
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE							:str										=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:List[Any]										=    '▁'

SCREAMING_SNAKE_CASE							:List[Any]										=    {'vocab_file': 'sentencepiece.bpe.model'}

SCREAMING_SNAKE_CASE							:str										=    {
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    }
}

SCREAMING_SNAKE_CASE							:Optional[Any]										=    {
    'facebook/xglm-564M': 2048,
}





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					VOCAB_FILES_NAMES
	snake_case_    =					PRETRAINED_VOCAB_FILES_MAP
	snake_case_    =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case_    =					["input_ids", "attention_mask"]






	def __init__(	self :			Optional[int]							,A :			List[Any]							,A :			Tuple="<s>"							,A :			Any="</s>"							,A :			Any="</s>"							,A :			Dict="<s>"							,A :			Tuple="<unk>"							,A :			str="<pad>"							,A :			Optional[Dict[str, Any]] = None							,**A :			List[Any]							,):
					__A             =			{} if sp_model_kwargs is None else sp_model_kwargs

					# Compatibility with the original tokenizer
					__A             =			7
					__A             =			[f'''<madeupword{i}>''' for i in range(self.num_madeup_words			)]

					__A             =			kwargs.get("additional_special_tokens"							,[]			)
					kwargs["additional_special_tokens"] += [
					    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
					]

					super().__init__(
					    bos_token=A							,eos_token=A							,unk_token=A							,sep_token=A							,cls_token=A							,pad_token=A							,sp_model_kwargs=self.sp_model_kwargs							,**A							,)

					__A             =			spm.SentencePieceProcessor(**self.sp_model_kwargs			)
					self.sp_model.Load(str(A			)			)
					__A             =			vocab_file

					# Original fairseq vocab and spm vocab must be "aligned":
					# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
					# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
					# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
					# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

					# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
					__A             =			1

					# Mimic fairseq token-to-id alignment for the first 4 token
					__A             =			{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

					__A             =			len(self.sp_model			)
					__A             =			{f'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words			)}
					self.fairseq_tokens_to_ids.update(A			)

					__A             =			{v: k for k, v in self.fairseq_tokens_to_ids.items()}






	def __getstate__(	self :			int			):
					__A             =			self.__dict__.copy()
					__A             =			None
					__A             =			self.sp_model.serialized_model_proto()
					return state






	def __setstate__(	self :			Optional[int]							,A :			int			):
					__A             =			d

					# for backward compatibility
					if not hasattr(self							,"sp_model_kwargs"			):
									__A             =			{}

					__A             =			spm.SentencePieceProcessor(**self.sp_model_kwargs			)
					self.sp_model.LoadFromSerializedProto(self.sp_model_proto			)






	def     UpperCamelCase_  (	self :			Optional[Any]							,A :			List[int]							,A :			Optional[List[int]] = None			):

					if token_ids_a is None:
									return [self.sep_token_id] + token_ids_a
					__A             =			[self.sep_token_id]
					return sep + token_ids_a + sep + sep + token_ids_a






	def     UpperCamelCase_  (	self :			Any							,A :			List[int]							,A :			Optional[List[int]] = None							,A :			bool = False			):

					if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=A							,token_ids_a=A							,already_has_special_tokens=A			)

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






	def     UpperCamelCase_  (	self :			Union[str, Any]							,A :			List[int]							,A :			Optional[List[int]] = None			):
					__A             =			[self.sep_token_id]

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






	@property
	def     UpperCamelCase_  (	self :			Tuple			):
					return len(self.sp_model			) + self.fairseq_offset + self.num_madeup_words






	def     UpperCamelCase_  (	self :			List[Any]			):
					__A             =			{self.convert_ids_to_tokens(A			): i for i in range(self.vocab_size			)}
					vocab.update(self.added_tokens_encoder			)
					return vocab






	def     UpperCamelCase_  (	self :			str							,A :			str			):
					return self.sp_model.encode(A							,out_type=A			)






	def     UpperCamelCase_  (	self :			Tuple							,A :			Optional[int]			):
					if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
					__A             =			self.sp_model.PieceToId(A			)

