



"""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__					(		__snake_case     ,					unittest.TestCase      ):
						_lowerCAmelCase							=					BarthezTokenizer
						_lowerCAmelCase							=					BarthezTokenizerFast
						_lowerCAmelCase							=					True
						_lowerCAmelCase							=					True



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													super().setUp()

													__a		:		Tuple									=       BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez"""							)
													tokenizer.save_pretrained(self.tmpdirname							)
													tokenizer.save_pretrained(self.tmpdirname						,     legacy_format=_lowercase							)
													__a		:		List[str]									=       tokenizer



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Tuple									=       """<pad>"""
													__a		:		Any									=       1

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



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Any									=       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(_lowercase							)						,     101122							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													self.assertEqual(self.get_tokenizer().vocab_size						,     101122							)



						@require_torch
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Dict									=       ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
													__a		:		str									=       [0, 57, 3018, 70307, 91, 2]

													__a		:		Any									=       self.tokenizer(
													    _lowercase						,     max_length=len(_lowercase							)						,     padding=_lowercase						,     truncation=_lowercase						,     return_tensors="""pt"""							)
													self.assertIsInstance(_lowercase						,     _lowercase							)

													self.assertEqual((2, 6)						,     batch.input_ids.shape							)
													self.assertEqual((2, 6)						,     batch.attention_mask.shape							)
													__a		:		Optional[int]									=       batch.input_ids.tolist()[0]
													self.assertListEqual(_lowercase						,     _lowercase							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													if not self.test_rust_tokenizer:
																				return

													__a		:		Optional[Any]									=       self.get_tokenizer()
													__a		:		List[str]									=       self.get_rust_tokenizer()

													__a		:		List[str]									=       """I was born in 92000, and this is falsé."""

													__a		:		Optional[Any]									=       tokenizer.tokenize(_lowercase							)
													__a		:		Tuple									=       rust_tokenizer.tokenize(_lowercase							)
													self.assertListEqual(_lowercase						,     _lowercase							)

													__a		:		Any									=       tokenizer.encode(_lowercase						,     add_special_tokens=_lowercase							)
													__a		:		List[Any]									=       rust_tokenizer.encode(_lowercase						,     add_special_tokens=_lowercase							)
													self.assertListEqual(_lowercase						,     _lowercase							)

													__a		:		Dict									=       self.get_rust_tokenizer()
													__a		:		Optional[Any]									=       tokenizer.encode(_lowercase							)
													__a		:		Tuple									=       rust_tokenizer.encode(_lowercase							)
													self.assertListEqual(_lowercase						,     _lowercase							)





						@slow
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		List[Any]									=       {"""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.
													__a		:		Union[str, Any]									=       [
													    """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=_lowercase						,     model_name="""moussaKam/mbarthez"""						,     revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"""						,     sequences=_lowercase						,     )






"""simple docstring"""

import functools
import operator

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


lowercase__			   =  logging.get_logger(__name__)

lowercase__			   =  {
    "microsoft/unispeech-large-1500h-cv": (
        "https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json"
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					"unispeech"



						def __init__(self						,     _lowercase=32						,     _lowercase=768						,     _lowercase=12						,     _lowercase=12						,     _lowercase=3072						,     _lowercase="gelu"						,     _lowercase=0.1						,     _lowercase=0.1						,     _lowercase=0.1						,     _lowercase=0.0						,     _lowercase=0.0						,     _lowercase=0.1						,     _lowercase=0.1						,     _lowercase=0.02						,     _lowercase=1e-5						,     _lowercase="group"						,     _lowercase="gelu"						,     _lowercase=(512, 512, 512, 512, 512, 512, 512)						,     _lowercase=(5, 2, 2, 2, 2, 2, 2)						,     _lowercase=(10, 3, 3, 3, 3, 2, 2)						,     _lowercase=False						,     _lowercase=128						,     _lowercase=16						,     _lowercase=False						,     _lowercase=True						,     _lowercase=0.05						,     _lowercase=10						,     _lowercase=2						,     _lowercase=0.0						,     _lowercase=10						,     _lowercase=0						,     _lowercase=320						,     _lowercase=2						,     _lowercase=0.1						,     _lowercase=100						,     _lowercase=256						,     _lowercase=256						,     _lowercase=0.1						,     _lowercase="mean"						,     _lowercase=False						,     _lowercase=False						,     _lowercase=256						,     _lowercase=80						,     _lowercase=0						,     _lowercase=1						,     _lowercase=2						,     _lowercase=0.5						,     **_lowercase						,     ):





													'''simple docstring'''

													super().__init__(**_lowercase						,     pad_token_id=_lowercase						,     bos_token_id=_lowercase						,     eos_token_id=_lowercase							)
													__a		:		Union[str, Any]									=       hidden_size
													__a		:		Any									=       feat_extract_norm
													__a		:		Union[str, Any]									=       feat_extract_activation
													__a		:		Tuple									=       list(_lowercase							)
													__a		:		Dict									=       list(_lowercase							)
													__a		:		List[Any]									=       list(_lowercase							)
													__a		:		List[Any]									=       conv_bias
													__a		:		Optional[Any]									=       num_conv_pos_embeddings
													__a		:		Union[str, Any]									=       num_conv_pos_embedding_groups
													__a		:		Dict									=       len(self.conv_dim							)
													__a		:		Dict									=       num_hidden_layers
													__a		:		Union[str, Any]									=       intermediate_size
													__a		:		List[str]									=       hidden_act
													__a		:		int									=       num_attention_heads
													__a		:		int									=       hidden_dropout
													__a		:		Any									=       attention_dropout
													__a		:		List[Any]									=       activation_dropout
													__a		:		List[Any]									=       feat_proj_dropout
													__a		:		Union[str, Any]									=       final_dropout
													__a		:		str									=       layerdrop
													__a		:		Dict									=       layer_norm_eps
													__a		:		Dict									=       initializer_range
													__a		:		Union[str, Any]									=       num_ctc_classes
													__a		:		List[Any]									=       vocab_size
													__a		:		Any									=       do_stable_layer_norm
													__a		:		List[str]									=       use_weighted_layer_sum
													__a		:		List[str]									=       classifier_proj_size

													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
													__a		:		Dict									=       apply_spec_augment
													__a		:		Union[str, Any]									=       mask_time_prob
													__a		:		List[str]									=       mask_time_length
													__a		:		Dict									=       mask_time_min_masks
													__a		:		List[Any]									=       mask_feature_prob
													__a		:		Tuple									=       mask_feature_length
													__a		:		int									=       mask_feature_min_masks

													# parameters for pretraining with codevector quantized representations
													__a		:		List[Any]									=       num_codevectors_per_group
													__a		:		Union[str, Any]									=       num_codevector_groups
													__a		:		List[Any]									=       contrastive_logits_temperature
													__a		:		Any									=       feat_quantizer_dropout
													__a		:		Optional[int]									=       num_negatives
													__a		:		List[str]									=       codevector_dim
													__a		:		List[Any]									=       proj_codevector_dim
													__a		:		Tuple									=       diversity_loss_weight

													# ctc loss
													__a		:		Any									=       ctc_loss_reduction
													__a		:		List[str]									=       ctc_zero_infinity

													# pretraining loss
													__a		:		Tuple									=       replace_prob





						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return functools.reduce(operator.mul						,     self.conv_stride						,     1							)

