


"""simple docstring"""







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


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

lowerCAmelCase__				    =       128_022
lowerCAmelCase__				    =       128_028


@require_sentencepiece
class 				SCREAMING_SNAKE_CASE__    (		lowercase       ,   unittest.TestCase    ):
       """simple docstring"""


       a  :    Optional[int]       =MaMaaaTokenizer
       a  :    int       =False
       a  :    str       =False
       a  :    int       =True

       def 	lowercase__							(						self						):






         """simple docstring"""





         super().setUp()

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

         lowerCAmelCase					:					int								=							MaMaaaTokenizer.from_pretrained(self.tmpdirname						)
         tokenizer.save_pretrained(self.tmpdirname						)

       def 	lowercase__							(						self		,					**snake_case__						):






         """simple docstring"""





         return MaMaaaTokenizer.from_pretrained(self.tmpdirname		,					**snake_case__						)

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         return (
             "This is a test",
             "This is a test",
         )

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[int]								=							"</s>"
         lowerCAmelCase					:					List[Any]								=							0

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

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					int								=							self.get_tokenizer()
         lowerCAmelCase					:					Optional[int]								=							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(snake_case__						)		,					tokenizer.vocab_size + len(tokenizer.get_added_vocab()						)						)

       @unittest.skip("Skip this test while all models are still to be uploaded."						)
       def 	lowercase__							(						self						):






         """simple docstring"""





         pass

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Union[str, Any]								=							self.get_tokenizer()

         lowerCAmelCase					:					Union[str, Any]								=							tokenizer.tokenize("This is a test"						)
         self.assertListEqual(snake_case__		,					["▁This", "▁is", "▁a", "▁t", "est"]						)

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

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

         lowerCAmelCase					:					List[Any]								=							tokenizer.convert_tokens_to_string(snake_case__						)
         self.assertEqual(snake_case__		,					"This is a test"						)







       @slow
       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					int								=							{"input_ids": [[128_022, 110_108, 397, 11, 38_272, 2_247, 124_811, 285, 18_105, 1_586, 207, 7, 39_534, 4_428, 397, 1_019, 18_105, 1_586, 207, 7, 41_337, 16_786, 241, 7, 20_214, 17, 125_690, 10_398, 7, 44_378, 58_069, 68_342, 7_798, 7_343, 11, 299, 33_310, 4, 158, 37_350, 94_077, 4_569, 299, 33_310, 90, 4, 52_840, 290, 4, 31_270, 112, 299, 682, 4, 52_840, 39_953, 14_079, 193, 52_519, 90_894, 17_894, 120_697, 11, 40_445, 551, 17, 1_019, 52_519, 90_894, 17_756, 963, 11, 40_445, 480, 17, 9_792, 1_120, 5_173, 1_393, 6_240, 16_786, 241, 120_996, 28, 1_245, 1_393, 118_240, 11_123, 1_019, 93_612, 2_691, 10_618, 98_058, 120_409, 1_928, 279, 4, 40_683, 367, 178, 207, 1_019, 103, 103_121, 506, 65_296, 5, 2], [128_022, 21_217, 367, 117, 125_450, 128, 719, 7, 7_308, 40, 93_612, 12_669, 1_116, 16_704, 71, 17_785, 3_699, 15_592, 35, 144, 9_584, 241, 11_943, 713, 950, 799, 2_247, 88_427, 150, 149, 118_813, 120_706, 1_019, 106_906, 81_518, 28, 1_224, 22_799, 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], [128_022, 1_658, 123_311, 5_155, 5_578, 4_722, 279, 14_947, 2_366, 1_120, 1_197, 14, 1_348, 9_232, 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=snake_case__		,					model_name="facebook/m2m100_418M"		,					revision="c168bae485c864188cf9aa0e4108b0b6934dc91e"		,					)


@require_torch
@require_sentencepiece
@require_tokenizers
class 				SCREAMING_SNAKE_CASE__    (		unittest.TestCase    ):
       """simple docstring"""


       a  :    Any       ="facebook/m2m100_418M"
       a  :    List[Any]       =[
           "In my opinion, there are two levels of response from the French government.",
           "NSA Affair Emphasizes Complete Lack of Debate on Intelligence",
       ]
       a  :    str       =[
           "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
       a  :    Optional[Any]       =[EN_CODE, 5_93, 19_49, 11_57_81, 4, 7_15_86, 42_34, 6_06_33, 12_62_33, 4_32, 12_38_08, 1_55_92, 11_97, 11_71_32, 12_06_18, 5, 2]

       @classmethod
       def 	lowercase__							(						cls						):






         """simple docstring"""





         lowerCAmelCase					:					MaMaaaTokenizer								=							MaMaaaTokenizer.from_pretrained(
             cls.checkpoint_name		,					src_lang="en"		,					tgt_lang="fr"						)
         lowerCAmelCase					:					List[Any]								=							1
         return cls

       def 	lowercase__							(						self						):






