



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							(   __magic_name__						:	Optional[Any]=None  ,			__magic_name__						:	List[str]=None    ):
							return field(default_factory=lambda: default  ,			metadata=__snake_case    )


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





def     __lowerCamelCase							(   __magic_name__						:	int    ):
							try:
														int(__snake_case    )
														return True
							except ValueError:
														return False





def     __lowerCamelCase							(   __magic_name__						:	Optional[Any]    ):
							try:
														float(__snake_case    )
														return True
							except ValueError:
														return False


class 						lowerCamelCase__			:


						def __init__(			self    :	int   ,							_a    :	List[str]      ):
													a__:							Optional[int]									=args
													a__:							Optional[Any]									=defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}      )

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







						def 	_lowerCamelCase      (			self    :	List[str]      ):
													a__				,      a__:							Union[str, Any]									=plt.subplots()
													a__:							str									="Time usage" if self.args.is_time else "Memory usage"
													a__:							int									=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__:							Optional[int]									=sorted(set(self.result_dict[model_name]["bsz"]      )      )
																				a__:							List[Any]									=sorted(set(self.result_dict[model_name]["seq_len"]      )      )
																				a__:							Tuple									=self.result_dict[model_name]["result"]

																				((a__)				,      (a__)):							Tuple									=(
																				    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
																				)

																				a__:							Optional[int]									=(
																				    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__:							Optional[Any]									=np.asarray(
																																		    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]   ,							dtype=_lowerCAmelCase   ,							)
																											else:
																																		a__:							str									=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__)):							Union[str, Any]									=(
																											    ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
																											)

																											a__:							List[str]									=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__:							List[Any]									=title_str[:-4]
													a__:							Dict									="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							(   ):
							a__:							List[str]									=HfArgumentParser(__snake_case    )
							a__:							Tuple									=parser.parse_args_into_dataclasses()[0]
							a__:							Dict									=Plot(args=__snake_case    )
							plot.plot()


if __name__ == "__main__":
		main()









import functools
import operator

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


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

UpperCamelCase			:					List[Any]         =							{
    '''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      __snake_case				(   _UpperCAmelCase):
					SCREAMING_SNAKE_CASE_		:       Tuple								=			"hubert"
					def __init__(						self       :						List[Any]	,      _lowerCAmelCase       :						Any=3_2	,      _lowerCAmelCase       :						Optional[Any]=7_6_8	,      _lowerCAmelCase       :						str=1_2	,      _lowerCAmelCase       :						List[Any]=1_2	,      _lowerCAmelCase       :						Optional[int]=3_0_7_2	,      _lowerCAmelCase       :						int="gelu"	,      _lowerCAmelCase       :						List[str]=0.1	,      _lowerCAmelCase       :						Union[str, Any]=0.1	,      _lowerCAmelCase       :						Optional[Any]=0.1	,      _lowerCAmelCase       :						Optional[int]=True	,      _lowerCAmelCase       :						Any=0.0	,      _lowerCAmelCase       :						Tuple=0.1	,      _lowerCAmelCase       :						Optional[int]=0.1	,      _lowerCAmelCase       :						str=0.02	,      _lowerCAmelCase       :						str=1E-5	,      _lowerCAmelCase       :						Dict="group"	,      _lowerCAmelCase       :						Tuple="gelu"	,      _lowerCAmelCase       :						List[str]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)	,      _lowerCAmelCase       :						List[str]=(5, 2, 2, 2, 2, 2, 2)	,      _lowerCAmelCase       :						Union[str, Any]=(1_0, 3, 3, 3, 3, 2, 2)	,      _lowerCAmelCase       :						Any=False	,      _lowerCAmelCase       :						Union[str, Any]=1_2_8	,      _lowerCAmelCase       :						Any=1_6	,      _lowerCAmelCase       :						Tuple=False	,      _lowerCAmelCase       :						Dict=True	,      _lowerCAmelCase       :						Union[str, Any]=0.05	,      _lowerCAmelCase       :						Optional[int]=1_0	,      _lowerCAmelCase       :						str=2	,      _lowerCAmelCase       :						Any=0.0	,      _lowerCAmelCase       :						Tuple=1_0	,      _lowerCAmelCase       :						int=0	,      _lowerCAmelCase       :						Tuple="sum"	,      _lowerCAmelCase       :						Tuple=False	,      _lowerCAmelCase       :						str=False	,      _lowerCAmelCase       :						Tuple=2_5_6	,      _lowerCAmelCase       :						List[str]=0	,      _lowerCAmelCase       :						int=1	,      _lowerCAmelCase       :						int=2	,      **_lowerCAmelCase       :						Optional[Any]	,      )							->					Dict:



											'''simple docstring'''





											super().__init__(**_lowerCAmelCase	,      pad_token_id=_lowerCAmelCase	,      bos_token_id=_lowerCAmelCase	,      eos_token_id=_lowerCAmelCase  )
											__magic_name__					=      hidden_size
											__magic_name__					=      feat_extract_norm
											__magic_name__					=      feat_extract_activation
											__magic_name__					=      list(_lowerCAmelCase  )
											__magic_name__					=      list(_lowerCAmelCase  )
											__magic_name__					=      list(_lowerCAmelCase  )
											__magic_name__					=      conv_bias
											__magic_name__					=      num_conv_pos_embeddings
											__magic_name__					=      num_conv_pos_embedding_groups
											__magic_name__					=      len(self.conv_dim  )
											__magic_name__					=      num_hidden_layers
											__magic_name__					=      intermediate_size
											__magic_name__					=      hidden_act
											__magic_name__					=      num_attention_heads
											__magic_name__					=      hidden_dropout
											__magic_name__					=      attention_dropout
											__magic_name__					=      activation_dropout
											__magic_name__					=      feat_proj_layer_norm
											__magic_name__					=      feat_proj_dropout
											__magic_name__					=      final_dropout
											__magic_name__					=      layerdrop
											__magic_name__					=      layer_norm_eps
											__magic_name__					=      initializer_range
											__magic_name__					=      vocab_size
											__magic_name__					=      do_stable_layer_norm
											__magic_name__					=      use_weighted_layer_sum
											__magic_name__					=      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
											__magic_name__					=      apply_spec_augment
											__magic_name__					=      mask_time_prob
											__magic_name__					=      mask_time_length
											__magic_name__					=      mask_time_min_masks
											__magic_name__					=      mask_feature_prob
											__magic_name__					=      mask_feature_length
											__magic_name__					=      mask_feature_min_masks

											# ctc loss
											__magic_name__					=      ctc_loss_reduction
											__magic_name__					=      ctc_zero_infinity







					@property
					def 	a			(						self       :						Optional[int]  )							->					Any:



											'''simple docstring'''





											return functools.reduce(operator.mul	,      self.conv_stride	,      1  )

