



'''simple docstring'''




import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


UpperCamelCase__       :	Union[str, Any]    = logging.getLogger(__name__)






@dataclass(frozen=lowerCamelCase__    )
class 						_UpperCamelCase  :


			'''simple docstring'''

			_A      :		str
			_A      :		str
			_A      :		Optional[str]			    =		None
			_A      :		Optional[str]			    =		None
			_A      :		Optional[str]			    =		None






@dataclass(frozen=lowerCamelCase__    )
class 						_UpperCamelCase  :


			'''simple docstring'''

			_A      :		List[int]
			_A      :		Optional[List[int]]			    =		None
			_A      :		Optional[List[int]]			    =		None
			_A      :		Optional[Union[int, float]]			    =		None
			_A      :		Optional[int]			    =		None


if is_torch_available():
						import torch
						from torch.utils.data import Dataset






						class 						_UpperCamelCase  (							lowerCamelCase__    ):


									'''simple docstring'''

									_A      :		List[InputFeatures]

									def __init__(							self     : Tuple							,     lowerCAmelCase__     : str							,     lowerCAmelCase__     : PreTrainedTokenizer							,     lowerCAmelCase__     : str							,     lowerCAmelCase__     : Optional[int] = None							,     lowerCAmelCase__     : int=False							,     lowerCAmelCase__     : bool = False							,     ):





											"""simple docstring"""






											__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		hans_processors[task]()

											__SCREAMING_SNAKE_CASE       :		Dict           =		os.path.join(
											    lowerCAmelCase__							,     """cached_{}_{}_{}_{}""".format(
											        """dev""" if evaluate else """train"""							,     tokenizer.__class__.__name__							,     str(lowerCAmelCase__						)							,     lowerCAmelCase__							,     )							,     )
											__SCREAMING_SNAKE_CASE       :		List[str]           =		processor.get_labels()
											if tokenizer.__class__ in (
											    RobertaTokenizer,
											    RobertaTokenizerFast,
											    XLMRobertaTokenizer,
											    BartTokenizer,
											    BartTokenizerFast,
											):
													# HACK(label indices are swapped in RoBERTa pretrained model)
													__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		int           =		label_list[2], label_list[1]
											__SCREAMING_SNAKE_CASE       :		int           =		label_list

											# Make sure only the first process in distributed training processes the dataset,
											# and the others will use the cache.
											__SCREAMING_SNAKE_CASE       :		Optional[int]           =		cached_features_file + """.lock"""
											with FileLock(lowerCAmelCase__						):
													if os.path.exists(lowerCAmelCase__						) and not overwrite_cache:
															logger.info(F"Loading features from cached file {cached_features_file}"						)
															__SCREAMING_SNAKE_CASE       :		Optional[int]           =		torch.load(lowerCAmelCase__						)
													else:
															logger.info(F"Creating features from dataset file at {data_dir}"						)

															__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		(
															    processor.get_dev_examples(lowerCAmelCase__						) if evaluate else processor.get_train_examples(lowerCAmelCase__						)
															)

															logger.info("""Training examples: %s"""							,     len(lowerCAmelCase__						)						)
															__SCREAMING_SNAKE_CASE       :		Tuple           =		hans_convert_examples_to_features(lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__						)
															logger.info("""Saving features into cached file %s"""							,     lowerCAmelCase__						)
															torch.save(self.features							,     lowerCAmelCase__						)

									def __len__(							self     : Optional[int]						):





											"""simple docstring"""






											return len(self.features						)

									def __getitem__(							self     : List[Any]							,     lowerCAmelCase__     : List[Any]						):





											"""simple docstring"""






											return self.features[i]



									def UpperCamelCase__					(							self     : int						):





											"""simple docstring"""






											return self.label_list


if is_tf_available():
						import tensorflow as tf






						class 						_UpperCamelCase  :


									'''simple docstring'''

									_A      :		List[InputFeatures]

									def __init__(							self     : Tuple							,     lowerCAmelCase__     : str							,     lowerCAmelCase__     : PreTrainedTokenizer							,     lowerCAmelCase__     : str							,     lowerCAmelCase__     : Optional[int] = 1_2_8							,     lowerCAmelCase__     : List[Any]=False							,     lowerCAmelCase__     : bool = False							,     ):





