import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


__a  :					Optional[int]          =  logging.get_logger(__name__)




@dataclass
class        _UpperCamelCase				:


			"""simple docstring"""






			__a  :			str
			__a  :			str
			__a  :			Optional[str]      			=						None
			__a  :			Optional[str]      			=						None





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> int:

							'''simple docstring'''


							return json.dumps(dataclasses.asdict(self						)				,       indent=2						) + "\n"




@dataclass(frozen=_UpperCAmelCase			)
class        _UpperCamelCase				:


			"""simple docstring"""






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





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Any:

							'''simple docstring'''


							return json.dumps(dataclasses.asdict(self						)						) + "\n"




class        _UpperCamelCase				:


			"""simple docstring"""






			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> str:

							'''simple docstring'''


							raise NotImplementedError()





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Optional[Any]:

							'''simple docstring'''


							raise NotImplementedError()





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> List[Any]:

							'''simple docstring'''


							raise NotImplementedError()





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> List[Any]:

							'''simple docstring'''


							raise NotImplementedError()





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Any:

							'''simple docstring'''


							raise NotImplementedError()





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Dict:

							'''simple docstring'''


							if len(self.get_labels()						) > 1:
											__lowercase			      =							self.get_labels()[int(example.label						)]
							return example





			@classmethod
			def 	_SCREAMING_SNAKE_CASE      (       cls				,       lowerCAmelCase__				,       lowerCAmelCase__=None						)					-> Dict:

							'''simple docstring'''


							with open(lowerCAmelCase__				,       '''r'''				,       encoding='''utf-8-sig'''						) as f:
											return list(csv.reader(lowerCAmelCase__				,       delimiter='''\t'''				,       quotechar=lowerCAmelCase__						)						)




class        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__="classification"				,       lowerCAmelCase__=False						)					-> Dict:

							'''simple docstring'''


							__lowercase			      =							[] if labels is None else labels
							__lowercase			      =							[] if examples is None else examples
							__lowercase			      =							mode
							__lowercase			      =							verbose





			def __len__(       self						)					-> Tuple:

							'''simple docstring'''


							return len(self.examples						)





			def __getitem__(       self				,       lowerCAmelCase__						)					-> Optional[int]:

							'''simple docstring'''


							if isinstance(lowerCAmelCase__				,       lowerCAmelCase__						):
											return SingleSentenceClassificationProcessor(labels=self.labels				,       examples=self.examples[idx]						)
							return self.examples[idx]





			@classmethod
			def 	_SCREAMING_SNAKE_CASE      (       cls				,       lowerCAmelCase__				,       lowerCAmelCase__=""				,       lowerCAmelCase__=0				,       lowerCAmelCase__=1				,       lowerCAmelCase__=None				,       lowerCAmelCase__=False				,       **lowerCAmelCase__						)					-> Any:

							'''simple docstring'''


							__lowercase			      =							cls(**lowerCAmelCase__						)
							processor.add_examples_from_csv(
							    lowerCAmelCase__				,       split_name=lowerCAmelCase__				,       column_label=lowerCAmelCase__				,       column_text=lowerCAmelCase__				,       column_id=lowerCAmelCase__				,       skip_first_row=lowerCAmelCase__				,       overwrite_labels=lowerCAmelCase__				,       overwrite_examples=lowerCAmelCase__				,       )
							return processor





			@classmethod
			def 	_SCREAMING_SNAKE_CASE      (       cls				,       lowerCAmelCase__				,       lowerCAmelCase__=None				,       **lowerCAmelCase__						)					-> List[Any]:

							'''simple docstring'''


							__lowercase			      =							cls(**lowerCAmelCase__						)
							processor.add_examples(lowerCAmelCase__				,       labels=lowerCAmelCase__						)
							return processor





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__=""				,       lowerCAmelCase__=0				,       lowerCAmelCase__=1				,       lowerCAmelCase__=None				,       lowerCAmelCase__=False				,       lowerCAmelCase__=False				,       lowerCAmelCase__=False				,       )					-> List[str]:

							'''simple docstring'''


							__lowercase			      =							self._read_tsv(lowerCAmelCase__						)
							if skip_first_row:
											__lowercase			      =							lines[1:]
							__lowercase			      =							[]
							__lowercase			      =							[]
							__lowercase			      =							[]
							for i, line in enumerate(lowerCAmelCase__						):
											texts.append(line[column_text]						)
											labels.append(line[column_label]						)
											if column_id is not None:
															ids.append(line[column_id]						)
											else:
															__lowercase			      =							F"{split_name}-{i}" if split_name else str(lowerCAmelCase__						)
															ids.append(lowerCAmelCase__						)

							return self.add_examples(
							    lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       overwrite_labels=lowerCAmelCase__				,       overwrite_examples=lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=False				,       lowerCAmelCase__=False						)					-> Any:

