



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        lowercase_							(A : List[Any]=None		,					A : List[Any]=None      ):
       return field(default_factory=lambda: default		,					metadata=A      )




@dataclass
class 					snake_case__						:






     """simple docstring"""


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


def        lowercase_							(A : Union[str, Any]      ):
       try:
              int(A      )
              return True
       except ValueError:
              return False
def        lowercase_							(A : List[str]      ):
       try:
              float(A      )
              return True
       except ValueError:
              return False




class 					snake_case__						:






     """simple docstring"""


     def __init__(       self  :  List[Any], _snake_case  :  Dict					)   ->Union[str, Any]:
            snake_case__							:	Union[str, Any]   	=	args
            snake_case__							:	Union[str, Any]   	=	defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}					)

            with open(self.args.csv_file, newline=''					) as csv_file:
                   snake_case__							:	List[str]   	=	csv.DictReader(_snake_case					)
                   for row in reader:
                          snake_case__							:	List[Any]   	=	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__							:	List[Any]   	=	int(row['result']					)
                          elif can_convert_to_float(row['result']					):
                                 # value is not None
                                 snake_case__							:	Union[str, Any]   	=	float(row['result']					)






     def    lowercase_   (       self  :  Any					)   ->List[str]:
            snake_case__	,				snake_case__							:	Dict   	=	plt.subplots()
            snake_case__							:	List[Any]   	=	'Time usage' if self.args.is_time else 'Memory usage'
            snake_case__							:	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()					):
                   snake_case__							:	Union[str, Any]   	=	sorted(set(self.result_dict[model_name]['bsz']					)					)
                   snake_case__							:	Optional[Any]   	=	sorted(set(self.result_dict[model_name]['seq_len']					)					)
                   snake_case__							:	Optional[Any]   	=	self.result_dict[model_name]['result']

                   ((snake_case__)	,				(snake_case__))							:	List[str]   	=	(
                       (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
                   )

                   snake_case__							:	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:
                                 snake_case__							:	int   	=	np.asarray(
                                     [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results], dtype=_snake_case, )
                          else:
                                 snake_case__							:	List[str]   	=	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__))							:	List[str]   	=	(
                              ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
                          )

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

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

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

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

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


def        lowercase_							():
       snake_case__							:	Optional[int]   	=	HfArgumentParser(A      )
       snake_case__							:	List[str]   	=	parser.parse_args_into_dataclasses()[0]
       snake_case__							:	Any   	=	Plot(args=A      )
       plot.plot()


if __name__ == "__main__":
 main()











from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_   :Optional[int]        =      logging.get_logger(__name__)

a_   :Dict        =      {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	"""openai-gpt"""
     _SCREAMING_SNAKE_CASE						=	{
         """max_position_embeddings""": """n_positions""",
         """hidden_size""": """n_embd""",
         """num_attention_heads""": """n_head""",
         """num_hidden_layers""": """n_layer""",
     }






     def __init__(       self  :  Optional[int], _snake_case  :  Dict=4_0_4_7_8, _snake_case  :  str=5_1_2, _snake_case  :  int=7_6_8, _snake_case  :  Tuple=1_2, _snake_case  :  Any=1_2, _snake_case  :  str="gelu", _snake_case  :  List[str]=0.1, _snake_case  :  Any=0.1, _snake_case  :  Dict=0.1, _snake_case  :  int=1e-5, _snake_case  :  Optional[Any]=0.0_2, _snake_case  :  List[Any]="cls_index", _snake_case  :  Any=True, _snake_case  :  Any=None, _snake_case  :  int=True, _snake_case  :  Optional[Any]=0.1, **_snake_case  :  List[Any], )   ->Optional[int]:
            snake_case__							:	int   	=	vocab_size
            snake_case__							:	Dict   	=	n_positions
            snake_case__							:	str   	=	n_embd
            snake_case__							:	str   	=	n_layer
            snake_case__							:	List[Any]   	=	n_head
            snake_case__							:	List[Any]   	=	afn
            snake_case__							:	Optional[Any]   	=	resid_pdrop
            snake_case__							:	List[str]   	=	embd_pdrop
            snake_case__							:	List[Any]   	=	attn_pdrop
            snake_case__							:	Optional[int]   	=	layer_norm_epsilon
            snake_case__							:	str   	=	initializer_range
            snake_case__							:	List[str]   	=	summary_type
            snake_case__							:	Optional[int]   	=	summary_use_proj
            snake_case__							:	List[str]   	=	summary_activation
            snake_case__							:	Optional[Any]   	=	summary_first_dropout
            snake_case__							:	int   	=	summary_proj_to_labels
            super().__init__(**_snake_case					)






