
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


_UpperCAmelCase   :					int    =			logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
_UpperCAmelCase   :					List[str]    =			direct_transformers_import(Path(__file__).parent)


_UpperCAmelCase   :					Optional[Any]    =			{
    "AutoTokenizer": "PreTrainedTokenizerBase",
    "AutoFeatureExtractor": "FeatureExtractionMixin",
    "AutoImageProcessor": "ImageProcessingMixin",
}


class 							lowercase   (						_SCREAMING_SNAKE_CASE  ):
		__lowercase			:			Optional[Any]      	=							["feature_extractor", "tokenizer"]
		# Names need to be attr_class for attr in attributes
		__lowercase			:			Union[str, Any]      	=							None
		__lowercase			:			List[Any]      	=							None
		__lowercase			:			Optional[Any]      	=							None



		def __init__(					self							, *A_							, **A_			)    ->   List[str]:
				"""simple docstring"""
				# Sanitize args and kwargs
				for key in kwargs:
						if key not in self.attributes:
								raise TypeError(F'''Unexpected keyword argument {key}.'''			)
				for arg, attribute_name in zip(A_							, self.attributes			):
						if attribute_name in kwargs:
								raise TypeError(F'''Got multiple values for argument {attribute_name}.'''			)
						else:
								UpperCamelCase            =	arg

				if len(A_			) != len(self.attributes			):
						raise ValueError(
						    F'''This processor requires {len(self.attributes			)} arguments: {', '.join(self.attributes			)}. Got '''
						    F'''{len(A_			)} 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():
						UpperCamelCase            =	getattr(self							, F'''{attribute_name}_class'''			)
						# Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
						UpperCamelCase            =	AUTO_TO_BASE_CLASS_MAPPING.get(A_							, A_			)
						if isinstance(A_							, A_			):
								UpperCamelCase            =	tuple(getattr(A_							, A_			) for n in class_name if n is not None			)
						else:
								UpperCamelCase            =	getattr(A_							, A_			)

						if not isinstance(A_							, A_			):
								raise ValueError(
								    F'''Received a {type(A_			).__name__} for argument {attribute_name}, but a {class_name} was expected.'''			)

						setattr(self							, A_							, A_			)



		def __repr__(					self			)    ->   str:
				"""simple docstring"""
				UpperCamelCase            =	[F'''- {name}: {repr(getattr(self							, A_			)			)}''' for name in self.attributes]
				UpperCamelCase            =	'\n'.join(A_			)
				return F'''{self.__class__.__name__}:\n{attributes_repr}'''



		def __UpperCamelCase					(					self							, A_							, A_ = False							, **A_			)    ->   str:
				"""simple docstring"""
				os.makedirs(A_							, exist_ok=A_			)

				if push_to_hub:
						UpperCamelCase            =	kwargs.pop('commit_message'							, A_			)
						UpperCamelCase            =	kwargs.pop('repo_id'							, save_directory.split(os.path.sep			)[-1]			)
						UpperCamelCase            =	self._create_repo(A_							, **A_			)
						UpperCamelCase            =	self._get_files_timestamps(A_			)
				# 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:
						UpperCamelCase            =	[getattr(self							, A_			) for attribute_name in self.attributes]
						UpperCamelCase            =	[(a.init_kwargs if isinstance(A_							, A_			) else a) for a in attrs]
						custom_object_save(self							, A_							, config=A_			)

				for attribute_name in self.attributes:
						UpperCamelCase            =	getattr(self							, A_			)
						# Include the processor class in the attribute config so this processor can then be reloaded with the
						# `AutoProcessor` API.
						if hasattr(A_							, '_set_processor_class'			):
								attribute._set_processor_class(self.__class__.__name__			)
						attribute.save_pretrained(A_			)

				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:
								UpperCamelCase            =	getattr(self							, A_			)
								if isinstance(A_							, A_			):
										del attribute.init_kwargs["auto_map"]

				if push_to_hub:
						self._upload_modified_files(
						    A_							, A_							, A_							, commit_message=A_							, token=kwargs.get('use_auth_token'			)							, )



		@classmethod
		def __UpperCamelCase					(					cls							, A_							, A_ = None							, A_ = False							, A_ = False							, A_ = None							, A_ = "main"							, **A_							, )    ->   Dict:
				"""simple docstring"""
				UpperCamelCase            =	cache_dir
				UpperCamelCase            =	force_download
				UpperCamelCase            =	local_files_only
				UpperCamelCase            =	revision

