






import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

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


__magic_name__							=     logging.get_logger(__name__)


__magic_name__							=     {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

__magic_name__							=     {
    "vocab_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

__magic_name__							=     {"facebook/blenderbot_small-90M": 512}

def    UpperCamelCase						(	_A			):






						"""simple docstring"""







						__magic_name__      :       Tuple       						= set()
						__magic_name__      :       str       						= word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)			)
												__magic_name__      :       Union[str, Any]       						= char

						__magic_name__      :       int       						= set(_A			)
						return pairs





class 	snake_case__ (							_lowerCAmelCase				):
	lowercase__						:					Union[str, Any]											=   VOCAB_FILES_NAMES
	lowercase__						:					Union[str, Any]											=   PRETRAINED_VOCAB_FILES_MAP
	lowercase__						:					Optional[Any]											=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase__						:					Dict											=   ['''input_ids''', '''attention_mask''']
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__="__start__"      ,       lowerCAmelCase__="__end__"      ,       lowerCAmelCase__="__unk__"      ,       lowerCAmelCase__="__null__"      ,       **lowerCAmelCase__      ,       )							->				int:
							super().__init__(unk_token=lowerCAmelCase__      ,       bos_token=lowerCAmelCase__      ,       eos_token=lowerCAmelCase__      ,       pad_token=lowerCAmelCase__      ,       **lowerCAmelCase__			)

							with open(lowerCAmelCase__      ,       encoding="""utf-8"""			) as vocab_handle:
													__magic_name__      :       Any       						= json.load(lowerCAmelCase__			)
							__magic_name__      :       str       						= {v: k for k, v in self.encoder.items()}
							with open(lowerCAmelCase__      ,       encoding="""utf-8"""			) as merges_handle:
													__magic_name__      :       Any       						= merges_handle.read().split("""\n"""			)[1:-1]
							__magic_name__      :       Tuple       						= [tuple(merge.split()			) for merge in merges]
							__magic_name__      :       Any       						= dict(zip(lowerCAmelCase__      ,       range(len(lowerCAmelCase__			)			)			)			)
							__magic_name__      :       Optional[Any]       						= {}
	@property
	def       __magic_name__    (    self			)							->				int:
							return len(self.encoder			)
	def       __magic_name__    (    self			)							->				Dict:
							return dict(self.encoder      ,       **self.added_tokens_encoder			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				str:
							if token in self.cache:
													return self.cache[token]
							__magic_name__      :       Optional[Any]       						= re.sub("""([.,!?()])"""      ,       R""" \1"""      ,       lowerCAmelCase__			)
							__magic_name__      :       str       						= re.sub("""(')"""      ,       R""" \1 """      ,       lowerCAmelCase__			)
							__magic_name__      :       List[Any]       						= re.sub(R"""\s{2,}"""      ,       """ """      ,       lowerCAmelCase__			)
							if "\n" in token:
													__magic_name__      :       Any       						= token.replace("""\n"""      ,       """ __newln__"""			)

							__magic_name__      :       Tuple       						= token.split(""" """			)
							__magic_name__      :       Any       						= []
							for token in tokens:
													if not len(lowerCAmelCase__			):
																			continue

													__magic_name__      :       str       						= token.lower()
													__magic_name__      :       Optional[Any]       						= tuple(lowerCAmelCase__			)
													__magic_name__      :       Optional[int]       						= tuple(list(word[:-1]			) + [word[-1] + """</w>"""]			)
													__magic_name__      :       List[Any]       						= get_pairs(lowerCAmelCase__			)

													if not pairs:
																			words.append(lowerCAmelCase__			)
																			continue

													while True:
																			__magic_name__      :       Optional[Any]       						= min(lowerCAmelCase__      ,       key=lambda lowerCAmelCase__			: self.bpe_ranks.get(lowerCAmelCase__      ,       float("""inf"""			)			)			)
																			if bigram not in self.bpe_ranks:
																									break
																			__magic_name__      :       Tuple       						= bigram
																			__magic_name__      :       Dict       						= []
																			__magic_name__      :       Dict       						= 0

																			while i < len(lowerCAmelCase__			):
																									try:
																															__magic_name__      :       List[str]       						= word.index(lowerCAmelCase__      ,       lowerCAmelCase__			)
																															new_word.extend(word[i:j]			)
																															__magic_name__      :       Tuple       						= j
																									except ValueError:
																															new_word.extend(word[i:]			)
																															break

