





'''simple docstring'''



import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_SCREAMING_SNAKE_CASE   =      logging.getLogger(__name__)




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Any						,    lowerCamelCase_     :					List[str]			):
			return (preds == labels).mean()






@dataclass
class 							__lowercase    :







	'''simple docstring'''

	a      :			str     =    field(
	    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}  )
	a      :			Optional[str]     =    field(
	    default=lowerCAmelCase__	, metadata={"help": "Pretrained config name or path if not the same as model_name"}  )
	a      :			Optional[str]     =    field(
	    default=lowerCAmelCase__	, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}  )
	a      :			Optional[str]     =    field(
	    default=lowerCAmelCase__	, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}	, )






@dataclass
class 							__lowercase    :







	'''simple docstring'''

	a      :			str     =    field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys()  )}  )
	a      :			str     =    field(metadata={"help": "Should contain the data files for the task."}  )
	a      :			int     =    field(
	    default=128	, metadata={
	        "help": (
	            "The maximum total input sequence length after tokenization. Sequences longer "
	            "than this will be truncated, sequences shorter will be padded."
	        )
	    }	, )
	a      :			bool     =    field(
	    default=lowerCAmelCase__	, metadata={"help": "Overwrite the cached training and evaluation sets"}  )





def 				_lowerCAmelCase  (			):
			# See all possible arguments in src/transformers/training_args.py
			# or by passing the --help flag to this script.
			# We now keep distinct sets of args, for a cleaner separation of concerns.

			__lowercase			  =							HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
			__lowercase  ,						__lowercase  ,						__lowercase			  =							parser.parse_args_into_dataclasses()

			if (
			    os.path.exists(training_args.output_dir			)
			    and os.listdir(training_args.output_dir			)
			    and training_args.do_train
			    and not training_args.overwrite_output_dir
			):
						raise ValueError(
						    f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
						    ''' --overwrite_output_dir to overcome.'''			)

			# Setup logging
			logging.basicConfig(
			    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''						,    datefmt='''%m/%d/%Y %H:%M:%S'''						,    level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN						,    )
			logger.warning(
			    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''						,    training_args.local_rank						,    training_args.device						,    training_args.n_gpu						,    bool(training_args.local_rank != -1			)						,    training_args.fpaa						,    )
			# Set the verbosity to info of the Transformers logger (on main process only):
			if is_main_process(training_args.local_rank			):
						transformers.utils.logging.set_verbosity_info()
						transformers.utils.logging.enable_default_handler()
						transformers.utils.logging.enable_explicit_format()
			logger.info('''Training/evaluation parameters %s'''						,    lowerCamelCase_			)

			# Set seed
			set_seed(training_args.seed			)

			try:
						__lowercase			  =							processors[data_args.task_name]()
						__lowercase			  =							processor.get_labels()
						__lowercase			  =							len(lowerCamelCase_			)
			except KeyError:
						raise ValueError('''Task not found: %s''' % (data_args.task_name)			)

			# Load pretrained model and tokenizer
			#
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.

			__lowercase			  =							AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path						,    num_labels=lowerCamelCase_						,    finetuning_task=data_args.task_name						,    cache_dir=model_args.cache_dir						,    )
			__lowercase			  =							AutoTokenizer.from_pretrained(
			    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path						,    cache_dir=model_args.cache_dir						,    )
			__lowercase			  =							AutoModelForMultipleChoice.from_pretrained(
			    model_args.model_name_or_path						,    from_tf=bool('''.ckpt''' in model_args.model_name_or_path			)						,    config=lowerCamelCase_						,    cache_dir=model_args.cache_dir						,    )

			# Get datasets
			__lowercase			  =							(
			    MultipleChoiceDataset(
			        data_dir=data_args.data_dir						,    tokenizer=lowerCamelCase_						,    task=data_args.task_name						,    max_seq_length=data_args.max_seq_length						,    overwrite_cache=data_args.overwrite_cache						,    mode=Split.train						,    )
			    if training_args.do_train
			    else None
			)
			__lowercase			  =							(
			    MultipleChoiceDataset(
			        data_dir=data_args.data_dir						,    tokenizer=lowerCamelCase_						,    task=data_args.task_name						,    max_seq_length=data_args.max_seq_length						,    overwrite_cache=data_args.overwrite_cache						,    mode=Split.dev						,    )
			    if training_args.do_eval
			    else None
			)

			def compute_metrics(lowerCamelCase_     :					EvalPrediction			) -> Dict:
						__lowercase			  =							np.argmax(p.predictions						,    axis=1			)
						return {"acc": simple_accuracy(lowerCamelCase_						,    p.label_ids			)}

