





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():
       UpperCamelCase__  			=get_tests_dir('fixtures/test_sentencepiece.model')


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

UpperCamelCase__  			=12_8022
UpperCamelCase__  			=12_8028





@require_sentencepiece
class       lowerCAmelCase__(			_snake_case	,  unittest.TestCase     ):






       '''simple docstring'''


       __snake_case			      =     MaMaaaTokenizer
       __snake_case			      =     False
       __snake_case			      =     False
       __snake_case			      =     True




       def      UpperCamelCase_      (   self				)      ->  Tuple:
            super().setUp()

            _SCREAMING_SNAKE_CASE							:						Dict         =				['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
            _SCREAMING_SNAKE_CASE							:						Any         =				dict(zip(_lowerCamelCase     ,    range(len(_lowerCamelCase				)				)				)				)
            _SCREAMING_SNAKE_CASE							:						Any         =				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"]				)

            _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				MaMaaaTokenizer.from_pretrained(self.tmpdirname				)
            tokenizer.save_pretrained(self.tmpdirname				)




       def      UpperCamelCase_      (   self     ,    **__lowerCamelCase				)      ->  Dict:
            return MaMaaaTokenizer.from_pretrained(self.tmpdirname     ,    **_lowerCamelCase				)




       def      UpperCamelCase_      (   self     ,    __lowerCamelCase				)      ->  str:
            return (
                "This is a test",
                "This is a test",
            )




       def      UpperCamelCase_      (   self				)      ->  Tuple:
            _SCREAMING_SNAKE_CASE							:						Tuple         =				'''</s>'''
            _SCREAMING_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      UpperCamelCase_      (   self				)      ->  str:
            _SCREAMING_SNAKE_CASE							:						Optional[int]         =				self.get_tokenizer()
            _SCREAMING_SNAKE_CASE							:						List[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      UpperCamelCase_      (   self				)      ->  Dict:
            pass




       def      UpperCamelCase_      (   self				)      ->  List[Any]:
            _SCREAMING_SNAKE_CASE							:						List[str]         =				self.get_tokenizer()

            _SCREAMING_SNAKE_CASE							:						Optional[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]     ,    )

            _SCREAMING_SNAKE_CASE							:						Optional[int]         =				tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]				)
            self.assertListEqual(_lowerCamelCase     ,    ["▁This", "▁is", "▁a", "▁t", "est"]				)

            _SCREAMING_SNAKE_CASE							:						List[str]         =				tokenizer.convert_tokens_to_string(_lowerCamelCase				)
            self.assertEqual(_lowerCamelCase     ,    "This is a test"				)







