    (   self		,      a_		,      a_ = None		,      a_ = False	):

			if already_has_special_tokens:
					if token_ids_a is not None:
							raise ValueError(
							    "You should not supply a second sequence if the provided sequence of "
							    "ids is already formatted with special tokens for the model."	)
					return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

			if token_ids_a is None:
					return [1] + ([0] * len(a_	)) + [1]
			return [1] + ([0] * len(a_	)) + [1] + ([0] * len(a_	)) + [1]



	def   _lowerCamelCase    (   self		,      a_		,      a_ = None	):
			lowerCAmelCase     :    Optional[Any]						=   [self.sep_token_id]
			lowerCAmelCase     :    Dict						=   [self.cls_token_id]

			if token_ids_a is None:
					return len(cls + token_ids_a + sep	) * [0]
			return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]





	def   _lowerCamelCase    (   self		,      a_		,      a_ = None	):
			if not self.can_save_slow_tokenizer:
					raise ValueError(
					    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
					    "tokenizer."	)

			if not os.path.isdir(a_	):
					logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
					return
			lowerCAmelCase     :    Optional[Any]						=   os.path.join(
			    a_		,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)

			if os.path.abspath(self.vocab_file	) != os.path.abspath(a_	):
					copyfile(self.vocab_file		,      a_	)

			return (out_vocab_file,)










'''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      __A						(							a_						:  Any=None      ,a_						:  List[Any]=None	):
		return field(default_factory=lambda: default      ,metadata=a_	)
@dataclass
class 		lowerCamelCase  :
	snake_case_      =   field(
	    metadata={"help": "The csv file to plot."}      ,   )
	snake_case_      =   field(
	    default=_A      ,   metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}      ,   )
	snake_case_      =   field(
	    default=_A      ,   metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}      ,   )
	snake_case_      =   field(
	    default=_A      ,   metadata={"help": "Disable logarithmic scale when plotting"}      ,   )
	snake_case_      =   field(
	    default=_A      ,   metadata={
	        "help": "Whether the csv file has training results or inference results. Defaults to inference results."
	    }      ,   )
	snake_case_      =   field(
	    default=_A      ,   metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}      ,   )
	snake_case_      =   list_field(
	    default=_A      ,   metadata={"help": "List of model names that are used instead of the ones in the csv file."}       )






def      __A						(							a_						:  Tuple	):
		try:
				int(a_	)
				return True
		except ValueError:
				return False






def      __A						(							a_						:  int	):
		try:
				float(a_	)
				return True
		except ValueError:
				return False
class 		lowerCamelCase  :



	def __init__(   self		,      a_	):
			lowerCAmelCase     :    Optional[Any]						=   args
			lowerCAmelCase     :    List[str]						=   defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}	)

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





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

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

					lowerCAmelCase     :    Union[str, 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:
									lowerCAmelCase     :    int						=   np.asarray(
									    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]		,      dtype=a_		,      )
							else:
									lowerCAmelCase     :    Any						=   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))     :    Any						=   (
							    ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
							)

							lowerCAmelCase     :    Union[str, Any]						=   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.'''

			lowerCAmelCase     :    List[str]						=   title_str[:-4]
			lowerCAmelCase     :    List[Any]						=   "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      __A						(							):
		lowerCAmelCase     :    Optional[Any]						=   HfArgumentParser(a_	)
		lowerCAmelCase     :    List[Any]						=   parser.parse_args_into_dataclasses()[0]
		lowerCAmelCase     :    str						=   Plot(args=a_	)
		plot.plot()


if __name__ == "__main__":
					main()



