





import math

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__						:			Tuple									=logging.get_logger(__name__)

UpperCAmelCase__						:			Tuple									={
    '''facebook/data2vec-base-960h''': '''https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json''',
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}

class       __A     (						a    ):
				__A      =				"""data2vec-audio"""


				def __init__(		self   , UpperCAmelCase_=32   , UpperCAmelCase_=768   , UpperCAmelCase_=12   , UpperCAmelCase_=12   , UpperCAmelCase_=3072   , UpperCAmelCase_="gelu"   , UpperCAmelCase_=0.1   , UpperCAmelCase_=0.1   , UpperCAmelCase_=0.1   , UpperCAmelCase_=0.0   , UpperCAmelCase_=0.1   , UpperCAmelCase_=0.1   , UpperCAmelCase_=0.0_2   , UpperCAmelCase_=1E-5   , UpperCAmelCase_="gelu"   , UpperCAmelCase_=(512, 512, 512, 512, 512, 512, 512)   , UpperCAmelCase_=(5, 2, 2, 2, 2, 2, 2)   , UpperCAmelCase_=(10, 3, 3, 3, 3, 2, 2)   , UpperCAmelCase_=False   , UpperCAmelCase_=16   , UpperCAmelCase_=19   , UpperCAmelCase_=5   , UpperCAmelCase_=0.0_5   , UpperCAmelCase_=10   , UpperCAmelCase_=2   , UpperCAmelCase_=0.0   , UpperCAmelCase_=10   , UpperCAmelCase_=0   , UpperCAmelCase_="sum"   , UpperCAmelCase_=False   , UpperCAmelCase_=False   , UpperCAmelCase_=256   , UpperCAmelCase_=(512, 512, 512, 512, 1500)   , UpperCAmelCase_=(5, 3, 3, 1, 1)   , UpperCAmelCase_=(1, 2, 3, 1, 1)   , UpperCAmelCase_=512   , UpperCAmelCase_=0   , UpperCAmelCase_=1   , UpperCAmelCase_=2   , UpperCAmelCase_=False   , UpperCAmelCase_=3   , UpperCAmelCase_=2   , UpperCAmelCase_=3   , UpperCAmelCase_=None   , **UpperCAmelCase_   , ):
								super().__init__(**UpperCAmelCase_   , pad_token_id=UpperCAmelCase_   , bos_token_id=UpperCAmelCase_   , eos_token_id=UpperCAmelCase_							)
								lowerCamelCase	 =hidden_size
								lowerCamelCase	 =feat_extract_activation
								lowerCamelCase	 =list(UpperCAmelCase_							)
								lowerCamelCase	 =list(UpperCAmelCase_							)
								lowerCamelCase	 =list(UpperCAmelCase_							)
								lowerCamelCase	 =conv_bias
								lowerCamelCase	 =num_conv_pos_embeddings
								lowerCamelCase	 =num_conv_pos_embedding_groups
								lowerCamelCase	 =conv_pos_kernel_size
								lowerCamelCase	 =len(self.conv_dim							)
								lowerCamelCase	 =num_hidden_layers
								lowerCamelCase	 =intermediate_size
								lowerCamelCase	 =hidden_act
								lowerCamelCase	 =num_attention_heads
								lowerCamelCase	 =hidden_dropout
								lowerCamelCase	 =attention_dropout
								lowerCamelCase	 =activation_dropout
								lowerCamelCase	 =feat_proj_dropout
								lowerCamelCase	 =final_dropout
								lowerCamelCase	 =layerdrop
								lowerCamelCase	 =layer_norm_eps
								lowerCamelCase	 =initializer_range
								lowerCamelCase	 =vocab_size
								lowerCamelCase	 =use_weighted_layer_sum

								if (
								    (len(self.conv_stride							) != self.num_feat_extract_layers)
								    or (len(self.conv_kernel							) != self.num_feat_extract_layers)
								    or (len(self.conv_dim							) != self.num_feat_extract_layers)
								):
												raise ValueError(
												    """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
												    """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
												    f""" {len(self.conv_dim							)}`, `len(config.conv_stride) = {len(self.conv_stride							)}`,"""
												    f""" `len(config.conv_kernel) = {len(self.conv_kernel							)}`."""							)

								# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
								lowerCamelCase	 =mask_time_prob
								lowerCamelCase	 =mask_time_length
								lowerCamelCase	 =mask_time_min_masks
								lowerCamelCase	 =mask_feature_prob
								lowerCamelCase	 =mask_feature_length
								lowerCamelCase	 =mask_feature_min_masks

