code: 
  '''simple docstring'''
  
  
  
  import os
  import warnings
  from pathlib import Path
  from typing import Optional, Union
  
  from .dynamic_module_utils import custom_object_save
  from .tokenization_utils_base import PreTrainedTokenizerBase
  from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging
  
  
  __A          =    logging.get_logger(__name__)
  
  # Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
  __A          =    direct_transformers_import(Path(__file__).parent)
  
  
  __A          =    {
      "AutoTokenizer": "PreTrainedTokenizerBase",
      "AutoFeatureExtractor": "FeatureExtractionMixin",
      "AutoImageProcessor": "ImageProcessingMixin",
  }
  
  
  
  
  
  class 			A							(       __UpperCAmelCase				):
  			lowerCamelCase							:							Optional[int]       		=  ["""feature_extractor""", """tokenizer"""]
  			# Names need to be attr_class for attr in attributes
  			lowerCamelCase							:							Optional[Any]       		=  None
  			lowerCamelCase							:							str       		=  None
  			lowerCamelCase							:							Dict       		=  None
  
  
  
  			def __init__(					self       ,     *lowerCamelCase__       ,     **lowerCamelCase__       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									for key in kwargs:
  															if key not in self.attributes:
  																					raise TypeError(F'''Unexpected keyword argument {key}.'''       )
  									for arg, attribute_name in zip(lowerCamelCase__       ,     self.attributes       ):
  															if attribute_name in kwargs:
  																					raise TypeError(F'''Got multiple values for argument {attribute_name}.'''       )
  															else:
  																					lowercase__							 =       arg
  
  									if len(lowerCamelCase__       ) != len(self.attributes       ):
  															raise ValueError(
  															    F'''This processor requires {len(self.attributes       )} arguments: {', '.join(self.attributes       )}. Got '''
  															    F'''{len(lowerCamelCase__       )} arguments instead.'''       )
  
  									# Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
  									for attribute_name, arg in kwargs.items():
  															lowercase__							 =       getattr(self       ,     F'''{attribute_name}_class'''       )
  															# Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
  															lowercase__							 =       AUTO_TO_BASE_CLASS_MAPPING.get(lowerCamelCase__       ,     lowerCamelCase__       )
  															if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  																					lowercase__							 =       tuple(getattr(lowerCamelCase__       ,     lowerCamelCase__       ) for n in class_name if n is not None       )
  															else:
  																					lowercase__							 =       getattr(lowerCamelCase__       ,     lowerCamelCase__       )
  
  															if not isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  																					raise ValueError(
  																					    F'''Received a {type(lowerCamelCase__       ).__name__} for argument {attribute_name}, but a {class_name} was expected.'''       )
  
  															setattr(self       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  
  
  
  			def __repr__(					self       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       [F'''- {name}: {repr(getattr(self       ,     lowerCamelCase__       )       )}''' for name in self.attributes]
  									lowercase__							 =       """\n""".join(lowerCamelCase__       )
  									return F'''{self.__class__.__name__}:\n{attributes_repr}'''
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__ = False       ,     **lowerCamelCase__       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									os.makedirs(lowerCamelCase__       ,     exist_ok=lowerCamelCase__       )
  
  									if push_to_hub:
  															lowercase__							 =       kwargs.pop("""commit_message"""       ,     lowerCamelCase__       )
  															lowercase__							 =       kwargs.pop("""repo_id"""       ,     save_directory.split(os.path.sep       )[-1]       )
  															lowercase__							 =       self._create_repo(lowerCamelCase__       ,     **lowerCamelCase__       )
  															lowercase__							 =       self._get_files_timestamps(lowerCamelCase__       )
  									# If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
  									# loaded from the Hub.
  									if self._auto_class is not None:
  															lowercase__							 =       [getattr(self       ,     lowerCamelCase__       ) for attribute_name in self.attributes]
  															lowercase__							 =       [(a.init_kwargs if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ) else a) for a in attrs]
  															custom_object_save(self       ,     lowerCamelCase__       ,     config=lowerCamelCase__       )
  
  									for attribute_name in self.attributes:
  															lowercase__							 =       getattr(self       ,     lowerCamelCase__       )
  															# Include the processor class in the attribute config so this processor can then be reloaded with the
  															# `AutoProcessor` API.
  															if hasattr(lowerCamelCase__       ,     """_set_processor_class"""       ):
  																					attribute._set_processor_class(self.__class__.__name__       )
  															attribute.save_pretrained(lowerCamelCase__       )
  
  									if self._auto_class is not None:
  															# We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
  															for attribute_name in self.attributes:
  																					lowercase__							 =       getattr(self       ,     lowerCamelCase__       )
  																					if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  																											del attribute.init_kwargs["auto_map"]
  
  									if push_to_hub:
  															self._upload_modified_files(
  															    lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     commit_message=lowerCamelCase__       ,     token=kwargs.get("""use_auth_token"""       )       ,     )
  
  
  
  			@classmethod
  			def A__ (					cls       ,     lowerCamelCase__       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = False       ,     lowerCamelCase__ = False       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = "main"       ,     **lowerCamelCase__       ,     )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       cache_dir
  									lowercase__							 =       force_download
  									lowercase__							 =       local_files_only
  									lowercase__							 =       revision
  
  									lowercase__							 =       kwargs.pop("""use_auth_token"""       ,     lowerCamelCase__       )
  									if use_auth_token is not None:
  															warnings.warn(
  															    """The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers."""       ,     lowerCamelCase__       )
  															if token is not None:
  																					raise ValueError(
  																					    """`token` and `use_auth_token` are both specified. Please set only the argument `token`."""       )
  															lowercase__							 =       use_auth_token
  
  									if token is not None:
  															# change to `token` in a follow-up PR
  															lowercase__							 =       token
  
  									lowercase__							 =       cls._get_arguments_from_pretrained(lowerCamelCase__       ,     **lowerCamelCase__       )
  									return cls(*lowerCamelCase__       )
  
