




'''simple docstring'''





import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase_						:      Optional[int]       =						get_tests_dir('''fixtures/test_sentencepiece.model''')
UpperCamelCase_						:      int       =						get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')

UpperCamelCase_						:      List[str]       =						'''pt''' if is_torch_available() else '''tf'''

@require_sentencepiece
@require_tokenizers
class        _a    (						__lowerCAmelCase , unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:   Dict       =						CamembertTokenizer
     SCREAMING_SNAKE_CASE_							:   Union[str, Any]       =						CamembertTokenizerFast
     SCREAMING_SNAKE_CASE_							:   Tuple       =						True
     SCREAMING_SNAKE_CASE_							:   Union[str, Any]       =						True






     def     _lowercase     (			self   )							->      str:
          super().setUp()

          # We have a SentencePiece fixture for testing
          _snake_case							=				CamembertTokenizer(_SCREAMING_SNAKE_CASE   )
          tokenizer.save_pretrained(self.tmpdirname   )






     def     _lowercase     (			self   )							->      Any:
          _snake_case							=				"<pad>"
          _snake_case							=				1

          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     _lowercase     (			self   )							->      Union[str, Any]:
          _snake_case							=				list(self.get_tokenizer().get_vocab().keys()   )

          self.assertEqual(vocab_keys[0] ,"<s>NOTUSED"   )
          self.assertEqual(vocab_keys[1] ,"<pad>"   )
          self.assertEqual(vocab_keys[-1] ,"<mask>"   )
          self.assertEqual(len(_SCREAMING_SNAKE_CASE   ) ,1_004   )






     def     _lowercase     (			self   )							->      int:
          self.assertEqual(self.get_tokenizer().vocab_size ,1_005   )






     def     _lowercase     (			self   )							->      List[Any]:
          _snake_case							=				CamembertTokenizer(_SCREAMING_SNAKE_CASE   )
          tokenizer.save_pretrained(self.tmpdirname   )
          _snake_case							=				CamembertTokenizerFast.from_pretrained(self.tmpdirname   )

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

          _snake_case							=				tokenizer.encode(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				rust_tokenizer.encode(_SCREAMING_SNAKE_CASE   )
          self.assertListEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )

          _snake_case							=				tokenizer.encode(_SCREAMING_SNAKE_CASE ,add_special_tokens=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				rust_tokenizer.encode(_SCREAMING_SNAKE_CASE ,add_special_tokens=_SCREAMING_SNAKE_CASE   )
          self.assertListEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )

          # <unk> tokens are not the same for `rust` than for `slow`.
          # Because spm gives back raw token instead of `unk` in EncodeAsPieces
          # tokens = tokenizer.tokenize(sequence)
          _snake_case							=				tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE   )
          self.assertListEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      Tuple:
          if not self.test_rust_tokenizer:
               return

          _snake_case							=				self.get_tokenizer()
          _snake_case							=				self.get_rust_tokenizer()

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

          _snake_case							=				tokenizer.tokenize(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE   )
          self.assertListEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )

          _snake_case							=				tokenizer.encode(_SCREAMING_SNAKE_CASE ,add_special_tokens=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				rust_tokenizer.encode(_SCREAMING_SNAKE_CASE ,add_special_tokens=_SCREAMING_SNAKE_CASE   )
          self.assertListEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )

          _snake_case							=				self.get_rust_tokenizer()
          _snake_case							=				tokenizer.encode(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				rust_tokenizer.encode(_SCREAMING_SNAKE_CASE   )
          self.assertListEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )






     @slow
     def     _lowercase     (			self   )							->      int:
          # fmt: off
          _snake_case							=				{"input_ids": [[5, 54, 7_196, 297, 30, 23, 776, 18, 11, 3_215, 3_705, 8_252, 22, 3_164, 1_181, 2_116, 29, 16, 813, 25, 791, 3_314, 20, 3_446, 38, 27_575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9_088, 20, 1_517, 8, 22_804, 18_818, 10, 38, 629, 607, 607, 142, 19, 7_196, 867, 56, 10_326, 24, 2_267, 20, 416, 5_072, 15_612, 233, 734, 7, 2_399, 27, 16, 3_015, 1_649, 7, 24, 20, 4_338, 2_399, 27, 13, 3_400, 14, 13, 6_189, 8, 930, 9, 6]], "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, 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]]}  # noqa: E501
          # fmt: on

