





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

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


lowerCAmelCase_    =    logging.getLogger(__name__)



@dataclass
class  __lowerCAmelCase	:
			lowerCamelCase_					:					str
			lowerCamelCase_					:					List[str]
			lowerCamelCase_					:					Optional[List[str]]




@dataclass
class  __lowerCAmelCase	:
			lowerCamelCase_					:					List[int]
			lowerCamelCase_					:					List[int]
			lowerCamelCase_					:					Optional[List[int]]             =				None
			lowerCamelCase_					:					Optional[List[int]]             =				None




class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					Optional[int]             =				'''train'''
			lowerCamelCase_					:					int             =				'''dev'''
			lowerCamelCase_					:					List[str]             =				'''test'''




class  __lowerCAmelCase	:






			@staticmethod
			def       lowerCamelCase  (__magic_name__ ,							__magic_name__   )					->       List[InputExample]:


							'''simple docstring'''



							raise NotImplementedError






			@staticmethod
			def       lowerCamelCase  (__magic_name__   )					->       List[str]:


							'''simple docstring'''



							raise NotImplementedError






			@staticmethod
			def       lowerCamelCase  (__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__=False ,							__magic_name__="[CLS]" ,							__magic_name__=1 ,							__magic_name__="[SEP]" ,							__magic_name__=False ,							__magic_name__=False ,							__magic_name__=0 ,							__magic_name__=0 ,							__magic_name__=-100 ,							__magic_name__=0 ,							__magic_name__=True ,							)					->       List[InputFeatures]:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  {label: i for i, label in enumerate(__magic_name__   )}

							snake_case_						:   Any           =  []
							for ex_index, example in enumerate(__magic_name__   ):
											if ex_index % 1_0000 == 0:
															logger.info('''Writing example %d of %d''' ,							__magic_name__ ,							len(__magic_name__   )   )

											snake_case_						:   List[Any]           =  []
											snake_case_						:   Optional[Any]           =  []
											for word, label in zip(example.words ,							example.labels   ):
															snake_case_						:   int           =  tokenizer.tokenize(__magic_name__   )

															# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
															if len(__magic_name__   ) > 0:
																			tokens.extend(__magic_name__   )
																			# Use the real label id for the first token of the word, and padding ids for the remaining tokens
																			label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(__magic_name__   ) - 1)   )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
											snake_case_						:   List[Any]           =  tokenizer.num_special_tokens_to_add()
											if len(__magic_name__   ) > max_seq_length - special_tokens_count:
															snake_case_						:   Optional[int]           =  tokens[: (max_seq_length - special_tokens_count)]
															snake_case_						:   Tuple           =  label_ids[: (max_seq_length - special_tokens_count)]

											# The convention in BERT is:
											# (a) For sequence pairs:
											#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
											#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
											# (b) For single sequences:
											#  tokens:   [CLS] the dog is hairy . [SEP]
											#  type_ids:   0   0   0   0  0     0   0
											#
											# Where "type_ids" are used to indicate whether this is the first
											# sequence or the second sequence. The embedding vectors for `type=0` and
											# `type=1` were learned during pre-training and are added to the wordpiece
											# embedding vector (and position vector). This is not *strictly* necessary
											# since the [SEP] token unambiguously separates the sequences, but it makes
											# it easier for the model to learn the concept of sequences.
											#
											# For classification tasks, the first vector (corresponding to [CLS]) is
											# used as the "sentence vector". Note that this only makes sense because
											# the entire model is fine-tuned.
											tokens += [sep_token]
											label_ids += [pad_token_label_id]
											if sep_token_extra:
															# roberta uses an extra separator b/w pairs of sentences
															tokens += [sep_token]
															label_ids += [pad_token_label_id]
											snake_case_						:   List[Any]           =  [sequence_a_segment_id] * len(__magic_name__   )

											if cls_token_at_end:
															tokens += [cls_token]
															label_ids += [pad_token_label_id]
															segment_ids += [cls_token_segment_id]
											else:
															snake_case_						:   Dict           =  [cls_token] + tokens
															snake_case_						:   Dict           =  [pad_token_label_id] + label_ids
															snake_case_						:   Union[str, Any]           =  [cls_token_segment_id] + segment_ids

											snake_case_						:   Union[str, Any]           =  tokenizer.convert_tokens_to_ids(__magic_name__   )

											# The mask has 1 for real tokens and 0 for padding tokens. Only real
											# tokens are attended to.
											snake_case_						:   str           =  [1 if mask_padding_with_zero else 0] * len(__magic_name__   )