         """simple docstring"""





         self.assertEqual(self.tokenizer.get_lang_id("ar"						)		,					128_006						)
         self.assertEqual(self.tokenizer.get_lang_id("en"						)		,					128_022						)
         self.assertEqual(self.tokenizer.get_lang_id("ro"						)		,					128_076						)
         self.assertEqual(self.tokenizer.get_lang_id("mr"						)		,					128_063						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Union[str, Any]								=							self.tokenizer.get_vocab()
         self.assertEqual(len(snake_case__						)		,					self.tokenizer.vocab_size						)
         self.assertEqual(vocab["<unk>"]		,					3						)
         self.assertIn(self.tokenizer.get_lang_token("en"						)		,					snake_case__						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[int]								=							"en"
         lowerCAmelCase					:					int								=							self.tokenizer.batch_encode_plus(self.src_text						).input_ids[0]
         self.assertListEqual(self.expected_src_tokens		,					snake_case__						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         self.assertIn(snake_case__		,					self.tokenizer.all_special_ids						)
         # fmt: off
         lowerCAmelCase					:					Any								=							[FR_CODE, 5_364, 82, 8_642, 4, 294, 47, 8, 14_028, 136, 3_286, 9_706, 6, 90_797, 6, 144_012, 162, 88_128, 30_061, 5, 2]
         # fmt: on
         lowerCAmelCase					:					str								=							self.tokenizer.decode(snake_case__		,					skip_special_tokens=snake_case__						)
         lowerCAmelCase					:					Tuple								=							self.tokenizer.decode(generated_ids[1:]		,					skip_special_tokens=snake_case__						)
         self.assertEqual(snake_case__		,					snake_case__						)
         self.assertNotIn(self.tokenizer.eos_token		,					snake_case__						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Any								=							tempfile.mkdtemp()
         lowerCAmelCase					:					List[str]								=							self.tokenizer.lang_token_to_id
         self.tokenizer.save_pretrained(snake_case__						)
         lowerCAmelCase					:					str								=							MaMaaaTokenizer.from_pretrained(snake_case__						)
         self.assertDictEqual(new_tok.lang_token_to_id		,					snake_case__						)

       @require_torch
       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Tuple								=							"en"
         lowerCAmelCase					:					Optional[int]								=							"fr"

         lowerCAmelCase					:					int								=							self.tokenizer(self.src_text		,					text_target=self.tgt_text		,					padding=snake_case__		,					return_tensors="pt"						)

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

         for k in batch:
           lowerCAmelCase					:					Any								=							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 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Dict								=							"mr"
         self.assertListEqual(self.tokenizer.prefix_tokens		,					[self.tokenizer.get_lang_id("mr"						)]						)
         self.assertListEqual(self.tokenizer.suffix_tokens		,					[self.tokenizer.eos_token_id]						)

         lowerCAmelCase					:					int								=							"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 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					str								=							"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						)]						)

         lowerCAmelCase					:					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 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Dict								=							self.tokenizer._build_translation_inputs("A test"		,					return_tensors="pt"		,					src_lang="en"		,					tgt_lang="ar"						)

         self.assertEqual(
             nested_simplify(snake_case__						)		,					{
                 # en_XX, A, test, EOS
                 "input_ids": [[128_022, 58, 4_183, 2]],
                 "attention_mask": [[1, 1, 1, 1]],
                 # ar_AR
                 "forced_bos_token_id": 128_006,
             }		,					)









"""simple docstring"""







# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
     import jax
     import jaxlib

lowerCAmelCase__				    =       get_logger()

lowerCAmelCase__				    =       None


class 				SCREAMING_SNAKE_CASE__    (		TensorFormatter[Mapping, "jax.Array", Mapping]    ):
       """simple docstring"""


       def __init__(						self		,					snake_case__=None		,					snake_case__=None		,					**snake_case__						):






         """simple docstring"""





         super().__init__(features=snake_case__						)
         import jax
         from jaxlib.xla_client import Device

         if isinstance(snake_case__		,					snake_case__						):
           raise ValueError(
               f"""Expected {device} to be a `str` not {type(snake_case__						)}, as `jaxlib.xla_extension.Device` """
               "is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
               "the device with `str()` to get its string identifier that will be internally mapped "
               "to the actual `jaxlib.xla_extension.Device`."						)
         lowerCAmelCase					:					List[str]								=							device if isinstance(snake_case__		,					snake_case__						) else str(jax.devices()[0]						)
         # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
         # with `pickle` nor with `dill`, so we need to use a global variable instead
         global DEVICE_MAPPING
         if DEVICE_MAPPING is None:
           lowerCAmelCase					:					Optional[Any]								=							self._map_devices_to_str()
         if self.device not in list(DEVICE_MAPPING.keys()						):
           logger.warning(
               f"""Device with string identifier {self.device} not listed among the available """
               f"""devices: {list(DEVICE_MAPPING.keys()						)}, so falling back to the default """
               f"""device: {str(jax.devices()[0]						)}."""						)
           lowerCAmelCase					:					Dict								=							str(jax.devices()[0]						)
         lowerCAmelCase					:					Union[str, Any]								=							jnp_array_kwargs

