


import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
    pass

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


A   :					str	=      logging.get_logger(__name__)

A   :					Optional[Any]	=      {
    "kakaobrain/align-base": "https://huggingface.co/kakaobrain/align-base/resolve/main/config.json",
}







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    "align_text_model"





       def __init__(     self							:    Tuple      ,   __lowerCamelCase							:    Optional[Any]=30522      ,   __lowerCamelCase							:    str=768      ,   __lowerCamelCase							:    Optional[int]=12      ,   __lowerCamelCase							:    Optional[int]=12      ,   __lowerCamelCase							:    int=3072      ,   __lowerCamelCase							:    int="gelu"      ,   __lowerCamelCase							:    Any=0.1      ,   __lowerCamelCase							:    Dict=0.1      ,   __lowerCamelCase							:    int=512      ,   __lowerCamelCase							:    str=2      ,   __lowerCamelCase							:    Any=0.0_2      ,   __lowerCamelCase							:    Union[str, Any]=1E-1_2      ,   __lowerCamelCase							:    Union[str, Any]=0      ,   __lowerCamelCase							:    Union[str, Any]="absolute"      ,   __lowerCamelCase							:    Optional[Any]=True      ,   **__lowerCamelCase							:    List[Any]      ,   ):


             '''simple docstring'''



             super().__init__(**__lowerCamelCase )

             lowerCamelCase__	:       str					=						vocab_size
             lowerCamelCase__	:       Any					=						hidden_size
             lowerCamelCase__	:       List[Any]					=						num_hidden_layers
             lowerCamelCase__	:       Optional[Any]					=						num_attention_heads
             lowerCamelCase__	:       List[Any]					=						hidden_act
             lowerCamelCase__	:       Dict					=						intermediate_size
             lowerCamelCase__	:       str					=						hidden_dropout_prob
             lowerCamelCase__	:       Union[str, Any]					=						attention_probs_dropout_prob
             lowerCamelCase__	:       Optional[int]					=						max_position_embeddings
             lowerCamelCase__	:       str					=						type_vocab_size
             lowerCamelCase__	:       int					=						initializer_range
             lowerCamelCase__	:       Optional[int]					=						layer_norm_eps
             lowerCamelCase__	:       Any					=						position_embedding_type
             lowerCamelCase__	:       Any					=						use_cache
             lowerCamelCase__	:       Optional[Any]					=						pad_token_id





       @classmethod
       def 		lowerCAmelCase  (     cls							:    List[Any]      ,   __lowerCamelCase							:    Union[str, os.PathLike]      ,   **__lowerCamelCase							:    Union[str, Any] ):


             '''simple docstring'''



             cls._set_token_in_kwargs(__lowerCamelCase )

             lowerCamelCase__					,     lowerCamelCase__	:       Tuple					=						cls.get_config_dict(__lowerCamelCase      ,   **__lowerCamelCase )

             # get the text config dict if we are loading from AlignConfig
             if config_dict.get("model_type" ) == "align":
                   lowerCamelCase__	:       Union[str, Any]					=						config_dict["text_config"]

             if "model_type" in config_dict and hasattr(cls      ,   "model_type" ) and config_dict["model_type"] != cls.model_type:
                   logger.warning(
                       f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                       f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." )

             return cls.from_dict(__lowerCamelCase      ,   **__lowerCamelCase )







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    "align_vision_model"





       def __init__(     self							:    Any      ,   __lowerCamelCase							:    int = 3      ,   __lowerCamelCase							:    int = 600      ,   __lowerCamelCase							:    float = 2.0      ,   __lowerCamelCase							:    float = 3.1      ,   __lowerCamelCase							:    int = 8      ,   __lowerCamelCase							:    List[int] = [3, 3, 5, 3, 5, 5, 3]      ,   __lowerCamelCase							:    List[int] = [32, 16, 24, 40, 80, 112, 192]      ,   __lowerCamelCase							:    List[int] = [16, 24, 40, 80, 112, 192, 320]      ,   __lowerCamelCase							:    List[int] = []      ,   __lowerCamelCase							:    List[int] = [1, 2, 2, 2, 1, 2, 1]      ,   __lowerCamelCase							:    List[int] = [1, 2, 2, 3, 3, 4, 1]      ,   __lowerCamelCase							:    List[int] = [1, 6, 6, 6, 6, 6, 6]      ,   __lowerCamelCase							:    float = 0.2_5      ,   __lowerCamelCase							:    str = "swish"      ,   __lowerCamelCase							:    int = 2560      ,   __lowerCamelCase							:    str = "mean"      ,   __lowerCamelCase							:    float = 0.0_2      ,   __lowerCamelCase							:    float = 0.0_0_1      ,   __lowerCamelCase							:    float = 0.9_9      ,   __lowerCamelCase							:    float = 0.2      ,   **__lowerCamelCase							:    List[str]      ,   ):


             '''simple docstring'''



             super().__init__(**__lowerCamelCase )

             lowerCamelCase__	:       List[str]					=						num_channels
             lowerCamelCase__	:       Optional[int]					=						image_size
             lowerCamelCase__	:       Tuple					=						width_coefficient
             lowerCamelCase__	:       Dict					=						depth_coefficient
             lowerCamelCase__	:       List[str]					=						depth_divisor
             lowerCamelCase__	:       Tuple					=						kernel_sizes
             lowerCamelCase__	:       Dict					=						in_channels
             lowerCamelCase__	:       Any					=						out_channels
             lowerCamelCase__	:       Dict					=						depthwise_padding
             lowerCamelCase__	:       str					=						strides
             lowerCamelCase__	:       List[Any]					=						num_block_repeats
             lowerCamelCase__	:       Any					=						expand_ratios
             lowerCamelCase__	:       Optional[Any]					=						squeeze_expansion_ratio
             lowerCamelCase__	:       List[Any]					=						hidden_act
             lowerCamelCase__	:       Tuple					=						hidden_dim
             lowerCamelCase__	:       str					=						pooling_type
             lowerCamelCase__	:       List[str]					=						initializer_range
             lowerCamelCase__	:       Optional[Any]					=						batch_norm_eps
             lowerCamelCase__	:       Optional[Any]					=						batch_norm_momentum
             lowerCamelCase__	:       Optional[Any]					=						drop_connect_rate
             lowerCamelCase__	:       Optional[Any]					=						sum(__lowerCamelCase ) * 4