       @slow
       def      UpperCamelCase_      (   self				)      ->  Optional[Any]:
            # fmt: off
            _SCREAMING_SNAKE_CASE							:						Any         =				{'''input_ids''': [[1_2_8_0_2_2, 1_1_0_1_0_8, 3_9_7, 1_1, 3_8_2_7_2, 2_2_4_7, 1_2_4_8_1_1, 2_8_5, 1_8_1_0_5, 1_5_8_6, 2_0_7, 7, 3_9_5_3_4, 4_4_2_8, 3_9_7, 1_0_1_9, 1_8_1_0_5, 1_5_8_6, 2_0_7, 7, 4_1_3_3_7, 1_6_7_8_6, 2_4_1, 7, 2_0_2_1_4, 1_7, 1_2_5_6_9_0, 1_0_3_9_8, 7, 4_4_3_7_8, 5_8_0_6_9, 6_8_3_4_2, 7_7_9_8, 7_3_4_3, 1_1, 2_9_9, 3_3_3_1_0, 4, 1_5_8, 3_7_3_5_0, 9_4_0_7_7, 4_5_6_9, 2_9_9, 3_3_3_1_0, 9_0, 4, 5_2_8_4_0, 2_9_0, 4, 3_1_2_7_0, 1_1_2, 2_9_9, 6_8_2, 4, 5_2_8_4_0, 3_9_9_5_3, 1_4_0_7_9, 1_9_3, 5_2_5_1_9, 9_0_8_9_4, 1_7_8_9_4, 1_2_0_6_9_7, 1_1, 4_0_4_4_5, 5_5_1, 1_7, 1_0_1_9, 5_2_5_1_9, 9_0_8_9_4, 1_7_7_5_6, 9_6_3, 1_1, 4_0_4_4_5, 4_8_0, 1_7, 9_7_9_2, 1_1_2_0, 5_1_7_3, 1_3_9_3, 6_2_4_0, 1_6_7_8_6, 2_4_1, 1_2_0_9_9_6, 2_8, 1_2_4_5, 1_3_9_3, 1_1_8_2_4_0, 1_1_1_2_3, 1_0_1_9, 9_3_6_1_2, 2_6_9_1, 1_0_6_1_8, 9_8_0_5_8, 1_2_0_4_0_9, 1_9_2_8, 2_7_9, 4, 4_0_6_8_3, 3_6_7, 1_7_8, 2_0_7, 1_0_1_9, 1_0_3, 1_0_3_1_2_1, 5_0_6, 6_5_2_9_6, 5, 2], [1_2_8_0_2_2, 2_1_2_1_7, 3_6_7, 1_1_7, 1_2_5_4_5_0, 1_2_8, 7_1_9, 7, 7_3_0_8, 4_0, 9_3_6_1_2, 1_2_6_6_9, 1_1_1_6, 1_6_7_0_4, 7_1, 1_7_7_8_5, 3_6_9_9, 1_5_5_9_2, 3_5, 1_4_4, 9_5_8_4, 2_4_1, 1_1_9_4_3, 7_1_3, 9_5_0, 7_9_9, 2_2_4_7, 8_8_4_2_7, 1_5_0, 1_4_9, 1_1_8_8_1_3, 1_2_0_7_0_6, 1_0_1_9, 1_0_6_9_0_6, 8_1_5_1_8, 2_8, 1_2_2_4, 2_2_7_9_9, 3_9_7, 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_2_8_0_2_2, 1_6_5_8, 1_2_3_3_1_1, 5_1_5_5, 5_5_7_8, 4_7_2_2, 2_7_9, 1_4_9_4_7, 2_3_6_6, 1_1_2_0, 1_1_9_7, 1_4, 1_3_4_8, 9_2_3_2, 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       lowerCAmelCase__(			unittest.TestCase     ):






       '''simple docstring'''


       __snake_case			      =     'facebook/m2m100_418M'
       __snake_case			      =     [
           'In my opinion, there are two levels of response from the French government.',
           'NSA Affair Emphasizes Complete Lack of Debate on Intelligence',
       ]
       __snake_case			      =     [
           '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
       __snake_case			      =     [EN_CODE, 5_9_3, 1_9_4_9, 1_1_5_7_8_1, 4, 7_1_5_8_6, 4_2_3_4, 6_0_6_3_3, 1_2_6_2_3_3, 4_3_2, 1_2_3_8_0_8, 1_5_5_9_2, 1_1_9_7, 1_1_7_1_3_2, 1_2_0_6_1_8, 5, 2]




       @classmethod
       def      UpperCamelCase_      (   cls				)      ->  Dict:
            _SCREAMING_SNAKE_CASE							:						MaMaaaTokenizer         =				MaMaaaTokenizer.from_pretrained(
                cls.checkpoint_name     ,    src_lang="en"     ,    tgt_lang="fr"				)
            _SCREAMING_SNAKE_CASE							:						Any         =				1
            return cls




       def      UpperCamelCase_      (   self				)      ->  str:
            self.assertEqual(self.tokenizer.get_lang_id("ar"				)     ,    1_2_8_0_0_6				)
            self.assertEqual(self.tokenizer.get_lang_id("en"				)     ,    1_2_8_0_2_2				)
            self.assertEqual(self.tokenizer.get_lang_id("ro"				)     ,    1_2_8_0_7_6				)
            self.assertEqual(self.tokenizer.get_lang_id("mr"				)     ,    1_2_8_0_6_3				)




       def      UpperCamelCase_      (   self				)      ->  Tuple:
            _SCREAMING_SNAKE_CASE							:						int         =				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      UpperCamelCase_      (   self				)      ->  Tuple:
            _SCREAMING_SNAKE_CASE							:						Any         =				'''en'''
            _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				self.tokenizer.batch_encode_plus(self.src_text				).input_ids[0]
            self.assertListEqual(self.expected_src_tokens     ,    _lowerCamelCase				)




