


import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation

UpperCAmelCase   :    Tuple     =		logging.get_logger(__name__)


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

UpperCAmelCase   :    str     =		{
    "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
    "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
    },
}

UpperCAmelCase   :    Dict     =		{"facebook/blenderbot-3B": 1_28}






@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 	__lowerCamelCase						(							):





			'''simple docstring'''




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






def 	__lowerCamelCase						(							lowerCamelCase__		:						Optional[Any]      ):





			'''simple docstring'''




			lowerCamelCase    		=  set()
			lowerCamelCase    		=  word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)      )
						lowerCamelCase    		=  char
			return pairs







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					Any			       =		VOCAB_FILES_NAMES
		UpperCamelCase     :					Optional[int]			       =		PRETRAINED_VOCAB_FILES_MAP
		UpperCamelCase     :					int			       =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCamelCase     :					Union[str, Any]			       =		["input_ids", "attention_mask"]







		def __init__( self		,      A		,      A		,      A="replace"		,      A="<s>"		,      A="</s>"		,      A="</s>"		,      A="<s>"		,      A="<unk>"		,      A="<pad>"		,      A="<mask>"		,      A=False		,      **A		,      )  -> List[Any]:







					'''simple docstring'''







					lowerCamelCase    		=  AddedToken(A		,      lstrip=A		,      rstrip=A	) if isinstance(A		,      A	) else bos_token
					lowerCamelCase    		=  AddedToken(A		,      lstrip=A		,      rstrip=A	) if isinstance(A		,      A	) else eos_token
					lowerCamelCase    		=  AddedToken(A		,      lstrip=A		,      rstrip=A	) if isinstance(A		,      A	) else sep_token
					lowerCamelCase    		=  AddedToken(A		,      lstrip=A		,      rstrip=A	) if isinstance(A		,      A	) else cls_token
					lowerCamelCase    		=  AddedToken(A		,      lstrip=A		,      rstrip=A	) if isinstance(A		,      A	) else unk_token
					lowerCamelCase    		=  AddedToken(A		,      lstrip=A		,      rstrip=A	) if isinstance(A		,      A	) else pad_token

					# Mask token behave like a normal word, i.e. include the space before it
					lowerCamelCase    		=  AddedToken(A		,      lstrip=A		,      rstrip=A	) if isinstance(A		,      A	) else mask_token

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

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

					# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
					lowerCamelCase    		=  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.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
		def 					__A   ( self	)  -> Optional[int]:







					'''simple docstring'''







					return len(self.encoder	)







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







					'''simple docstring'''







					return dict(self.encoder		,      **self.added_tokens_encoder	)







		def 					__A   ( self		,      A	)  -> int:







					'''simple docstring'''







					if token in self.cache:
								return self.cache[token]
					lowerCamelCase    		=  tuple(A	)
					lowerCamelCase    		=  get_pairs(A	)

					if not pairs:
								return token

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

											if word[i] == first and i < len(A	) - 1 and word[i + 1] == second:
														new_word.append(first + second	)
														i += 2
											else:
														new_word.append(word[i]	)
														i += 1
								lowerCamelCase    		=  tuple(A	)
								lowerCamelCase    		=  new_word
								if len(A	) == 1:
											break
								else:
											lowerCamelCase    		=  get_pairs(A	)
					lowerCamelCase    		=  """ """.join(A	)
					lowerCamelCase    		=  word
					return word







		def 					__A   ( self		,      A	)  -> List[str]:







					'''simple docstring'''







					lowerCamelCase    		=  []
					for token in re.findall(self.pat		,      A	):
								lowerCamelCase    		=  """""".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(A	).split(""" """	)	)
					return bpe_tokens







		def 					__A   ( self		,      A	)  -> Optional[int]:







					'''simple docstring'''







					return self.encoder.get(A		,      self.encoder.get(self.unk_token	)	)







		def 					__A   ( self		,      A	)  -> Optional[int]:







					'''simple docstring'''







					return self.decoder.get(A	)







		def 					__A   ( self		,      A	)  -> Optional[Any]:







