




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  (		lowerCAmelCase_	:   Tuple=None  ,							lowerCAmelCase_	:   Any=None     ):
     """simple docstring"""

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





@dataclass
class 					__lowerCamelCase			:




    """simple docstring"""

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






def 				_A  (		lowerCAmelCase_	:   Optional[int]     ):
     """simple docstring"""

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



def 				_A  (		lowerCAmelCase_	:   Optional[int]     ):
     """simple docstring"""

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





class 					__lowerCamelCase			:




    """simple docstring"""

    def __init__(			self	:	int  ,      SCREAMING_SNAKE_CASE__	:	int  ) ->			Dict:
         lowerCAmelCase__            =					args
         lowerCAmelCase__            =					defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}  )

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


    def   a (			self	:	Tuple  ) ->			Optional[int]:
         lowerCAmelCase__      ,						lowerCAmelCase__            =					plt.subplots()
         lowerCAmelCase__            =					"Time usage" if self.args.is_time else "Memory usage"
         lowerCAmelCase__            =					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__            =					sorted(set(self.result_dict[model_name]["bsz"]  )  )
              lowerCAmelCase__            =					sorted(set(self.result_dict[model_name]["seq_len"]  )  )
              lowerCAmelCase__            =					self.result_dict[model_name]["result"]

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

              lowerCAmelCase__            =					(
                  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__            =					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:
                        lowerCAmelCase__            =					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__))            =					(
                       ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
                   )

                   lowerCAmelCase__            =					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.'

         lowerCAmelCase__            =					title_str[:-4]
         lowerCAmelCase__            =					"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  (		):
     """simple docstring"""

     lowerCAmelCase__            =					HfArgumentParser(lowerCAmelCase_     )
     lowerCAmelCase__            =					parser.parse_args_into_dataclasses()[0]
     lowerCAmelCase__            =					Plot(args=lowerCAmelCase_     )
     plot.plot()


if __name__ == "__main__":
     main()






'''simple docstring'''


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer


_lowercase  =			{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_lowercase  =			{
    """vocab_file""": {
        """google/electra-small-generator""": (
            """https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt"""
        ),
        """google/electra-base-generator""": """https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt""",
        """google/electra-large-generator""": (
            """https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt"""
        ),
        """google/electra-small-discriminator""": (
            """https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt"""
        ),
        """google/electra-base-discriminator""": (
            """https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt"""
        ),
        """google/electra-large-discriminator""": (
            """https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """google/electra-small-generator""": (
            """https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json"""
        ),
        """google/electra-base-generator""": (
            """https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json"""
        ),
        """google/electra-large-generator""": (
            """https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json"""
        ),
        """google/electra-small-discriminator""": (
            """https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json"""
        ),
        """google/electra-base-discriminator""": (
            """https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json"""
        ),
        """google/electra-large-discriminator""": (
            """https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json"""
        ),
    },
}

_lowercase  =			{
    """google/electra-small-generator""": 5_12,
    """google/electra-base-generator""": 5_12,
    """google/electra-large-generator""": 5_12,
    """google/electra-small-discriminator""": 5_12,
    """google/electra-base-discriminator""": 5_12,
    """google/electra-large-discriminator""": 5_12,
}

_lowercase  =			{
    """google/electra-small-generator""": {"""do_lower_case""": True},
    """google/electra-base-generator""": {"""do_lower_case""": True},
    """google/electra-large-generator""": {"""do_lower_case""": True},
    """google/electra-small-discriminator""": {"""do_lower_case""": True},
    """google/electra-base-discriminator""": {"""do_lower_case""": True},
    """google/electra-large-discriminator""": {"""do_lower_case""": True},
}
class 						a_				(			UpperCAmelCase__						):
	lowercase_       :     Tuple			  =      VOCAB_FILES_NAMES
	lowercase_       :     Optional[int]			  =      PRETRAINED_VOCAB_FILES_MAP
	lowercase_       :     int			  =      PRETRAINED_INIT_CONFIGURATION
	lowercase_       :     str			  =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase_       :     List[Any]			  =      ElectraTokenizer




	def __init__(       self     :  Optional[Any]       ,		__lowerCAmelCase     :  Optional[Any]=None       ,		__lowerCAmelCase     :  str=None       ,		__lowerCAmelCase     :  Union[str, Any]=True       ,		__lowerCAmelCase     :  int="[UNK]"       ,		__lowerCAmelCase     :  str="[SEP]"       ,		__lowerCAmelCase     :  Optional[Any]="[PAD]"       ,		__lowerCAmelCase     :  Optional[Any]="[CLS]"       ,		__lowerCAmelCase     :  str="[MASK]"       ,		__lowerCAmelCase     :  List[Any]=True       ,		__lowerCAmelCase     :  Optional[int]=None       ,		**__lowerCAmelCase     :  List[Any]       ,		):
							super().__init__(
							    __lowerCAmelCase       ,		tokenizer_file=__lowerCAmelCase       ,		do_lower_case=__lowerCAmelCase       ,		unk_token=__lowerCAmelCase       ,		sep_token=__lowerCAmelCase       ,		pad_token=__lowerCAmelCase       ,		cls_token=__lowerCAmelCase       ,		mask_token=__lowerCAmelCase       ,		tokenize_chinese_chars=__lowerCAmelCase       ,		strip_accents=__lowerCAmelCase       ,		**__lowerCAmelCase       ,		)

							__snake_case						 =   json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
							if (
							    normalizer_state.get('lowercase'       ,		__lowerCAmelCase      ) != do_lower_case
							    or normalizer_state.get('strip_accents'       ,		__lowerCAmelCase      ) != strip_accents
							    or normalizer_state.get('handle_chinese_chars'       ,		__lowerCAmelCase      ) != tokenize_chinese_chars
							):
													__snake_case						 =   getattr(__lowerCAmelCase       ,		normalizer_state.pop('type'      )      )
													__snake_case						 =   do_lower_case
													__snake_case						 =   strip_accents
													__snake_case						 =   tokenize_chinese_chars
													__snake_case						 =   normalizer_class(**__lowerCAmelCase      )

							__snake_case						 =   do_lower_case




	def   lowercase__   (       self     :  Union[str, Any]       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  Tuple=None      ):
							__snake_case						 =   [self.cls_token_id] + token_ids_a + [self.sep_token_id]

							if token_ids_a:
													output += token_ids_a + [self.sep_token_id]

							return output




	def   lowercase__   (       self     :  Any       ,		__lowerCAmelCase     :  List[int]       ,		__lowerCAmelCase     :  Optional[List[int]] = 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   lowercase__   (       self     :  List[Any]       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  Optional[str] = None      ):
							__snake_case						 =   self._tokenizer.model.save(__lowerCAmelCase       ,		name=__lowerCAmelCase      )
							return tuple(__lowerCAmelCase      )

