


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_fnet import FNetTokenizer
else:
   UpperCAmelCase    =					None

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

UpperCAmelCase    =					{
    """vocab_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/spiece.model""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json""",
    },
}

UpperCAmelCase    =					{
    """google/fnet-base""": 512,
    """google/fnet-large""": 512,
}

UpperCAmelCase    =					"""▁"""







class lowerCAmelCase_		(				lowerCamelCase__ ):

 '''simple docstring'''

 __snake_case				=						VOCAB_FILES_NAMES
 __snake_case				=						PRETRAINED_VOCAB_FILES_MAP
 __snake_case				=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __snake_case				=						["input_ids", "token_type_ids"]
 __snake_case				=						FNetTokenizer



 def __init__(			self	,   _UpperCAmelCase=None	,   _UpperCAmelCase=None	,   _UpperCAmelCase=False	,   _UpperCAmelCase=True	,   _UpperCAmelCase=True	,   _UpperCAmelCase="<unk>"	,   _UpperCAmelCase="[SEP]"	,   _UpperCAmelCase="<pad>"	,   _UpperCAmelCase="[CLS]"	,   _UpperCAmelCase="[MASK]"	,   **_UpperCAmelCase	,   ):
       # Mask token behave like a normal word, i.e. include the space before it and
       # is included in the raw text, there should be a match in a non-normalized sentence.
       snake_case_ =   (
           AddedToken(_UpperCAmelCase	,   lstrip=_UpperCAmelCase	,   rstrip=_UpperCAmelCase	,   normalized=_UpperCAmelCase      )
           if isinstance(_UpperCAmelCase	,   _UpperCAmelCase      )
           else mask_token
       )

       super().__init__(
           _UpperCAmelCase	,   tokenizer_file=_UpperCAmelCase	,   do_lower_case=_UpperCAmelCase	,   remove_space=_UpperCAmelCase	,   keep_accents=_UpperCAmelCase	,   unk_token=_UpperCAmelCase	,   sep_token=_UpperCAmelCase	,   pad_token=_UpperCAmelCase	,   cls_token=_UpperCAmelCase	,   mask_token=_UpperCAmelCase	,   **_UpperCAmelCase	,   )

       snake_case_ =   do_lower_case
       snake_case_ =   remove_space
       snake_case_ =   keep_accents
       snake_case_ =   vocab_file
       snake_case_ =   False if not self.vocab_file else True



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase = None      ):
       snake_case_ =   [self.sep_token_id]
       snake_case_ =   [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 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase = None      ):
       snake_case_ =   [self.sep_token_id]
       snake_case_ =   [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 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase = None      ):
       if not os.path.isdir(_UpperCAmelCase      ):
             logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''      )
             return
       snake_case_ =   os.path.join(
           _UpperCAmelCase	,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

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

       return (out_vocab_file,)



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					(SCREAMING_SNAKE_CASE=None      ,      SCREAMING_SNAKE_CASE=None      )->						Tuple:




      """simple docstring"""

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







@dataclass
class lowerCAmelCase_		:

 '''simple docstring'''

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






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						Optional[Any]:




      """simple docstring"""

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






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						Tuple:




      """simple docstring"""

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







class lowerCAmelCase_		:

 '''simple docstring'''

 def __init__(			self	,   _UpperCAmelCase      ):
       snake_case_ =   args
       snake_case_ =   defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}      )

       with open(self.args.csv_file	,   newline=''''''      ) as csv_file:
             snake_case_ =   csv.DictReader(_UpperCAmelCase      )
             for row in reader:
                   snake_case_ =   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
                         snake_case_ =   int(row['''result''']      )
                   elif can_convert_to_float(row['''result''']      ):
                         # value is not None
                         snake_case_ =   float(row['''result''']      )





 def 		UpperCamelCase__  (			self      ):
       snake_case_ ,					snake_case_ =   plt.subplots()
       snake_case_ =   '''Time usage''' if self.args.is_time else '''Memory usage'''
       snake_case_ =   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()      ):
             snake_case_ =   sorted(set(self.result_dict[model_name]['''bsz''']      )      )
             snake_case_ =   sorted(set(self.result_dict[model_name]['''seq_len''']      )      )
             snake_case_ =   self.result_dict[model_name]['''result''']

             ((snake_case_) ,					(snake_case_)) =   (
                 (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
             )

             snake_case_ =   (
                 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:
                         snake_case_ =   np.asarray(
                             [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]	,   dtype=_UpperCAmelCase	,   )
                   else:
                         snake_case_ =   np.asarray(
                             [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]	,   dtype=np.floataa	,   )

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

                   snake_case_ =   np.asarray(_UpperCAmelCase	,   _UpperCAmelCase      )[: len(_UpperCAmelCase      )]
                   plt.scatter(
                       _UpperCAmelCase	,   _UpperCAmelCase	,   label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''      )
                   plt.plot(_UpperCAmelCase	,   _UpperCAmelCase	,   '''--'''      )

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

       snake_case_ =   title_str[:-4]
       snake_case_ =   '''Time in s''' if self.args.is_time else '''Memory in MB'''

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

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






def  __lowerCAmelCase					()->						int:




      """simple docstring"""

      snake_case_ =   HfArgumentParser(SCREAMING_SNAKE_CASE      )
      snake_case_ =   parser.parse_args_into_dataclasses()[0]
      snake_case_ =   Plot(args=SCREAMING_SNAKE_CASE      )
      plot.plot()


if __name__ == "__main__":
   main()