






'''simple docstring'''

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer


A:      int  				=				logging.get_logger(__name__)

A:      str  				=				{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

A:      List[Any]  				=				{
    "vocab_file": {
        "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt",
        "distilbert-base-uncased-distilled-squad": (
            "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt"
        ),
        "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt",
        "distilbert-base-cased-distilled-squad": (
            "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt"
        ),
        "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt",
        "distilbert-base-multilingual-cased": (
            "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json",
        "distilbert-base-uncased-distilled-squad": (
            "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json"
        ),
        "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json",
        "distilbert-base-cased-distilled-squad": (
            "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json"
        ),
        "distilbert-base-german-cased": (
            "https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json"
        ),
        "distilbert-base-multilingual-cased": (
            "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json"
        ),
    },
}

A:      Tuple  				=				{
    "distilbert-base-uncased": 5_1_2,
    "distilbert-base-uncased-distilled-squad": 5_1_2,
    "distilbert-base-cased": 5_1_2,
    "distilbert-base-cased-distilled-squad": 5_1_2,
    "distilbert-base-german-cased": 5_1_2,
    "distilbert-base-multilingual-cased": 5_1_2,
}


A:      List[Any]  				=				{
    "distilbert-base-uncased": {"do_lower_case": True},
    "distilbert-base-uncased-distilled-squad": {"do_lower_case": True},
    "distilbert-base-cased": {"do_lower_case": False},
    "distilbert-base-cased-distilled-squad": {"do_lower_case": False},
    "distilbert-base-german-cased": {"do_lower_case": False},
    "distilbert-base-multilingual-cased": {"do_lower_case": False},
}






class 							__magic_name__     (	__a				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      Optional[int]        =  VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE_					:      List[str]        =  PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE_					:      Any        =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE_					:      str        =  PRETRAINED_INIT_CONFIGURATION
	SCREAMING_SNAKE_CASE_					:      Optional[int]        =  ['input_ids', 'attention_mask']
	SCREAMING_SNAKE_CASE_					:      List[Any]        =  DistilBertTokenizer
	def __init__(							self  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=True  ,      _lowercase="[UNK]"  ,      _lowercase="[SEP]"  ,      _lowercase="[PAD]"  ,      _lowercase="[CLS]"  ,      _lowercase="[MASK]"  ,      _lowercase=True  ,      _lowercase=None  ,      **_lowercase  ,      )						->  Union[str, Any]:
							super().__init__(
							    a_  ,      tokenizer_file=a_  ,      do_lower_case=a_  ,      unk_token=a_  ,      sep_token=a_  ,      pad_token=a_  ,      cls_token=a_  ,      mask_token=a_  ,      tokenize_chinese_chars=a_  ,      strip_accents=a_  ,      **a_  ,      )

							lowercase_      :   int        =			json.loads(self.backend_tokenizer.normalizer.__getstate__()       )
							if (
							    normalizer_state.get('lowercase'  ,      a_       ) != do_lower_case
							    or normalizer_state.get('strip_accents'  ,      a_       ) != strip_accents
							    or normalizer_state.get('handle_chinese_chars'  ,      a_       ) != tokenize_chinese_chars
							):
													lowercase_      :   Optional[Any]        =			getattr(a_  ,      normalizer_state.pop('type'       )       )
													lowercase_      :   List[Any]        =			do_lower_case
													lowercase_      :   Optional[Any]        =			strip_accents
													lowercase_      :   Optional[int]        =			tokenize_chinese_chars
													lowercase_      :   Dict        =			normalizer_class(**a_       )

							lowercase_      :   int        =			do_lower_case
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase=None       )						->  List[Any]:
							lowercase_      :   Tuple        =			[self.cls_token_id] + token_ids_a + [self.sep_token_id]

							if token_ids_a:
													output += token_ids_a + [self.sep_token_id]

							return output
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase = None       )						->  List[str]:
							lowercase_      :   List[Any]        =			[self.sep_token_id]
							lowercase_      :   Dict        =			[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       ) * [0] + len(token_ids_a + sep       ) * [1]







	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase = None       )						->  Optional[int]:
							lowercase_      :   Optional[Any]        =			self._tokenizer.model.save(a_  ,      name=a_       )
							return tuple(a_       )








'''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       )