			# Data collator
			__lowercase			  =							DataCollatorWithPadding(lowerCamelCase_						,    pad_to_multiple_of=8			) if training_args.fpaa else None

			# Initialize our Trainer
			__lowercase			  =							Trainer(
			    model=lowerCamelCase_						,    args=lowerCamelCase_						,    train_dataset=lowerCamelCase_						,    eval_dataset=lowerCamelCase_						,    compute_metrics=lowerCamelCase_						,    data_collator=lowerCamelCase_						,    )

			# Training
			if training_args.do_train:
						trainer.train(
						    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path			) else None			)
						trainer.save_model()
						# For convenience, we also re-save the tokenizer to the same directory,
						# so that you can share your model easily on huggingface.co/models =)
						if trainer.is_world_master():
									tokenizer.save_pretrained(training_args.output_dir			)

    # Evaluation
			__lowercase			  =							{}
			if training_args.do_eval:
						logger.info('''*** Evaluate ***'''			)

						__lowercase			  =							trainer.evaluate()

						__lowercase			  =							os.path.join(training_args.output_dir						,    '''eval_results.txt'''			)
						if trainer.is_world_master():
									with open(lowerCamelCase_						,    '''w'''			) as writer:
												logger.info('''***** Eval results *****'''			)
												for key, value in result.items():
															logger.info('''  %s = %s'''						,    lowerCamelCase_						,    lowerCamelCase_			)
															writer.write('''%s = %s\n''' % (key, value)			)

												results.update(lowerCamelCase_			)

			return results




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Tuple			):
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
	main()












'''simple docstring'''



import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin






class 							__lowercase    ( lowerCAmelCase__	, unittest.TestCase  ):







	'''simple docstring'''

	a      :			List[Any]     =    CanineTokenizer
	a      :			Union[str, Any]     =    False


	def   _UpperCAmelCase       (self	)							->							Dict:







				'''simple docstring'''





				super().setUp()
				__lowercase			  =							CanineTokenizer()
				tokenizer.save_pretrained(self.tmpdirname	)


	@cached_property
	def   _UpperCAmelCase       (self	)							->							List[Any]:







				'''simple docstring'''





				return CanineTokenizer.from_pretrained('''google/canine-s'''	)


	def   _UpperCAmelCase       (self       ,**_lowerCamelCase	)							->							CanineTokenizer:







				'''simple docstring'''





				__lowercase			  =							self.tokenizer_class.from_pretrained(self.tmpdirname       ,**_lowerCamelCase	)
				__lowercase			  =							1024
				return tokenizer


	@require_torch
	def   _UpperCAmelCase       (self	)							->							Optional[int]:







				'''simple docstring'''





				__lowercase			  =							self.canine_tokenizer
				__lowercase			  =							['''Life is like a box of chocolates.''', '''You never know what you\'re gonna get.''']
				# fmt: off
				__lowercase			  =							[57344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 57345, 0, 0, 0, 0]
				# fmt: on
				__lowercase			  =							tokenizer(_lowerCamelCase       ,padding=_lowerCamelCase       ,return_tensors='''pt'''	)
				self.assertIsInstance(_lowerCamelCase       ,_lowerCamelCase	)

				__lowercase			  =							list(batch.input_ids.numpy()[0]	)

				self.assertListEqual(_lowerCamelCase       ,_lowerCamelCase	)

				self.assertEqual((2, 39)       ,batch.input_ids.shape	)
				self.assertEqual((2, 39)       ,batch.attention_mask.shape	)


	@require_torch
	def   _UpperCAmelCase       (self	)							->							Tuple:







				'''simple docstring'''





				__lowercase			  =							self.canine_tokenizer
				__lowercase			  =							['''Once there was a man.''', '''He wrote a test in HuggingFace Tranformers.''']
				__lowercase			  =							tokenizer(_lowerCamelCase       ,padding=_lowerCamelCase       ,return_tensors='''pt'''	)
				# check if input_ids, attention_mask and token_type_ids are returned
				self.assertIn('''input_ids'''       ,_lowerCamelCase	)
				self.assertIn('''attention_mask'''       ,_lowerCamelCase	)
				self.assertIn('''token_type_ids'''       ,_lowerCamelCase	)


	@require_torch
	def   _UpperCAmelCase       (self	)							->							int:







				'''simple docstring'''