       @staticmethod
       def 	lowercase__							(						):






         """simple docstring"""





         import jax

         return {str(snake_case__						): device for device in jax.devices()}

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         import jax
         import jax.numpy as jnp

         if isinstance(snake_case__		,					snake_case__						) and column:
           if all(
               isinstance(snake_case__		,					jax.Array						) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column						):
             return jnp.stack(snake_case__		,					axis=0						)
         return column

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         import jax
         import jax.numpy as jnp

         if isinstance(snake_case__		,					(str, bytes, type(snake_case__						))						):
           return value
         elif isinstance(snake_case__		,					(np.character, np.ndarray)						) and np.issubdtype(value.dtype		,					np.character						):
           return value.tolist()

         lowerCAmelCase					:					Tuple								=							{}

         if isinstance(snake_case__		,					(np.number, np.ndarray)						) and np.issubdtype(value.dtype		,					np.integer						):
           # the default int precision depends on the jax config
           # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
           if jax.config.jax_enable_xaa:
             lowerCAmelCase					:					List[Any]								=							{"dtype": jnp.intaa}
           else:
             lowerCAmelCase					:					Optional[Any]								=							{"dtype": jnp.intaa}
         elif isinstance(snake_case__		,					(np.number, np.ndarray)						) and np.issubdtype(value.dtype		,					np.floating						):
           lowerCAmelCase					:					int								=							{"dtype": jnp.floataa}
         elif config.PIL_AVAILABLE and "PIL" in sys.modules:
           import PIL.Image

           if isinstance(snake_case__		,					PIL.Image.Image						):
             lowerCAmelCase					:					Dict								=							np.asarray(snake_case__						)

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
         global DEVICE_MAPPING
         if DEVICE_MAPPING is None:
           lowerCAmelCase					:					str								=							self._map_devices_to_str()

         with jax.default_device(DEVICE_MAPPING[self.device]						):
           # calling jnp.array on a np.ndarray does copy the data
           # see https://github.com/google/jax/issues/4486
           return jnp.array(snake_case__		,					**{**default_dtype, **self.jnp_array_kwargs}						)

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         import jax

         # support for torch, tf, jax etc.
         if config.TORCH_AVAILABLE and "torch" in sys.modules:
           import torch

           if isinstance(snake_case__		,					torch.Tensor						):
             return self._tensorize(data_struct.detach().cpu().numpy()[()]						)
         if hasattr(snake_case__		,					"__array__"						) and not isinstance(snake_case__		,					jax.Array						):
           lowerCAmelCase					:					Dict								=							data_struct.__array__()
         # support for nested types like struct of list of struct
         if isinstance(snake_case__		,					np.ndarray						):
           if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
             return self._consolidate([self.recursive_tensorize(snake_case__						) for substruct in data_struct]						)
         elif isinstance(snake_case__		,					(list, tuple)						):
           return self._consolidate([self.recursive_tensorize(snake_case__						) for substruct in data_struct]						)
         return self._tensorize(snake_case__						)

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         return map_nested(self._recursive_tensorize		,					snake_case__		,					map_list=snake_case__						)

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					List[Any]								=							self.numpy_arrow_extractor().extract_row(snake_case__						)
         lowerCAmelCase					:					Tuple								=							self.python_features_decoder.decode_row(snake_case__						)
         return self.recursive_tensorize(snake_case__						)

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[int]								=							self.numpy_arrow_extractor().extract_column(snake_case__						)
         lowerCAmelCase					:					Dict								=							self.python_features_decoder.decode_column(snake_case__		,					pa_table.column_names[0]						)
         lowerCAmelCase					:					Optional[Any]								=							self.recursive_tensorize(snake_case__						)
         lowerCAmelCase					:					Dict								=							self._consolidate(snake_case__						)
         return column







       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[Any]								=							self.numpy_arrow_extractor().extract_batch(snake_case__						)
         lowerCAmelCase					:					str								=							self.python_features_decoder.decode_batch(snake_case__						)
         lowerCAmelCase					:					Tuple								=							self.recursive_tensorize(snake_case__						)
         for column_name in batch:
           lowerCAmelCase					:					Optional[int]								=							self._consolidate(batch[column_name]						)
         return batch





