






'''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

A:      List[Any]  				=				logging.get_logger(__name__)


A:      Optional[int]  				=				{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

A:      int  				=				{
    "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"
    },
}

A:      List[str]  				=				{"facebook/blenderbot-3B": 1_2_8}






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      Union[str, Any]        =  VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE_					:      Any        =  PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE_					:      List[str]        =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE_					:      Union[str, Any]        =  ['input_ids', 'attention_mask']
	SCREAMING_SNAKE_CASE_					:      Optional[Any]        =  BlenderbotTokenizer
	def __init__(							self  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase="replace"  ,      _lowercase="<s>"  ,      _lowercase="</s>"  ,      _lowercase="</s>"  ,      _lowercase="<s>"  ,      _lowercase="<unk>"  ,      _lowercase="<pad>"  ,      _lowercase="<mask>"  ,      _lowercase=False  ,      _lowercase=True  ,      **_lowercase  ,      )						->  Optional[Any]:
							super().__init__(
							    _lowercase  ,      _lowercase  ,      tokenizer_file=_lowercase  ,      errors=_lowercase  ,      bos_token=_lowercase  ,      eos_token=_lowercase  ,      sep_token=_lowercase  ,      cls_token=_lowercase  ,      unk_token=_lowercase  ,      pad_token=_lowercase  ,      mask_token=_lowercase  ,      add_prefix_space=_lowercase  ,      trim_offsets=_lowercase  ,      **_lowercase  ,      )

							lowercase_      :   Optional[int]        =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
							if pre_tok_state.get('add_prefix_space'  ,      _lowercase       ) != add_prefix_space:
													lowercase_      :   str        =			getattr(_lowercase  ,      pre_tok_state.pop('type'       )       )
													lowercase_      :   str        =			add_prefix_space
													lowercase_      :   Any        =			pre_tok_class(**_lowercase       )

							lowercase_      :   List[Any]        =			add_prefix_space

							lowercase_      :   Optional[int]        =			'post_processor'
							lowercase_      :   Optional[int]        =			getattr(self.backend_tokenizer  ,      _lowercase  ,      _lowercase       )
							if tokenizer_component_instance:
													lowercase_      :   Any        =			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:
																			lowercase_      :   Dict        =			tuple(state['sep']       )
													if "cls" in state:
																			lowercase_      :   Dict        =			tuple(state['cls']       )

													lowercase_      :   Optional[int]        =			False

													if state.get('add_prefix_space'  ,      _lowercase       ) != add_prefix_space:
																			lowercase_      :   int        =			add_prefix_space
																			lowercase_      :   str        =			True

													if state.get('trim_offsets'  ,      _lowercase       ) != trim_offsets:
																			lowercase_      :   Dict        =			trim_offsets
																			lowercase_      :   Any        =			True

													if changes_to_apply:
																			lowercase_      :   Optional[Any]        =			getattr(_lowercase  ,      state.pop('type'       )       )
																			lowercase_      :   Tuple        =			component_class(**_lowercase       )
																			setattr(self.backend_tokenizer  ,      _lowercase  ,      _lowercase       )
	@property
	# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
	def        lowerCamelCase__		(							self       )						->  str:
							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        lowerCamelCase__		(							self  ,      _lowercase       )						->  str:
							lowercase_      :   Dict        =			AddedToken(_lowercase  ,      lstrip=_lowercase  ,      rstrip=_lowercase       ) if isinstance(_lowercase  ,      _lowercase       ) else value
							lowercase_      :   Any        =			value
	def        lowerCamelCase__		(							self  ,      *_lowercase  ,      **_lowercase       )						->  BatchEncoding:
							lowercase_      :   Tuple        =			kwargs.get('is_split_into_words'  ,      _lowercase       )
							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(*_lowercase  ,      **_lowercase       )
	def        lowerCamelCase__		(							self  ,      *_lowercase  ,      **_lowercase       )						->  BatchEncoding:
							lowercase_      :   str        =			kwargs.get('is_split_into_words'  ,      _lowercase       )

							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(*_lowercase  ,      **_lowercase       )
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase = None       )						->  Tuple[str]:
							lowercase_      :   List[str]        =			self._tokenizer.model.save(_lowercase  ,      name=_lowercase       )
							return tuple(_lowercase       )
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase = None       )						->  List[int]:
							lowercase_      :   Tuple        =			[self.sep_token_id]
							lowercase_      :   Union[str, 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  ,      _lowercase  ,      _lowercase = None       )						->  Dict:
							return token_ids_a + [self.eos_token_id]







	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  List[int]:
							lowercase_      :   Dict        =			[]
							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       )

							lowercase_      :   Optional[Any]        =			'  '.join(_lowercase       )
							lowercase_      :   Optional[int]        =			self.encode(_lowercase       )
							if len(_lowercase       ) > self.model_max_length:
													lowercase_      :   Dict        =			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 io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


A:      Any  				=				get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
						A:      List[Any]  				=				json.load(f)






@require_torch
class 							__magic_name__     (	unittest.TestCase				):







	"""simple docstring"""



	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  Tuple:
							return FSMTTokenizer.from_pretrained(_lowercase       )
	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  Optional[int]:
							lowercase_      :   str        =			FSMTForConditionalGeneration.from_pretrained(_lowercase       ).to(_lowercase       )
							if torch_device == "cuda":
													model.half()
							return model







	@parameterized.expand(
	    [
	        ['en-ru', 26.0],
	        ['ru-en', 22.0],
	        ['en-de', 22.0],
	        ['de-en', 29.0],
	    ]       )
	@slow
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase       )						->  Optional[int]:
							# note: this test is not testing the best performance since it only evals a small batch
							# but it should be enough to detect a regression in the output quality
							lowercase_      :   Optional[Any]        =			f"facebook/wmt19-{pair}"
							lowercase_      :   str        =			self.get_tokenizer(_lowercase       )
							lowercase_      :   Any        =			self.get_model(_lowercase       )

							lowercase_      :   Any        =			bleu_data[pair]['src']
							lowercase_      :   Any        =			bleu_data[pair]['tgt']

							lowercase_      :   Dict        =			tokenizer(_lowercase  ,      return_tensors='pt'  ,      truncation=_lowercase  ,      padding='longest'       ).to(_lowercase       )
							lowercase_      :   str        =			model.generate(
							    input_ids=batch.input_ids  ,      num_beams=8  ,      )
							lowercase_      :   Any        =			tokenizer.batch_decode(
							    _lowercase  ,      skip_special_tokens=_lowercase  ,      clean_up_tokenization_spaces=_lowercase       )
							lowercase_      :   Union[str, Any]        =			calculate_bleu(_lowercase  ,      _lowercase       )
							print(_lowercase       )
							self.assertGreaterEqual(scores['bleu']  ,      _lowercase       )