					# Need to return unknown token if the SP model returned 0
					return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






	def     UpperCamelCase_  (	self :			List[Any]							,A :			Dict			):
					if index in self.fairseq_ids_to_tokens:
									return self.fairseq_ids_to_tokens[index]
					return self.sp_model.IdToPiece(index - self.fairseq_offset			)






	def     UpperCamelCase_  (	self :			List[Any]							,A :			List[Any]			):
					__A             =			"".join(A			).replace(A							," "			).strip()
					return out_string







	def     UpperCamelCase_  (	self :			str							,A :			str							,A :			Optional[str] = None			):
					if not os.path.isdir(A			):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
									return
					__A             =			os.path.join(
					    A							,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]			)

					if os.path.abspath(self.vocab_file			) != os.path.abspath(A			) and os.path.isfile(self.vocab_file			):
									copyfile(self.vocab_file							,A			)
					elif not os.path.isfile(self.vocab_file			):
									with open(A							,"wb"			) as fi:
													__A             =			self.sp_model.serialized_model_proto()
													fi.write(A			)

					return (out_vocab_file,)


from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase


def 						UpperCAmelCase      (     a_						)		->     None:







				"""simple docstring"""



				__A      ,							__A             =			analyze_text(a_						)
				__A             =			list(" " + ascii_lowercase						)
				# what is our total sum of probabilities.
				__A             =			sum(single_char_strings.values()						)

				# one length string
				__A             =			0
				# for each alpha we go in our dict and if it is in it we calculate entropy
				for ch in my_alphas:
								if ch in single_char_strings:
												__A             =			single_char_strings[ch]
												__A             =			my_str / all_sum
												my_fir_sum += prob * math.loga(a_						)  # entropy formula.

    # print entropy
				print(F'''{round(-1 * my_fir_sum						):.1f}'''						)

				# two len string
				__A             =			sum(two_char_strings.values()						)
				__A             =			0
				# for each alpha (two in size) calculate entropy.
				for cha in my_alphas:
								for cha in my_alphas:
												__A             =			cha + cha
												if sequence in two_char_strings:
																__A             =			two_char_strings[sequence]
																__A             =			int(a_						) / all_sum
																my_sec_sum += prob * math.loga(a_						)

    # print second entropy
				print(F'''{round(-1 * my_sec_sum						):.1f}'''						)

				# print the difference between them
				print(F'''{round((-1 * my_sec_sum) - (-1 * my_fir_sum)						):.1f}'''						)


def 						UpperCAmelCase      (     a_						)		->     tuple[dict, dict]:







				"""simple docstring"""



				__A             =			Counter()  # type: ignore
				__A             =			Counter()  # type: ignore
				single_char_strings[text[-1]] += 1

				# first case when we have space at start.
				two_char_strings[" " + text[0]] += 1
				for i in range(0		,       len(a_						) - 1						):
								single_char_strings[text[i]] += 1
								two_char_strings[text[i : i + 2]] += 1
				return single_char_strings, two_char_strings


def 						UpperCAmelCase      (     )		->     Optional[Any]:







				"""simple docstring"""



				import doctest

				doctest.testmod()
				# text = (
				#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
				#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
				#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
				#     "beloved evident through no service elderly is. Blind there if every no so "
				#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
				#     "of message cottage windows do besides against uncivil.  Delightful "
				#     "unreserved impossible few estimating men favourable see entreaties. She "
				#     "propriety immediate was improving. He or entrance humoured likewise "
				#     "moderate. Much nor game son say feel. Fat make met can must form into "
				#     "gate. Me we offending prevailed discovery. "
				# )

				# calculate_prob(text)


if __name__ == "__main__":
					main()