											# Zero-pad up to the sequence length.
											snake_case_						:   Any           =  max_seq_length - len(__magic_name__   )
											if pad_on_left:
															snake_case_						:   List[str]           =  ([pad_token] * padding_length) + input_ids
															snake_case_						:   Optional[Any]           =  ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
															snake_case_						:   int           =  ([pad_token_segment_id] * padding_length) + segment_ids
															snake_case_						:   List[str]           =  ([pad_token_label_id] * padding_length) + label_ids
											else:
															input_ids += [pad_token] * padding_length
															input_mask += [0 if mask_padding_with_zero else 1] * padding_length
															segment_ids += [pad_token_segment_id] * padding_length
															label_ids += [pad_token_label_id] * padding_length

											assert len(__magic_name__   ) == max_seq_length
											assert len(__magic_name__   ) == max_seq_length
											assert len(__magic_name__   ) == max_seq_length
											assert len(__magic_name__   ) == max_seq_length

											if ex_index < 5:
															logger.info('''*** Example ***'''   )
															logger.info('''guid: %s''' ,							example.guid   )
															logger.info('''tokens: %s''' ,							''' '''.join([str(__magic_name__   ) for x in tokens]   )   )
															logger.info('''input_ids: %s''' ,							''' '''.join([str(__magic_name__   ) for x in input_ids]   )   )
															logger.info('''input_mask: %s''' ,							''' '''.join([str(__magic_name__   ) for x in input_mask]   )   )
															logger.info('''segment_ids: %s''' ,							''' '''.join([str(__magic_name__   ) for x in segment_ids]   )   )
															logger.info('''label_ids: %s''' ,							''' '''.join([str(__magic_name__   ) for x in label_ids]   )   )

											if "token_type_ids" not in tokenizer.model_input_names:
															snake_case_						:   Optional[Any]           =  None

											features.append(
											    InputFeatures(
											        input_ids=__magic_name__ ,							attention_mask=__magic_name__ ,							token_type_ids=__magic_name__ ,							label_ids=__magic_name__   )   )
							return features


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



	class  __lowerCAmelCase	(      _a					):
				lowerCamelCase_					:					List[InputFeatures]
				lowerCamelCase_					:					int             =				nn.CrossEntropyLoss().ignore_index






				def __init__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = None ,							__magic_name__=False ,							__magic_name__ = Split.train ,							)					->       Optional[Any]:


								'''simple docstring'''



								snake_case_						:   List[Any]           =  os.path.join(
								    __magic_name__ ,							'''cached_{}_{}_{}'''.format(mode.value ,							tokenizer.__class__.__name__ ,							str(__magic_name__   )   ) ,							)

								# Make sure only the first process in distributed training processes the dataset,
								# and the others will use the cache.
								snake_case_						:   List[str]           =  cached_features_file + '''.lock'''
								with FileLock(__magic_name__   ):
												if os.path.exists(__magic_name__   ) and not overwrite_cache:
																logger.info(F'''Loading features from cached file {cached_features_file}'''   )
																snake_case_						:   Union[str, Any]           =  torch.load(__magic_name__   )
												else:
																logger.info(F'''Creating features from dataset file at {data_dir}'''   )
																snake_case_						:   Optional[Any]           =  token_classification_task.read_examples_from_file(__magic_name__ ,							__magic_name__   )
																# TODO clean up all this to leverage built-in features of tokenizers
																snake_case_						:   Dict           =  token_classification_task.convert_examples_to_features(
																    __magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							cls_token_at_end=bool(model_type in ['''xlnet''']   ) ,							cls_token=tokenizer.cls_token ,							cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0 ,							sep_token=tokenizer.sep_token ,							sep_token_extra=__magic_name__ ,							pad_on_left=bool(tokenizer.padding_side == '''left'''   ) ,							pad_token=tokenizer.pad_token_id ,							pad_token_segment_id=tokenizer.pad_token_type_id ,							pad_token_label_id=self.pad_token_label_id ,							)
																logger.info(F'''Saving features into cached file {cached_features_file}'''   )
																torch.save(self.features ,							__magic_name__   )






				def __len__(self   )					->       str:


								'''simple docstring'''



								return len(self.features   )






				def __getitem__(self ,							__magic_name__   )					->       InputFeatures:


								'''simple docstring'''



								return self.features[i]


if is_tf_available():
	import tensorflow as tf



	class  __lowerCAmelCase	:
				lowerCamelCase_					:					List[InputFeatures]
				lowerCamelCase_					:					int             =				-100






				def __init__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = None ,							__magic_name__=False ,							__magic_name__ = Split.train ,							)					->       str:


								'''simple docstring'''



								snake_case_						:   Tuple           =  token_classification_task.read_examples_from_file(__magic_name__ ,							__magic_name__   )
								# TODO clean up all this to leverage built-in features of tokenizers
								snake_case_						:   Dict           =  token_classification_task.convert_examples_to_features(
								    __magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							cls_token_at_end=bool(model_type in ['''xlnet''']   ) ,							cls_token=tokenizer.cls_token ,							cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0 ,							sep_token=tokenizer.sep_token ,							sep_token_extra=__magic_name__ ,							pad_on_left=bool(tokenizer.padding_side == '''left'''   ) ,							pad_token=tokenizer.pad_token_id ,							pad_token_segment_id=tokenizer.pad_token_type_id ,							pad_token_label_id=self.pad_token_label_id ,							)

								def gen():
												for ex in self.features:
																if ex.token_type_ids is None:
																				yield (
																				    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
																				    ex.label_ids,
																				)
																else:
																				yield (
																				    {
																				        "input_ids": ex.input_ids,
																				        "attention_mask": ex.attention_mask,
																				        "token_type_ids": ex.token_type_ids,
																				    },
																				    ex.label_ids,
																				)

								if "token_type_ids" not in tokenizer.model_input_names:
												snake_case_						:   List[str]           =  tf.data.Dataset.from_generator(
												    __magic_name__ ,							({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa) ,							(
												        {'''input_ids''': tf.TensorShape([None]   ), '''attention_mask''': tf.TensorShape([None]   )},
												        tf.TensorShape([None]   ),
												    ) ,							)
								else:
												snake_case_						:   Dict           =  tf.data.Dataset.from_generator(
												    __magic_name__ ,							({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa, '''token_type_ids''': tf.intaa}, tf.intaa) ,							(
												        {
												            '''input_ids''': tf.TensorShape([None]   ),
												            '''attention_mask''': tf.TensorShape([None]   ),
												            '''token_type_ids''': tf.TensorShape([None]   ),
												        },
												        tf.TensorShape([None]   ),
												    ) ,							)






				def       lowerCamelCase  (self   )					->       str:


								'''simple docstring'''



								snake_case_						:   Union[str, Any]           =  self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features   )   )   )

								return self.dataset






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


								'''simple docstring'''



								return len(self.features   )






				def __getitem__(self ,							__magic_name__   )					->       InputFeatures:


								'''simple docstring'''



								return self.features[i]







from math import isclose, sqrt






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						tuple[float, float, float]:






				"""simple docstring"""


				snake_case_						:   Dict           =  point_y / 4 / point_x
				snake_case_						:   List[str]           =  2 * normal_gradient / (1 + normal_gradient * normal_gradient)
				snake_case_						:   Union[str, Any]           =  (1 - normal_gradient * normal_gradient) / (
				    1 + normal_gradient * normal_gradient
				)
				snake_case_						:   Tuple           =  (sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)

				# to find the next point, solve the simultaeneous equations:
				# y^2 + 4x^2 = 100
				# y - b = m * (x - a)
				# ==> A x^2 + B x + C = 0
				snake_case_						:   Union[str, Any]           =  outgoing_gradient**2 + 4
				snake_case_						:   Tuple           =  2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
				snake_case_						:   Optional[Any]           =  (point_y - outgoing_gradient * point_x) ** 2 - 100

				snake_case_						:   Dict           =  (
				    -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term			)
				) / (2 * quadratic_term)
				snake_case_						:   Optional[int]           =  (
				    -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term			)
				) / (2 * quadratic_term)

				# two solutions, one of which is our input point
				snake_case_						:   Any           =  x_minus if isclose(_UpperCamelCase			,    _UpperCamelCase			) else x_plus
				snake_case_						:   int           =  point_y + outgoing_gradient * (next_x - point_x)

				return next_x, next_y, outgoing_gradient






def 					lowerCamelCase_						(    _UpperCamelCase = 1.4			,    _UpperCamelCase = -9.6			)		->						int:






				"""simple docstring"""


				snake_case_						:   int           =  0
				snake_case_						:   float           =  first_x_coord
				snake_case_						:   float           =  first_y_coord
				snake_case_						:   float           =  (10.1 - point_y) / (0.0 - point_x)

				while not (-0.01 <= point_x <= 0.01 and point_y > 0):
								snake_case_      ,							snake_case_      ,							snake_case_						:   str           =  next_point(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)
								num_reflections += 1

				return num_reflections


if __name__ == "__main__":
	print(F'''{solution() = }''')
