




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


UpperCamelCase__    			=			logging.getLogger(__name__)
@dataclass
class 			a__    :
						_a							:    str
						_a							:    List[str]
						_a							:    Optional[List[str]]






@dataclass
class 			a__    :
						_a							:    List[int]
						_a							:    List[int]
						_a							:    Optional[List[int]]					=    None
						_a							:    Optional[List[int]]					=    None






class 			a__    (					snake_case__      ):
						_a							:    Optional[int]					=    """train"""
						_a							:    int					=    """dev"""
						_a							:    Tuple					=    """test"""






class 			a__    :






						@staticmethod
						def 				__SCREAMING_SNAKE_CASE(   _A		,			_A			):




											"""simple docstring"""



											raise NotImplementedError






						@staticmethod
						def 				__SCREAMING_SNAKE_CASE(   _A			):




											"""simple docstring"""



											raise NotImplementedError






						@staticmethod
						def 				__SCREAMING_SNAKE_CASE(   _A		,			_A		,			_A		,			_A		,			_A=False		,			_A="[CLS]"		,			_A=1		,			_A="[SEP]"		,			_A=False		,			_A=False		,			_A=0		,			_A=0		,			_A=-1_0_0		,			_A=0		,			_A=True		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       {label: i for i, label in enumerate(_A			)}

											__lowerCAmelCase				   =       []
											for ex_index, example in enumerate(_A			):
																if ex_index % 1_0_0_0_0 == 0:
																					logger.info("Writing example %d of %d"		,			_A		,			len(_A			)			)

																__lowerCAmelCase				   =       []
																__lowerCAmelCase				   =       []
																for word, label in zip(example.words		,			example.labels			):
																					__lowerCAmelCase				   =       tokenizer.tokenize(_A			)

																					# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
																					if len(_A			) > 0:
																										tokens.extend(_A			)
																										# 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(_A			) - 1)			)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
																__lowerCAmelCase				   =       tokenizer.num_special_tokens_to_add()
																if len(_A			) > max_seq_length - special_tokens_count:
																					__lowerCAmelCase				   =       tokens[: (max_seq_length - special_tokens_count)]
																					__lowerCAmelCase				   =       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]
																__lowerCAmelCase				   =       [sequence_a_segment_id] * len(_A			)

																if cls_token_at_end:
																					tokens += [cls_token]
																					label_ids += [pad_token_label_id]
																					segment_ids += [cls_token_segment_id]
																else:
																					__lowerCAmelCase				   =       [cls_token] + tokens
																					__lowerCAmelCase				   =       [pad_token_label_id] + label_ids
																					__lowerCAmelCase				   =       [cls_token_segment_id] + segment_ids

																__lowerCAmelCase				   =       tokenizer.convert_tokens_to_ids(_A			)

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

																# Zero-pad up to the sequence length.
																__lowerCAmelCase				   =       max_seq_length - len(_A			)
																if pad_on_left:
																					__lowerCAmelCase				   =       ([pad_token] * padding_length) + input_ids
																					__lowerCAmelCase				   =       ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
																					__lowerCAmelCase				   =       ([pad_token_segment_id] * padding_length) + segment_ids
																					__lowerCAmelCase				   =       ([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(_A			) == max_seq_length
																assert len(_A			) == max_seq_length
																assert len(_A			) == max_seq_length
																assert len(_A			) == max_seq_length

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

																if "token_type_ids" not in tokenizer.model_input_names:
																					__lowerCAmelCase				   =       None

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


if is_torch_available():
					import torch
					from torch import nn
					from torch.utils.data import Dataset
					class 			a__    (					snake_case__      ):
											_a							:    List[InputFeatures]
											_a							:    int					=    nn.CrossEntropyLoss().ignore_index






											def __init__(   self		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A = None		,			_A=False		,			_A = Split.train		,			):




																"""simple docstring"""



																__lowerCAmelCase				   =       os.path.join(
																    _A		,			"cached_{}_{}_{}".format(mode.value		,			tokenizer.__class__.__name__		,			str(_A			)			)		,			)

