





"""simple docstring"""




import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


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


a		:				Union[str, Any]  			=    {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all LED models at https://huggingface.co/models?filter=LED
a		:				int  			=    {
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

a		:				Optional[Any]  			=    {
    '''allenai/led-base-16384''': 16384,
}






@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def    _SCREAMING_SNAKE_CASE	(			)				->int:






	'''simple docstring'''






	a   :	Dict			=       (
	    list(range(ord("!"      )					, ord("~"      ) + 1      )      ) + list(range(ord("¡"      )					, ord("¬"      ) + 1      )      ) + list(range(ord("®"      )					, ord("ÿ"      ) + 1      )      )
	)
	a   :	List[Any]			=       bs[:]
	a   :	Union[str, Any]			=       0
	for b in range(2**8      ):
		if b not in bs:
			bs.append(_lowercase      )
			cs.append(2**8 + n      )
			n += 1
	a   :	Union[str, Any]			=       [chr(_lowercase      ) for n in cs]
	return dict(zip(_lowercase					, _lowercase      )      )






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Tuple      )				->int:






	'''simple docstring'''






	a   :	List[str]			=       set()
	a   :	Optional[Any]			=       word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)      )
		a   :	List[str]			=       char
	return pairs







class  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      Optional[Any]         =VOCAB_FILES_NAMES
							lowerCamelCase     :      Union[str, Any]         =PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase     :      Optional[Any]         =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCamelCase     :      str         =["""input_ids""", """attention_mask"""]






							def __init__(     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__="replace"   ,      lowerCAmelCase__="<s>"   ,      lowerCAmelCase__="</s>"   ,      lowerCAmelCase__="</s>"   ,      lowerCAmelCase__="<s>"   ,      lowerCAmelCase__="<unk>"   ,      lowerCAmelCase__="<pad>"   ,      lowerCAmelCase__="<mask>"   ,      lowerCAmelCase__=False   ,      **lowerCAmelCase__   ,      )			->				Optional[Any]:
								a   :	Any			=       AddedToken(lowerCAmelCase__   ,      lstrip=lowerCAmelCase__   ,      rstrip=lowerCAmelCase__   ) if isinstance(lowerCAmelCase__   ,      lowerCAmelCase__   ) else bos_token
								a   :	str			=       AddedToken(lowerCAmelCase__   ,      lstrip=lowerCAmelCase__   ,      rstrip=lowerCAmelCase__   ) if isinstance(lowerCAmelCase__   ,      lowerCAmelCase__   ) else eos_token
								a   :	List[str]			=       AddedToken(lowerCAmelCase__   ,      lstrip=lowerCAmelCase__   ,      rstrip=lowerCAmelCase__   ) if isinstance(lowerCAmelCase__   ,      lowerCAmelCase__   ) else sep_token
								a   :	List[Any]			=       AddedToken(lowerCAmelCase__   ,      lstrip=lowerCAmelCase__   ,      rstrip=lowerCAmelCase__   ) if isinstance(lowerCAmelCase__   ,      lowerCAmelCase__   ) else cls_token
								a   :	List[Any]			=       AddedToken(lowerCAmelCase__   ,      lstrip=lowerCAmelCase__   ,      rstrip=lowerCAmelCase__   ) if isinstance(lowerCAmelCase__   ,      lowerCAmelCase__   ) else unk_token
								a   :	int			=       AddedToken(lowerCAmelCase__   ,      lstrip=lowerCAmelCase__   ,      rstrip=lowerCAmelCase__   ) if isinstance(lowerCAmelCase__   ,      lowerCAmelCase__   ) else pad_token

								# Mask token behave like a normal word, i.e. include the space before it
								a   :	Optional[Any]			=       AddedToken(lowerCAmelCase__   ,      lstrip=lowerCAmelCase__   ,      rstrip=lowerCAmelCase__   ) if isinstance(lowerCAmelCase__   ,      lowerCAmelCase__   ) else mask_token

								super().__init__(
								    errors=lowerCAmelCase__   ,      bos_token=lowerCAmelCase__   ,      eos_token=lowerCAmelCase__   ,      unk_token=lowerCAmelCase__   ,      sep_token=lowerCAmelCase__   ,      cls_token=lowerCAmelCase__   ,      pad_token=lowerCAmelCase__   ,      mask_token=lowerCAmelCase__   ,      add_prefix_space=lowerCAmelCase__   ,      **lowerCAmelCase__   ,      )