							'''simple docstring'''


							if labels is not None and len(lowerCAmelCase__						) != len(lowerCAmelCase__						):
											raise ValueError(
											    F"Text and labels have mismatched lengths {len(lowerCAmelCase__						)} and {len(lowerCAmelCase__						)}"						)
							if ids is not None and len(lowerCAmelCase__						) != len(lowerCAmelCase__						):
											raise ValueError(F"Text and ids have mismatched lengths {len(lowerCAmelCase__						)} and {len(lowerCAmelCase__						)}"						)
							if ids is None:
											__lowercase			      =							[None] * len(lowerCAmelCase__						)
							if labels is None:
											__lowercase			      =							[None] * len(lowerCAmelCase__						)
							__lowercase			      =							[]
							__lowercase			      =							set()
							for text_or_text_and_label, label, guid in zip(lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__						):
											if isinstance(lowerCAmelCase__				,       (tuple, list)						) and label is None:
															__lowercase      ,					__lowercase			      =							text_or_text_and_label
											else:
															__lowercase			      =							text_or_text_and_label
											added_labels.add(lowerCAmelCase__						)
											examples.append(InputExample(guid=lowerCAmelCase__				,       text_a=lowerCAmelCase__				,       text_b=lowerCAmelCase__				,       label=lowerCAmelCase__						)						)

							# Update examples
							if overwrite_examples:
											__lowercase			      =							examples
							else:
											self.examples.extend(lowerCAmelCase__						)

							# Update labels
							if overwrite_labels:
											__lowercase			      =							list(lowerCAmelCase__						)
							else:
											__lowercase			      =							list(set(self.labels						).union(lowerCAmelCase__						)						)

							return self.examples





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__=None				,       lowerCAmelCase__=False				,       lowerCAmelCase__=0				,       lowerCAmelCase__=True				,       lowerCAmelCase__=None				,       )					-> str:

							'''simple docstring'''


							if max_length is None:
											__lowercase			      =							tokenizer.max_len

							__lowercase			      =							{label: i for i, label in enumerate(self.labels						)}

							__lowercase			      =							[]
							for ex_index, example in enumerate(self.examples						):
											if ex_index % 1_00_00 == 0:
															logger.info(F"Tokenizing example {ex_index}"						)

											__lowercase			      =							tokenizer.encode(
											    example.text_a				,       add_special_tokens=lowerCAmelCase__				,       max_length=min(lowerCAmelCase__				,       tokenizer.max_len						)				,       )
											all_input_ids.append(lowerCAmelCase__						)

							__lowercase			      =							max(len(lowerCAmelCase__						) for input_ids in all_input_ids						)

							__lowercase			      =							[]
							for ex_index, (input_ids, example) in enumerate(zip(lowerCAmelCase__				,       self.examples						)						):
											if ex_index % 1_00_00 == 0:
															logger.info(F"Writing example {ex_index}/{len(self.examples						)}"						)
											# The mask has 1 for real tokens and 0 for padding tokens. Only real
											# tokens are attended to.
											__lowercase			      =							[1 if mask_padding_with_zero else 0] * len(lowerCAmelCase__						)

											# Zero-pad up to the sequence length.
											__lowercase			      =							batch_length - len(lowerCAmelCase__						)
											if pad_on_left:
															__lowercase			      =							([pad_token] * padding_length) + input_ids
															__lowercase			      =							([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
											else:
															__lowercase			      =							input_ids + ([pad_token] * padding_length)
															__lowercase			      =							attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

											if len(lowerCAmelCase__						) != batch_length:
															raise ValueError(F"Error with input length {len(lowerCAmelCase__						)} vs {batch_length}"						)
											if len(lowerCAmelCase__						) != batch_length:
															raise ValueError(F"Error with input length {len(lowerCAmelCase__						)} vs {batch_length}"						)

											if self.mode == "classification":
															__lowercase			      =							label_map[example.label]
											elif self.mode == "regression":
															__lowercase			      =							float(example.label						)
											else:
															raise ValueError(self.mode						)

											if ex_index < 5 and self.verbose:
															logger.info('''*** Example ***'''						)
															logger.info(F"guid: {example.guid}"						)
															logger.info(F"input_ids: {' '.join([str(lowerCAmelCase__						) for x in input_ids]						)}"						)
															logger.info(F"attention_mask: {' '.join([str(lowerCAmelCase__						) for x in attention_mask]						)}"						)
															logger.info(F"label: {example.label} (id = {label})"						)

											features.append(InputFeatures(input_ids=lowerCAmelCase__				,       attention_mask=lowerCAmelCase__				,       label=lowerCAmelCase__						)						)

							if return_tensors is None:
											return features
							elif return_tensors == "tf":
											if not is_tf_available():
															raise RuntimeError('''return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported'''						)
											import tensorflow as tf

											def gen():
															for ex in features:
																			yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

