



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 	__UpperCamelCase	(  _lowerCAmelCase=None      ,     _lowerCAmelCase=None		)       ->  Tuple:



    """simple docstring"""






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






@dataclass
class SCREAMING_SNAKE_CASE__     :




    '''simple docstring'''






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




def 	__UpperCamelCase	(  _lowerCAmelCase		)       ->  List[str]:



    """simple docstring"""






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




def 	__UpperCamelCase	(  _lowerCAmelCase		)       ->  int:



    """simple docstring"""






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






class SCREAMING_SNAKE_CASE__     :




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__		):
        A :       List[Any]          =   args
        A :       Optional[int]          =   defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}		)

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






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

            ((A)     ,							(A)) :       int          =   (
                (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
            )

            A :       List[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:
                    A :       str          =   np.asarray(
                        [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results],		dtype=lowerCamelCase__,		)
                else:
                    A :       Dict          =   np.asarray(
                        [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results],		dtype=np.floataa,		)

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

                A :       str          =   np.asarray(lowerCamelCase__,		lowerCamelCase__		)[: len(lowerCamelCase__		)]
                plt.scatter(
                    lowerCamelCase__,		lowerCamelCase__,		label=f'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''		)
                plt.plot(lowerCamelCase__,		lowerCamelCase__,		"""--"""		)

            title_str += f''' {label_model_name} vs.'''

        A :       Dict          =   title_str[:-4]
        A :       Dict          =   """Time in s""" if self.args.is_time else """Memory in MB"""

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

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




def 	__UpperCamelCase	(  )       ->  Union[str, Any]:



    """simple docstring"""






    A :       List[str]          =   HfArgumentParser(_lowerCAmelCase		)
    A :       str          =   parser.parse_args_into_dataclasses()[0]
    A :       Union[str, Any]          =   Plot(args=_lowerCAmelCase		)
    plot.plot()


if __name__ == "__main__":
     main()







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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE_:List[Any]             =						"""▁"""

SCREAMING_SNAKE_CASE_:int             =						{"""vocab_file""": """spiece.model"""}

SCREAMING_SNAKE_CASE_:Any             =						{
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""}
}

SCREAMING_SNAKE_CASE_:Optional[Any]             =						{
    """google/pegasus-xsum""": 512,
}


SCREAMING_SNAKE_CASE_:Tuple             =						logging.get_logger(__name__)






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Union[str, Any]					=      VOCAB_FILES_NAMES

    __lowerCamelCase			: List[Any]					=      VOCAB_FILES_NAMES
    __lowerCamelCase			: Optional[Any]					=      PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase			: str					=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase			: str					=      ["input_ids", "attention_mask"]






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__="<pad>",		lowerCamelCase__="</s>",		lowerCamelCase__="<unk>",		lowerCamelCase__="<mask_2>",		lowerCamelCase__="<mask_1>",		lowerCamelCase__=None,		lowerCamelCase__=103,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        A :       int          =   offset
        if additional_special_tokens is not None:
            if not isinstance(lowerCamelCase__,		lowerCamelCase__		):
                raise TypeError(
                    f'''additional_special_tokens should be of type {type(lowerCamelCase__		)}, but is'''
                    f''' {type(lowerCamelCase__		)}'''		)

            A :       Dict          =   (
                ([mask_token_sent] + additional_special_tokens)
                if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                else additional_special_tokens
            )
            # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
            additional_special_tokens_extended += [
                f'''<unk_{i}>''' for i in range(len(lowerCamelCase__		),		self.offset - 1		)
            ]

            if len(set(lowerCamelCase__		)		) != len(lowerCamelCase__		):
                raise ValueError(
                    """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
                    f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''		)
            A :       int          =   additional_special_tokens_extended
        else:
            A :       Optional[int]          =   [mask_token_sent] if mask_token_sent is not None else []
            additional_special_tokens += [f'''<unk_{i}>''' for i in range(2,		self.offset		)]