								with open(lowerCAmelCase__   ,      encoding="utf-8"   ) as vocab_handle:
									a   :	Optional[int]			=       json.load(lowerCAmelCase__   )
								a   :	Any			=       {v: k for k, v in self.encoder.items()}
								a   :	Tuple			=       errors  # how to handle errors in decoding
								a   :	List[Any]			=       bytes_to_unicode()
								a   :	int			=       {v: k for k, v in self.byte_encoder.items()}
								with open(lowerCAmelCase__   ,      encoding="utf-8"   ) as merges_handle:
									a   :	Tuple			=       merges_handle.read().split("\n"   )[1:-1]
								a   :	str			=       [tuple(merge.split()   ) for merge in bpe_merges]
								a   :	Tuple			=       dict(zip(lowerCAmelCase__   ,      range(len(lowerCAmelCase__   )   )   )   )
								a   :	int			=       {}
								a   :	int			=       add_prefix_space

								# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
								a   :	Tuple			=       re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"   )






							@property
							# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
							def 					__a     (     self   )			->				int:
								return len(self.encoder   )






							def 					__a     (     self   )			->				List[Any]:
								return dict(self.encoder   ,      **self.added_tokens_encoder   )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				List[str]:
								if token in self.cache:
									return self.cache[token]
								a   :	str			=       tuple(lowerCAmelCase__   )
								a   :	Dict			=       get_pairs(lowerCAmelCase__   )

								if not pairs:
									return token

								while True:
									a   :	Tuple			=       min(lowerCAmelCase__   ,      key=lambda lowerCAmelCase__   : self.bpe_ranks.get(lowerCAmelCase__   ,      float("inf"   )   )   )
									if bigram not in self.bpe_ranks:
										break
									a, a   :	List[str]			=       bigram
									a   :	List[Any]			=       []
									a   :	List[Any]			=       0
									while i < len(lowerCAmelCase__   ):
										try:
											a   :	str			=       word.index(lowerCAmelCase__   ,      lowerCAmelCase__   )
										except ValueError:
											new_word.extend(word[i:]   )
											break
										else:
											new_word.extend(word[i:j]   )
											a   :	Tuple			=       j

										if word[i] == first and i < len(lowerCAmelCase__   ) - 1 and word[i + 1] == second:
											new_word.append(first + second   )
											i += 2
										else:
											new_word.append(word[i]   )
											i += 1
									a   :	Union[str, Any]			=       tuple(lowerCAmelCase__   )
									a   :	str			=       new_word
									if len(lowerCAmelCase__   ) == 1:
										break
									else:
										a   :	List[Any]			=       get_pairs(lowerCAmelCase__   )
								a   :	Any			=       " ".join(lowerCAmelCase__   )
								a   :	Optional[Any]			=       word
								return word






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Union[str, Any]:
								a   :	Union[str, Any]			=       []
								for token in re.findall(self.pat   ,      lowerCAmelCase__   ):
									a   :	List[Any]			=       "".join(
									    self.byte_encoder[b] for b in token.encode("utf-8"   )   )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
									bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCAmelCase__   ).split(" "   )   )
								return bpe_tokens






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Dict:
								return self.encoder.get(lowerCAmelCase__   ,      self.encoder.get(self.unk_token   )   )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				str:
								return self.decoder.get(lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				List[str]:
								a   :	Union[str, Any]			=       "".join(lowerCAmelCase__   )
								a   :	Any			=       bytearray([self.byte_decoder[c] for c in text]   ).decode("utf-8"   ,      errors=self.errors   )
								return text






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				Tuple[str]:
								if not os.path.isdir(lowerCAmelCase__   ):
									logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
									return
								a   :	str			=       os.path.join(
								    lowerCAmelCase__   ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )
								a   :	Tuple			=       os.path.join(
								    lowerCAmelCase__   ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]   )

								with open(lowerCAmelCase__   ,      "w"   ,      encoding="utf-8"   ) as f:
									f.write(json.dumps(self.encoder   ,      indent=2   ,      sort_keys=lowerCAmelCase__   ,      ensure_ascii=lowerCAmelCase__   ) + "\n"   )

								a   :	List[Any]			=       0
								with open(lowerCAmelCase__   ,      "w"   ,      encoding="utf-8"   ) as writer:
									writer.write("#version: 0.2\n"   )
									for bpe_tokens, token_index in sorted(self.bpe_ranks.items()   ,      key=lambda lowerCAmelCase__   : kv[1]   ):
										if index != token_index:
											logger.warning(
											    f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
											    " Please check that the tokenizer is not corrupted!"   )
											a   :	Union[str, Any]			=       token_index
										writer.write(" ".join(lowerCAmelCase__   ) + "\n"   )
										index += 1