       def      UpperCamelCase_      (   self				)      ->  str:
            self.assertIn(_lowerCamelCase     ,    self.tokenizer.all_special_ids				)
            # fmt: off
            _SCREAMING_SNAKE_CASE							:						Tuple         =				[FR_CODE, 5_3_6_4, 8_2, 8_6_4_2, 4, 2_9_4, 4_7, 8, 1_4_0_2_8, 1_3_6, 3_2_8_6, 9_7_0_6, 6, 9_0_7_9_7, 6, 1_4_4_0_1_2, 1_6_2, 8_8_1_2_8, 3_0_0_6_1, 5, 2]
            # fmt: on
            _SCREAMING_SNAKE_CASE							:						List[Any]         =				self.tokenizer.decode(_lowerCamelCase     ,    skip_special_tokens=_lowerCamelCase				)
            _SCREAMING_SNAKE_CASE							:						List[Any]         =				self.tokenizer.decode(generated_ids[1:]     ,    skip_special_tokens=_lowerCamelCase				)
            self.assertEqual(_lowerCamelCase     ,    _lowerCamelCase				)
            self.assertNotIn(self.tokenizer.eos_token     ,    _lowerCamelCase				)




       def      UpperCamelCase_      (   self				)      ->  int:
            _SCREAMING_SNAKE_CASE							:						str         =				tempfile.mkdtemp()
            _SCREAMING_SNAKE_CASE							:						Dict         =				self.tokenizer.lang_token_to_id
            self.tokenizer.save_pretrained(_lowerCamelCase				)
            _SCREAMING_SNAKE_CASE							:						List[str]         =				MaMaaaTokenizer.from_pretrained(_lowerCamelCase				)
            self.assertDictEqual(new_tok.lang_token_to_id     ,    _lowerCamelCase				)




       @require_torch
       def      UpperCamelCase_      (   self				)      ->  List[Any]:
            _SCREAMING_SNAKE_CASE							:						Any         =				'''en'''
            _SCREAMING_SNAKE_CASE							:						str         =				'''fr'''

            _SCREAMING_SNAKE_CASE							:						Dict         =				self.tokenizer(self.src_text     ,    text_target=self.tgt_text     ,    padding=_lowerCamelCase     ,    return_tensors="pt"				)

            _SCREAMING_SNAKE_CASE							:						Dict         =				shift_tokens_right(
                batch["labels"]     ,    self.tokenizer.pad_token_id     ,    self.tokenizer.eos_token_id				)

            for k in batch:
                 _SCREAMING_SNAKE_CASE							:						Tuple         =				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      UpperCamelCase_      (   self				)      ->  List[str]:
            _SCREAMING_SNAKE_CASE							:						Optional[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]				)

            _SCREAMING_SNAKE_CASE							:						List[str]         =				'''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      UpperCamelCase_      (   self				)      ->  Union[str, Any]:
            _SCREAMING_SNAKE_CASE							:						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				)]				)

            _SCREAMING_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      UpperCamelCase_      (   self				)      ->  Tuple:
            _SCREAMING_SNAKE_CASE							:						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": [[1_2_8_0_2_2, 5_8, 4_1_8_3, 2]],
                    "attention_mask": [[1, 1, 1, 1]],
                    # ar_AR
                    "forced_bos_token_id": 1_2_8_0_0_6,
                }     ,    )



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


snake_case							:       Any										=	{
    '''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case							:       List[Any]										=	[
         '''GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''GPTNeoForCausalLM''',
         '''GPTNeoForQuestionAnswering''',
         '''GPTNeoForSequenceClassification''',
         '''GPTNeoForTokenClassification''',
         '''GPTNeoModel''',
         '''GPTNeoPreTrainedModel''',
         '''load_tf_weights_in_gpt_neo''',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case							:       Any										=	[
         '''FlaxGPTNeoForCausalLM''',
         '''FlaxGPTNeoModel''',
         '''FlaxGPTNeoPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gpt_neo import (
              GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTNeoForCausalLM,
              GPTNeoForQuestionAnswering,
              GPTNeoForSequenceClassification,
              GPTNeoForTokenClassification,
              GPTNeoModel,
              GPTNeoPreTrainedModel,
              load_tf_weights_in_gpt_neo,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
     import sys

     snake_case							:       Optional[int]										=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