				__lowercase			  =							self.canine_tokenizer
				__lowercase			  =							[
				    '''What\'s the weater?''',
				    '''It\'s about 25 degrees.''',
				]
				__lowercase			  =							tokenizer(
				    text_target=_lowerCamelCase       ,max_length=32       ,padding='''max_length'''       ,truncation=_lowerCamelCase       ,return_tensors='''pt'''	)
				self.assertEqual(32       ,targets['''input_ids'''].shape[1]	)


	def   _UpperCAmelCase       (self	)							->							int:







				'''simple docstring'''





				__lowercase			  =							self.get_tokenizers()
				for tokenizer in tokenizers:
							with self.subTest(f"{tokenizer.__class__.__name__}"	):
										self.assertNotEqual(tokenizer.model_max_length       ,42	)

        # Now let's start the test
				__lowercase			  =							self.get_tokenizers()
				for tokenizer in tokenizers:
							with self.subTest(f"{tokenizer.__class__.__name__}"	):
										# Isolate this from the other tests because we save additional tokens/etc
										__lowercase			  =							tempfile.mkdtemp()

										__lowercase			  =							''' He is very happy, UNwant\u00E9d,running'''
										__lowercase			  =							tokenizer.encode(_lowerCamelCase       ,add_special_tokens=_lowerCamelCase	)
										tokenizer.save_pretrained(_lowerCamelCase	)

										__lowercase			  =							tokenizer.__class__.from_pretrained(_lowerCamelCase	)
										__lowercase			  =							after_tokenizer.encode(_lowerCamelCase       ,add_special_tokens=_lowerCamelCase	)
										self.assertListEqual(_lowerCamelCase       ,_lowerCamelCase	)

										shutil.rmtree(_lowerCamelCase	)

				__lowercase			  =							self.get_tokenizers(model_max_length=42	)
				for tokenizer in tokenizers:
							with self.subTest(f"{tokenizer.__class__.__name__}"	):
										# Isolate this from the other tests because we save additional tokens/etc
										__lowercase			  =							tempfile.mkdtemp()

										__lowercase			  =							''' He is very happy, UNwant\u00E9d,running'''

										__lowercase			  =							tokenizer.additional_special_tokens

										# We can add a new special token for Canine as follows:
										__lowercase			  =							chr(0xe_0_0_7	)
										additional_special_tokens.append(_lowerCamelCase	)
										tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}	)
										__lowercase			  =							tokenizer.encode(_lowerCamelCase       ,add_special_tokens=_lowerCamelCase	)
										tokenizer.save_pretrained(_lowerCamelCase	)

										__lowercase			  =							tokenizer.__class__.from_pretrained(_lowerCamelCase	)
										__lowercase			  =							after_tokenizer.encode(_lowerCamelCase       ,add_special_tokens=_lowerCamelCase	)
										self.assertListEqual(_lowerCamelCase       ,_lowerCamelCase	)
										self.assertIn(_lowerCamelCase       ,after_tokenizer.additional_special_tokens	)
										self.assertEqual(after_tokenizer.model_max_length       ,42	)

										__lowercase			  =							tokenizer.__class__.from_pretrained(_lowerCamelCase       ,model_max_length=43	)
										self.assertEqual(tokenizer.model_max_length       ,43	)

										shutil.rmtree(_lowerCamelCase	)


	def   _UpperCAmelCase       (self	)							->							Dict:







				'''simple docstring'''





				__lowercase			  =							self.get_tokenizers(do_lower_case=_lowerCamelCase	)
				for tokenizer in tokenizers:
							with self.subTest(f"{tokenizer.__class__.__name__}"	):
										__lowercase  ,						__lowercase			  =							self.get_clean_sequence(_lowerCamelCase	)

										# a special token for Canine can be defined as follows:
										__lowercase			  =							0xe_0_0_5
										__lowercase			  =							chr(_lowerCamelCase	)

										tokenizer.add_special_tokens({'''cls_token''': special_token}	)
										__lowercase			  =							tokenizer.encode(_lowerCamelCase       ,add_special_tokens=_lowerCamelCase	)
										self.assertEqual(len(_lowerCamelCase	)       ,1	)

										__lowercase			  =							tokenizer.decode(ids + encoded_special_token       ,clean_up_tokenization_spaces=_lowerCamelCase	)
										__lowercase			  =							tokenizer.encode(_lowerCamelCase       ,add_special_tokens=_lowerCamelCase	)

