





'''simple docstring'''





import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class        __SCREAMING_SNAKE_CASE					(     _SCREAMING_SNAKE_CASE   ,		unittest.TestCase       ):
							snake_case     :      Optional[int]						     =       BarthezTokenizer
							snake_case     :      Dict						     =       BarthezTokenizerFast
							snake_case     :      Optional[Any]						     =       True
							snake_case     :      Dict						     =       True






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

														UpperCamelCase__				     =			BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez"""  )
														tokenizer.save_pretrained(self.tmpdirname  )
														tokenizer.save_pretrained(self.tmpdirname  , legacy_format=A_  )
														UpperCamelCase__				     =			tokenizer






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			"""<pad>"""
														UpperCamelCase__				     =			1

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






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			list(self.get_tokenizer().get_vocab().keys()  )

														self.assertEqual(vocab_keys[0]  , """<s>"""  )
														self.assertEqual(vocab_keys[1]  , """<pad>"""  )
														self.assertEqual(vocab_keys[-1]  , """<mask>"""  )
														self.assertEqual(len(A_  )  , 101122  )






							def 				_lowerCamelCase		(						self  ):
														self.assertEqual(self.get_tokenizer().vocab_size  , 101122  )






							@require_torch
							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
														UpperCamelCase__				     =			[0, 57, 3018, 70307, 91, 2]

														UpperCamelCase__				     =			self.tokenizer(
														    A_  , max_length=len(A_  )  , padding=A_  , truncation=A_  , return_tensors="""pt"""  )
														self.assertIsInstance(A_  , A_  )

														self.assertEqual((2, 6)  , batch.input_ids.shape  )
														self.assertEqual((2, 6)  , batch.attention_mask.shape  )
														UpperCamelCase__				     =			batch.input_ids.tolist()[0]
														self.assertListEqual(A_  , A_  )






							def 				_lowerCamelCase		(						self  ):
														if not self.test_rust_tokenizer:
																					return

														UpperCamelCase__				     =			self.get_tokenizer()
														UpperCamelCase__				     =			self.get_rust_tokenizer()

														UpperCamelCase__				     =			"""I was born in 92000, and this is falsé."""

														UpperCamelCase__				     =			tokenizer.tokenize(A_  )
														UpperCamelCase__				     =			rust_tokenizer.tokenize(A_  )
														self.assertListEqual(A_  , A_  )

														UpperCamelCase__				     =			tokenizer.encode(A_  , add_special_tokens=A_  )
														UpperCamelCase__				     =			rust_tokenizer.encode(A_  , add_special_tokens=A_  )
														self.assertListEqual(A_  , A_  )

														UpperCamelCase__				     =			self.get_rust_tokenizer()
														UpperCamelCase__				     =			tokenizer.encode(A_  )
														UpperCamelCase__				     =			rust_tokenizer.encode(A_  )
														self.assertListEqual(A_  , A_  )







							@slow
							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			{"""input_ids""": [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 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], [0, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], """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, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
														# fmt: on

														# moussaKam/mbarthez is a french model. So we also use french texts.
														UpperCamelCase__				     =			[
														    """Le transformeur est un modèle d\'apprentissage profond introduit en 2017, """
														    """utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
														    """À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
														    """pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
														    """telles que la traduction et la synthèse de texte.""",
														]

														self.tokenizer_integration_test_util(
														    expected_encoding=A_  , model_name="""moussaKam/mbarthez"""  , revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"""  , sequences=A_  , )











import math

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


UpperCamelCase__											= logging.get_logger(__name__)