				UpperCamelCase            =	kwargs.pop('use_auth_token'							, A_			)
				if use_auth_token is not None:
						warnings.warn(
						    'The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.'							, A_			)
						if token is not None:
								raise ValueError(
								    '`token` and `use_auth_token` are both specified. Please set only the argument `token`.'			)
						UpperCamelCase            =	use_auth_token

				if token is not None:
						# change to `token` in a follow-up PR
						UpperCamelCase            =	token

				UpperCamelCase            =	cls._get_arguments_from_pretrained(A_							, **A_			)
				return cls(*A_			)



		@classmethod
		def __UpperCamelCase					(					cls							, A_="AutoProcessor"			)    ->   List[str]:
				"""simple docstring"""
				if not isinstance(A_							, A_			):
						UpperCamelCase            =	auto_class.__name__

				import transformers.models.auto as auto_module

				if not hasattr(A_							, A_			):
						raise ValueError(F'''{auto_class} is not a valid auto class.'''			)

				UpperCamelCase            =	auto_class



		@classmethod
		def __UpperCamelCase					(					cls							, A_							, **A_			)    ->   Optional[Any]:
				"""simple docstring"""
				UpperCamelCase            =	[]
				for attribute_name in cls.attributes:
						UpperCamelCase            =	getattr(cls							, F'''{attribute_name}_class'''			)
						if isinstance(A_							, A_			):
								UpperCamelCase            =	tuple(getattr(A_							, A_			) if n is not None else None for n in class_name			)
								UpperCamelCase            =	kwargs.get('use_fast'							, A_			)
								if use_fast and classes[1] is not None:
										UpperCamelCase            =	classes[1]
								else:
										UpperCamelCase            =	classes[0]
						else:
								UpperCamelCase            =	getattr(A_							, A_			)

						args.append(attribute_class.from_pretrained(A_							, **A_			)			)
				return args



		@property
		def __UpperCamelCase					(					self			)    ->   List[str]:
				"""simple docstring"""
				UpperCamelCase            =	getattr(self							, self.attributes[0]			)
				return getattr(A_							, 'model_input_names'							, A_			)






_UpperCAmelCase   :					Optional[Any]    =			copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
					_UpperCAmelCase   :					Union[str, Any]    =			ProcessorMixin.push_to_hub.__doc__.format(
					    object="processor", object_class="AutoProcessor", object_files="processor files"
					)


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					)  ->    Tuple:
		'''simple docstring'''

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


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






def  A			(   lowercase					)  ->    List[Any]:
		'''simple docstring'''

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


def  A			(   lowercase					)  ->    Optional[Any]:
		'''simple docstring'''

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


class 							lowercase   :



		def __init__(					self							, A_			)    ->   List[str]:
				"""simple docstring"""
				UpperCamelCase            =	args
				UpperCamelCase            =	defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}			)

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



		def __UpperCamelCase					(					self			)    ->   Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase      ,    UpperCamelCase            =	plt.subplots()
				UpperCamelCase            =	'Time usage' if self.args.is_time else 'Memory usage'
				UpperCamelCase            =	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()			):
						UpperCamelCase            =	sorted(set(self.result_dict[model_name]['bsz']			)			)
						UpperCamelCase            =	sorted(set(self.result_dict[model_name]['seq_len']			)			)
						UpperCamelCase            =	self.result_dict[model_name]['result']

						((UpperCamelCase)      ,    (UpperCamelCase))            =	(
						    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
						)

						UpperCamelCase            =	(
						    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:
										UpperCamelCase            =	np.asarray(
										    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]							, dtype=A_							, )
								else:
										UpperCamelCase            =	np.asarray(
										    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]							, dtype=np.floataa							, )

								((UpperCamelCase)      ,    (UpperCamelCase))            =	(
								    ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
								)

								UpperCamelCase            =	np.asarray(A_							, A_			)[: len(A_			)]
								plt.scatter(
								    A_							, A_							, label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''			)
								plt.plot(A_							, A_							, '--'			)

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

				UpperCamelCase            =	title_str[:-4]
				UpperCamelCase            =	'Time in s' if self.args.is_time else 'Memory in MB'

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

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






def  A			(   )  ->    Dict:
		'''simple docstring'''

		UpperCamelCase            =	HfArgumentParser(lowercase					)
		UpperCamelCase            =	parser.parse_args_into_dataclasses()[0]
		UpperCamelCase            =	Plot(args=lowercase					)
		plot.plot()


if __name__ == "__main__":
					main()