  
  
  			@classmethod
  			def A__ (					cls       ,     lowerCamelCase__="AutoProcessor"       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if not isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  															lowercase__							 =       auto_class.__name__
  
  									import transformers.models.auto as auto_module
  
  									if not hasattr(lowerCamelCase__       ,     lowerCamelCase__       ):
  															raise ValueError(F'''{auto_class} is not a valid auto class.'''       )
  
  									lowercase__							 =       auto_class
  
  
  
  			@classmethod
  			def A__ (					cls       ,     lowerCamelCase__       ,     **lowerCamelCase__       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       []
  									for attribute_name in cls.attributes:
  															lowercase__							 =       getattr(cls       ,     F'''{attribute_name}_class'''       )
  															if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  																					lowercase__							 =       tuple(getattr(lowerCamelCase__       ,     lowerCamelCase__       ) if n is not None else None for n in class_name       )
  																					lowercase__							 =       kwargs.get("""use_fast"""       ,     lowerCamelCase__       )
  																					if use_fast and classes[1] is not None:
  																											lowercase__							 =       classes[1]
  																					else:
  																											lowercase__							 =       classes[0]
  															else:
  																					lowercase__							 =       getattr(lowerCamelCase__       ,     lowerCamelCase__       )
  
  															args.append(attribute_class.from_pretrained(lowerCamelCase__       ,     **lowerCamelCase__       )       )
  									return args
  
  
  
  			@property
  			def A__ (					self       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       getattr(self       ,     self.attributes[0]       )
  									return getattr(lowerCamelCase__       ,     """model_input_names"""       ,     lowerCamelCase__       )
  
  
  
  
  __A          =    copy_func(ProcessorMixin.push_to_hub)
  if ProcessorMixin.push_to_hub.__doc__ is not None:
  				__A          =    ProcessorMixin.push_to_hub.__doc__.format(
  				    object="processor", object_class="AutoProcessor", object_files="processor files"
  				)
  
  
  
  
  
  
  
style_context: 
  '''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  _A		(							lowercase__=None		,		lowercase__=None	):
  						return field(default_factory=lambda: default		,		metadata=lowercase__	)
  
  
  
  
  
  @dataclass
  class 			A							:
  			lowerCamelCase							:							str       		=  field(
  			    metadata={"""help""": """The csv file to plot."""}		,						)
  			lowerCamelCase							:							bool       		=  field(
  			    default=__UpperCAmelCase		,						metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}		,						)
  			lowerCamelCase							:							bool       		=  field(
  			    default=__UpperCAmelCase		,						metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}		,						)
  			lowerCamelCase							:							bool       		=  field(
  			    default=__UpperCAmelCase		,						metadata={"""help""": """Disable logarithmic scale when plotting"""}		,						)
  			lowerCamelCase							:							bool       		=  field(
  			    default=__UpperCAmelCase		,						metadata={
  			        """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
  			    }		,						)
  			lowerCamelCase							:							Optional[str]       		=  field(
  			    default=__UpperCAmelCase		,						metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}		,						)
  			lowerCamelCase							:							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  _A		(							lowercase__	):
  						try:
  												int(lowercase__	)
  												return True
  						except ValueError:
  												return False
  
  
  
  
  
  def  _A		(							lowercase__	):
  						try:
  												float(lowercase__	)
  												return True
  						except ValueError:
  												return False
  
  
  
  
  
  class 			A							:
  
  
  
  			def __init__(					self       ,     lowerCamelCase__       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       args
  									lowercase__							 =       defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}       )
  
  									with open(self.args.csv_file       ,     newline=""""""       ) as csv_file:
  															lowercase__							 =       csv.DictReader(lowerCamelCase__       )
  															for row in reader:
  																					lowercase__							 =       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
  																											lowercase__							 =       int(row["""result"""]       )
  																					elif can_convert_to_float(row["""result"""]       ):
  																											# value is not None
  																											lowercase__							 =       float(row["""result"""]       )
  
  
  
  			def A__ (					self       )			->		Tuple:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       plt.subplots()
  									lowercase__							 =       """Time usage""" if self.args.is_time else """Memory usage"""
  									lowercase__							 =       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()       ):
  															lowercase__							 =       sorted(set(self.result_dict[model_name]["""bsz"""]       )       )
  															lowercase__							 =       sorted(set(self.result_dict[model_name]["""seq_len"""]       )       )
  															lowercase__							 =       self.result_dict[model_name]["""result"""]
  
  															((lowercase__) ,						(lowercase__))							 =       (
  															    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
  															)
  
  															lowercase__							 =       (
  															    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:
  																											lowercase__							 =       np.asarray(
  																											    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]       ,     dtype=lowerCamelCase__       ,     )
  																					else:
  																											lowercase__							 =       np.asarray(
  																											    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]       ,     dtype=np.floataa       ,     )
  
  																					((lowercase__) ,						(lowercase__))							 =       (
  																					    ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
  																					)
  
  																					lowercase__							 =       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.'''
  
  									lowercase__							 =       title_str[:-4]
  									lowercase__							 =       """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  _A		(							):
  						lowercase__							 =       HfArgumentParser(lowercase__	)
  						lowercase__							 =       parser.parse_args_into_dataclasses()[0]
  						lowercase__							 =       Plot(args=lowercase__	)
  						plot.plot()
  
  
  if __name__ == "__main__":
  				main()
  
  
  
  
  
  
  