



"""simple docstring"""





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

__lowerCAmelCase       :					Any      					=					logging.get_logger(__name__)


__lowerCAmelCase       :					Any      					=					{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

__lowerCAmelCase       :					Tuple      					=					{
    '''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'''
    },
}

__lowerCAmelCase       :					str      					=					{'''facebook/blenderbot-3B''': 128}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 	__lowerCAmelCase						(		):


						'''simple docstring'''






						snake_case_   : Tuple						=			(
						    list(range(ord("""!"""    )					, ord("""~"""    ) + 1    )    ) + list(range(ord("""¡"""    )					, ord("""¬"""    ) + 1    )    ) + list(range(ord("""®"""    )					, ord("""ÿ"""    ) + 1    )    )
						)
						snake_case_   : Optional[Any]						=			bs[:]
						snake_case_   : int						=			0
						for b in range(2**8    ):
												if b not in bs:
																		bs.append(__UpperCamelCase    )
																		cs.append(2**8 + n    )
																		n += 1
						snake_case_   : List[Any]						=			[chr(__UpperCamelCase    ) for n in cs]
						return dict(zip(__UpperCamelCase					, __UpperCamelCase    )    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[str]    ):


						'''simple docstring'''






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






class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase			  =       VOCAB_FILES_NAMES
		_lowerCamelCase			  =       PRETRAINED_VOCAB_FILES_MAP
		_lowerCamelCase			  =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_lowerCamelCase			  =       ['''input_ids''', '''attention_mask''']





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







								'''simple docstring'''

								snake_case_   : Union[str, Any]						=			AddedToken(_lowercase							,    lstrip=_lowercase							,    rstrip=_lowercase   ) if isinstance(_lowercase							,    _lowercase   ) else bos_token
								snake_case_   : Union[str, Any]						=			AddedToken(_lowercase							,    lstrip=_lowercase							,    rstrip=_lowercase   ) if isinstance(_lowercase							,    _lowercase   ) else eos_token
								snake_case_   : List[Any]						=			AddedToken(_lowercase							,    lstrip=_lowercase							,    rstrip=_lowercase   ) if isinstance(_lowercase							,    _lowercase   ) else sep_token
								snake_case_   : List[str]						=			AddedToken(_lowercase							,    lstrip=_lowercase							,    rstrip=_lowercase   ) if isinstance(_lowercase							,    _lowercase   ) else cls_token
								snake_case_   : Tuple						=			AddedToken(_lowercase							,    lstrip=_lowercase							,    rstrip=_lowercase   ) if isinstance(_lowercase							,    _lowercase   ) else unk_token
								snake_case_   : Dict						=			AddedToken(_lowercase							,    lstrip=_lowercase							,    rstrip=_lowercase   ) if isinstance(_lowercase							,    _lowercase   ) else pad_token

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

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

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

								# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
								snake_case_   : List[str]						=			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    UpperCAmelCase__							(					self   ) ->						Dict:







								'''simple docstring'''

								return len(self.encoder   )





		def    UpperCAmelCase__							(					self   ) ->						Union[str, Any]:







								'''simple docstring'''

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





		def    UpperCAmelCase__							(					self							,    _lowercase   ) ->						str:







								'''simple docstring'''

								if token in self.cache:
														return self.cache[token]
								snake_case_   : str						=			tuple(_lowercase   )
								snake_case_   : Optional[int]						=			get_pairs(_lowercase   )

								if not pairs:
														return token

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

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





		def    UpperCAmelCase__							(					self							,    _lowercase   ) ->						Union[str, Any]:







								'''simple docstring'''

								snake_case_   : Tuple						=			[]
								for token in re.findall(self.pat							,    _lowercase   ):
														snake_case_   : Dict						=			"""""".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(_lowercase   ).split(""" """   )   )
								return bpe_tokens





		def    UpperCAmelCase__							(					self							,    _lowercase   ) ->						Union[str, Any]:







								'''simple docstring'''

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





		def    UpperCAmelCase__							(					self							,    _lowercase   ) ->						int:







								'''simple docstring'''

								return self.decoder.get(_lowercase   )





		def    UpperCAmelCase__							(					self							,    _lowercase   ) ->						List[Any]:







								'''simple docstring'''

								snake_case_   : Any						=			"""""".join(_lowercase   )
								snake_case_   : Dict						=			bytearray([self.byte_decoder[c] for c in text]   ).decode("""utf-8"""							,    errors=self.errors   )
								return text





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase = None   ) ->						Tuple[str]:







								'''simple docstring'''

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

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

								snake_case_   : List[Any]						=			0
								with open(_lowercase							,    """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 _lowercase   : 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!"""   )
																										snake_case_   : Dict						=			token_index
																				writer.write(""" """.join(_lowercase   ) + """\n"""   )
																				index += 1

								return vocab_file, merge_file





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase = None							,    _lowercase = False   ) ->						List[int]:







								'''simple docstring'''

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

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





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase = None   ) ->						List[int]:







								'''simple docstring'''

								snake_case_   : List[str]						=			[self.sep_token_id]
								snake_case_   : Optional[Any]						=			[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    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase=False							,    **_lowercase   ) ->						Tuple:







								'''simple docstring'''

								snake_case_   : Union[str, Any]						=			kwargs.pop("""add_prefix_space"""							,    self.add_prefix_space   )
								if (is_split_into_words or add_prefix_space) and (len(_lowercase   ) > 0 and not text[0].isspace()):
														snake_case_   : int						=			""" """ + text
								return (text, kwargs)





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase = None   ) ->						str:







								'''simple docstring'''

								return token_ids_a + [self.eos_token_id]





		def    UpperCAmelCase__							(					self							,    _lowercase   ) ->						List[int]:







								'''simple docstring'''

								snake_case_   : str						=			[]
								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(_lowercase   )

								snake_case_   : Tuple						=			"""  """.join(_lowercase   )
								snake_case_   : Optional[int]						=			self.encode(_lowercase   )
								if len(_lowercase   ) > self.model_max_length:
														snake_case_   : str						=			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






"""simple docstring"""





import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
					import optax
					from flax.training.common_utils import onehot

					from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
					from transformers.models.ta.modeling_flax_ta import shift_tokens_right



@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class    _lowerCAmelCase    (				unittest.TestCase		):





		"""simple docstring"""






		@slow
		def    UpperCAmelCase__							(					self   ) ->						Optional[int]:







								'''simple docstring'''

								snake_case_   : int						=			FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small"""   )
								snake_case_   : List[Any]						=			AutoTokenizer.from_pretrained("""google/mt5-small"""   )

								snake_case_   : Dict						=			tokenizer("""Hello there"""							,    return_tensors="""np"""   ).input_ids
								snake_case_   : List[str]						=			tokenizer("""Hi I am"""							,    return_tensors="""np"""   ).input_ids

								snake_case_   : Optional[Any]						=			shift_tokens_right(_lowercase							,    model.config.pad_token_id							,    model.config.decoder_start_token_id   )

								snake_case_   : Tuple						=			model(_lowercase							,    decoder_input_ids=_lowercase   ).logits
								snake_case_   : Tuple						=			optax.softmax_cross_entropy(_lowercase							,    onehot(_lowercase							,    logits.shape[-1]   )   ).mean()

								snake_case_   : List[str]						=			-(labels.shape[-1] * loss.item())

								snake_case_   : Optional[int]						=			-84.9127
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE   ) < 1E-4   )

