
"""simple docstring"""




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation

lowerCAmelCase__				=					logging.get_logger(__name__)


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

lowerCAmelCase__				=					{
    '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__				=					{'facebook/blenderbot-3B': 128}





class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   VOCAB_FILES_NAMES
							UpperCAmelCase__      =   PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase__      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase__      =   ['''input_ids''', '''attention_mask''']
							UpperCAmelCase__      =   BlenderbotTokenizer

							def __init__(self							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_="replace"							,							SCREAMING_SNAKE_CASE_="<s>"							,							SCREAMING_SNAKE_CASE_="</s>"							,							SCREAMING_SNAKE_CASE_="</s>"							,							SCREAMING_SNAKE_CASE_="<s>"							,							SCREAMING_SNAKE_CASE_="<unk>"							,							SCREAMING_SNAKE_CASE_="<pad>"							,							SCREAMING_SNAKE_CASE_="<mask>"							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=True							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







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

									SCREAMING_SNAKE_CASE_    			=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
									if pre_tok_state.get('''add_prefix_space'''							,							SCREAMING_SNAKE_CASE_     ) != add_prefix_space:
											SCREAMING_SNAKE_CASE_    			=			getattr(SCREAMING_SNAKE_CASE_							,							pre_tok_state.pop('''type'''     )     )
											SCREAMING_SNAKE_CASE_    			=			add_prefix_space
											SCREAMING_SNAKE_CASE_    			=			pre_tok_class(**SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			add_prefix_space

									SCREAMING_SNAKE_CASE_    			=			'''post_processor'''
									SCREAMING_SNAKE_CASE_    			=			getattr(self.backend_tokenizer							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )
									if tokenizer_component_instance:
											SCREAMING_SNAKE_CASE_    			=			json.loads(tokenizer_component_instance.__getstate__()     )

											# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
											if "sep" in state:
													SCREAMING_SNAKE_CASE_    			=			tuple(state['''sep''']     )
											if "cls" in state:
													SCREAMING_SNAKE_CASE_    			=			tuple(state['''cls''']     )

											SCREAMING_SNAKE_CASE_    			=			False

											if state.get('''add_prefix_space'''							,							SCREAMING_SNAKE_CASE_     ) != add_prefix_space:
													SCREAMING_SNAKE_CASE_    			=			add_prefix_space
													SCREAMING_SNAKE_CASE_    			=			True

											if state.get('''trim_offsets'''							,							SCREAMING_SNAKE_CASE_     ) != trim_offsets:
													SCREAMING_SNAKE_CASE_    			=			trim_offsets
													SCREAMING_SNAKE_CASE_    			=			True

											if changes_to_apply:
													SCREAMING_SNAKE_CASE_    			=			getattr(SCREAMING_SNAKE_CASE_							,							state.pop('''type'''     )     )
													SCREAMING_SNAKE_CASE_    			=			component_class(**SCREAMING_SNAKE_CASE_     )
													setattr(self.backend_tokenizer							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

							@property
							# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
							def _lowercase       (self     ):

									"""simple docstring"""







									if self._mask_token is None:
											if self.verbose:
													logger.error('''Using mask_token, but it is not set yet.'''     )
											return None
									return str(self._mask_token     )

							@mask_token.setter
							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			AddedToken(SCREAMING_SNAKE_CASE_							,							lstrip=SCREAMING_SNAKE_CASE_							,							rstrip=SCREAMING_SNAKE_CASE_     ) if isinstance(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ) else value
									SCREAMING_SNAKE_CASE_    			=			value

							def _lowercase       (self							,							*SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			kwargs.get('''is_split_into_words'''							,							SCREAMING_SNAKE_CASE_     )
									assert self.add_prefix_space or not is_split_into_words, (
									    f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
									    "to use it with pretokenized inputs."
									)

									return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self							,							*SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			kwargs.get('''is_split_into_words'''							,							SCREAMING_SNAKE_CASE_     )

									assert self.add_prefix_space or not is_split_into_words, (
									    f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
									    "to use it with pretokenized inputs."
									)

									return super()._encode_plus(*SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = None     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self._tokenizer.model.save(SCREAMING_SNAKE_CASE_							,							name=SCREAMING_SNAKE_CASE_     )
									return tuple(SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = None     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			[self.sep_token_id]
									SCREAMING_SNAKE_CASE_    			=			[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 _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = None     ):

									"""simple docstring"""







									return token_ids_a + [self.eos_token_id]







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			[]
									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(SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			'''  '''.join(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			self.encode(SCREAMING_SNAKE_CASE_     )
									if len(SCREAMING_SNAKE_CASE_     ) > self.model_max_length:
											SCREAMING_SNAKE_CASE_    			=			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"""




from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
						import numpy as np
						import tensorflow as tf

						from transformers import TFCamembertModel





@require_tf
@require_sentencepiece
@require_tokenizers
class 			snake_case  (				unittest.TestCase ):

							@slow
							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			TFCamembertModel.from_pretrained('''jplu/tf-camembert-base'''     )

									SCREAMING_SNAKE_CASE_    			=			tf.convert_to_tensor(
									    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]							,							dtype=tf.intaa							,							)  # J'aime le camembert !"

									SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_     )['''last_hidden_state''']
									SCREAMING_SNAKE_CASE_    			=			tf.TensorShape((1, 10, 7_68)     )
									self.assertEqual(output.shape							,							SCREAMING_SNAKE_CASE_     )
									# compare the actual values for a slice.
									SCREAMING_SNAKE_CASE_    			=			tf.convert_to_tensor(
									    [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]							,							dtype=tf.floataa							,							)
									# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
									# camembert.eval()
									# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

									self.assertTrue(np.allclose(output[:, :3, :3].numpy()							,							expected_slice.numpy()							,							atol=1e-4     )     )