										__lowercase			  =							tokenizer.encode(_lowerCamelCase       ,add_special_tokens=_lowerCamelCase	)
										__lowercase			  =							tokenizer.encode(_lowerCamelCase       ,add_special_tokens=_lowerCamelCase	)
										self.assertEqual(_lowerCamelCase       ,input_encoded + special_token_id	)

										__lowercase			  =							tokenizer.decode(_lowerCamelCase       ,skip_special_tokens=_lowerCamelCase	)
										self.assertTrue(special_token not in decoded	)


	def   _UpperCAmelCase       (self	)							->							str:







				'''simple docstring'''





				__lowercase			  =							self.get_tokenizers(do_lower_case=_lowerCamelCase	)
				for tokenizer in tokenizers:
							with self.subTest(f"{tokenizer.__class__.__name__}"	):
										__lowercase			  =							chr(0xe_0_0_5	)
										__lowercase			  =							chr(0xe_0_0_6	)

										# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
										tokenizer.add_tokens([SPECIAL_TOKEN_1]       ,special_tokens=_lowerCamelCase	)
										# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
										# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
										tokenizer.add_special_tokens({'''additional_special_tokens''': [SPECIAL_TOKEN_2]}	)

										__lowercase			  =							tokenizer.tokenize(_lowerCamelCase	)
										__lowercase			  =							tokenizer.tokenize(_lowerCamelCase	)

										self.assertEqual(len(_lowerCamelCase	)       ,1	)
										self.assertEqual(len(_lowerCamelCase	)       ,1	)
										self.assertEqual(token_a[0]       ,_lowerCamelCase	)
										self.assertEqual(token_a[0]       ,_lowerCamelCase	)


	@require_tokenizers
	def   _UpperCAmelCase       (self	)							->							Dict:







				'''simple docstring'''





				__lowercase			  =							self.get_tokenizers(do_lower_case=_lowerCamelCase	)
				for tokenizer in tokenizers:
							with self.subTest(f"{tokenizer.__class__.__name__}"	):
										# a special token for Canine can be defined as follows:
										__lowercase			  =							0xe_0_0_6
										__lowercase			  =							chr(_lowerCamelCase	)

										__lowercase			  =							AddedToken(_lowerCamelCase       ,lstrip=_lowerCamelCase	)
										tokenizer.add_special_tokens({'''additional_special_tokens''': [new_token]}	)

										with tempfile.TemporaryDirectory() as tmp_dir_name:
													tokenizer.save_pretrained(_lowerCamelCase	)
													tokenizer.from_pretrained(_lowerCamelCase	)


	def   _UpperCAmelCase       (self	)							->							int:







				'''simple docstring'''





				__lowercase			  =							[]
				if self.test_slow_tokenizer:
							tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())	)

				if self.test_rust_tokenizer:
							tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())	)

				for tokenizer_class, tokenizer_utils in tokenizer_list:
							with tempfile.TemporaryDirectory() as tmp_dir:
										tokenizer_utils.save_pretrained(_lowerCamelCase	)

										with open(os.path.join(_lowerCamelCase       ,'''special_tokens_map.json'''	)       ,encoding='''utf-8'''	) as json_file:
													__lowercase			  =							json.load(_lowerCamelCase	)

										with open(os.path.join(_lowerCamelCase       ,'''tokenizer_config.json'''	)       ,encoding='''utf-8'''	) as json_file:
													__lowercase			  =							json.load(_lowerCamelCase	)

										# a special token for Canine can be defined as follows:
										__lowercase			  =							0xe_0_0_6
										__lowercase			  =							chr(_lowerCamelCase	)

										__lowercase			  =							[new_token_a]
										__lowercase			  =							[new_token_a]

										with open(os.path.join(_lowerCamelCase       ,'''special_tokens_map.json'''	)       ,'''w'''       ,encoding='''utf-8'''	) as outfile:
													json.dump(_lowerCamelCase       ,_lowerCamelCase	)
										with open(os.path.join(_lowerCamelCase       ,'''tokenizer_config.json'''	)       ,'''w'''       ,encoding='''utf-8'''	) as outfile:
													json.dump(_lowerCamelCase       ,_lowerCamelCase	)

										# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
										# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
										# "special_tokens_map.json" files
										__lowercase			  =							tokenizer_class.from_pretrained(_lowerCamelCase       ,extra_ids=0	)
										self.assertIn(_lowerCamelCase       ,tokenizer_without_change_in_init.additional_special_tokens	)
										# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
										self.assertEqual(
										    [new_token_a]       ,tokenizer_without_change_in_init.convert_ids_to_tokens(
										        tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]	)	)       ,)

