


import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
			from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
			_snake_case	:						List[str]         =							get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
			from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

_snake_case	:						Optional[Any]         =							128_022
_snake_case	:						Optional[Any]         =							128_028

@require_sentencepiece
class 							a						(_lowerCAmelCase	,							unittest.TestCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				Dict	    =    MaMaaaTokenizer
							__UpperCAmelCase       :				Tuple	    =    False
							__UpperCAmelCase       :				Optional[Any]	    =    False
							__UpperCAmelCase       :				Dict	    =    True

							def        __snake_case   (				self			:    Union[str, Any]							)      ->       int:
														super().setUp()

														__snake_case						:     Optional[Any]									=  ["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
														__snake_case						:     List[Any]									=  dict(zip(lowerCamelCase       ,      range(len(lowerCamelCase							)							)							)							)
														__snake_case						:     Dict									=  Path(self.tmpdirname							)
														save_json(lowerCamelCase       ,      save_dir / VOCAB_FILES_NAMES["vocab_file"]							)
														if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
																					copyfile(lowerCamelCase       ,      save_dir / VOCAB_FILES_NAMES["spm_file"]							)

														__snake_case						:     Any									=  MaMaaaTokenizer.from_pretrained(self.tmpdirname							)
														tokenizer.save_pretrained(self.tmpdirname							)

							def        __snake_case   (				self			:    Optional[Any]       ,      **lowerCamelCase			:    Dict							)      ->       Tuple:
														return MaMaaaTokenizer.from_pretrained(self.tmpdirname       ,      **lowerCamelCase							)

							def        __snake_case   (				self			:    Any       ,      lowerCamelCase			:    str							)      ->       List[Any]:
														return (
														    "This is a test",
														    "This is a test",
														)

							def        __snake_case   (				self			:    Tuple							)      ->       Optional[Any]:
														__snake_case						:     Dict									=  "</s>"
														__snake_case						:     int									=  0

														self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase							)       ,      lowerCamelCase							)
														self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase							)       ,      lowerCamelCase							)

							def        __snake_case   (				self			:    str							)      ->       str:
														__snake_case						:     Tuple									=  self.get_tokenizer()
														__snake_case						:     str									=  list(tokenizer.get_vocab().keys()							)

														self.assertEqual(vocab_keys[0]       ,      "</s>"							)
														self.assertEqual(vocab_keys[1]       ,      "<unk>"							)
														self.assertEqual(vocab_keys[-1]       ,      "<s>"							)
														self.assertEqual(len(lowerCamelCase							)       ,      tokenizer.vocab_size + len(tokenizer.get_added_vocab()							)							)

							@unittest.skip("Skip this test while all models are still to be uploaded."							)
							def        __snake_case   (				self			:    Dict							)      ->       str:
														pass

							def        __snake_case   (				self			:    Any							)      ->       Any:
														__snake_case						:     Any									=  self.get_tokenizer()

														__snake_case						:     List[Any]									=  tokenizer.tokenize("This is a test"							)
														self.assertListEqual(lowerCamelCase       ,      ["▁This", "▁is", "▁a", "▁t", "est"]							)

														self.assertListEqual(
														    tokenizer.convert_tokens_to_ids(lowerCamelCase							)       ,      [2, 3, 4, 5, 6]       ,      )

														__snake_case						:     Dict									=  tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]							)
														self.assertListEqual(lowerCamelCase       ,      ["▁This", "▁is", "▁a", "▁t", "est"]							)

														__snake_case						:     Optional[Any]									=  tokenizer.convert_tokens_to_string(lowerCamelCase							)
														self.assertEqual(lowerCamelCase       ,      "This is a test"							)






							@slow
							def        __snake_case   (				self			:    Optional[int]							)      ->       Optional[Any]:
														# fmt: off
														__snake_case						:     List[str]									=  {"input_ids": [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 5, 2, 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], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 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]], "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, 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, 0, 0, 0, 0], [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, 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=lowerCamelCase       ,      model_name="facebook/m2m100_418M"       ,      revision="c168bae485c864188cf9aa0e4108b0b6934dc91e"       ,      )

