

"""simple docstring"""





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  :				Optional[Any]=None   ,				UpperCAmelCase  :				Optional[Any]=None					):


					'''simple docstring'''






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

@dataclass
class      __a :

			"""simple docstring"""

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



def 							_lowerCAmelCase   (    UpperCAmelCase  :				Union[str, Any]					):


					'''simple docstring'''






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



def 							_lowerCAmelCase   (    UpperCAmelCase  :				Union[str, Any]					):


					'''simple docstring'''






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

class      __a :

			"""simple docstring"""

			def __init__(				self  :       int    ,	lowercase_  :       List[str]    ):
								UpperCamelCase__		:     Tuple								=args
								UpperCamelCase__		:     Optional[int]								=defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}    )

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






			def  _lowerCAmelCase      (				self  :       Any    ):
								UpperCamelCase__	,      UpperCamelCase__		:     Optional[int]								=plt.subplots()
								UpperCamelCase__		:     List[str]								='''Time usage''' if self.args.is_time else '''Memory usage'''
								UpperCamelCase__		:     Tuple								=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()    ):
													UpperCamelCase__		:     Optional[int]								=sorted(set(self.result_dict[model_name]['''bsz''']    )    )
													UpperCamelCase__		:     Dict								=sorted(set(self.result_dict[model_name]['''seq_len''']    )    )
													UpperCamelCase__		:     int								=self.result_dict[model_name]['''result''']

													((UpperCamelCase__)	,      (UpperCamelCase__))		:     Dict								=(
													    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
													)

													UpperCamelCase__		:     Union[str, 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:
																							UpperCamelCase__		:     str								=np.asarray(
																							    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]    ,	dtype=lowercase_    ,	)
																		else:
																							UpperCamelCase__		:     Tuple								=np.asarray(
																							    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]    ,	dtype=np.floataa    ,	)

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

																		UpperCamelCase__		:     Dict								=np.asarray(lowercase_    ,	lowercase_    )[: len(lowercase_    )]
																		plt.scatter(
																		    lowercase_    ,	lowercase_    ,	label=f'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''    )
																		plt.plot(lowercase_    ,	lowercase_    ,	'''--'''    )

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

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

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

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



def 							_lowerCAmelCase   (    ):


					'''simple docstring'''






					UpperCamelCase__		:     List[Any]								=HfArgumentParser(UpperCAmelCase					)
					UpperCamelCase__		:     List[Any]								=parser.parse_args_into_dataclasses()[0]
					UpperCamelCase__		:     Dict								=Plot(args=UpperCAmelCase					)
					plot.plot()


if __name__ == "__main__":
				main()





"""simple docstring"""





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


_SCREAMING_SNAKE_CASE							:	Any										=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:	Optional[Any]										=			{
    # pcqm4mv1 now deprecated
    """graphormer-base""": """https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json""",
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}

class      __a (   snake_case__     ):

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_      =    'graphormer'
			SCREAMING_SNAKE_CASE_      =    ['past_key_values']






			def __init__(				self  :       Optional[int]    ,	lowercase_  :       int = 1    ,	lowercase_  :       int = 512 * 9    ,	lowercase_  :       int = 512 * 3    ,	lowercase_  :       int = 512    ,	lowercase_  :       int = 512    ,	lowercase_  :       int = 512    ,	lowercase_  :       int = 128    ,	lowercase_  :       int = 5    ,	lowercase_  :       int = 1024    ,	lowercase_  :       str = "multi_hop"    ,	lowercase_  :       int = 512    ,	lowercase_  :       bool = False    ,	lowercase_  :       int = 12    ,	lowercase_  :       int = 768    ,	lowercase_  :       int = 768    ,	lowercase_  :       int = 32    ,	lowercase_  :       float = 0.1    ,	lowercase_  :       float = 0.1    ,	lowercase_  :       float = 0.0    ,	lowercase_  :       bool = False    ,	lowercase_  :       bool = False    ,	lowercase_  :       bool = False    ,	lowercase_  :       str = "gelu"    ,	lowercase_  :       float = None    ,	lowercase_  :       bool = False    ,	lowercase_  :       int = 0    ,	lowercase_  :       bool = False    ,	lowercase_  :       float = 0.0    ,	lowercase_  :       int = 8    ,	lowercase_  :       int = None    ,	lowercase_  :       int = None    ,	lowercase_  :       bool = True    ,	lowercase_  :       bool = True    ,	lowercase_  :       List[str]=0    ,	lowercase_  :       Union[str, Any]=1    ,	lowercase_  :       str=2    ,	**lowercase_  :       str    ,	):
								UpperCamelCase__		:     Dict								=num_classes
								UpperCamelCase__		:     str								=num_atoms
								UpperCamelCase__		:     Union[str, Any]								=num_in_degree
								UpperCamelCase__		:     Optional[int]								=num_out_degree
								UpperCamelCase__		:     List[str]								=num_edges
								UpperCamelCase__		:     List[str]								=num_spatial
								UpperCamelCase__		:     Dict								=num_edge_dis
								UpperCamelCase__		:     Tuple								=edge_type
								UpperCamelCase__		:     str								=multi_hop_max_dist
								UpperCamelCase__		:     Optional[Any]								=spatial_pos_max
								UpperCamelCase__		:     Tuple								=max_nodes
								UpperCamelCase__		:     Optional[Any]								=num_hidden_layers
								UpperCamelCase__		:     Optional[Any]								=embedding_dim
								UpperCamelCase__		:     Dict								=embedding_dim
								UpperCamelCase__		:     Dict								=ffn_embedding_dim
								UpperCamelCase__		:     str								=num_attention_heads
								UpperCamelCase__		:     Tuple								=dropout
								UpperCamelCase__		:     List[str]								=attention_dropout
								UpperCamelCase__		:     List[str]								=layerdrop
								UpperCamelCase__		:     Tuple								=encoder_normalize_before
								UpperCamelCase__		:     List[Any]								=pre_layernorm
								UpperCamelCase__		:     Optional[Any]								=apply_graphormer_init
								UpperCamelCase__		:     Tuple								=activation_fn
								UpperCamelCase__		:     Any								=embed_scale
								UpperCamelCase__		:     List[str]								=freeze_embeddings
								UpperCamelCase__		:     Optional[Any]								=num_trans_layers_to_freeze
								UpperCamelCase__		:     List[Any]								=share_input_output_embed
								UpperCamelCase__		:     Union[str, Any]								=traceable
								UpperCamelCase__		:     List[Any]								=q_noise
								UpperCamelCase__		:     Union[str, Any]								=qn_block_size

								# These parameters are here for future extensions
								# atm, the model only supports self attention
								UpperCamelCase__		:     Any								=kdim
								UpperCamelCase__		:     Tuple								=vdim
								UpperCamelCase__		:     Any								=self_attention
								UpperCamelCase__		:     List[str]								=bias

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


