




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    snake_case   (				A=None		,  A=None     )				->				str:




	'''simple docstring'''


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


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






def    snake_case   (				A     )				->				Any:




	'''simple docstring'''


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






def    snake_case   (				A     )				->				Any:




	'''simple docstring'''


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


class 							__SCREAMING_SNAKE_CASE				:

					def __init__(							self					,			__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[str]				  =				args
						lowerCamelCase_				:						int				  =				defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}   )

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





					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_  ,     lowerCamelCase_				:						Tuple				  =				plt.subplots()
						lowerCamelCase_				:						int				  =				'Time usage' if self.args.is_time else 'Memory usage'
						lowerCamelCase_				:						str				  =				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_				:						Union[str, Any]				  =				sorted(set(self.result_dict[model_name]['bsz']   )   )
							lowerCamelCase_				:						Tuple				  =				sorted(set(self.result_dict[model_name]['seq_len']   )   )
							lowerCamelCase_				:						Optional[Any]				  =				self.result_dict[model_name]['result']

							((lowerCamelCase_)  ,     (lowerCamelCase_))				:						Optional[Any]				  =				(
							    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
							)

							lowerCamelCase_				:						List[str]				  =				(
							    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_				:						List[Any]				  =				np.asarray(
									    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]					,			dtype=__lowercase					,			)
								else:
									lowerCamelCase_				:						int				  =				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_))				:						Dict				  =				(
								    ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
								)

								lowerCamelCase_				:						Union[str, Any]				  =				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."

						lowerCamelCase_				:						Tuple				  =				title_str[:-4]
						lowerCamelCase_				:						Optional[Any]				  =				'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    snake_case   (				)				->				int:




	'''simple docstring'''


	lowerCamelCase_				:						Tuple				  =				HfArgumentParser(A     )
	lowerCamelCase_				:						Optional[Any]				  =				parser.parse_args_into_dataclasses()[0]
	lowerCamelCase_				:						Optional[int]				  =				Plot(args=A     )
	plot.plot()


if __name__ == "__main__":
							main()





'''simple docstring'''




import functools
import operator

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


a  :     Union[str, Any]       =       logging.get_logger(__name__)

a  :     int       =       {
    """facebook/hubert-base-ls960""": """https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json""",
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}

class      UpperCamelCase_					(				__magic_name__     ):
	lowercase		=					'hubert'





	def __init__(    self						,						A=32						,						A=768						,						A=12						,						A=12						,						A=3072						,						A="gelu"						,						A=0.1						,						A=0.1						,						A=0.1						,						A=True						,						A=0.0						,						A=0.1						,						A=0.1						,						A=0.0_2						,						A=1e-5						,						A="group"						,						A="gelu"						,						A=(512, 512, 512, 512, 512, 512, 512)						,						A=(5, 2, 2, 2, 2, 2, 2)						,						A=(10, 3, 3, 3, 3, 2, 2)						,						A=False						,						A=128						,						A=16						,						A=False						,						A=True						,						A=0.0_5						,						A=10						,						A=2						,						A=0.0						,						A=10						,						A=0						,						A="sum"						,						A=False						,						A=False						,						A=256						,						A=0						,						A=1						,						A=2						,						**A						,						)     ->     List[str]:
								super().__init__(**A						,						pad_token_id=A						,						bos_token_id=A						,						eos_token_id=A       )
								UpperCAmelCase	:						List[Any]					=						hidden_size
								UpperCAmelCase	:						List[Any]					=						feat_extract_norm
								UpperCAmelCase	:						Optional[Any]					=						feat_extract_activation
								UpperCAmelCase	:						Optional[int]					=						list(A       )
								UpperCAmelCase	:						int					=						list(A       )
								UpperCAmelCase	:						Any					=						list(A       )
								UpperCAmelCase	:						Any					=						conv_bias
								UpperCAmelCase	:						Dict					=						num_conv_pos_embeddings
								UpperCAmelCase	:						Tuple					=						num_conv_pos_embedding_groups
								UpperCAmelCase	:						str					=						len(self.conv_dim       )
								UpperCAmelCase	:						Optional[Any]					=						num_hidden_layers
								UpperCAmelCase	:						int					=						intermediate_size
								UpperCAmelCase	:						Any					=						hidden_act
								UpperCAmelCase	:						Tuple					=						num_attention_heads
								UpperCAmelCase	:						int					=						hidden_dropout
								UpperCAmelCase	:						Any					=						attention_dropout
								UpperCAmelCase	:						Any					=						activation_dropout
								UpperCAmelCase	:						str					=						feat_proj_layer_norm
								UpperCAmelCase	:						int					=						feat_proj_dropout
								UpperCAmelCase	:						List[Any]					=						final_dropout
								UpperCAmelCase	:						int					=						layerdrop
								UpperCAmelCase	:						Dict					=						layer_norm_eps
								UpperCAmelCase	:						Union[str, Any]					=						initializer_range
								UpperCAmelCase	:						Any					=						vocab_size
								UpperCAmelCase	:						Optional[int]					=						do_stable_layer_norm
								UpperCAmelCase	:						Optional[int]					=						use_weighted_layer_sum
								UpperCAmelCase	:						Dict					=						classifier_proj_size

								if (
								    (len(self.conv_stride       ) != self.num_feat_extract_layers)
								    or (len(self.conv_kernel       ) != self.num_feat_extract_layers)
								    or (len(self.conv_dim       ) != self.num_feat_extract_layers)
								):
															raise ValueError(
															    """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
															    """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
															    f''' {len(self.conv_dim       )}`, `len(config.conv_stride) = {len(self.conv_stride       )}`,'''
															    f''' `len(config.conv_kernel) = {len(self.conv_kernel       )}`.'''       )

								# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
								UpperCAmelCase	:						Union[str, Any]					=						apply_spec_augment
								UpperCAmelCase	:						Tuple					=						mask_time_prob
								UpperCAmelCase	:						Optional[Any]					=						mask_time_length
								UpperCAmelCase	:						List[Any]					=						mask_time_min_masks
								UpperCAmelCase	:						Any					=						mask_feature_prob
								UpperCAmelCase	:						Any					=						mask_feature_length
								UpperCAmelCase	:						Tuple					=						mask_feature_min_masks

								# ctc loss
								UpperCAmelCase	:						int					=						ctc_loss_reduction
								UpperCAmelCase	:						Any					=						ctc_zero_infinity






	@property
	def 					_lowercase(    self       )     ->     Union[str, Any]:
								return functools.reduce(operator.mul						,						self.conv_stride						,						1       )