											__lowercase			      =							tf.data.Dataset.from_generator(
											    lowerCAmelCase__				,       ({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)				,       ({'''input_ids''': tf.TensorShape([None]						), '''attention_mask''': tf.TensorShape([None]						)}, tf.TensorShape([]						))				,       )
											return dataset
							elif return_tensors == "pt":
											if not is_torch_available():
															raise RuntimeError('''return_tensors set to \'pt\' but PyTorch can\'t be imported'''						)
											import torch
											from torch.utils.data import TensorDataset

											__lowercase			      =							torch.tensor([f.input_ids for f in features]				,       dtype=torch.long						)
											__lowercase			      =							torch.tensor([f.attention_mask for f in features]				,       dtype=torch.long						)
											if self.mode == "classification":
															__lowercase			      =							torch.tensor([f.label for f in features]				,       dtype=torch.long						)
											elif self.mode == "regression":
															__lowercase			      =							torch.tensor([f.label for f in features]				,       dtype=torch.float						)

											__lowercase			      =							TensorDataset(lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__						)
											return dataset
							else:
											raise ValueError('''return_tensors should be one of \'tf\' or \'pt\''''						)
import timeit




class        _UpperCamelCase				:


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__						)					-> List[str]:

							'''simple docstring'''


							if isinstance(lowerCAmelCase__				,       lowerCAmelCase__						):  # Initializes a matrix identical to the one provided.
											__lowercase			      =							arg
											__lowercase			      =							len(lowerCAmelCase__						)
							else:  # Initializes a square matrix of the given size and set values to zero.
											__lowercase			      =							arg
											__lowercase			      =							[[0 for _ in range(self.n						)] for _ in range(self.n						)]





			def __mul__(       self				,       lowerCAmelCase__						)					-> str:

							'''simple docstring'''


							__lowercase			      =							Matrix(self.n						)
							for i in range(self.n						):
											for j in range(self.n						):
															for k in range(self.n						):
																			matrix.t[i][j] += self.t[i][k] * b.t[k][j]
							return matrix





def     UpperCAmelCase						(   lowercase							,      lowercase							):







				"""simple docstring"""



				__lowercase			      =							Matrix([[1, 0], [0, 1]]							)
				while b > 0:
								if b & 1:
												matrix *= a
								a *= a
								b >>= 1
				return matrix





def     UpperCAmelCase						(   lowercase							,      lowercase							,      lowercase							):







				"""simple docstring"""



				if n == 1:
								return fa
				elif n == 2:
								return fa
				__lowercase			      =							Matrix([[1, 1], [1, 0]]							)
				__lowercase			      =							modular_exponentiation(lowercase							,      n - 2							)
				return fa * matrix.t[0][0] + fa * matrix.t[0][1]





def     UpperCAmelCase						(   lowercase							,      lowercase							,      lowercase							):







				"""simple docstring"""



				if n == 1:
								return fa
				elif n == 2:
								return fa

				__lowercase			      =							fa
				__lowercase			      =							fa
				n -= 2

				while n > 0:
								__lowercase      ,					__lowercase			      =							fn_a + fn_a, fn_a
								n -= 1

				return fn_a





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				__lowercase			      =							'''
from random import randint
from __main__ import fibonacci_with_matrix_exponentiation
'''
				__lowercase			      =							'''fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)'''
				__lowercase			      =							timeit.timeit(setup=lowercase							,      stmt=lowercase							,      number=100							)
				print('''With matrix exponentiation the average execution time is '''							,      exec_time / 100							)
				return exec_time





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				__lowercase			      =							'''
from random import randint
from __main__ import simple_fibonacci
'''
				__lowercase			      =							'''simple_fibonacci(randint(1,70000), 1, 1)'''
				__lowercase			      =							timeit.timeit(setup=lowercase							,      stmt=lowercase							,      number=100							)
				print(
				    '''Without matrix exponentiation the average execution time is '''							,      exec_time / 100							)
				return exec_time





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				matrix_exponentiation_time()
				simple_fibonacci_time()


if __name__ == "__main__":
		main()