											"""simple docstring"""






											__SCREAMING_SNAKE_CASE       :		str           =		hans_processors[task]()
											__SCREAMING_SNAKE_CASE       :		Optional[int]           =		processor.get_labels()
											if tokenizer.__class__ in (
											    RobertaTokenizer,
											    RobertaTokenizerFast,
											    XLMRobertaTokenizer,
											    BartTokenizer,
											    BartTokenizerFast,
											):
													# HACK(label indices are swapped in RoBERTa pretrained model)
													__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Tuple           =		label_list[2], label_list[1]
											__SCREAMING_SNAKE_CASE       :		List[Any]           =		label_list

											__SCREAMING_SNAKE_CASE       :		List[str]           =		processor.get_dev_examples(lowerCAmelCase__						) if evaluate else processor.get_train_examples(lowerCAmelCase__						)
											__SCREAMING_SNAKE_CASE       :		Tuple           =		hans_convert_examples_to_features(lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__						)

											def gen():
													for ex_index, ex in tqdm.tqdm(enumerate(self.features						)							,     desc="""convert examples to features"""						):
															if ex_index % 1_0_0_0_0 == 0:
																	logger.info("""Writing example %d of %d""" % (ex_index, len(lowerCAmelCase__						))						)

															yield (
															    {
															        "example_id": 0,
															        "input_ids": ex.input_ids,
															        "attention_mask": ex.attention_mask,
															        "token_type_ids": ex.token_type_ids,
															    },
															    ex.label,
															)

											__SCREAMING_SNAKE_CASE       :		List[Any]           =		tf.data.Dataset.from_generator(
											    lowerCAmelCase__							,     (
											        {
											            """example_id""": tf.intaa,
											            """input_ids""": tf.intaa,
											            """attention_mask""": tf.intaa,
											            """token_type_ids""": tf.intaa,
											        },
											        tf.intaa,
											    )							,     (
											        {
											            """example_id""": tf.TensorShape([]						),
											            """input_ids""": tf.TensorShape([None, None]						),
											            """attention_mask""": tf.TensorShape([None, None]						),
											            """token_type_ids""": tf.TensorShape([None, None]						),
											        },
											        tf.TensorShape([]						),
											    )							,     )

									def UpperCamelCase__					(							self     : Dict						):





											"""simple docstring"""






											return self.dataset

									def __len__(							self     : List[Any]						):





											"""simple docstring"""






											return len(self.features						)

									def __getitem__(							self     : Union[str, Any]							,     lowerCAmelCase__     : List[str]						):





											"""simple docstring"""






											return self.features[i]



									def UpperCamelCase__					(							self     : Optional[int]						):





											"""simple docstring"""






											return self.label_list






class 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			def UpperCamelCase__					(							self     : Optional[int]							,     lowerCAmelCase__     : List[Any]						):





					"""simple docstring"""






					return self._create_examples(self._read_tsv(os.path.join(lowerCAmelCase__							,     """heuristics_train_set.txt"""						)						)							,     """train"""						)

			def UpperCamelCase__					(							self     : List[str]							,     lowerCAmelCase__     : Any						):





					"""simple docstring"""






					return self._create_examples(self._read_tsv(os.path.join(lowerCAmelCase__							,     """heuristics_evaluation_set.txt"""						)						)							,     """dev"""						)

			def UpperCamelCase__					(							self     : Optional[int]						):