UpperCamelCase__											= {
    "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        __SCREAMING_SNAKE_CASE					(     _a       ):
							snake_case     :      Optional[Any]						     =       """data2vec-audio"""






							def __init__(						self  , __lowerCAmelCase=32  , __lowerCAmelCase=768  , __lowerCAmelCase=12  , __lowerCAmelCase=12  , __lowerCAmelCase=3072  , __lowerCAmelCase="gelu"  , __lowerCAmelCase=0.1  , __lowerCAmelCase=0.1  , __lowerCAmelCase=0.1  , __lowerCAmelCase=0.0  , __lowerCAmelCase=0.1  , __lowerCAmelCase=0.1  , __lowerCAmelCase=0.02  , __lowerCAmelCase=1E-5  , __lowerCAmelCase="gelu"  , __lowerCAmelCase=(512, 512, 512, 512, 512, 512, 512)  , __lowerCAmelCase=(5, 2, 2, 2, 2, 2, 2)  , __lowerCAmelCase=(10, 3, 3, 3, 3, 2, 2)  , __lowerCAmelCase=False  , __lowerCAmelCase=16  , __lowerCAmelCase=19  , __lowerCAmelCase=5  , __lowerCAmelCase=0.05  , __lowerCAmelCase=10  , __lowerCAmelCase=2  , __lowerCAmelCase=0.0  , __lowerCAmelCase=10  , __lowerCAmelCase=0  , __lowerCAmelCase="sum"  , __lowerCAmelCase=False  , __lowerCAmelCase=False  , __lowerCAmelCase=256  , __lowerCAmelCase=(512, 512, 512, 512, 1500)  , __lowerCAmelCase=(5, 3, 3, 1, 1)  , __lowerCAmelCase=(1, 2, 3, 1, 1)  , __lowerCAmelCase=512  , __lowerCAmelCase=0  , __lowerCAmelCase=1  , __lowerCAmelCase=2  , __lowerCAmelCase=False  , __lowerCAmelCase=3  , __lowerCAmelCase=2  , __lowerCAmelCase=3  , __lowerCAmelCase=None  , **__lowerCAmelCase  , ):
														super().__init__(**__lowerCAmelCase  , pad_token_id=__lowerCAmelCase  , bos_token_id=__lowerCAmelCase  , eos_token_id=__lowerCAmelCase  )
														UpperCamelCase__				     =			hidden_size
														UpperCamelCase__				     =			feat_extract_activation
														UpperCamelCase__				     =			list(__lowerCAmelCase  )
														UpperCamelCase__				     =			list(__lowerCAmelCase  )
														UpperCamelCase__				     =			list(__lowerCAmelCase  )
														UpperCamelCase__				     =			conv_bias
														UpperCamelCase__				     =			num_conv_pos_embeddings
														UpperCamelCase__				     =			num_conv_pos_embedding_groups
														UpperCamelCase__				     =			conv_pos_kernel_size
														UpperCamelCase__				     =			len(self.conv_dim  )
														UpperCamelCase__				     =			num_hidden_layers
														UpperCamelCase__				     =			intermediate_size
														UpperCamelCase__				     =			hidden_act
														UpperCamelCase__				     =			num_attention_heads
														UpperCamelCase__				     =			hidden_dropout
														UpperCamelCase__				     =			attention_dropout
														UpperCamelCase__				     =			activation_dropout
														UpperCamelCase__				     =			feat_proj_dropout
														UpperCamelCase__				     =			final_dropout
														UpperCamelCase__				     =			layerdrop
														UpperCamelCase__				     =			layer_norm_eps
														UpperCamelCase__				     =			initializer_range
														UpperCamelCase__				     =			vocab_size
														UpperCamelCase__				     =			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
														UpperCamelCase__				     =			mask_time_prob
														UpperCamelCase__				     =			mask_time_length
														UpperCamelCase__				     =			mask_time_min_masks
														UpperCamelCase__				     =			mask_feature_prob
														UpperCamelCase__				     =			mask_feature_length
														UpperCamelCase__				     =			mask_feature_min_masks

														# ctc loss
														UpperCamelCase__				     =			ctc_loss_reduction
														UpperCamelCase__				     =			ctc_zero_infinity

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

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

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







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




