



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase							=       logging.get_logger(__name__)

UpperCAmelCase							=       {
    """google/electra-small-generator""": """https://huggingface.co/google/electra-small-generator/resolve/main/config.json""",
    """google/electra-base-generator""": """https://huggingface.co/google/electra-base-generator/resolve/main/config.json""",
    """google/electra-large-generator""": """https://huggingface.co/google/electra-large-generator/resolve/main/config.json""",
    """google/electra-small-discriminator""": (
        """https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json"""
    ),
    """google/electra-base-discriminator""": (
        """https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json"""
    ),
    """google/electra-large-discriminator""": (
        """https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json"""
    ),
}

class 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__					:   Union[str, Any]       =       """electra"""


							def __init__(				self	,	a_=30_522	,	a_=128	,	a_=256	,	a_=12	,	a_=4	,	a_=1_024	,	a_="gelu"	,	a_=0.1	,	a_=0.1	,	a_=512	,	a_=2	,	a_=0.02	,	a_=1E-1_2	,	a_="first"	,	a_=True	,	a_="gelu"	,	a_=0.1	,	a_=0	,	a_="absolute"	,	a_=True	,	a_=None	,	**a_	,	):
											super().__init__(pad_token_id=a_	,	**a_		)

											a__					   =		vocab_size
											a__					   =		embedding_size
											a__					   =		hidden_size
											a__					   =		num_hidden_layers
											a__					   =		num_attention_heads
											a__					   =		intermediate_size
											a__					   =		hidden_act
											a__					   =		hidden_dropout_prob
											a__					   =		attention_probs_dropout_prob
											a__					   =		max_position_embeddings
											a__					   =		type_vocab_size
											a__					   =		initializer_range
											a__					   =		layer_norm_eps

											a__					   =		summary_type
											a__					   =		summary_use_proj
											a__					   =		summary_activation
											a__					   =		summary_last_dropout
											a__					   =		position_embedding_type
											a__					   =		use_cache
											a__					   =		classifier_dropout




class 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							@property
							def _a   (				self		):
											if self.task == "multiple-choice":
															a__					   =		{0: """batch""", 1: """choice""", 2: """sequence"""}
											else:
															a__					   =		{0: """batch""", 1: """sequence"""}
											return OrderedDict(
											    [
											        ("""input_ids""", dynamic_axis),
											        ("""attention_mask""", dynamic_axis),
											        ("""token_type_ids""", dynamic_axis),
											    ]		)







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    (							UpperCAmelCase_	:      Union[str, Any]=None   ,       UpperCAmelCase_	:      Union[str, Any]=None      )->			Tuple:

       """simple docstring"""




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


@dataclass
class        UpperCAmelCase__				:



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





def 							lowerCamelCase    (							UpperCAmelCase_	:      Dict      )->			int:

       """simple docstring"""




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





def 							lowerCamelCase    (							UpperCAmelCase_	:      List[str]      )->			str:

       """simple docstring"""




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


class        UpperCAmelCase__				:



     '''simple docstring'''
     def __init__(				self ,    _lowerCAmelCase	):
            a			   =args
            a			   =defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}	)

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






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

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

                   a			   =(
                       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			   =np.asarray(
                                     [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] ,    dtype=_lowerCAmelCase ,    )
                          else:
                                 a			   =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))			   =(
                              ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
                          )

                          a			   =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			   =title_str[:-4]
            a			   ="""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 							lowerCamelCase    (							)->			Optional[Any]:

       """simple docstring"""




       a			   =HfArgumentParser(UpperCAmelCase_      )
       a			   =parser.parse_args_into_dataclasses()[0]
       a			   =Plot(args=UpperCAmelCase_      )
       plot.plot()


if __name__ == "__main__":
    main()