@require_torch
@require_sentencepiece
@require_tokenizers
class 							a						(unittest.TestCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				Optional[int]	    =    "facebook/m2m100_418M"
							__UpperCAmelCase       :				Optional[Any]	    =    [
							    "In my opinion, there are two levels of response from the French government.",
							    "NSA Affair Emphasizes Complete Lack of Debate on Intelligence",
							]
							__UpperCAmelCase       :				Dict	    =    [
							    "Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
							    "L'affaire NSA souligne l'absence totale de débat sur le renseignement",
							]

							# fmt: off
							__UpperCAmelCase       :				Optional[Any]	    =    [EN_CODE, 593, 1949, 11_5781, 4, 7_1586, 4234, 6_0633, 12_6233, 432, 12_3808, 1_5592, 1197, 11_7132, 12_0618, 5, 2]

							@classmethod
							def        __snake_case   (				cls			:    Tuple							)      ->       int:
														__snake_case						:     MaMaaaTokenizer									=  MaMaaaTokenizer.from_pretrained(
														    cls.checkpoint_name       ,      src_lang="en"       ,      tgt_lang="fr"							)
														__snake_case						:     Optional[Any]									=  1
														return cls

							def        __snake_case   (				self			:    Optional[Any]							)      ->       Dict:
														self.assertEqual(self.tokenizer.get_lang_id("ar"							)       ,      128006							)
														self.assertEqual(self.tokenizer.get_lang_id("en"							)       ,      128022							)
														self.assertEqual(self.tokenizer.get_lang_id("ro"							)       ,      128076							)
														self.assertEqual(self.tokenizer.get_lang_id("mr"							)       ,      128063							)

							def        __snake_case   (				self			:    Tuple							)      ->       List[str]:
														__snake_case						:     List[str]									=  self.tokenizer.get_vocab()
														self.assertEqual(len(lowerCamelCase							)       ,      self.tokenizer.vocab_size							)
														self.assertEqual(vocab["<unk>"]       ,      3							)
														self.assertIn(self.tokenizer.get_lang_token("en"							)       ,      lowerCamelCase							)

							def        __snake_case   (				self			:    Tuple							)      ->       int:
														__snake_case						:     str									=  "en"
														__snake_case						:     Any									=  self.tokenizer.batch_encode_plus(self.src_text							).input_ids[0]
														self.assertListEqual(self.expected_src_tokens       ,      lowerCamelCase							)

							def        __snake_case   (				self			:    Dict							)      ->       str:
														self.assertIn(lowerCamelCase       ,      self.tokenizer.all_special_ids							)
														# fmt: off
														__snake_case						:     Any									=  [FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2]
														# fmt: on
														__snake_case						:     Any									=  self.tokenizer.decode(lowerCamelCase       ,      skip_special_tokens=lowerCamelCase							)
														__snake_case						:     Optional[Any]									=  self.tokenizer.decode(generated_ids[1:]       ,      skip_special_tokens=lowerCamelCase							)
														self.assertEqual(lowerCamelCase       ,      lowerCamelCase							)
														self.assertNotIn(self.tokenizer.eos_token       ,      lowerCamelCase							)

							def        __snake_case   (				self			:    int							)      ->       int:
														__snake_case						:     Dict									=  tempfile.mkdtemp()
														__snake_case						:     Optional[Any]									=  self.tokenizer.lang_token_to_id
														self.tokenizer.save_pretrained(lowerCamelCase							)
														__snake_case						:     Optional[Any]									=  MaMaaaTokenizer.from_pretrained(lowerCamelCase							)
														self.assertDictEqual(new_tok.lang_token_to_id       ,      lowerCamelCase							)

							@require_torch
							def        __snake_case   (				self			:    List[str]							)      ->       str:
														__snake_case						:     Tuple									=  "en"
														__snake_case						:     List[str]									=  "fr"

														__snake_case						:     Any									=  self.tokenizer(self.src_text       ,      text_target=self.tgt_text       ,      padding=lowerCamelCase       ,      return_tensors="pt"							)

