
"""simple docstring"""




import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_A     :			List[Any]  						=			"""▁"""

_A     :			Optional[Any]  						=			get_tests_dir("""fixtures/test_sentencepiece.model""")

@require_sentencepiece
class 						a__		( a_,     unittest.TestCase		):
   __lowerCAmelCase				=							BertGenerationTokenizer
   __lowerCAmelCase				=							False
   __lowerCAmelCase				=							True
   def      __magic_name__     (     self							):
          super().setUp()

          lowercase       :							Optional[int]									=				BertGenerationTokenizer(_a      ,       keep_accents=_a							)
          tokenizer.save_pretrained(self.tmpdirname							)
   def      __magic_name__     (     self							):
          lowercase       :							Optional[Any]									=				"<s>"
          lowercase       :							Tuple									=				1

          self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a							)      ,       _a							)
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a							)      ,       _a							)
   def      __magic_name__     (     self							):
          lowercase       :							Union[str, Any]									=				list(self.get_tokenizer().get_vocab().keys()							)

          self.assertEqual(vocab_keys[0]      ,       "<unk>"							)
          self.assertEqual(vocab_keys[1]      ,       "<s>"							)
          self.assertEqual(vocab_keys[-1]      ,       "<pad>"							)
          self.assertEqual(len(_a							)      ,       1_002							)
   def      __magic_name__     (     self							):
          self.assertEqual(self.get_tokenizer().vocab_size      ,       1_000							)
   def      __magic_name__     (     self							):
          lowercase       :							Tuple									=				BertGenerationTokenizer(_a      ,       keep_accents=_a							)

          lowercase       :							str									=				tokenizer.tokenize("This is a test"							)
          self.assertListEqual(_a      ,       ["▁This", "▁is", "▁a", "▁t", "est"]							)

          self.assertListEqual(
              tokenizer.convert_tokens_to_ids(_a							)      ,       [285, 46, 10, 170, 382]      ,       )

          lowercase       :							Optional[int]									=				tokenizer.tokenize("I was born in 92000, and this is falsé."							)
          self.assertListEqual(
              _a      ,       [
                  SPIECE_UNDERLINE + "I",
                  SPIECE_UNDERLINE + "was",
                  SPIECE_UNDERLINE + "b",
                  "or",
                  "n",
                  SPIECE_UNDERLINE + "in",
                  SPIECE_UNDERLINE + "",
                  "9",
                  "2",
                  "0",
                  "0",
                  "0",
                  ",",
                  SPIECE_UNDERLINE + "and",
                  SPIECE_UNDERLINE + "this",
                  SPIECE_UNDERLINE + "is",
                  SPIECE_UNDERLINE + "f",
                  "al",
                  "s",
                  "é",
                  ".",
              ]      ,       )
          lowercase       :							Optional[int]									=				tokenizer.convert_tokens_to_ids(_a							)
          self.assertListEqual(
              _a      ,       [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]      ,       )

          lowercase       :							Tuple									=				tokenizer.convert_ids_to_tokens(_a							)
          self.assertListEqual(
              _a      ,       [
                  SPIECE_UNDERLINE + "I",
                  SPIECE_UNDERLINE + "was",
                  SPIECE_UNDERLINE + "b",
                  "or",
                  "n",
                  SPIECE_UNDERLINE + "in",
                  SPIECE_UNDERLINE + "",
                  "<unk>",
                  "2",
                  "0",
                  "0",
                  "0",
                  ",",
                  SPIECE_UNDERLINE + "and",
                  SPIECE_UNDERLINE + "this",
                  SPIECE_UNDERLINE + "is",
                  SPIECE_UNDERLINE + "f",
                  "al",
                  "s",
                  "<unk>",
                  ".",
              ]      ,       )
   @cached_property
   def      __magic_name__     (     self							):
          return BertGenerationTokenizer.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"							)
   @slow
   def      __magic_name__     (     self							):
          lowercase       :							List[str]									=				"Hello World!"
          lowercase       :							Optional[int]									=				[18_536, 2_260, 101]

          self.assertListEqual(_a      ,       self.big_tokenizer.encode(_a							)							)
   @slow
   def      __magic_name__     (     self							):
          lowercase       :							Tuple									=				(
              "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
              " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
          )
          lowercase       :							Optional[Any]									=				[
              871,
              419,
              358,
              946,
              991,
              2_521,
              452,
              358,
              1_357,
              387,
              7_751,
              3_536,
              112,
              985,
              456,
              126,
              865,
              938,
              5_400,
              5_734,
              458,
              1_368,
              467,
              786,
              2_462,
              5_246,
              1_159,
              633,
              865,
              4_519,
              457,
              582,
              852,
              2_557,
              427,
              916,
              508,
              405,
              34_324,
              497,
              391,
              408,
              11_342,
              1_244,
              385,
              100,
              938,
              985,
              456,
              574,
              362,
              12_597,
              3_200,
              3_129,
              1_172,
          ]

          self.assertListEqual(_a      ,       self.big_tokenizer.encode(_a							)							)
   @require_torch
   @slow
   def      __magic_name__     (     self							):
          import torch

          from transformers import BertGenerationConfig, BertGenerationEncoder

          # Build sequence
          lowercase       :							Dict									=				list(self.big_tokenizer.get_vocab().keys()							)[:10]
          lowercase       :							Optional[int]									=				" ".join(_a							)
          lowercase       :							Any									=				self.big_tokenizer.encode_plus(_a      ,       return_tensors="pt"      ,       return_token_type_ids=_a							)
          lowercase       :							List[str]									=				self.big_tokenizer.batch_encode_plus(
              [sequence + " " + sequence]      ,       return_tensors="pt"      ,       return_token_type_ids=_a							)

          lowercase       :							Optional[int]									=				BertGenerationConfig()
          lowercase       :							Optional[Any]									=				BertGenerationEncoder(_a							)

          assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

          with torch.no_grad():
                 model(**_a							)
                 model(**_a							)

   @slow
   def      __magic_name__     (     self							):
          # fmt: off
          lowercase       :							Tuple									=				{"input_ids": [[39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114], [448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
          # fmt: on

          self.tokenizer_integration_test_util(
              expected_encoding=_a      ,       model_name="google/bert_for_seq_generation_L-24_bbc_encoder"      ,       revision="c817d1fd1be2ffa69431227a1fe320544943d4db"      ,       )




"""simple docstring"""




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer

@require_torch
@require_sentencepiece
@require_tokenizers
class 						a__		( unittest.TestCase		):
   @slow
   def      __magic_name__     (     self							):
          lowercase       :							Tuple									=				AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"      ,       return_dict=_a							).to(_a							)
          lowercase       :							Union[str, Any]									=				AutoTokenizer.from_pretrained("google/mt5-small"							)

          lowercase       :							Optional[Any]									=				tokenizer("Hello there"      ,       return_tensors="pt"							).input_ids
          lowercase       :							Union[str, Any]									=				tokenizer("Hi I am"      ,       return_tensors="pt"							).input_ids

          lowercase       :							int									=				model(input_ids.to(_a							)      ,       labels=labels.to(_a							)							).loss
          lowercase       :							str									=				-(labels.shape[-1] * loss.item())

          lowercase       :							List[str]									=				-8_4.9_1_2_7
          self.assertTrue(abs(mtf_score - EXPECTED_SCORE							) < 1E-4							)


