


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_	(					__snake_case      :    Optional[Any]=None						,      __snake_case      :    List[str]=None  )->   Optional[int]:




						'''simple docstring'''
						return field(default_factory=lambda: default						,      metadata=__snake_case  )

@dataclass
class      __snake_case				:
					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 				UpperCamelCase_	(					__snake_case      :    int  )->   str:




						'''simple docstring'''
						try:
												int(__snake_case  )
												return True
						except ValueError:
												return False
def 				UpperCamelCase_	(					__snake_case      :    Optional[Any]  )->   Dict:




						'''simple docstring'''
						try:
												float(__snake_case  )
												return True
						except ValueError:
												return False

class      __snake_case				:
					def __init__(						self       :						int	,      _lowerCAmelCase       :						List[str]  )							->					List[Any]:



											'''simple docstring'''





											__magic_name__					=      args
											__magic_name__					=      defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}  )

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







					def 	a			(						self       :						List[str]  )							->					Tuple:



											'''simple docstring'''





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

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

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

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

																							__magic_name__					=      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.'''

											__magic_name__					=      title_str[:-4]
											__magic_name__					=      '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_	(					)->   Optional[int]:




						'''simple docstring'''
						__magic_name__					=      HfArgumentParser(__snake_case  )
						__magic_name__					=      parser.parse_args_into_dataclasses()[0]
						__magic_name__					=      Plot(args=__snake_case  )
						plot.plot()


if __name__ == "__main__":
					main()





from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase			:					List[Any]         =							logging.get_logger(__name__)

UpperCamelCase			:					Tuple         =							{
    '''mnaylor/mega-base-wikitext''': '''https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json''',
}

class      __snake_case				(   _UpperCAmelCase):
					SCREAMING_SNAKE_CASE_		:       str								=			"mega"
					def __init__(						self       :						Optional[int]	,      _lowerCAmelCase       :						List[Any]=3_0_5_2_2	,      _lowerCAmelCase       :						List[str]=1_2_8	,      _lowerCAmelCase       :						Dict=4	,      _lowerCAmelCase       :						Optional[Any]=2_5_6	,      _lowerCAmelCase       :						Optional[Any]=1_6	,      _lowerCAmelCase       :						Dict=True	,      _lowerCAmelCase       :						Optional[Any]=6_4	,      _lowerCAmelCase       :						Optional[Any]=False	,      _lowerCAmelCase       :						Optional[int]=-1	,      _lowerCAmelCase       :						Any=None	,      _lowerCAmelCase       :						List[Any]=True	,      _lowerCAmelCase       :						List[Any]="scalenorm"	,      _lowerCAmelCase       :						str=True	,      _lowerCAmelCase       :						Union[str, Any]="silu"	,      _lowerCAmelCase       :						Optional[Any]="softmax"	,      _lowerCAmelCase       :						Optional[Any]=0.1	,      _lowerCAmelCase       :						Dict=0.1	,      _lowerCAmelCase       :						Any=0.1	,      _lowerCAmelCase       :						Optional[Any]=False	,      _lowerCAmelCase       :						Any=True	,      _lowerCAmelCase       :						int=2_5_6	,      _lowerCAmelCase       :						List[str]=True	,      _lowerCAmelCase       :						str=0.1	,      _lowerCAmelCase       :						List[str]=2_0_4_8	,      _lowerCAmelCase       :						Optional[Any]=False	,      _lowerCAmelCase       :						str=2	,      _lowerCAmelCase       :						Dict=0.02	,      _lowerCAmelCase       :						str=0.2	,      _lowerCAmelCase       :						List[str]=0.02	,      _lowerCAmelCase       :						int=1.0	,      _lowerCAmelCase       :						Optional[int]=1	,      _lowerCAmelCase       :						List[str]=0	,      _lowerCAmelCase       :						str=2	,      _lowerCAmelCase       :						Optional[Any]="rotary"	,      _lowerCAmelCase       :						int=None	,      _lowerCAmelCase       :						int=True	,      _lowerCAmelCase       :						Optional[Any]=True	,      **_lowerCAmelCase       :						str	,      )							->					Any:



											'''simple docstring'''





											super().__init__(pad_token_id=_lowerCAmelCase	,      bos_token_id=_lowerCAmelCase	,      eos_token_id=_lowerCAmelCase	,      **_lowerCAmelCase  )

											__magic_name__					=      vocab_size
											__magic_name__					=      hidden_size
											__magic_name__					=      num_hidden_layers
											__magic_name__					=      activation
											__magic_name__					=      attention_activation
											__magic_name__					=      intermediate_size
											__magic_name__					=      ema_projection_size
											__magic_name__					=      bidirectional
											__magic_name__					=      shared_representation_size
											__magic_name__					=      use_chunking
											__magic_name__					=      chunk_size
											__magic_name__					=      truncation
											__magic_name__					=      normalize_before_mega
											__magic_name__					=      normalization_type
											__magic_name__					=      norm_affine
											__magic_name__					=      dropout_prob
											__magic_name__					=      hidden_dropout_prob
											__magic_name__					=      attention_probs_dropout_prob
											__magic_name__					=      use_feature_dropout
											__magic_name__					=      use_normalized_ffn
											__magic_name__					=      nffn_hidden_size
											__magic_name__					=      normalize_before_ffn
											__magic_name__					=      nffn_activation_dropout_prob
											__magic_name__					=      max_positions
											__magic_name__					=      add_token_type_embeddings
											__magic_name__					=      type_vocab_size
											__magic_name__					=      initializer_range
											__magic_name__					=      ema_delta_alpha_range
											__magic_name__					=      ema_beta_range
											__magic_name__					=      ema_gamma_omega_range
											__magic_name__					=      relative_positional_bias
											__magic_name__					=      use_cache
											__magic_name__					=      classifier_dropout
											__magic_name__					=      add_lm_hidden_dense_layer
											__magic_name__					=      1  # not used but required by Hugging Face







class      __snake_case				(   _UpperCAmelCase):
					@property
					def 	a			(						self       :						int  )							->					Mapping[str, Mapping[int, str]]:



											'''simple docstring'''





											if self.task == "multiple-choice":
																	__magic_name__					=      {0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
																	__magic_name__					=      {0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('input_ids', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											    ]  )