								return vocab_file, merge_file






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				List[int]:
								if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								a   :	Optional[Any]			=       [self.cls_token_id]
								a   :	Any			=       [self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = False   )			->				List[int]:
								if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=lowerCAmelCase__   ,      token_ids_a=lowerCAmelCase__   ,      already_has_special_tokens=lowerCAmelCase__   )

								if token_ids_a is None:
									return [1] + ([0] * len(lowerCAmelCase__   )) + [1]
								return [1] + ([0] * len(lowerCAmelCase__   )) + [1, 1] + ([0] * len(lowerCAmelCase__   )) + [1]






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				List[int]:
								a   :	Optional[int]			=       [self.sep_token_id]
								a   :	str			=       [self.cls_token_id]

								if token_ids_a is None:
									return len(cls + token_ids_a + sep   ) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__=False   ,      **lowerCAmelCase__   )			->				List[str]:
								a   :	List[Any]			=       kwargs.pop("add_prefix_space"   ,      self.add_prefix_space   )
								if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase__   ) > 0 and not text[0].isspace()):
									a   :	Tuple			=       " " + text
								return (text, kwargs)






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = PaddingStrategy.DO_NOT_PAD   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      )			->				dict:
								a   :	Dict			=       super()._pad(
								    encoded_inputs=lowerCAmelCase__   ,      max_length=lowerCAmelCase__   ,      padding_strategy=lowerCAmelCase__   ,      pad_to_multiple_of=lowerCAmelCase__   ,      return_attention_mask=lowerCAmelCase__   ,      )

								# Load from model defaults
								if return_attention_mask is None:
									a   :	List[Any]			=       "attention_mask" in self.model_input_names

								if return_attention_mask and "global_attention_mask" in encoded_inputs:
									a   :	List[Any]			=       encoded_inputs[self.model_input_names[0]]
									# `global_attention_mask` need to have the same length as other (sequential) inputs.
									a   :	Tuple			=       len(encoded_inputs["global_attention_mask"]   ) != len(lowerCAmelCase__   )

									if needs_to_be_padded:
										a   :	List[str]			=       len(lowerCAmelCase__   ) - len(encoded_inputs["global_attention_mask"]   )

										if self.padding_side == "right":
											# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
											a   :	Union[str, Any]			=       (
											    encoded_inputs["global_attention_mask"] + [-1] * difference
											)
										elif self.padding_side == "left":
											a   :	str			=       [-1] * difference + encoded_inputs[
											    "global_attention_mask"
											]
										else:
											raise ValueError("Invalid padding strategy:" + str(self.padding_side   )   )

								return encoded_inputs









"""simple docstring"""




from random import randint
from tempfile import TemporaryFile

import numpy as np






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[int]					, _lowercase		:					Optional[Any]					, _lowercase		:					Union[str, Any]      )				->Dict:






	'''simple docstring'''






	a   :	List[str]			=       0
	if start < end:
		a   :	Tuple			=       randint(_lowercase					, _lowercase      )
		a   :	List[str]			=       a[end]
		a   :	str			=       a[pivot]
		a   :	Optional[int]			=       temp

		a, a   :	Dict			=       _in_place_partition(_lowercase					, _lowercase					, _lowercase      )
		count += _in_place_quick_sort(_lowercase					, _lowercase					, p - 1      )
		count += _in_place_quick_sort(_lowercase					, p + 1					, _lowercase      )
	return count






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[Any]					, _lowercase		:					str					, _lowercase		:					List[Any]      )				->str:






	'''simple docstring'''






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

	a   :	Tuple			=       a[new_pivot_index + 1]
	a   :	str			=       a[end]
	a   :	Dict			=       temp
	return new_pivot_index + 1, count


a		:				int  			=    TemporaryFile()
a		:				Tuple  			=    100  # 1000 elements are to be sorted


a			,				a		:				int  			=    0, 1  # mean and standard deviation
a		:				List[Any]  			=    np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)


outfile.seek(0)  # using the same array
a		:				int  			=    np.load(outfile)
a		:				Tuple  			=    len(M) - 1
a		:				Union[str, Any]  			=    _in_place_quick_sort(M, 0, r)

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







print(z)