								# ctc loss
								lowerCamelCase	 =ctc_loss_reduction
								lowerCamelCase	 =ctc_zero_infinity

								# adapter
								lowerCamelCase	 =add_adapter
								lowerCamelCase	 =adapter_kernel_size
								lowerCamelCase	 =adapter_stride
								lowerCamelCase	 =num_adapter_layers
								lowerCamelCase	 =output_hidden_size or hidden_size

								# SequenceClassification-specific parameter. Feel free to ignore for other classes.
								lowerCamelCase	 =classifier_proj_size

								# XVector-specific parameters. Feel free to ignore for other classes.
								lowerCamelCase	 =list(UpperCAmelCase_							)
								lowerCamelCase	 =list(UpperCAmelCase_							)
								lowerCamelCase	 =list(UpperCAmelCase_							)
								lowerCamelCase	 =xvector_output_dim


				@property
				def _snake_case       (		self							):
								return math.prod(self.conv_stride							)












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__						:			Union[str, Any]									=get_tests_dir('''fixtures/test_sentencepiece.model''')


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

UpperCAmelCase__						:			Union[str, Any]									=12_80_22
UpperCAmelCase__						:			Optional[Any]									=12_80_28

@require_sentencepiece
class       __A     (						a    ,					unittest.TestCase    ):
				__A      =				MaMaaaTokenizer
				__A      =				False
				__A      =				False
				__A      =				True


				def _snake_case       (		self							):
								super().setUp()

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

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


				def _snake_case       (		self   , **UpperCAmelCase_							):
								return MaMaaaTokenizer.from_pretrained(self.tmpdirname   , **UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_							):
								return (
								    "This is a test",
								    "This is a test",
								)


				def _snake_case       (		self							):
								lowerCamelCase	 ="""</s>"""
								lowerCamelCase	 =0

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


				def _snake_case       (		self							):
								lowerCamelCase	 =self.get_tokenizer()
								lowerCamelCase	 =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(UpperCAmelCase_							)   , 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							):
								pass


				def _snake_case       (		self							):
								lowerCamelCase	 =self.get_tokenizer()

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

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

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

								lowerCamelCase	 =tokenizer.convert_tokens_to_string(UpperCAmelCase_							)
								self.assertEqual(UpperCAmelCase_   , """This is a test"""							)


				@slow
				def _snake_case       (		self							):
								# fmt: off
								lowerCamelCase	 ={"""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=UpperCAmelCase_   , model_name="""facebook/m2m100_418M"""   , revision="""c168bae485c864188cf9aa0e4108b0b6934dc91e"""   , )




@require_torch
@require_sentencepiece
@require_tokenizers
class       __A     (						unittest.TestCase    ):
				__A      =				"""facebook/m2m100_418M"""
				__A      =				[
				    """In my opinion, there are two levels of response from the French government.""",
				    """NSA Affair Emphasizes Complete Lack of Debate on Intelligence""",
				]
				__A      =				[
				    """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      =				[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 _snake_case       (		cls							):
								lowerCamelCase	 =MaMaaaTokenizer.from_pretrained(
								    cls.checkpoint_name   , src_lang="""en"""   , tgt_lang="""fr"""							)
								lowerCamelCase	 =1
								return cls


				def _snake_case       (		self							):
								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							):
								lowerCamelCase	 =self.tokenizer.get_vocab()
								self.assertEqual(len(UpperCAmelCase_							)   , self.tokenizer.vocab_size							)
								self.assertEqual(vocab["""<unk>"""]   , 3							)
								self.assertIn(self.tokenizer.get_lang_token("""en"""							)   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 ="""en"""
								lowerCamelCase	 =self.tokenizer.batch_encode_plus(self.src_text							).input_ids[0]
								self.assertListEqual(self.expected_src_tokens   , UpperCAmelCase_							)


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


				def _snake_case       (		self							):
								lowerCamelCase	 =tempfile.mkdtemp()
								lowerCamelCase	 =self.tokenizer.lang_token_to_id
								self.tokenizer.save_pretrained(UpperCAmelCase_							)
								lowerCamelCase	 =MaMaaaTokenizer.from_pretrained(UpperCAmelCase_							)
								self.assertDictEqual(new_tok.lang_token_to_id   , UpperCAmelCase_							)


				@require_torch
				def _snake_case       (		self							):
								lowerCamelCase	 ="""en"""
								lowerCamelCase	 ="""fr"""

								lowerCamelCase	 =self.tokenizer(self.src_text   , text_target=self.tgt_text   , padding=UpperCAmelCase_   , return_tensors="""pt"""							)

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

								for k in batch:
												lowerCamelCase	 =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							):
								lowerCamelCase	 ="""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	 ="""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							):
								lowerCamelCase	 ="""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	 ="""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							):
								lowerCamelCase	 =self.tokenizer._build_translation_inputs("""A test"""   , return_tensors="""pt"""   , src_lang="""en"""   , tgt_lang="""ar"""							)

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





