"""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  _A						(       lowerCAmelCase  ,    unittest.TestCase	):
      snake_case__			:   List[str]							  =	BarthezTokenizer
      snake_case__			:   str							  =	BarthezTokenizerFast
      snake_case__			:   List[Any]							  =	True
      snake_case__			:   str							  =	True

      def 				A__       (						self						):







            """simple docstring"""



            super().setUp()

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

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	"""<pad>"""
            lowercase														=	1

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

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	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(__lowerCAmelCase						)		,       10_1122						)

      def 				A__       (						self						):







            """simple docstring"""



            self.assertEqual(self.get_tokenizer().vocab_size		,       10_1122						)

      @require_torch
      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
            lowercase														=	[0, 57, 3018, 7_0307, 91, 2]

            lowercase														=	self.tokenizer(
                __lowerCAmelCase		,       max_length=len(__lowerCAmelCase						)		,       padding=__lowerCAmelCase		,       truncation=__lowerCAmelCase		,       return_tensors="""pt"""						)
            self.assertIsInstance(__lowerCAmelCase		,       __lowerCAmelCase						)

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

      def 				A__       (						self						):







            """simple docstring"""



            if not self.test_rust_tokenizer:
                  return

            lowercase														=	self.get_tokenizer()
            lowercase														=	self.get_rust_tokenizer()

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

            lowercase														=	tokenizer.tokenize(__lowerCAmelCase						)
            lowercase														=	rust_tokenizer.tokenize(__lowerCAmelCase						)
            self.assertListEqual(__lowerCAmelCase		,       __lowerCAmelCase						)

            lowercase														=	tokenizer.encode(__lowerCAmelCase		,       add_special_tokens=__lowerCAmelCase						)
            lowercase														=	rust_tokenizer.encode(__lowerCAmelCase		,       add_special_tokens=__lowerCAmelCase						)
            self.assertListEqual(__lowerCAmelCase		,       __lowerCAmelCase						)

            lowercase														=	self.get_rust_tokenizer()
            lowercase														=	tokenizer.encode(__lowerCAmelCase						)
            lowercase														=	rust_tokenizer.encode(__lowerCAmelCase						)
            self.assertListEqual(__lowerCAmelCase		,       __lowerCAmelCase						)







      @slow
      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	{"""input_ids""": [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 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, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 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.
            lowercase														=	[
                """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=__lowerCAmelCase		,       model_name="""moussaKam/mbarthez"""		,       revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"""		,       sequences=__lowerCAmelCase		,       )




"""simple docstring"""






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


__lowerCAmelCase    :       List[Any]					     =logging.get_logger(__name__)

__lowerCAmelCase    :       Union[str, Any]					     ={
    """s-JoL/Open-Llama-V1""": """https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json""",
}





class  _A						(       lowerCAmelCase	):
      snake_case__			:   Union[str, Any]							  =	'open-llama'

      def __init__(						self		,       __lowerCAmelCase=10_0000		,       __lowerCAmelCase=4096		,       __lowerCAmelCase=1_1008		,       __lowerCAmelCase=32		,       __lowerCAmelCase=32		,       __lowerCAmelCase="silu"		,       __lowerCAmelCase=2048		,       __lowerCAmelCase=0.0_2		,       __lowerCAmelCase=1E-6		,       __lowerCAmelCase=True		,       __lowerCAmelCase=0		,       __lowerCAmelCase=1		,       __lowerCAmelCase=2		,       __lowerCAmelCase=False		,       __lowerCAmelCase=True		,       __lowerCAmelCase=0.1		,       __lowerCAmelCase=0.1		,       __lowerCAmelCase=True		,       __lowerCAmelCase=True		,       __lowerCAmelCase=None		,       **__lowerCAmelCase		,       ):







            """simple docstring"""



            lowercase														=	vocab_size
            lowercase														=	max_position_embeddings
            lowercase														=	hidden_size
            lowercase														=	intermediate_size
            lowercase														=	num_hidden_layers
            lowercase														=	num_attention_heads
            lowercase														=	hidden_act
            lowercase														=	initializer_range
            lowercase														=	rms_norm_eps
            lowercase														=	use_cache
            lowercase														=	kwargs.pop(
                """use_memorry_efficient_attention"""		,       __lowerCAmelCase						)
            lowercase														=	hidden_dropout_prob
            lowercase														=	attention_dropout_prob
            lowercase														=	use_stable_embedding
            lowercase														=	shared_input_output_embedding
            lowercase														=	rope_scaling
            self._rope_scaling_validation()

            super().__init__(
                pad_token_id=__lowerCAmelCase		,       bos_token_id=__lowerCAmelCase		,       eos_token_id=__lowerCAmelCase		,       tie_word_embeddings=__lowerCAmelCase		,       **__lowerCAmelCase		,       )







      def 				A__       (						self						):







            """simple docstring"""



            if self.rope_scaling is None:
                  return

            if not isinstance(self.rope_scaling		,       __lowerCAmelCase						) or len(self.rope_scaling						) != 2:
                  raise ValueError(
                      """`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
                      f'got {self.rope_scaling}'						)
            lowercase														=	self.rope_scaling.get("""type"""		,       __lowerCAmelCase						)
            lowercase														=	self.rope_scaling.get("""factor"""		,       __lowerCAmelCase						)
            if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
                  raise ValueError(
                      f'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'						)
            if rope_scaling_factor is None or not isinstance(__lowerCAmelCase		,       __lowerCAmelCase						) or rope_scaling_factor <= 1.0:
                  raise ValueError(f'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'						)



