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():
     _SCREAMING_SNAKE_CASE      						=	get_tests_dir("""fixtures/test_sentencepiece.model""")


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

_SCREAMING_SNAKE_CASE      						=	1_2_8_0_2_2
_SCREAMING_SNAKE_CASE      						=	1_2_8_0_2_8
@require_sentencepiece
class SCREAMING_SNAKE_CASE_						(	lowercase_   ,			unittest.TestCase						):
   __lowerCAmelCase      =							MaMaaaTokenizer
   __lowerCAmelCase      =							False
   __lowerCAmelCase      =							False
   __lowerCAmelCase      =							True







   def 		lowerCamelCase_     (			self		:		Any							):





    """simple docstring"""


    super().setUp()

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

    UpperCamelCase          =	MaMaaaTokenizer.from_pretrained(self.tmpdirname							)
    tokenizer.save_pretrained(self.tmpdirname							)







   def 		lowerCamelCase_     (			self		:		str					,					**lowerCamelCase_		:		str							):





    """simple docstring"""


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







   def 		lowerCamelCase_     (			self		:		Tuple					,					lowerCamelCase_		:		int							):





    """simple docstring"""


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







   def 		lowerCamelCase_     (			self		:		Tuple							):





    """simple docstring"""


    UpperCamelCase          =	'</s>'
    UpperCamelCase          =	0

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







   def 		lowerCamelCase_     (			self		:		Union[str, Any]							):





    """simple docstring"""


    UpperCamelCase          =	self.get_tokenizer()
    UpperCamelCase          =	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(SCREAMING_SNAKE_CASE_							)					,					tokenizer.vocab_size + len(tokenizer.get_added_vocab()							)							)







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





    """simple docstring"""


    pass







   def 		lowerCamelCase_     (			self		:		List[Any]							):





    """simple docstring"""


    UpperCamelCase          =	self.get_tokenizer()

    UpperCamelCase          =	tokenizer.tokenize("""This is a test"""							)
    self.assertListEqual(SCREAMING_SNAKE_CASE_					,					["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]							)

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

    UpperCamelCase          =	tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]							)
    self.assertListEqual(SCREAMING_SNAKE_CASE_					,					["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]							)

    UpperCamelCase          =	tokenizer.convert_tokens_to_string(SCREAMING_SNAKE_CASE_							)
    self.assertEqual(SCREAMING_SNAKE_CASE_					,					"""This is a test"""							)







   @slow
   def 		lowerCamelCase_     (			self		:		Optional[Any]							):





    """simple docstring"""


    UpperCamelCase          =	{'input_ids': [[12_8022, 11_0108, 397, 11, 3_8272, 2247, 12_4811, 285, 1_8105, 1586, 207, 7, 3_9534, 4428, 397, 1019, 1_8105, 1586, 207, 7, 4_1337, 1_6786, 241, 7, 2_0214, 17, 12_5690, 1_0398, 7, 4_4378, 5_8069, 6_8342, 7798, 7343, 11, 299, 3_3310, 4, 158, 3_7350, 9_4077, 4569, 299, 3_3310, 90, 4, 5_2840, 290, 4, 3_1270, 112, 299, 682, 4, 5_2840, 3_9953, 1_4079, 193, 5_2519, 9_0894, 1_7894, 12_0697, 11, 4_0445, 551, 17, 1019, 5_2519, 9_0894, 1_7756, 963, 11, 4_0445, 480, 17, 9792, 1120, 5173, 1393, 6240, 1_6786, 241, 12_0996, 28, 1245, 1393, 11_8240, 1_1123, 1019, 9_3612, 2691, 1_0618, 9_8058, 12_0409, 1928, 279, 4, 4_0683, 367, 178, 207, 1019, 103, 10_3121, 506, 6_5296, 5, 2], [12_8022, 2_1217, 367, 117, 12_5450, 128, 719, 7, 7308, 40, 9_3612, 1_2669, 1116, 1_6704, 71, 1_7785, 3699, 1_5592, 35, 144, 9584, 241, 1_1943, 713, 950, 799, 2247, 8_8427, 150, 149, 11_8813, 12_0706, 1019, 10_6906, 8_1518, 28, 1224, 2_2799, 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], [12_8022, 1658, 12_3311, 5155, 5578, 4722, 279, 1_4947, 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=SCREAMING_SNAKE_CASE_					,					model_name="""facebook/m2m100_418M"""					,					revision="""c168bae485c864188cf9aa0e4108b0b6934dc91e"""					,					)





@require_torch
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE_						(	unittest.TestCase						):
   __lowerCAmelCase      =							"""facebook/m2m100_418M"""
   __lowerCAmelCase      =							[
       """In my opinion, there are two levels of response from the French government.""",
       """NSA Affair Emphasizes Complete Lack of Debate on Intelligence""",
   ]
   __lowerCAmelCase      =							[
       """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
   __lowerCAmelCase      =							[EN_CODE, 593, 1_949, 115_781, 4, 71_586, 4_234, 60_633, 126_233, 432, 123_808, 15_592, 1_197, 117_132, 120_618, 5, 2]







