






'''simple docstring'''







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


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

_A     :      Optional[Any]   					={'''vocab_file''': '''spm_char.model'''}

_A     :      Optional[Any]   					={
    '''vocab_file''': {
        '''microsoft/speecht5_asr''': '''https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model''',
        '''microsoft/speecht5_tts''': '''https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model''',
        '''microsoft/speecht5_vc''': '''https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model''',
    }
}

_A     :      str   					={
    '''microsoft/speecht5_asr''': 1_024,
    '''microsoft/speecht5_tts''': 1_024,
    '''microsoft/speecht5_vc''': 1_024,
}

class _lowercase     (    _lowercase		):
		a    				=	VOCAB_FILES_NAMES
		a    				=	PRETRAINED_VOCAB_FILES_MAP
		a    				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		a    				=	["""input_ids""", """attention_mask"""]




		def __init__(							self:    Dict      ,							UpperCamelCase__:    str      ,							UpperCamelCase__:    Any="<s>"      ,							UpperCamelCase__:    Tuple="</s>"      ,							UpperCamelCase__:    str="<unk>"      ,							UpperCamelCase__:    Optional[int]="<pad>"      ,							UpperCamelCase__:    Optional[Dict[str, Any]] = None      ,							**UpperCamelCase__:    List[str]      ,							):
									lowerCamelCase__ :     int	  =	{} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    bos_token=UpperCamelCase__      ,							eos_token=UpperCamelCase__      ,							unk_token=UpperCamelCase__      ,							pad_token=UpperCamelCase__      ,							sp_model_kwargs=self.sp_model_kwargs      ,							**UpperCamelCase__      ,							)

									lowerCamelCase__ :     List[Any]	  =	vocab_file

									lowerCamelCase__ :     Tuple	  =	spm.SentencePieceProcessor(**self.sp_model_kwargs					)
									self.sp_model.Load(UpperCamelCase__					)




		@property
		def 		lowerCamelCase_    (							self:    Optional[Any]					):
									return self.sp_model.get_piece_size()




		def 		lowerCamelCase_    (							self:    Any					):
									lowerCamelCase__ :     Dict	  =	{self.convert_ids_to_tokens(UpperCamelCase__					): i for i in range(self.vocab_size					)}
									vocab.update(self.added_tokens_encoder					)
									return vocab




		def __getstate__(							self:    Any					):
									lowerCamelCase__ :     Dict	  =	self.__dict__.copy()
									lowerCamelCase__ :     str	  =	None
									return state




		def __setstate__(							self:    int      ,							UpperCamelCase__:    Optional[Any]					):
									lowerCamelCase__ :     Union[str, Any]	  =	d

									# for backward compatibility
									if not hasattr(self      ,							"""sp_model_kwargs"""					):
																lowerCamelCase__ :     Optional[Any]	  =	{}

									lowerCamelCase__ :     Union[str, Any]	  =	spm.SentencePieceProcessor(**self.sp_model_kwargs					)
									self.sp_model.Load(self.vocab_file					)




		def 		lowerCamelCase_    (							self:    Optional[int]      ,							UpperCamelCase__:    str					):
									return self.sp_model.encode(UpperCamelCase__      ,							out_type=UpperCamelCase__					)




		def 		lowerCamelCase_    (							self:    Any      ,							UpperCamelCase__:    Any					):
									return self.sp_model.piece_to_id(UpperCamelCase__					)




		def 		lowerCamelCase_    (							self:    str      ,							UpperCamelCase__:    Dict					):
									lowerCamelCase__ :     List[Any]	  =	self.sp_model.IdToPiece(UpperCamelCase__					)
									return token




		def 		lowerCamelCase_    (							self:    Optional[Any]      ,							UpperCamelCase__:    List[str]					):
									lowerCamelCase__ :     List[Any]	  =	[]
									lowerCamelCase__ :     Optional[Any]	  =	""""""
									for token in tokens:
																# make sure that special tokens are not decoded using sentencepiece model
																if token in self.all_special_tokens:
																							out_string += self.sp_model.decode(UpperCamelCase__					) + token
																							lowerCamelCase__ :     Dict	  =	[]
																else:
																							current_sub_tokens.append(UpperCamelCase__					)
									out_string += self.sp_model.decode(UpperCamelCase__					)
									return out_string.strip()




		def 		lowerCamelCase_    (							self:    Tuple      ,							UpperCamelCase__:    List[Any]      ,							UpperCamelCase__:    Dict=None					):
									if token_ids_a is None:
																return token_ids_a + [self.eos_token_id]
									# We don't expect to process pairs, but leave the pair logic for API consistency
									return token_ids_a + token_ids_a + [self.eos_token_id]




		def 		lowerCamelCase_    (							self:    List[str]      ,							UpperCamelCase__:    List[int]      ,							UpperCamelCase__:    Optional[List[int]] = None      ,							UpperCamelCase__:    bool = False					):
									if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=UpperCamelCase__      ,							token_ids_a=UpperCamelCase__      ,							already_has_special_tokens=UpperCamelCase__					)

									lowerCamelCase__ :     Dict	  =	[1]
									if token_ids_a is None:
																return ([0] * len(UpperCamelCase__					)) + suffix_ones
									return ([0] * len(UpperCamelCase__					)) + ([0] * len(UpperCamelCase__					)) + suffix_ones




		def 		lowerCamelCase_    (							self:    Union[str, Any]      ,							UpperCamelCase__:    str      ,							UpperCamelCase__:    Optional[str] = None					):
									if not os.path.isdir(UpperCamelCase__					):
																logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
																return
									lowerCamelCase__ :     List[str]	  =	os.path.join(
									    UpperCamelCase__      ,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

									if os.path.abspath(self.vocab_file					) != os.path.abspath(UpperCamelCase__					) and os.path.isfile(self.vocab_file					):
																copyfile(self.vocab_file      ,							UpperCamelCase__					)
									elif not os.path.isfile(self.vocab_file					):
																with open(UpperCamelCase__      ,							"""wb"""					) as fi:
																							lowerCamelCase__ :     Union[str, Any]	  =	self.sp_model.serialized_model_proto()
																							fi.write(UpperCamelCase__					)

									return (out_vocab_file,)









'''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 			SCREAMING_SNAKE_CASE_	(UpperCamelCase=None    ,      UpperCamelCase=None				)				->	Any:
							return field(default_factory=lambda: default    ,      metadata=UpperCamelCase				)

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





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Dict:
							try:
														int(UpperCamelCase				)
														return True
							except ValueError:
														return False





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	int:
							try:
														float(UpperCamelCase				)
														return True
							except ValueError:
														return False

class _lowercase     :




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

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




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

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

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

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

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

														lowerCamelCase__ :     Any	  =	title_str[:-4]
														lowerCamelCase__ :     Optional[int]	  =	"""Time in s""" if self.args.is_time else """Memory in MB"""

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

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





def 			SCREAMING_SNAKE_CASE_	()				->	str:
							lowerCamelCase__ :     str	  =	HfArgumentParser(UpperCamelCase				)
							lowerCamelCase__ :     str	  =	parser.parse_args_into_dataclasses()[0]
							lowerCamelCase__ :     Any	  =	Plot(args=UpperCamelCase				)
							plot.plot()


if __name__ == "__main__":
			main()