										__lowercase			  =							0xe_0_0_7
										__lowercase			  =							chr(_lowerCamelCase	)
										# Now we test that we can change the value of additional_special_tokens in the from_pretrained
										__lowercase			  =							[AddedToken(_lowerCamelCase       ,lstrip=_lowerCamelCase	)]
										__lowercase			  =							tokenizer_class.from_pretrained(
										    _lowerCamelCase       ,additional_special_tokens=_lowerCamelCase       ,extra_ids=0	)

										self.assertIn(_lowerCamelCase       ,tokenizer.additional_special_tokens	)
										# self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
										self.assertEqual(
										    [new_token_a]       ,tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]	)	)	)


	@require_tokenizers
	def   _UpperCAmelCase       (self	)							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase			  =							self.get_tokenizers(do_lower_case=_lowerCamelCase	)
				for tokenizer in tokenizers:
							with self.subTest(f"{tokenizer.__class__.__name__}"	):
										__lowercase			  =							'''hello world'''
										if self.space_between_special_tokens:
													__lowercase			  =							'''[CLS] hello world [SEP]'''
										else:
													__lowercase			  =							input
										__lowercase			  =							tokenizer.encode(_lowerCamelCase       ,add_special_tokens=_lowerCamelCase	)
										__lowercase			  =							tokenizer.decode(_lowerCamelCase       ,spaces_between_special_tokens=self.space_between_special_tokens	)
										self.assertIn(_lowerCamelCase       ,[output, output.lower()]	)


	def   _UpperCAmelCase       (self	)							->							Tuple:







				'''simple docstring'''





				__lowercase			  =							self.get_tokenizers()
				for tokenizer in tokenizers:
							with self.subTest(f"{tokenizer.__class__.__name__}"	):
										__lowercase			  =							[
										    '''bos_token''',
										    '''eos_token''',
										    '''unk_token''',
										    '''sep_token''',
										    '''pad_token''',
										    '''cls_token''',
										    '''mask_token''',
										]

				__lowercase			  =							'''a'''
				__lowercase			  =							ord(_lowerCamelCase	)

				for attr in attributes_list:
							setattr(_lowerCamelCase       ,attr + '''_id'''       ,_lowerCamelCase	)
							self.assertEqual(getattr(_lowerCamelCase       ,_lowerCamelCase	)       ,_lowerCamelCase	)
							self.assertEqual(getattr(_lowerCamelCase       ,attr + '''_id'''	)       ,_lowerCamelCase	)

							setattr(_lowerCamelCase       ,attr + '''_id'''       ,_lowerCamelCase	)
							self.assertEqual(getattr(_lowerCamelCase       ,_lowerCamelCase	)       ,_lowerCamelCase	)
							self.assertEqual(getattr(_lowerCamelCase       ,attr + '''_id'''	)       ,_lowerCamelCase	)

				setattr(_lowerCamelCase       ,'''additional_special_tokens_ids'''       ,[]	)
				self.assertListEqual(getattr(_lowerCamelCase       ,'''additional_special_tokens'''	)       ,[]	)
				self.assertListEqual(getattr(_lowerCamelCase       ,'''additional_special_tokens_ids'''	)       ,[]	)

				__lowercase			  =							0xe_0_0_6
				__lowercase			  =							chr(_lowerCamelCase	)
				setattr(_lowerCamelCase       ,'''additional_special_tokens_ids'''       ,[additional_special_token_id]	)
				self.assertListEqual(getattr(_lowerCamelCase       ,'''additional_special_tokens'''	)       ,[additional_special_token]	)
				self.assertListEqual(getattr(_lowerCamelCase       ,'''additional_special_tokens_ids'''	)       ,[additional_special_token_id]	)


	def   _UpperCAmelCase       (self	)							->							List[Any]:







				'''simple docstring'''





				pass


	def   _UpperCAmelCase       (self	)							->							Dict:







				'''simple docstring'''





				pass


	def   _UpperCAmelCase       (self	)							->							Tuple:







				'''simple docstring'''





				pass


	def   _UpperCAmelCase       (self	)							->							List[str]:







				'''simple docstring'''





				pass


	def   _UpperCAmelCase       (self	)							->							int:







				'''simple docstring'''





				pass


	def   _UpperCAmelCase       (self	)							->							Any:







				'''simple docstring'''





				pass


	def   _UpperCAmelCase       (self	)							->							int:







				'''simple docstring'''





				pass


	def   _UpperCAmelCase       (self	)							->							Dict:







				'''simple docstring'''





				pass