   @classmethod
   def 		lowerCamelCase_     (			cls		:		Optional[Any]							):





    """simple docstring"""


    UpperCamelCase          =	MaMaaaTokenizer.from_pretrained(
        cls.checkpoint_name					,					src_lang="""en"""					,					tgt_lang="""fr"""							)
    UpperCamelCase          =	1
    return cls







   def 		lowerCamelCase_     (			self		:		List[Any]							):





    """simple docstring"""


    self.assertEqual(self.tokenizer.get_lang_id("""ar"""							)					,					12_8006							)
    self.assertEqual(self.tokenizer.get_lang_id("""en"""							)					,					12_8022							)
    self.assertEqual(self.tokenizer.get_lang_id("""ro"""							)					,					12_8076							)
    self.assertEqual(self.tokenizer.get_lang_id("""mr"""							)					,					12_8063							)







   def 		lowerCamelCase_     (			self		:		List[str]							):





    """simple docstring"""


    UpperCamelCase          =	self.tokenizer.get_vocab()
    self.assertEqual(len(SCREAMING_SNAKE_CASE_							)					,					self.tokenizer.vocab_size							)
    self.assertEqual(vocab["""<unk>"""]					,					3							)
    self.assertIn(self.tokenizer.get_lang_token("""en"""							)					,					SCREAMING_SNAKE_CASE_							)







   def 		lowerCamelCase_     (			self		:		List[str]							):





    """simple docstring"""


    UpperCamelCase          =	'en'
    UpperCamelCase          =	self.tokenizer.batch_encode_plus(self.src_text							).input_ids[0]
    self.assertListEqual(self.expected_src_tokens					,					SCREAMING_SNAKE_CASE_							)







   def 		lowerCamelCase_     (			self		:		List[Any]							):





    """simple docstring"""


    self.assertIn(SCREAMING_SNAKE_CASE_					,					self.tokenizer.all_special_ids							)
    # fmt: off
    UpperCamelCase          =	[FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 1_4028, 136, 3286, 9706, 6, 9_0797, 6, 14_4012, 162, 8_8128, 3_0061, 5, 2]
    # fmt: on
    UpperCamelCase          =	self.tokenizer.decode(SCREAMING_SNAKE_CASE_					,					skip_special_tokens=SCREAMING_SNAKE_CASE_							)
    UpperCamelCase          =	self.tokenizer.decode(generated_ids[1:]					,					skip_special_tokens=SCREAMING_SNAKE_CASE_							)
    self.assertEqual(SCREAMING_SNAKE_CASE_					,					SCREAMING_SNAKE_CASE_							)
    self.assertNotIn(self.tokenizer.eos_token					,					SCREAMING_SNAKE_CASE_							)







   def 		lowerCamelCase_     (			self		:		List[str]							):





    """simple docstring"""


    UpperCamelCase          =	tempfile.mkdtemp()
    UpperCamelCase          =	self.tokenizer.lang_token_to_id
    self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_							)
    UpperCamelCase          =	MaMaaaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_							)
    self.assertDictEqual(new_tok.lang_token_to_id					,					SCREAMING_SNAKE_CASE_							)







   @require_torch
   def 		lowerCamelCase_     (			self		:		int							):





    """simple docstring"""


    UpperCamelCase          =	'en'
    UpperCamelCase          =	'fr'

    UpperCamelCase          =	self.tokenizer(self.src_text					,					text_target=self.tgt_text					,					padding=SCREAMING_SNAKE_CASE_					,					return_tensors="""pt"""							)

    UpperCamelCase          =	shift_tokens_right(
        batch["""labels"""]					,					self.tokenizer.pad_token_id					,					self.tokenizer.eos_token_id							)

    for k in batch:
     UpperCamelCase          =	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 		lowerCamelCase_     (			self		:		str							):





    """simple docstring"""


    UpperCamelCase          =	'mr'
    self.assertListEqual(self.tokenizer.prefix_tokens					,					[self.tokenizer.get_lang_id("""mr"""							)]							)
    self.assertListEqual(self.tokenizer.suffix_tokens					,					[self.tokenizer.eos_token_id]							)

    UpperCamelCase          =	'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 		lowerCamelCase_     (			self		:		Tuple							):





    """simple docstring"""


    UpperCamelCase          =	'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							)]							)

    UpperCamelCase          =	'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 		lowerCamelCase_     (			self		:		Optional[Any]							):





    """simple docstring"""