					'''simple docstring'''







					lowerCamelCase    		=  """""".join(A	)
					lowerCamelCase    		=  bytearray([self.byte_decoder[c] for c in text]	).decode("""utf-8"""		,      errors=self.errors	)
					return text







		def 					__A   ( self		,      A		,      A = None	)  -> Tuple[str]:







					'''simple docstring'''







					if not os.path.isdir(A	):
								logger.error(F'Vocabulary path ({save_directory}) should be a directory'	)
								return
					lowerCamelCase    		=  os.path.join(
					    A		,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]	)
					lowerCamelCase    		=  os.path.join(
					    A		,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]	)

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

					lowerCamelCase    		=  0
					with open(A		,      """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 A	: 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!"""	)
														lowerCamelCase    		=  token_index
											writer.write(""" """.join(A	) + """\n"""	)
											index += 1

					return vocab_file, merge_file







		def 					__A   ( self		,      A		,      A = None		,      A = False	)  -> List[int]:







					'''simple docstring'''







					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	)) + [1]
					return [1] + ([0] * len(A	)) + [1, 1] + ([0] * len(A	)) + [1]







		def 					__A   ( self		,      A		,      A = None	)  -> List[int]:







					'''simple docstring'''







					lowerCamelCase    		=  [self.sep_token_id]
					lowerCamelCase    		=  [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		,      A		,      A=False		,      **A	)  -> Union[str, Any]:







					'''simple docstring'''







					lowerCamelCase    		=  kwargs.pop("""add_prefix_space"""		,      self.add_prefix_space	)
					if (is_split_into_words or add_prefix_space) and (len(A	) > 0 and not text[0].isspace()):
								lowerCamelCase    		=  """ """ + text
					return (text, kwargs)







		def 					__A   ( self		,      A		,      A = None	)  -> Optional[int]:







					'''simple docstring'''







					return token_ids_a + [self.eos_token_id]







		def 					__A   ( self		,      A	)  -> List[int]:







					'''simple docstring'''







					lowerCamelCase    		=  []
					for is_user, text in conversation.iter_texts():
								if is_user:
											# We need to space prefix as it's being done within blenderbot
											inputs.append(""" """ + text	)
								else:
											# Generated responses should contain them already.
											inputs.append(A	)

					lowerCamelCase    		=  """  """.join(A	)
					lowerCamelCase    		=  self.encode(A	)
					if len(A	) > self.model_max_length:
								lowerCamelCase    		=  input_ids[-self.model_max_length :]
								logger.warning(F'Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'	)
					return input_ids





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase   :    Union[str, Any]     =		logging.get_logger(__name__)

UpperCAmelCase   :    List[Any]     =		{
    "SCUT-DLVCLab/lilt-roberta-en-base": (
        "https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
    ),
}







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					List[str]			       =		"lilt"







		def __init__( self		,      A=3_05_22		,      A=7_68		,      A=12		,      A=12		,      A=30_72		,      A="gelu"		,      A=0.1		,      A=0.1		,      A=5_12		,      A=2		,      A=0.02		,      A=1e-1_2		,      A=0		,      A="absolute"		,      A=None		,      A=4		,      A=10_24		,      **A		,      )  -> List[Any]:







					'''simple docstring'''







					super().__init__(pad_token_id=A		,      **A	)

					lowerCamelCase    		=  vocab_size
					lowerCamelCase    		=  hidden_size
					lowerCamelCase    		=  num_hidden_layers
					lowerCamelCase    		=  num_attention_heads
					lowerCamelCase    		=  hidden_act
					lowerCamelCase    		=  intermediate_size
					lowerCamelCase    		=  hidden_dropout_prob
					lowerCamelCase    		=  attention_probs_dropout_prob
					lowerCamelCase    		=  max_position_embeddings
					lowerCamelCase    		=  type_vocab_size
					lowerCamelCase    		=  initializer_range
					lowerCamelCase    		=  layer_norm_eps
					lowerCamelCase    		=  position_embedding_type
					lowerCamelCase    		=  classifier_dropout
					lowerCamelCase    		=  channel_shrink_ratio
					lowerCamelCase    		=  max_ad_position_embeddings

