


"""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

__snake_case         =					logging.get_logger(__name__)


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

__snake_case         =					{
    """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"""
    },
}

__snake_case         =					{"""facebook/blenderbot-3B""": 128}







@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 						__lowerCAmelCase		(		)			->		Optional[int]:


    """simple docstring"""





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







def 						__lowerCAmelCase		(		lowercase						:						List[str]				)			->		Any:


    """simple docstring"""





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







class _lowerCAmelCase       (   __SCREAMING_SNAKE_CASE			):
   __UpperCAmelCase				: str													=						VOCAB_FILES_NAMES
   __UpperCAmelCase				: List[str]													=						PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase				: Optional[Any]													=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase				: Dict													=						['''input_ids''', '''attention_mask''']







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

       '''simple docstring'''




       snake_case					:   Any         =					AddedToken(_a		,			lstrip=_a		,			rstrip=_a		) if isinstance(_a		,			_a		) else bos_token
       snake_case					:   Dict         =					AddedToken(_a		,			lstrip=_a		,			rstrip=_a		) if isinstance(_a		,			_a		) else eos_token
       snake_case					:   List[Any]         =					AddedToken(_a		,			lstrip=_a		,			rstrip=_a		) if isinstance(_a		,			_a		) else sep_token
       snake_case					:   Optional[int]         =					AddedToken(_a		,			lstrip=_a		,			rstrip=_a		) if isinstance(_a		,			_a		) else cls_token
       snake_case					:   List[Any]         =					AddedToken(_a		,			lstrip=_a		,			rstrip=_a		) if isinstance(_a		,			_a		) else unk_token
       snake_case					:   List[str]         =					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
       snake_case					:   Dict         =					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:
           snake_case					:   Any         =					json.load(_a		)
       snake_case					:   str         =					{v: k for k, v in self.encoder.items()}
       snake_case					:   Union[str, Any]         =					errors  # how to handle errors in decoding
       snake_case					:   Any         =					bytes_to_unicode()
       snake_case					:   int         =					{v: k for k, v in self.byte_encoder.items()}
       with open(_a		,			encoding="utf-8"		) as merges_handle:
           snake_case					:   int         =					merges_handle.read().split("\n"		)[1:-1]
       snake_case					:   Optional[int]         =					[tuple(merge.split()		) for merge in bpe_merges]
       snake_case					:   Union[str, Any]         =					dict(zip(_a		,			range(len(_a		)		)		)		)
       snake_case					:   Any         =					{}
       snake_case					:   int         =					add_prefix_space

       # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
       snake_case					:   int         =					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 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       return len(self.encoder		)







   def 				lowerCamelCase	(			self		)			->	Tuple:

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Any:

       '''simple docstring'''




       if token in self.cache:
           return self.cache[token]
       snake_case					:   Optional[int]         =					tuple(_a		)
       snake_case					:   int         =					get_pairs(_a		)

       if not pairs:
           return token

       while True:
           snake_case					:   List[str]         =					min(_a		,			key=lambda UpperCamelCase__		: self.bpe_ranks.get(_a		,			float("inf"		)		)		)
           if bigram not in self.bpe_ranks:
               break
           snake_case					:   Dict         =					bigram
           snake_case					:   Optional[Any]         =					[]
           snake_case					:   Dict         =					0
           while i < len(_a		):
               try:
                   snake_case					:   int         =					word.index(_a		,			_a		)
               except ValueError:
                   new_word.extend(word[i:]		)
                   break
               else:
                   new_word.extend(word[i:j]		)
                   snake_case					:   Any         =					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
           snake_case					:   Union[str, Any]         =					tuple(_a		)
           snake_case					:   Optional[Any]         =					new_word
           if len(_a		) == 1:
               break
           else:
               snake_case					:   str         =					get_pairs(_a		)
       snake_case					:   List[Any]         =					""" """.join(_a		)
       snake_case					:   str         =					word
       return word







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	int:

       '''simple docstring'''




       snake_case					:   Union[str, Any]         =					[]
       for token in re.findall(self.pat		,			_a		):
           snake_case					:   str         =					"""""".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 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	List[Any]:

       '''simple docstring'''




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







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

       '''simple docstring'''




       return self.decoder.get(_a		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	List[str]:

       '''simple docstring'''




       snake_case					:   int         =					"""""".join(_a		)
       snake_case					:   Tuple         =					bytearray([self.byte_decoder[c] for c in text]		).decode("utf-8"		,			errors=self.errors		)
       return text







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__ = None		)			->	List[Any]:

       '''simple docstring'''




       if not os.path.isdir(_a		):
           logger.error(F'Vocabulary path ({save_directory}) should be a directory'		)
           return
       snake_case					:   Union[str, Any]         =					os.path.join(
           _a		,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
       snake_case					:   Dict         =					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"		)

       snake_case					:   List[Any]         =					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 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!"		)
                   snake_case					:   Tuple         =					token_index
               writer.write(" ".join(_a		) + "\n"		)
               index += 1

       return vocab_file, merge_file







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__ = None		,			UpperCamelCase__ = False		)			->	Dict:

       '''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 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__ = None		)			->	Any:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					[self.sep_token_id]
       snake_case					:   List[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 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__=False		,			**UpperCamelCase__		)			->	str:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					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()):
           snake_case					:   List[str]         =					""" """ + text
       return (text, kwargs)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__ = None		)			->	Optional[int]:

       '''simple docstring'''




       return token_ids_a + [self.eos_token_id]







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	str:

       '''simple docstring'''




       snake_case					:   Tuple         =					[]
       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		)

       snake_case					:   List[Any]         =					"""  """.join(_a		)
       snake_case					:   List[str]         =					self.encode(_a		)
       if len(_a		) > self.model_max_length:
           snake_case					:   List[Any]         =					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

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
  from transformers.models.roberta.modeling_flax_roberta import (
      FlaxRobertaForCausalLM,
      FlaxRobertaForMaskedLM,
      FlaxRobertaForMultipleChoice,
      FlaxRobertaForQuestionAnswering,
      FlaxRobertaForSequenceClassification,
      FlaxRobertaForTokenClassification,
      FlaxRobertaModel,
  )







class _lowerCAmelCase       (   unittest.TestCase			):







   def __init__(			self		,			UpperCamelCase__		,			UpperCamelCase__=13		,			UpperCamelCase__=7		,			UpperCamelCase__=True		,			UpperCamelCase__=True		,			UpperCamelCase__=True		,			UpperCamelCase__=True		,			UpperCamelCase__=99		,			UpperCamelCase__=32		,			UpperCamelCase__=5		,			UpperCamelCase__=4		,			UpperCamelCase__=37		,			UpperCamelCase__="gelu"		,			UpperCamelCase__=0.1		,			UpperCamelCase__=0.1		,			UpperCamelCase__=512		,			UpperCamelCase__=16		,			UpperCamelCase__=2		,			UpperCamelCase__=0.02		,			UpperCamelCase__=4		,			)			->	Dict:

       '''simple docstring'''




       snake_case					:   Any         =					parent
       snake_case					:   int         =					batch_size
       snake_case					:   Optional[Any]         =					seq_length
       snake_case					:   Dict         =					is_training
       snake_case					:   str         =					use_attention_mask
       snake_case					:   List[Any]         =					use_token_type_ids
       snake_case					:   Tuple         =					use_labels
       snake_case					:   str         =					vocab_size
       snake_case					:   Optional[Any]         =					hidden_size
       snake_case					:   Optional[Any]         =					num_hidden_layers
       snake_case					:   Dict         =					num_attention_heads
       snake_case					:   int         =					intermediate_size
       snake_case					:   Union[str, Any]         =					hidden_act
       snake_case					:   Optional[Any]         =					hidden_dropout_prob
       snake_case					:   int         =					attention_probs_dropout_prob
       snake_case					:   Optional[int]         =					max_position_embeddings
       snake_case					:   Dict         =					type_vocab_size
       snake_case					:   str         =					type_sequence_label_size
       snake_case					:   List[Any]         =					initializer_range
       snake_case					:   Dict         =					num_choices







   def 				lowerCamelCase	(			self		)			->	Tuple:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size		)

       snake_case					:   Optional[int]         =					None
       if self.use_attention_mask:
           snake_case					:   Optional[Any]         =					random_attention_mask([self.batch_size, self.seq_length]		)

       snake_case					:   List[Any]         =					None
       if self.use_token_type_ids:
           snake_case					:   Dict         =					ids_tensor([self.batch_size, self.seq_length]		,			self.type_vocab_size		)

       snake_case					:   Union[str, Any]         =					RobertaConfig(
           vocab_size=self.vocab_size		,			hidden_size=self.hidden_size		,			num_hidden_layers=self.num_hidden_layers		,			num_attention_heads=self.num_attention_heads		,			intermediate_size=self.intermediate_size		,			hidden_act=self.hidden_act		,			hidden_dropout_prob=self.hidden_dropout_prob		,			attention_probs_dropout_prob=self.attention_probs_dropout_prob		,			max_position_embeddings=self.max_position_embeddings		,			type_vocab_size=self.type_vocab_size		,			is_decoder=UpperCamelCase__		,			initializer_range=self.initializer_range		,			)

       return config, input_ids, token_type_ids, attention_mask







   def 				lowerCamelCase	(			self		)			->	Tuple:

       '''simple docstring'''




       snake_case					:   List[str]         =					self.prepare_config_and_inputs()
       snake_case      ,snake_case      ,snake_case      ,snake_case					:   List[Any]         =					config_and_inputs
       snake_case					:   Tuple         =					{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
       return config, inputs_dict







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   List[str]         =					self.prepare_config_and_inputs()
       snake_case      ,snake_case      ,snake_case      ,snake_case					:   List[Any]         =					config_and_inputs

       snake_case					:   int         =					True
       snake_case					:   Optional[int]         =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
       snake_case					:   Optional[Any]         =					ids_tensor([self.batch_size, self.seq_length]		,			vocab_size=2		)

       return (
           config,
           input_ids,
           token_type_ids,
           encoder_hidden_states,
           encoder_attention_mask,
       )







@require_flax
class _lowerCAmelCase       (   snake_case_					,							unittest.TestCase			):
   __UpperCAmelCase				: int													=						True

   __UpperCAmelCase				: Optional[Any]													=						(
       (
           FlaxRobertaModel,
           FlaxRobertaForCausalLM,
           FlaxRobertaForMaskedLM,
           FlaxRobertaForSequenceClassification,
           FlaxRobertaForTokenClassification,
           FlaxRobertaForMultipleChoice,
           FlaxRobertaForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       snake_case					:   Dict         =					FlaxRobertaModelTester(self		)







   @slow
   def 				lowerCamelCase	(			self		)			->	Union[str, Any]:

       '''simple docstring'''




       for model_class_name in self.all_model_classes:
           snake_case					:   Dict         =					model_class_name.from_pretrained("roberta-base"		,			from_pt=UpperCamelCase__		)
           snake_case					:   List[str]         =					model(np.ones((1, 1)		)		)
           self.assertIsNotNone(UpperCamelCase__		)