       @classmethod
       def 		lowerCAmelCase  (     cls							:    List[Any]      ,   __lowerCamelCase							:    Union[str, os.PathLike]      ,   **__lowerCamelCase							:    Tuple ):


             '''simple docstring'''



             cls._set_token_in_kwargs(__lowerCamelCase )

             lowerCamelCase__					,     lowerCamelCase__	:       Dict					=						cls.get_config_dict(__lowerCamelCase      ,   **__lowerCamelCase )

             # get the vision config dict if we are loading from AlignConfig
             if config_dict.get("model_type" ) == "align":
                   lowerCamelCase__	:       Optional[int]					=						config_dict["vision_config"]

             if "model_type" in config_dict and hasattr(cls      ,   "model_type" ) and config_dict["model_type"] != cls.model_type:
                   logger.warning(
                       f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                       f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." )

             return cls.from_dict(__lowerCamelCase      ,   **__lowerCamelCase )







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    "align"
       A__      		=    True





       def __init__(     self							:    List[Any]      ,   __lowerCamelCase							:    Dict=None      ,   __lowerCamelCase							:    Optional[int]=None      ,   __lowerCamelCase							:    Union[str, Any]=640      ,   __lowerCamelCase							:    int=1.0      ,   __lowerCamelCase							:    List[str]=0.0_2      ,   **__lowerCamelCase							:    Optional[Any]      ,   ):


             '''simple docstring'''



             super().__init__(**__lowerCamelCase )

             if text_config is None:
                   lowerCamelCase__	:       Union[str, Any]					=						{}
                   logger.info("text_config is None. Initializing the AlignTextConfig with default values." )

             if vision_config is None:
                   lowerCamelCase__	:       Tuple					=						{}
                   logger.info("vision_config is None. Initializing the AlignVisionConfig with default values." )

             lowerCamelCase__	:       Optional[int]					=						AlignTextConfig(**__lowerCamelCase )
             lowerCamelCase__	:       int					=						AlignVisionConfig(**__lowerCamelCase )

             lowerCamelCase__	:       Union[str, Any]					=						projection_dim
             lowerCamelCase__	:       int					=						temperature_init_value
             lowerCamelCase__	:       Optional[Any]					=						initializer_range





       @classmethod
       def 		lowerCAmelCase  (     cls							:    List[Any]      ,   __lowerCamelCase							:    AlignTextConfig      ,   __lowerCamelCase							:    AlignVisionConfig      ,   **__lowerCamelCase							:    Optional[Any] ):


             '''simple docstring'''



             return cls(text_config=text_config.to_dict()      ,   vision_config=vision_config.to_dict()      ,   **__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    str ):


             '''simple docstring'''



             lowerCamelCase__	:       Any					=						copy.deepcopy(self.__dict__ )
             lowerCamelCase__	:       Union[str, Any]					=						self.text_config.to_dict()
             lowerCamelCase__	:       Tuple					=						self.vision_config.to_dict()
             lowerCamelCase__	:       Union[str, Any]					=						self.__class__.model_type
             return output






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     _lowercase				(							lowercase__							,						unittest.TestCase):


       """simple docstring"""




       A__      		=    BarthezTokenizer
       A__      		=    BarthezTokenizerFast
       A__      		=    True
       A__      		=    True





       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             super().setUp()

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





       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



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

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





       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						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 )      ,   101122 )





       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



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





       @require_torch
       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



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

             lowerCamelCase__	:       Tuple					=						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 )
             lowerCamelCase__	:       Any					=						batch.input_ids.tolist()[0]
             self.assertListEqual(__lowerCamelCase      ,   __lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             if not self.test_rust_tokenizer:
                   return

             lowerCamelCase__	:       Any					=						self.get_tokenizer()
             lowerCamelCase__	:       Tuple					=						self.get_rust_tokenizer()

             lowerCamelCase__	:       Union[str, Any]					=						"I was born in 92000, and this is falsé."

             lowerCamelCase__	:       Dict					=						tokenizer.tokenize(__lowerCamelCase )
             lowerCamelCase__	:       Optional[int]					=						rust_tokenizer.tokenize(__lowerCamelCase )
             self.assertListEqual(__lowerCamelCase      ,   __lowerCamelCase )

             lowerCamelCase__	:       Tuple					=						tokenizer.encode(__lowerCamelCase      ,   add_special_tokens=__lowerCamelCase )
             lowerCamelCase__	:       List[Any]					=						rust_tokenizer.encode(__lowerCamelCase      ,   add_special_tokens=__lowerCamelCase )
             self.assertListEqual(__lowerCamelCase      ,   __lowerCamelCase )

             lowerCamelCase__	:       List[str]					=						self.get_rust_tokenizer()
             lowerCamelCase__	:       Optional[Any]					=						tokenizer.encode(__lowerCamelCase )
             lowerCamelCase__	:       List[Any]					=						rust_tokenizer.encode(__lowerCamelCase )
             self.assertListEqual(__lowerCamelCase      ,   __lowerCamelCase )





       @slow
       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						{"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.
             lowerCamelCase__	:       List[str]					=						[
                 "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      ,   )