					"""simple docstring"""






					return ["contradiction", "entailment", "neutral"]



			def UpperCamelCase__					(							self     : Dict							,     lowerCAmelCase__     : Tuple							,     lowerCAmelCase__     : Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Dict           =		[]
					for i, line in enumerate(lowerCAmelCase__						):
							if i == 0:
									continue
							__SCREAMING_SNAKE_CASE       :		Any           =		"""%s-%s""" % (set_type, line[0])
							__SCREAMING_SNAKE_CASE       :		Tuple           =		line[5]
							__SCREAMING_SNAKE_CASE       :		Dict           =		line[6]
							__SCREAMING_SNAKE_CASE       :		Tuple           =		line[7][2:] if line[7].startswith("""ex"""						) else line[7]
							__SCREAMING_SNAKE_CASE       :		Tuple           =		line[0]
							examples.append(InputExample(guid=lowerCAmelCase__							,     text_a=lowerCAmelCase__							,     text_b=lowerCAmelCase__							,     label=lowerCAmelCase__							,     pairID=lowerCAmelCase__						)						)
					return examples


def 				lowerCAmelCase_   (	_lowerCamelCase:   List[InputExample]       ,      _lowerCamelCase:   List[str]       ,      _lowerCamelCase:   int       ,      _lowerCamelCase:   PreTrainedTokenizer       ,      ):
		__SCREAMING_SNAKE_CASE       :		Dict           =		{label: i for i, label in enumerate(_lowerCamelCase					)}

		__SCREAMING_SNAKE_CASE       :		str           =		[]
		for ex_index, example in tqdm.tqdm(enumerate(_lowerCamelCase					)       ,      desc="""convert examples to features"""					):
				if ex_index % 1_00_00 == 0:
						logger.info("""Writing example %d""" % (ex_index)					)

				__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		tokenizer(
				    example.text_a       ,      example.text_b       ,      add_special_tokens=_lowerCamelCase       ,      max_length=_lowerCamelCase       ,      padding="""max_length"""       ,      truncation=_lowerCamelCase       ,      return_overflowing_tokens=_lowerCamelCase       ,      )

				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		label_map[example.label] if example.label in label_map else 0

				__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		int(example.pairID					)

				features.append(InputFeatures(**_lowerCamelCase       ,      label=_lowerCamelCase       ,      pairID=_lowerCamelCase					)					)

		for i, example in enumerate(examples[:5]					):
				logger.info("""*** Example ***"""					)
				logger.info(F"guid: {example}"					)
				logger.info(F"features: {features[i]}"					)

		return features


UpperCamelCase__       :	List[str]    = {
    '''hans''': 3,
}

UpperCamelCase__       :	str    = {
    '''hans''': HansProcessor,
}




'''simple docstring'''




def 				lowerCAmelCase_   (	_lowerCamelCase:   list[int]       ,      _lowerCamelCase:   str					):
		__SCREAMING_SNAKE_CASE       :		str           =		int(_lowerCamelCase					)

		# Initialize Result
		__SCREAMING_SNAKE_CASE       :		Tuple           =		[]

		# Traverse through all denomination
		for denomination in reversed(_lowerCamelCase					):
				# Find denominations
				while int(_lowerCamelCase					) >= int(_lowerCamelCase					):
						total_value -= int(_lowerCamelCase					)
						answer.append(_lowerCamelCase					)  # Append the "answers" array

		return answer


# Driver Code
if __name__ == "__main__":
						UpperCamelCase__       :	int    = []
						UpperCamelCase__       :	List[Any]    = '''0'''

						if (
						    input('''Do you want to enter your denominations ? (yY/n): ''').strip().lower()
						    == "y"
						):
												UpperCamelCase__       :	Tuple    = int(input('''Enter the number of denominations you want to add: ''').strip())

												for i in range(0, n):
																		denominations.append(int(input(f"Denomination {i}: ").strip()))
												UpperCamelCase__       :	str    = input('''Enter the change you want to make in Indian Currency: ''').strip()
						else:
												# All denominations of Indian Currency if user does not enter
												UpperCamelCase__       :	List[Any]    = [1, 2, 5, 10, 20, 50, 1_00, 5_00, 20_00]
												UpperCamelCase__       :	str    = input('''Enter the change you want to make: ''').strip()

						if int(value) == 0 or int(value) < 0:
												print('''The total value cannot be zero or negative.''')

						else:
												print(f"Following is minimal change for {value}: ")
												UpperCamelCase__       :	int    = find_minimum_change(denominations, value)
												# Print result
												for i in range(len(answer)):
																		print(answer[i], end=''' ''')