
'''simple docstring'''







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

import regex as re

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


_snake_case				:   Any     =				logging.get_logger(__name__)


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

_snake_case				:   Dict     =				{
    "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"
        )
    },
}

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



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Optional[int]  ):
						_SCREAMING_SNAKE_CASE				= set()
						_SCREAMING_SNAKE_CASE				= word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)  )
												_SCREAMING_SNAKE_CASE				= char

						_SCREAMING_SNAKE_CASE				= set(_SCREAMING_SNAKE_CASE  )
						return pairs




class 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					Union[str, Any]       =					VOCAB_FILES_NAMES
		a							:					Union[str, Any]       =					PRETRAINED_VOCAB_FILES_MAP
		a							:					int       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		a							:					Optional[int]       =					["""input_ids""", """attention_mask"""]




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase="__start__"					,						UpperCamelCase="__end__"					,						UpperCamelCase="__unk__"					,						UpperCamelCase="__null__"					,						**UpperCamelCase					,						):
								super().__init__(unk_token=__UpperCamelCase					,						bos_token=__UpperCamelCase					,						eos_token=__UpperCamelCase					,						pad_token=__UpperCamelCase					,						**__UpperCamelCase      )

								with open(__UpperCamelCase					,						encoding="utf-8"      ) as vocab_handle:
														_SCREAMING_SNAKE_CASE				= json.load(__UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= {v: k for k, v in self.encoder.items()}
								with open(__UpperCamelCase					,						encoding="utf-8"      ) as merges_handle:
														_SCREAMING_SNAKE_CASE				= merges_handle.read().split("\n"      )[1:-1]
								_SCREAMING_SNAKE_CASE				= [tuple(merge.split()      ) for merge in merges]
								_SCREAMING_SNAKE_CASE				= dict(zip(__UpperCamelCase					,						range(len(__UpperCamelCase      )      )      )      )
								_SCREAMING_SNAKE_CASE				= {}




		@property
		def     lowercase       (				self      ):
								return len(self.encoder      )




		def     lowercase       (				self      ):
								return dict(self.encoder					,						**self.added_tokens_encoder      )




		def     lowercase       (				self					,						UpperCamelCase      ):
								if token in self.cache:
														return self.cache[token]
								_SCREAMING_SNAKE_CASE				= re.sub("([.,!?()])"					,						R" \1"					,						__UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= re.sub("(\')"					,						R" \1 "					,						__UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= re.sub(R"\s{2,}"					,						" "					,						__UpperCamelCase      )
								if "\n" in token:
														_SCREAMING_SNAKE_CASE				= token.replace("\n"					,						" __newln__"      )

								_SCREAMING_SNAKE_CASE				= token.split(" "      )
								_SCREAMING_SNAKE_CASE				= []
								for token in tokens:
														if not len(__UpperCamelCase      ):
																				continue

														_SCREAMING_SNAKE_CASE				= token.lower()
														_SCREAMING_SNAKE_CASE				= tuple(__UpperCamelCase      )
														_SCREAMING_SNAKE_CASE				= tuple(list(word[:-1]      ) + [word[-1] + "</w>"]      )
														_SCREAMING_SNAKE_CASE				= get_pairs(__UpperCamelCase      )

														if not pairs:
																				words.append(__UpperCamelCase      )
																				continue

														while True:
																				_SCREAMING_SNAKE_CASE				= min(__UpperCamelCase					,						key=lambda UpperCamelCase      : self.bpe_ranks.get(__UpperCamelCase					,						float("inf"      )      )      )
																				if bigram not in self.bpe_ranks:
																										break
																				_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= bigram
																				_SCREAMING_SNAKE_CASE				= []
																				_SCREAMING_SNAKE_CASE				= 0

																				while i < len(__UpperCamelCase      ):
																										try:
																																_SCREAMING_SNAKE_CASE				= word.index(__UpperCamelCase					,						__UpperCamelCase      )
																																new_word.extend(word[i:j]      )
																																_SCREAMING_SNAKE_CASE				= j
																										except ValueError:
																																new_word.extend(word[i:]      )
																																break

																										if word[i] == first and i < len(__UpperCamelCase      ) - 1 and word[i + 1] == second:
																																new_word.append(first + second      )
																																i += 2
																										else:
																																new_word.append(word[i]      )
																																i += 1
																				_SCREAMING_SNAKE_CASE				= tuple(__UpperCamelCase      )
																				_SCREAMING_SNAKE_CASE				= new_word
																				if len(__UpperCamelCase      ) == 1:
																										break
																				else:
																										_SCREAMING_SNAKE_CASE				= get_pairs(__UpperCamelCase      )
														_SCREAMING_SNAKE_CASE				= "@@ ".join(__UpperCamelCase      )
														_SCREAMING_SNAKE_CASE				= word[:-4]

														_SCREAMING_SNAKE_CASE				= word
														words.append(__UpperCamelCase      )
								return " ".join(__UpperCamelCase      )




		def     lowercase       (				self					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= []

								_SCREAMING_SNAKE_CASE				= re.findall(R"\S+\n?"					,						__UpperCamelCase      )

								for token in words:
														split_tokens.extend(list(self.bpe(__UpperCamelCase      ).split(" "      )      )      )
								return split_tokens




		def     lowercase       (				self					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= token.lower()
								return self.encoder.get(__UpperCamelCase					,						self.encoder.get(self.unk_token      )      )




		def     lowercase       (				self					,						UpperCamelCase      ):
								return self.decoder.get(__UpperCamelCase					,						self.unk_token      )




		def     lowercase       (				self					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= " ".join(__UpperCamelCase      ).replace("@@ "					,						""      ).strip()
								return out_string




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase = None      ):
								if not os.path.isdir(__UpperCamelCase      ):
														logger.error(F'Vocabulary path ({save_directory}) should be a directory'      )
														return
								_SCREAMING_SNAKE_CASE				= os.path.join(
								    __UpperCamelCase					,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )
								_SCREAMING_SNAKE_CASE				= os.path.join(
								    __UpperCamelCase					,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]      )

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

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

								return vocab_file, merge_file

'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_snake_case				:   Optional[Any]     =				{
    """configuration_rag""": ["""RagConfig"""],
    """retrieval_rag""": ["""RagRetriever"""],
    """tokenization_rag""": ["""RagTokenizer"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   int     =				[
						    """RagModel""",
						    """RagPreTrainedModel""",
						    """RagSequenceForGeneration""",
						    """RagTokenForGeneration""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   Dict     =				[
						    """TFRagModel""",
						    """TFRagPreTrainedModel""",
						    """TFRagSequenceForGeneration""",
						    """TFRagTokenForGeneration""",
						]


if TYPE_CHECKING:
						from .configuration_rag import RagConfig
						from .retrieval_rag import RagRetriever
						from .tokenization_rag import RagTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_rag import (
												    TFRagModel,
												    TFRagPreTrainedModel,
												    TFRagSequenceForGeneration,
												    TFRagTokenForGeneration,
												)

else:
						import sys

						_snake_case				:   str     =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)