


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

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




							"""simple docstring"""


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

							def    __snake_case (							self	:  Optional[int]    )     ->   Any:
														super().setUp()

														__snake_case		:      Any										=       ["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
														__snake_case		:      str										=       dict(zip(lowerCamelCase   ,     range(len(lowerCamelCase    )    )    )    )
														__snake_case		:      List[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"]    )

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

							def    __snake_case (							self	:  int   ,     **lowerCamelCase	:  int    )     ->   Dict:
														return MaMaaaTokenizer.from_pretrained(self.tmpdirname   ,     **lowerCamelCase    )

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

							def    __snake_case (							self	:  Union[str, Any]    )     ->   Union[str, Any]:
														__snake_case		:      Optional[int]										=       "</s>"
														__snake_case		:      str										=       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    )     ->   int:
														__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	:  Tuple    )     ->   List[str]:
														pass

							def    __snake_case (							self	:  Union[str, Any]    )     ->   Optional[int]:
														__snake_case		:      Union[str, Any]										=       self.get_tokenizer()

														__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]   ,     )

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

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






							@slow
							def    __snake_case (							self	:  Any    )     ->   Dict:
														# fmt: off
														__snake_case		:      List[Any]										=       {"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   :							List[Any]					  =  "facebook/m2m100_418M"
							__UpperCAmelCase   :							str					  =  [
							    "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   :							Dict					  =  [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	:  int    )     ->   Tuple:
														__snake_case		:      MaMaaaTokenizer										=       MaMaaaTokenizer.from_pretrained(
														    cls.checkpoint_name   ,     src_lang="en"   ,     tgt_lang="fr"    )
														__snake_case		:      int										=       1
														return cls

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

							def    __snake_case (							self	:  str    )     ->   Tuple:
														self.assertIn(lowerCamelCase   ,     self.tokenizer.all_special_ids    )
														# fmt: off
														__snake_case		:      Optional[int]										=       [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		:      Optional[Any]										=       self.tokenizer.decode(lowerCamelCase   ,     skip_special_tokens=lowerCamelCase    )
														__snake_case		:      Union[str, 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	:  List[Any]    )     ->   List[Any]:
														__snake_case		:      Any										=       tempfile.mkdtemp()
														__snake_case		:      List[str]										=       self.tokenizer.lang_token_to_id
														self.tokenizer.save_pretrained(lowerCamelCase    )
														__snake_case		:      List[Any]										=       MaMaaaTokenizer.from_pretrained(lowerCamelCase    )
														self.assertDictEqual(new_tok.lang_token_to_id   ,     lowerCamelCase    )

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

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

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

														for k in batch:
																					__snake_case		:      Optional[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    __snake_case (							self	:  List[str]    )     ->   Dict:
														__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]    )

														__snake_case		:      Union[str, Any]										=       "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	:  str    )     ->   str:
														__snake_case		:      Tuple										=       "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	:  Optional[Any]    )     ->   int:
														__snake_case		:      int										=       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,
														    }   ,     )





import math
from typing import Optional

import numpy as np

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


_snake_case					:		Union[str, Any]      =    logging.get_logger(__name__)

_snake_case					:		List[str]      =    {
    "facebook/encodec_24khz": "https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json",
    "facebook/encodec_48khz": "https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json",
}

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Tuple					  =  "encodec"

							def __init__(							self	:  Any   ,     lowerCamelCase	:  Optional[int]=[1.5, 3.0, 6.0, 12.0, 24.0]   ,     lowerCamelCase	:  List[str]=24000   ,     lowerCamelCase	:  int=1   ,     lowerCamelCase	:  Optional[int]=False   ,     lowerCamelCase	:  Dict=None   ,     lowerCamelCase	:  Tuple=None   ,     lowerCamelCase	:  Optional[int]=128   ,     lowerCamelCase	:  Optional[int]=32   ,     lowerCamelCase	:  List[str]=1   ,     lowerCamelCase	:  str=[8, 5, 4, 2]   ,     lowerCamelCase	:  List[str]="weight_norm"   ,     lowerCamelCase	:  Any=7   ,     lowerCamelCase	:  Tuple=7   ,     lowerCamelCase	:  int=3   ,     lowerCamelCase	:  int=2   ,     lowerCamelCase	:  Union[str, Any]=True   ,     lowerCamelCase	:  List[Any]="reflect"   ,     lowerCamelCase	:  Union[str, Any]=2   ,     lowerCamelCase	:  Optional[int]=2   ,     lowerCamelCase	:  int=1.0   ,     lowerCamelCase	:  Optional[Any]=1024   ,     lowerCamelCase	:  Optional[Any]=None   ,     lowerCamelCase	:  str=True   ,     **lowerCamelCase	:  Dict   ,     )     ->   Any:
														__snake_case		:      Tuple										=       target_bandwidths
														__snake_case		:      Union[str, Any]										=       sampling_rate
														__snake_case		:      Union[str, Any]										=       audio_channels
														__snake_case		:      Dict										=       normalize
														__snake_case		:      List[Any]										=       chunk_length_s
														__snake_case		:      Tuple										=       overlap
														__snake_case		:      Optional[int]										=       hidden_size
														__snake_case		:      List[Any]										=       num_filters
														__snake_case		:      Union[str, Any]										=       num_residual_layers
														__snake_case		:      Optional[int]										=       upsampling_ratios
														__snake_case		:      List[str]										=       norm_type
														__snake_case		:      Optional[int]										=       kernel_size
														__snake_case		:      Dict										=       last_kernel_size
														__snake_case		:      Tuple										=       residual_kernel_size
														__snake_case		:      List[Any]										=       dilation_growth_rate
														__snake_case		:      Optional[int]										=       use_causal_conv
														__snake_case		:      Tuple										=       pad_mode
														__snake_case		:      Union[str, Any]										=       compress
														__snake_case		:      Union[str, Any]										=       num_lstm_layers
														__snake_case		:      int										=       trim_right_ratio
														__snake_case		:      Tuple										=       codebook_size
														__snake_case		:      Optional[Any]										=       codebook_dim if codebook_dim is not None else hidden_size
														__snake_case		:      int										=       use_conv_shortcut

														if self.norm_type not in ["weight_norm", "time_group_norm"]:
																					raise ValueError(
																					    F'self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'    )

														super().__init__(**lowerCamelCase    )

							@property
							def    __snake_case (							self	:  int    )     ->   Optional[int]:
														if self.chunk_length_s is None:
																					return None
														else:
																					return int(self.chunk_length_s * self.sampling_rate    )

							@property
							def    __snake_case (							self	:  Union[str, Any]    )     ->   Optional[int]:
														if self.chunk_length_s is None or self.overlap is None:
																					return None
														else:
																					return max(1   ,     int((1.0 - self.overlap) * self.chunk_length    )    )

							@property
							def    __snake_case (							self	:  Optional[Any]    )     ->   int:
														__snake_case		:      Union[str, Any]										=       np.prod(self.upsampling_ratios    )
														return math.ceil(self.sampling_rate / hop_length    )






							@property
							def    __snake_case (							self	:  Optional[Any]    )     ->   int:
														return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10)    )

