



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


a_      :      List[str]    =     get_tests_dir('fixtures/test_sentencepiece.model')
a_      :      Dict    =     get_tests_dir('fixtures/test_sentencepiece_bpe.model')

a_      :      List[Any]    =     'pt' if is_torch_available() else 'tf'
@require_sentencepiece
@require_tokenizers
class    __UpperCamelCase			(       _lowercase	,	unittest.TestCase				):

 """simple docstring"""



 _lowercase   :    Tuple      						=					CamembertTokenizer
 _lowercase   :    Tuple      						=					CamembertTokenizerFast
 _lowercase   :    Dict      						=					True
 _lowercase   :    List[Any]      						=					True

 def 						_UpperCAmelCase       (	self      ) ->			List[Any]:
  super().setUp()

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

 def 						_UpperCAmelCase       (	self      ) ->			Union[str, Any]:
  a__												= '''<pad>'''
  a__												= 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 						_UpperCAmelCase       (	self      ) ->			int:
  a__												= 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_0_0_4      )

 def 						_UpperCAmelCase       (	self      ) ->			Any:
  self.assertEqual(self.get_tokenizer().vocab_size		,				1_0_0_5      )

 def 						_UpperCAmelCase       (	self      ) ->			List[Any]:
  a__												= CamembertTokenizer(SCREAMING_SNAKE_CASE      )
  tokenizer.save_pretrained(self.tmpdirname      )
  a__												= CamembertTokenizerFast.from_pretrained(self.tmpdirname      )

  a__												= '''I was born in 92000, and this is falsé.'''

  a__												= tokenizer.encode(SCREAMING_SNAKE_CASE      )
  a__												= rust_tokenizer.encode(SCREAMING_SNAKE_CASE      )
  self.assertListEqual(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )

  a__												= tokenizer.encode(SCREAMING_SNAKE_CASE		,				add_special_tokens=SCREAMING_SNAKE_CASE      )
  a__												= 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)
  a__												= tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE      )
  a__												= rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE      )
  self.assertListEqual(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self      ) ->			Optional[Any]:
  if not self.test_rust_tokenizer:
   return

  a__												= self.get_tokenizer()
  a__												= self.get_rust_tokenizer()

  a__												= '''I was born in 92000, and this is falsé.'''

  a__												= tokenizer.tokenize(SCREAMING_SNAKE_CASE      )
  a__												= rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE      )
  self.assertListEqual(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )

  a__												= tokenizer.encode(SCREAMING_SNAKE_CASE		,				add_special_tokens=SCREAMING_SNAKE_CASE      )
  a__												= rust_tokenizer.encode(SCREAMING_SNAKE_CASE		,				add_special_tokens=SCREAMING_SNAKE_CASE      )
  self.assertListEqual(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )

  a__												= self.get_rust_tokenizer()
  a__												= tokenizer.encode(SCREAMING_SNAKE_CASE      )
  a__												= rust_tokenizer.encode(SCREAMING_SNAKE_CASE      )
  self.assertListEqual(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )



 @slow
 def 						_UpperCAmelCase       (	self      ) ->			Dict:
  # fmt: off
  a__												= {'''input_ids''': [[5, 5_4, 7_1_9_6, 2_9_7, 3_0, 2_3, 7_7_6, 1_8, 1_1, 3_2_1_5, 3_7_0_5, 8_2_5_2, 2_2, 3_1_6_4, 1_1_8_1, 2_1_1_6, 2_9, 1_6, 8_1_3, 2_5, 7_9_1, 3_3_1_4, 2_0, 3_4_4_6, 3_8, 2_7_5_7_5, 1_2_0, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_6_8, 1_7, 1_1, 9_0_8_8, 2_0, 1_5_1_7, 8, 2_2_8_0_4, 1_8_8_1_8, 1_0, 3_8, 6_2_9, 6_0_7, 6_0_7, 1_4_2, 1_9, 7_1_9_6, 8_6_7, 5_6, 1_0_3_2_6, 2_4, 2_2_6_7, 2_0, 4_1_6, 5_0_7_2, 1_5_6_1_2, 2_3_3, 7_3_4, 7, 2_3_9_9, 2_7, 1_6, 3_0_1_5, 1_6_4_9, 7, 2_4, 2_0, 4_3_3_8, 2_3_9_9, 2_7, 1_3, 3_4_0_0, 1_4, 1_3, 6_1_8_9, 8, 9_3_0, 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.
  a__												= [
      '''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		,				)










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_      :      Union[str, Any]    =     logging.get_logger(__name__)

a_      :      Optional[Any]    =     {
    'kakaobrain/align-base': 'https://huggingface.co/kakaobrain/align-base/resolve/main/config.json',
}
class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 _lowercase   :    int      						=					'''align_text_model'''

 def __init__(	self		,				SCREAMING_SNAKE_CASE=3_0_5_2_2		,				SCREAMING_SNAKE_CASE=7_6_8		,				SCREAMING_SNAKE_CASE=1_2		,				SCREAMING_SNAKE_CASE=1_2		,				SCREAMING_SNAKE_CASE=3_0_7_2		,				SCREAMING_SNAKE_CASE="gelu"		,				SCREAMING_SNAKE_CASE=0.1		,				SCREAMING_SNAKE_CASE=0.1		,				SCREAMING_SNAKE_CASE=5_1_2		,				SCREAMING_SNAKE_CASE=2		,				SCREAMING_SNAKE_CASE=0.02		,				SCREAMING_SNAKE_CASE=1e-1_2		,				SCREAMING_SNAKE_CASE=0		,				SCREAMING_SNAKE_CASE="absolute"		,				SCREAMING_SNAKE_CASE=True		,				**SCREAMING_SNAKE_CASE		,				) ->			List[str]:
  super().__init__(**SCREAMING_SNAKE_CASE      )