														__snake_case						:     Union[str, Any]									=  shift_tokens_right(
														    batch["labels"]       ,      self.tokenizer.pad_token_id       ,      self.tokenizer.eos_token_id							)

														for k in batch:
																					__snake_case						:     str									=  batch[k].tolist()
														# batch = {k: v.tolist() for k,v in batch.items()}
														# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
														# batch.decoder_inputs_ids[0][0] ==
														assert batch.input_ids[1][0] == EN_CODE
														assert batch.input_ids[1][-1] == 2
														assert batch.labels[1][0] == FR_CODE
														assert batch.labels[1][-1] == 2
														assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]

							@require_torch
							def        __snake_case   (				self			:    str							)      ->       List[str]:
														__snake_case						:     Any									=  "mr"
														self.assertListEqual(self.tokenizer.prefix_tokens       ,      [self.tokenizer.get_lang_id("mr"							)]							)
														self.assertListEqual(self.tokenizer.suffix_tokens       ,      [self.tokenizer.eos_token_id]							)

														__snake_case						:     Tuple									=  "zh"
														self.assertListEqual(self.tokenizer.prefix_tokens       ,      [self.tokenizer.get_lang_id("zh"							)]							)
														self.assertListEqual(self.tokenizer.suffix_tokens       ,      [self.tokenizer.eos_token_id]							)

							@require_torch
							def        __snake_case   (				self			:    Optional[Any]							)      ->       int:
														__snake_case						:     Optional[Any]									=  "mr"
														self.tokenizer._switch_to_target_mode()
														self.assertListEqual(self.tokenizer.prefix_tokens       ,      [self.tokenizer.get_lang_id("mr"							)]							)
														self.assertListEqual(self.tokenizer.suffix_tokens       ,      [self.tokenizer.eos_token_id]							)
														self.tokenizer._switch_to_input_mode()
														self.assertListEqual(self.tokenizer.prefix_tokens       ,      [self.tokenizer.get_lang_id(self.tokenizer.src_lang							)]							)

														__snake_case						:     Dict									=  "zh"
														self.tokenizer._switch_to_target_mode()
														self.assertListEqual(self.tokenizer.prefix_tokens       ,      [self.tokenizer.get_lang_id("zh"							)]							)
														self.assertListEqual(self.tokenizer.suffix_tokens       ,      [self.tokenizer.eos_token_id]							)
														self.tokenizer._switch_to_input_mode()
														self.assertListEqual(self.tokenizer.prefix_tokens       ,      [self.tokenizer.get_lang_id(self.tokenizer.src_lang							)]							)






							@require_torch
							def        __snake_case   (				self			:    List[str]							)      ->       List[Any]:
														__snake_case						:     List[Any]									=  self.tokenizer._build_translation_inputs("A test"       ,      return_tensors="pt"       ,      src_lang="en"       ,      tgt_lang="ar"							)

														self.assertEqual(
														    nested_simplify(lowerCamelCase							)       ,      {
														        # en_XX, A, test, EOS
														        "input_ids": [[128022, 58, 4183, 2]],
														        "attention_mask": [[1, 1, 1, 1]],
														        # ar_AR
														        "forced_bos_token_id": 128006,
														    }       ,      )





'''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 				UpperCAmelCase			(			unittest.TestCase						):


 @slow
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    UpperCAmelCase__	:List[str]      =      TFCamembertModel.from_pretrained('''jplu/tf-camembert-base''' )

    UpperCAmelCase__	:Optional[Any]      =      tf.convert_to_tensor(
        [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]			,       dtype=tf.intaa			,       )  # J'aime le camembert !"

    UpperCAmelCase__	:Any      =      model(__lowerCamelCase )['''last_hidden_state''']
    UpperCAmelCase__	:Dict      =      tf.TensorShape((1, 1_0, 7_6_8) )
    self.assertEqual(output.shape			,       __lowerCamelCase )
    # compare the actual values for a slice.
    UpperCAmelCase__	:Any      =      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 ) )



