
'''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"
				)








'''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()






