
import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class     __UpperCamelCase (							lowerCAmelCase__ ,						unittest.TestCase       ):






     """simple docstring"""







     lowerCAmelCase_							   =			MgpstrTokenizer
     lowerCAmelCase_							   =			False
     lowerCAmelCase_							   =			{}
     lowerCAmelCase_							   =			False



     def  UpperCAmelCase__							(				self							:							Optional[Any] ):







       """simple docstring"""




       super().setUp()

       # fmt: off
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
       # fmt: on
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		dict(zip(_A     ,						range(len(_A ) ) ) )

       __SCREAMING_SNAKE_CASE      :	int					=		os.path.join(self.tmpdirname     ,						VOCAB_FILES_NAMES['''vocab_file'''] )
       with open(self.vocab_file     ,						'''w'''     ,						encoding='''utf-8''' ) as fp:
         fp.write(json.dumps(_A ) + '''\n''' )



     def  UpperCAmelCase__							(				self							:							Tuple     ,						**_A							:							List[str] ):







       """simple docstring"""




       return MgpstrTokenizer.from_pretrained(self.tmpdirname     ,						**_A )



     def  UpperCAmelCase__							(				self							:							int     ,						_A							:							Optional[int] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Dict					=		'''tester'''
       __SCREAMING_SNAKE_CASE      :	str					=		'''tester'''
       return input_text, output_text



     @unittest.skip('''MGP-STR always lower cases letters.''' )
     def  UpperCAmelCase__							(				self							:							Optional[Any] ):







       """simple docstring"""




       pass



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







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Any					=		self.get_tokenizers(do_lower_case=_A )
       for tokenizer in tokenizers:
         with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
           __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		'''[SPECIAL_TOKEN]'''

           tokenizer.add_special_tokens({'''cls_token''': special_token} )
           __SCREAMING_SNAKE_CASE      :	List[Any]					=		tokenizer.encode([special_token]     ,						add_special_tokens=_A )
           self.assertEqual(len(_A )     ,						1 )

           __SCREAMING_SNAKE_CASE      :	Tuple					=		tokenizer.decode(_A     ,						skip_special_tokens=_A )
           self.assertTrue(special_token not in decoded )



     def  UpperCAmelCase__							(				self							:							List[Any] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	str					=		self.get_tokenizers()
       for tokenizer in tokenizers:
         with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
           __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Dict					=		self.get_input_output_texts(_A )

           __SCREAMING_SNAKE_CASE      :	List[str]					=		tokenizer.tokenize(_A )
           __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		tokenizer.convert_tokens_to_ids(_A )
           __SCREAMING_SNAKE_CASE      :	str					=		tokenizer.encode(_A     ,						add_special_tokens=_A )
           self.assertListEqual(_A     ,						_A )

           __SCREAMING_SNAKE_CASE      :	List[str]					=		tokenizer.convert_ids_to_tokens(_A )
           self.assertNotEqual(len(_A )     ,						0 )
           __SCREAMING_SNAKE_CASE      :	Tuple					=		tokenizer.decode(_A )
           self.assertIsInstance(_A     ,						_A )

           self.assertEqual(text_a.replace(''' '''     ,						'''''' )     ,						_A )



     @unittest.skip('''MGP-STR tokenizer only handles one sequence.''' )
     def  UpperCAmelCase__							(				self							:							Dict ):







       """simple docstring"""




       pass




     @unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer''' )
     def  UpperCAmelCase__							(				self							:							Union[str, Any] ):







       """simple docstring"""




       pass





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