        A :       Any          =   {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            eos_token=lowerCamelCase__,		unk_token=lowerCamelCase__,		mask_token=lowerCamelCase__,		pad_token=lowerCamelCase__,		mask_token_sent=lowerCamelCase__,		offset=lowerCamelCase__,		additional_special_tokens=lowerCamelCase__,		sp_model_kwargs=self.sp_model_kwargs,		**lowerCamelCase__,		)
        A :       Union[str, Any]          =   mask_token_sent
        A :       Optional[Any]          =   vocab_file
        A :       Optional[int]          =   spm.SentencePieceProcessor(**self.sp_model_kwargs		)
        self.sp_model.Load(lowerCamelCase__		)

        # add special tokens to encoder dict
        A :       Dict[int, str]          =   {
            0: self.pad_token,
            1: self.eos_token,
        }

        if self.mask_token_sent is not None:
            self.encoder.update(
                {
                    2: self.mask_token_sent,
                    3: self.mask_token,
                }		)

        if self.offset > 0:
            # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
            # mask_token_sent is already added to list -> so start at 1
            self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1,		self.offset - 1		)}		)

        A :       Dict[str, int]          =   {v: k for k, v in self.encoder.items()}






    @property
    def 			_lowerCAmelCase					(       self		):
        return len(self.sp_model		) + self.offset






    def 			_lowerCAmelCase					(       self		):
        A :       Optional[int]          =   {self.convert_ids_to_tokens(lowerCamelCase__		): i for i in range(self.vocab_size		)}
        vocab.update(self.added_tokens_encoder		)
        return vocab






    def __getstate__(       self		):
        A :       List[Any]          =   self.__dict__.copy()
        A :       Union[str, Any]          =   None
        return state






    def __setstate__(       self,		lowerCamelCase__		):
        A :       List[Any]          =   d

        # for backward compatibility
        if not hasattr(self,		"""sp_model_kwargs"""		):
            A :       int          =   {}

        A :       Tuple          =   spm.SentencePieceProcessor(**self.sp_model_kwargs		)
        self.sp_model.Load(self.vocab_file		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        return self.sp_model.encode(lowerCamelCase__,		out_type=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        if token in self.decoder:
            return self.decoder[token]
        elif token in self.added_tokens_decoder:
            return self.added_tokens_decoder[token]
        A :       List[str]          =   self.sp_model.piece_to_id(lowerCamelCase__		)
        return sp_id + self.offset






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        if index in self.encoder:
            return self.encoder[index]
        elif index in self.added_tokens_encoder:
            return self.added_tokens_encoder[index]
        else:
            A :       Dict          =   self.sp_model.IdToPiece(index - self.offset		)
        return token






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       Optional[int]          =   []
        A :       Optional[Any]          =   """"""
        for token in tokens:
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
                out_string += self.sp_model.decode(lowerCamelCase__		) + token
                A :       int          =   []
            else:
                current_sub_tokens.append(lowerCamelCase__		)
        out_string += self.sp_model.decode(lowerCamelCase__		)
        return out_string.strip()






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__=False		):
        return 1






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       Optional[int]          =   set(self.all_special_ids		)  # call it once instead of inside list comp
        all_special_ids.remove(self.unk_token_id		)  # <unk> is only sometimes special

        return [1 if x in all_special_ids else 0 for x in seq]






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__ = None,		lowerCamelCase__ = False		):
        if already_has_special_tokens:
            return self._special_token_mask(lowerCamelCase__		)
        elif token_ids_a is None:
            return self._special_token_mask(lowerCamelCase__		) + [1]
        else:
            return self._special_token_mask(token_ids_a + token_ids_a		) + [1]






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__=None		):
        if token_ids_a is None:
            return token_ids_a + [self.eos_token_id]
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return token_ids_a + token_ids_a + [self.eos_token_id]






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__ = None		):
        if not os.path.isdir(lowerCamelCase__		):
            logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
            return
        A :       Any          =   os.path.join(
            lowerCamelCase__,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)

        if os.path.abspath(self.vocab_file		) != os.path.abspath(lowerCamelCase__		) and os.path.isfile(self.vocab_file		):
            copyfile(self.vocab_file,		lowerCamelCase__		)
        elif not os.path.isfile(self.vocab_file		):
            with open(lowerCamelCase__,		"""wb"""		) as fi:
                A :       Dict          =   self.sp_model.serialized_model_proto()
                fi.write(lowerCamelCase__		)

        return (out_vocab_file,)