          # camembert is a french model. So we also use french texts.
          _snake_case							=				[
              "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=_SCREAMING_SNAKE_CASE ,model_name="camembert-base" ,revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf" ,sequences=_SCREAMING_SNAKE_CASE ,)








'''simple docstring'''





import functools
import operator

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


UpperCamelCase_						:      str       =						logging.get_logger(__name__)

UpperCamelCase_						:      Optional[Any]       =						{
    '''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
    # See all SEW models at https://huggingface.co/models?filter=sew
}

class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   Dict       =						"""sew"""






     def __init__(			self ,_SCREAMING_SNAKE_CASE=32 ,_SCREAMING_SNAKE_CASE=768 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=3_072 ,_SCREAMING_SNAKE_CASE=2 ,_SCREAMING_SNAKE_CASE="gelu" ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=0.0_2 ,_SCREAMING_SNAKE_CASE=1e-5 ,_SCREAMING_SNAKE_CASE="group" ,_SCREAMING_SNAKE_CASE="gelu" ,_SCREAMING_SNAKE_CASE=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) ,_SCREAMING_SNAKE_CASE=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) ,_SCREAMING_SNAKE_CASE=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE=128 ,_SCREAMING_SNAKE_CASE=16 ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=0.0_5 ,_SCREAMING_SNAKE_CASE=10 ,_SCREAMING_SNAKE_CASE=2 ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE=10 ,_SCREAMING_SNAKE_CASE=0 ,_SCREAMING_SNAKE_CASE="mean" ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE=256 ,_SCREAMING_SNAKE_CASE=0 ,_SCREAMING_SNAKE_CASE=1 ,_SCREAMING_SNAKE_CASE=2 ,**_SCREAMING_SNAKE_CASE ,)							->      str:
          super().__init__(**_SCREAMING_SNAKE_CASE ,pad_token_id=_SCREAMING_SNAKE_CASE ,bos_token_id=_SCREAMING_SNAKE_CASE ,eos_token_id=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				hidden_size
          _snake_case							=				feat_extract_norm
          _snake_case							=				feat_extract_activation
          _snake_case							=				list(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				list(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				list(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				conv_bias
          _snake_case							=				num_conv_pos_embeddings
          _snake_case							=				num_conv_pos_embedding_groups
          _snake_case							=				len(self.conv_dim   )
          _snake_case							=				num_hidden_layers
          _snake_case							=				intermediate_size
          _snake_case							=				squeeze_factor
          _snake_case							=				hidden_act
          _snake_case							=				num_attention_heads
          _snake_case							=				hidden_dropout
          _snake_case							=				attention_dropout
          _snake_case							=				activation_dropout
          _snake_case							=				feat_proj_dropout
          _snake_case							=				final_dropout
          _snake_case							=				layerdrop
          _snake_case							=				layer_norm_eps
          _snake_case							=				initializer_range
          _snake_case							=				vocab_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)`,"
                   f"""but is `len(config.conv_dim) = {len(self.conv_dim   )}`, `len(config.conv_stride)"""
                   f"""= {len(self.conv_stride   )}`, `len(config.conv_kernel) = {len(self.conv_kernel   )}`."""   )

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

          # ctc loss
          _snake_case							=				ctc_loss_reduction
          _snake_case							=				ctc_zero_infinity

          # sequence classification
          _snake_case							=				use_weighted_layer_sum
          _snake_case							=				classifier_proj_size






     @property
     def     _lowercase     (			self   )							->      Optional[Any]:
          return functools.reduce(operator.mul ,self.conv_stride ,1   )