  a__												= vocab_size
  a__												= hidden_size
  a__												= num_hidden_layers
  a__												= num_attention_heads
  a__												= hidden_act
  a__												= intermediate_size
  a__												= hidden_dropout_prob
  a__												= attention_probs_dropout_prob
  a__												= max_position_embeddings
  a__												= type_vocab_size
  a__												= initializer_range
  a__												= layer_norm_eps
  a__												= position_embedding_type
  a__												= use_cache
  a__												= pad_token_id



 @classmethod
 def 						_UpperCAmelCase       (	cls		,				SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      ) ->			"PretrainedConfig":
  cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE      )

  a__							,			a__												= cls.get_config_dict(SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )

  # get the text config dict if we are loading from AlignConfig
  if config_dict.get('''model_type'''      ) == "align":
   a__												= 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(SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )





class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 _lowercase   :    str      						=					'''align_vision_model'''

 def __init__(	self		,				SCREAMING_SNAKE_CASE = 3		,				SCREAMING_SNAKE_CASE = 6_0_0		,				SCREAMING_SNAKE_CASE = 2.0		,				SCREAMING_SNAKE_CASE = 3.1		,				SCREAMING_SNAKE_CASE = 8		,				SCREAMING_SNAKE_CASE = [3, 3, 5, 3, 5, 5, 3]		,				SCREAMING_SNAKE_CASE = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2]		,				SCREAMING_SNAKE_CASE = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0]		,				SCREAMING_SNAKE_CASE = []		,				SCREAMING_SNAKE_CASE = [1, 2, 2, 2, 1, 2, 1]		,				SCREAMING_SNAKE_CASE = [1, 2, 2, 3, 3, 4, 1]		,				SCREAMING_SNAKE_CASE = [1, 6, 6, 6, 6, 6, 6]		,				SCREAMING_SNAKE_CASE = 0.25		,				SCREAMING_SNAKE_CASE = "swish"		,				SCREAMING_SNAKE_CASE = 2_5_6_0		,				SCREAMING_SNAKE_CASE = "mean"		,				SCREAMING_SNAKE_CASE = 0.02		,				SCREAMING_SNAKE_CASE = 0.0_01		,				SCREAMING_SNAKE_CASE = 0.99		,				SCREAMING_SNAKE_CASE = 0.2		,				**SCREAMING_SNAKE_CASE		,				) ->			str:
  super().__init__(**SCREAMING_SNAKE_CASE      )

  a__												= num_channels
  a__												= image_size
  a__												= width_coefficient
  a__												= depth_coefficient
  a__												= depth_divisor
  a__												= kernel_sizes
  a__												= in_channels
  a__												= out_channels
  a__												= depthwise_padding
  a__												= strides
  a__												= num_block_repeats
  a__												= expand_ratios
  a__												= squeeze_expansion_ratio
  a__												= hidden_act
  a__												= hidden_dim
  a__												= pooling_type
  a__												= initializer_range
  a__												= batch_norm_eps
  a__												= batch_norm_momentum
  a__												= drop_connect_rate
  a__												= sum(SCREAMING_SNAKE_CASE      ) * 4



 @classmethod
 def 						_UpperCAmelCase       (	cls		,				SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      ) ->			"PretrainedConfig":
  cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE      )

  a__							,			a__												= cls.get_config_dict(SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )

  # get the vision config dict if we are loading from AlignConfig
  if config_dict.get('''model_type'''      ) == "align":
   a__												= 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(SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )





class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 _lowercase   :    Dict      						=					'''align'''
 _lowercase   :    Optional[Any]      						=					True

 def __init__(	self		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=6_4_0		,				SCREAMING_SNAKE_CASE=1.0		,				SCREAMING_SNAKE_CASE=0.02		,				**SCREAMING_SNAKE_CASE		,				) ->			List[str]:
  super().__init__(**SCREAMING_SNAKE_CASE      )

  if text_config is None:
   a__												= {}
   logger.info('''text_config is None. Initializing the AlignTextConfig with default values.'''      )

  if vision_config is None:
   a__												= {}
   logger.info('''vision_config is None. Initializing the AlignVisionConfig with default values.'''      )

  a__												= AlignTextConfig(**SCREAMING_SNAKE_CASE      )
  a__												= AlignVisionConfig(**SCREAMING_SNAKE_CASE      )

  a__												= projection_dim
  a__												= temperature_init_value
  a__												= initializer_range

 @classmethod
 def 						_UpperCAmelCase       (	cls		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      ) ->			Optional[int]:
  return cls(text_config=text_config.to_dict()		,				vision_config=vision_config.to_dict()		,				**SCREAMING_SNAKE_CASE      )



 def 						_UpperCAmelCase       (	self      ) ->			int:
  a__												= copy.deepcopy(self.__dict__      )
  a__												= self.text_config.to_dict()
  a__												= self.vision_config.to_dict()
  a__												= self.__class__.model_type
  return output