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

    self.assertEqual(
        nested_simplify(SCREAMING_SNAKE_CASE_							)					,					{
            # en_XX, A, test, EOS
            """input_ids""": [[12_8022, 58, 4183, 2]],
            """attention_mask""": [[1, 1, 1, 1]],
            # ar_AR
            """forced_bos_token_id""": 12_8006,
        }					,					)





"""simple docstring"""







import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
     import tensorflow as tf
     from tensorflow.python.eager import context
     from tensorflow.python.framework import ops

     from transformers import GradientAccumulator, create_optimizer







@require_tf
class       UpperCamelCase__   (	unittest.TestCase  ):




      """simple docstring"""




      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      str    ,			SCREAMING_SNAKE_CASE_				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      List[str]       ):
        self.assertEqual(len(SCREAMING_SNAKE_CASE_       )    ,			len(SCREAMING_SNAKE_CASE_       )       )
        for a, b in zip(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       ):
          self.assertAlmostEqual(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			delta=SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      int       ):
        lowerCAmelCase_   :				int								= GradientAccumulator()
        accumulator([tf.constant([1.0, 2.0]       )]       )
        accumulator([tf.constant([-2.0, 1.0]       )]       )
        accumulator([tf.constant([-1.0, 2.0]       )]       )
        with self.assertRaises(SCREAMING_SNAKE_CASE_       ):
          accumulator([tf.constant([1.0, 1.0]       ), tf.constant([2.0, 2.0]       )]       )
        self.assertEqual(accumulator.step    ,			3       )
        self.assertEqual(len(accumulator.gradients       )    ,			1       )
        self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()    ,			[-2.0, 5.0]    ,			tol=1E-2       )
        accumulator.reset()
        self.assertEqual(accumulator.step    ,			0       )
        self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()    ,			[0.0, 0.0]    ,			tol=1E-2       )





      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        lowerCAmelCase_   :				Optional[int]								= None
        ops.enable_eager_execution_internal()
        lowerCAmelCase_   :				str								= tf.config.list_physical_devices('CPU'       )
        if len(SCREAMING_SNAKE_CASE_       ) == 1:
          tf.config.set_logical_device_configuration(
              physical_devices[0]    ,			[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]       )
        lowerCAmelCase_   :				Dict								= tf.config.list_logical_devices(device_type='CPU'       )
        lowerCAmelCase_   :				Optional[int]								= tf.distribute.MirroredStrategy(devices=devices[:2]       )

        with strategy.scope():
          lowerCAmelCase_   :				Union[str, Any]								= GradientAccumulator()
          lowerCAmelCase_   :				int								= tf.Variable([4.0, 3.0]       )
          lowerCAmelCase_						,lowerCAmelCase_   :				Optional[int]								= create_optimizer(5E-5    ,			1_0    ,			5       )
          lowerCAmelCase_   :				Union[str, Any]								= tf.Variable([0.0, 0.0]    ,			trainable=SCREAMING_SNAKE_CASE_       )

        def accumulate_on_replica(SCREAMING_SNAKE_CASE_				:      Optional[int]       ):
          accumulator([gradient]       )

        def apply_on_replica():
          optimizer.apply_gradients(list(zip(accumulator.gradients    ,			[variable]       )       )       )

        @tf.function
        def accumulate(SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]       ):
          with strategy.scope():
            lowerCAmelCase_   :				Tuple								= strategy.experimental_local_results(SCREAMING_SNAKE_CASE_       )
            local_variables[0].assign(SCREAMING_SNAKE_CASE_       )
            local_variables[1].assign(SCREAMING_SNAKE_CASE_       )
            strategy.run(SCREAMING_SNAKE_CASE_    ,			args=(gradient_placeholder,)       )

        @tf.function
        def apply_grad():
          with strategy.scope():
            strategy.run(SCREAMING_SNAKE_CASE_       )

        def _check_local_values(SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]       ):
          lowerCAmelCase_   :				List[Any]								= strategy.experimental_local_results(accumulator._gradients[0]       )
          self.assertListAlmostEqual(values[0].value()    ,			SCREAMING_SNAKE_CASE_    ,			tol=1E-2       )
          self.assertListAlmostEqual(values[1].value()    ,			SCREAMING_SNAKE_CASE_    ,			tol=1E-2       )

        accumulate([1.0, 2.0]    ,			[-1.0, 1.0]       )
        accumulate([3.0, -1.0]    ,			[-1.0, -1.0]       )
        accumulate([-2.0, 2.0]    ,			[3.0, -2.0]       )
        self.assertEqual(accumulator.step    ,			3       )
        _check_local_values([2.0, 3.0]    ,			[1.0, -2.0]       )
        apply_grad()
        self.assertListAlmostEqual(variable.value()    ,			[4.0, 3.0]    ,			tol=1E-2       )
        accumulator.reset()
        self.assertEqual(accumulator.step    ,			0       )
        _check_local_values([0.0, 0.0]    ,			[0.0, 0.0]       )