																									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
																			__magic_name__      :       str       						= tuple(lowerCAmelCase__			)
																			__magic_name__      :       Union[str, Any]       						= new_word
																			if len(lowerCAmelCase__			) == 1:
																									break
																			else:
																									__magic_name__      :       Any       						= get_pairs(lowerCAmelCase__			)
													__magic_name__      :       Optional[int]       						= """@@ """.join(lowerCAmelCase__			)
													__magic_name__      :       List[Any]       						= word[:-4]

													__magic_name__      :       Union[str, Any]       						= word
													words.append(lowerCAmelCase__			)
							return " ".join(lowerCAmelCase__			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				List[str]:
							__magic_name__      :       Union[str, Any]       						= []

							__magic_name__      :       Tuple       						= re.findall(R"""\S+\n?"""      ,       lowerCAmelCase__			)

							for token in words:
													split_tokens.extend(list(self.bpe(lowerCAmelCase__			).split(""" """			)			)			)
							return split_tokens
	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				int:
							__magic_name__      :       Dict       						= token.lower()
							return self.encoder.get(lowerCAmelCase__      ,       self.encoder.get(self.unk_token			)			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				str:
							return self.decoder.get(lowerCAmelCase__      ,       self.unk_token			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				str:
							__magic_name__      :       Dict       						= """ """.join(lowerCAmelCase__			).replace("""@@ """      ,       """"""			).strip()
							return out_string







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

							__magic_name__      :       List[str]       						= 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!"""			)
																									__magic_name__      :       List[str]       						= token_index
																			writer.write(""" """.join(lowerCAmelCase__			) + """\n"""			)
																			index += 1

							return vocab_file, merge_file








import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer





class 	snake_case__ (							tf.keras.layers.Layer				):
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = None			)							->				int:
							super().__init__()
							__magic_name__      :       Any       						= pad_token_id
							__magic_name__      :       Any       						= max_length
							__magic_name__      :       List[str]       						= vocab
							__magic_name__      :       List[Any]       						= merges
							__magic_name__      :       int       						= BytePairTokenizer(lowerCAmelCase__      ,       lowerCAmelCase__      ,       sequence_length=lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls      ,       lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				Any:
							__magic_name__      :       Union[str, Any]       						= [""" """.join(lowerCAmelCase__			) for m in tokenizer.bpe_ranks.keys()]
							__magic_name__      :       Union[str, Any]       						= tokenizer.get_vocab()
							return cls(lowerCAmelCase__      ,       lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls      ,       lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				List[str]:
							__magic_name__      :       Optional[Any]       						= GPTaTokenizer.from_pretrained(lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)
							return cls.from_tokenizer(lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls      ,       lowerCAmelCase__			)							->				List[Any]:
							return cls(**lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				int:
							return {
							    "vocab": self.vocab,
							    "merges": self.merges,
							    "max_length": self.max_length,
							    "pad_token_id": self.pad_token_id,
							}







	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				int:
							__magic_name__      :       Dict       						= self.tf_tokenizer(lowerCAmelCase__			)
							__magic_name__      :       Dict       						= tf.ones_like(lowerCAmelCase__			)

							if self.pad_token_id is not None:
													# pad the tokens up to max length
													__magic_name__      :       List[Any]       						= max_length if max_length is not None else self.max_length

													if max_length is not None:
																			__magic_name__						,__magic_name__      :       List[Any]       						= pad_model_inputs(
																			    lowerCAmelCase__      ,       max_seq_length=lowerCAmelCase__      ,       pad_value=self.pad_token_id			)

							return {"attention_mask": attention_mask, "input_ids": input_ids}