																# Make sure only the first process in distributed training processes the dataset,
																# and the others will use the cache.
																__lowerCAmelCase				   =       cached_features_file + ".lock"
																with FileLock(_A			):
																					if os.path.exists(_A			) and not overwrite_cache:
																										logger.info(f"""Loading features from cached file {cached_features_file}"""			)
																										__lowerCAmelCase				   =       torch.load(_A			)
																					else:
																										logger.info(f"""Creating features from dataset file at {data_dir}"""			)
																										__lowerCAmelCase				   =       token_classification_task.read_examples_from_file(_A		,			_A			)
																										# TODO clean up all this to leverage built-in features of tokenizers
																										__lowerCAmelCase				   =       token_classification_task.convert_examples_to_features(
																										    _A		,			_A		,			_A		,			_A		,			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=_A		,			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		,			_A			)






											def __len__(   self			):




																"""simple docstring"""



																return len(self.features			)






											def __getitem__(   self		,			_A			):




																"""simple docstring"""



																return self.features[i]


if is_tf_available():
					import tensorflow as tf
					class 			a__    :
											_a							:    List[InputFeatures]
											_a							:    int					=    -1_0_0






											def __init__(   self		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A = None		,			_A=False		,			_A = Split.train		,			):




																"""simple docstring"""



																__lowerCAmelCase				   =       token_classification_task.read_examples_from_file(_A		,			_A			)
																# TODO clean up all this to leverage built-in features of tokenizers
																__lowerCAmelCase				   =       token_classification_task.convert_examples_to_features(
																    _A		,			_A		,			_A		,			_A		,			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=_A		,			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:
																					__lowerCAmelCase				   =       tf.data.Dataset.from_generator(
																					    _A		,			({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)		,			(
																					        {"input_ids": tf.TensorShape([None]			), "attention_mask": tf.TensorShape([None]			)},
																					        tf.TensorShape([None]			),
																					    )		,			)
																else:
																					__lowerCAmelCase				   =       tf.data.Dataset.from_generator(
																					    _A		,			({"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 				__SCREAMING_SNAKE_CASE(   self			):




																"""simple docstring"""



																__lowerCAmelCase				   =       self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features			)			)			)

																return self.dataset






											def __len__(   self			):




																"""simple docstring"""



																return len(self.features			)






											def __getitem__(   self		,			_A			):




																"""simple docstring"""



																return self.features[i]










from random import randint
from tempfile import TemporaryFile

import numpy as np







def 	_a      (						SCREAMING_SNAKE_CASE_							:				int      ,      SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				List[str]     ):
					__lowerCAmelCase				   =       0
					if start < end:
										__lowerCAmelCase				   =       randint(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
										__lowerCAmelCase				   =       a[end]
										__lowerCAmelCase				   =       a[pivot]
										__lowerCAmelCase				   =       temp

										__lowerCAmelCase   ,							__lowerCAmelCase				   =       _in_place_partition(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
										count += _in_place_quick_sort(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      p - 1     )
										count += _in_place_quick_sort(SCREAMING_SNAKE_CASE_      ,      p + 1      ,      SCREAMING_SNAKE_CASE_     )
					return count







def 	_a      (						SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				List[Any]      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]     ):
					__lowerCAmelCase				   =       0
					__lowerCAmelCase				   =       randint(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase				   =       a[end]
					__lowerCAmelCase				   =       a[pivot]
					__lowerCAmelCase				   =       temp
					__lowerCAmelCase				   =       start - 1
					for index in range(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     ):
										count += 1
										if a[index] < a[end]:  # check if current val is less than pivot value
															__lowerCAmelCase				   =       new_pivot_index + 1
															__lowerCAmelCase				   =       a[new_pivot_index]
															__lowerCAmelCase				   =       a[index]
															__lowerCAmelCase				   =       temp

					__lowerCAmelCase				   =       a[new_pivot_index + 1]
					__lowerCAmelCase				   =       a[end]
					__lowerCAmelCase				   =       temp
					return new_pivot_index + 1, count


UpperCamelCase__    			=			TemporaryFile()
UpperCamelCase__    			=			100  # 1000 elements are to be sorted


UpperCamelCase__			,	UpperCamelCase__    			=			0, 1  # mean and standard deviation
UpperCamelCase__    			=			np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("""The array is""")
print(X)


outfile.seek(0)  # using the same array
UpperCamelCase__    			=			np.load(outfile)
UpperCamelCase__    			=			len(M) - 1
UpperCamelCase__    			=			_in_place_quick_sort(M, 0, r)

print(
    """No of Comparisons for 100 elements selected from a standard normal distribution"""
    """is :"""
)


print(z)




