






import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
     lowerCamelCase__							=     'pt'
elif is_tf_available():
     lowerCamelCase__							=     'tf'
else:
     lowerCamelCase__							=     'jax'




class 			snake_case__   (							UpperCamelCase_	,    unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase							:				str 				= PerceiverTokenizer
       lowerCamelCase							:				Optional[int] 				= False



       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            super().setUp()
            __snake_case			:Any          =						PerceiverTokenizer()
            tokenizer.save_pretrained(self.tmpdirname    )



       @cached_property
       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            return PerceiverTokenizer.from_pretrained("""deepmind/language-perceiver"""    )



       def 					__lowercase						(							self			,						**a__    ) ->      List[Any]:






            '''simple docstring'''





            return self.tokenizer_class.from_pretrained(self.tmpdirname			,						**UpperCamelCase__    )



       def 					__lowercase						(							self			,						a__			,						a__=False			,						a__=20			,						a__=5    ) ->      str:






            '''simple docstring'''





            __snake_case			:str          =						[]
            for i in range(len(UpperCamelCase__    )    ):
                 try:
                      __snake_case			:Dict          =						tokenizer.decode([i]			,						clean_up_tokenization_spaces=UpperCamelCase__    )
                 except UnicodeDecodeError:
                      pass
                 toks.append((i, tok)    )

            __snake_case			:Dict          =						list(filter(lambda a__    : re.match(R"""^[ a-zA-Z]+$"""			,						t[1]    )			,						UpperCamelCase__    )    )
            __snake_case			:List[str]          =						list(filter(lambda a__    : [t[0]] == tokenizer.encode(t[1]			,						add_special_tokens=UpperCamelCase__    )			,						UpperCamelCase__    )    )
            if max_length is not None and len(UpperCamelCase__    ) > max_length:
                 __snake_case			:Any          =						toks[:max_length]
            if min_length is not None and len(UpperCamelCase__    ) < min_length and len(UpperCamelCase__    ) > 0:
                 while len(UpperCamelCase__    ) < min_length:
                      __snake_case			:Optional[int]          =						toks + toks
        # toks_str = [t[1] for t in toks]
            __snake_case			:List[Any]          =						[t[0] for t in toks]

            # Ensure consistency
            __snake_case			:Optional[int]          =						tokenizer.decode(UpperCamelCase__			,						clean_up_tokenization_spaces=UpperCamelCase__    )
            if " " not in output_txt and len(UpperCamelCase__    ) > 1:
                 __snake_case			:Tuple          =						(
                     tokenizer.decode([toks_ids[0]]			,						clean_up_tokenization_spaces=UpperCamelCase__    )
                     + ''' '''
                     + tokenizer.decode(toks_ids[1:]			,						clean_up_tokenization_spaces=UpperCamelCase__    )
                 )
            if with_prefix_space:
                 __snake_case			:Tuple          =						''' ''' + output_txt
            __snake_case			:Dict          =						tokenizer.encode(UpperCamelCase__			,						add_special_tokens=UpperCamelCase__    )
            return output_txt, output_ids



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            __snake_case			:List[Any]          =						self.perceiver_tokenizer
            __snake_case			:Optional[Any]          =						'''Unicode €.'''
            __snake_case			:int          =						tokenizer(UpperCamelCase__    )
            __snake_case			:Union[str, Any]          =						[4, 91, 1_16, 1_11, 1_05, 1_17, 1_06, 1_07, 38, 2_32, 1_36, 1_78, 52, 5]
            self.assertEqual(encoded["""input_ids"""]			,						UpperCamelCase__    )

            # decoding
            __snake_case			:List[str]          =						tokenizer.decode(UpperCamelCase__    )
            self.assertEqual(UpperCamelCase__			,						"""[CLS]Unicode €.[SEP]"""    )

            __snake_case			:Optional[int]          =						tokenizer("""e è é ê ë"""    )
            __snake_case			:Any          =						[4, 1_07, 38, 2_01, 1_74, 38, 2_01, 1_75, 38, 2_01, 1_76, 38, 2_01, 1_77, 5]
            self.assertEqual(encoded["""input_ids"""]			,						UpperCamelCase__    )
            # decoding
            __snake_case			:Any          =						tokenizer.decode(UpperCamelCase__    )
            self.assertEqual(UpperCamelCase__			,						"""[CLS]e è é ê ë[SEP]"""    )

            # encode/decode, but with `encode` instead of `__call__`
            self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë"""    )    )			,						"""[CLS]e è é ê ë[SEP]"""    )



       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:Any          =						self.perceiver_tokenizer
            __snake_case			:List[str]          =						['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
            # fmt: off
            __snake_case			:Any          =						[4, 71, 38, 1_14, 1_17, 1_16, 1_09, 38, 1_18, 1_03, 1_20, 1_03, 1_09, 1_20, 1_03, 1_18, 1_10, 38, 1_08, 1_17, 1_20, 38, 1_21, 1_23, 1_15, 1_15, 1_03, 1_20, 1_11, 1_28, 1_03, 1_22, 1_11, 1_17, 1_16, 52, 5, 0]
            # fmt: on
            __snake_case			:Union[str, Any]          =						tokenizer(UpperCamelCase__			,						padding=UpperCamelCase__			,						return_tensors=UpperCamelCase__    )
            self.assertIsInstance(UpperCamelCase__			,						UpperCamelCase__    )

            if FRAMEWORK != "jax":
                 __snake_case			:Optional[int]          =						list(batch.input_ids.numpy()[0]    )
            else:
                 __snake_case			:List[str]          =						list(batch.input_ids.tolist()[0]    )

            self.assertListEqual(UpperCamelCase__			,						UpperCamelCase__    )

            self.assertEqual((2, 38)			,						batch.input_ids.shape    )
            self.assertEqual((2, 38)			,						batch.attention_mask.shape    )



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            __snake_case			:Union[str, Any]          =						self.perceiver_tokenizer
            __snake_case			:List[str]          =						['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
            __snake_case			:Dict          =						tokenizer(UpperCamelCase__			,						padding=UpperCamelCase__			,						return_tensors=UpperCamelCase__    )
            # check if input_ids are returned and no decoder_input_ids
            self.assertIn("""input_ids"""			,						UpperCamelCase__    )
            self.assertIn("""attention_mask"""			,						UpperCamelCase__    )
            self.assertNotIn("""decoder_input_ids"""			,						UpperCamelCase__    )
            self.assertNotIn("""decoder_attention_mask"""			,						UpperCamelCase__    )



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            __snake_case			:int          =						self.perceiver_tokenizer
            __snake_case			:Optional[int]          =						[
                '''Summary of the text.''',
                '''Another summary.''',
            ]
            __snake_case			:Dict          =						tokenizer(
                text_target=UpperCamelCase__			,						max_length=32			,						padding="""max_length"""			,						truncation=UpperCamelCase__			,						return_tensors=UpperCamelCase__    )
            self.assertEqual(32			,						targets["""input_ids"""].shape[1]    )



       def 					__lowercase						(							self    ) ->      List[str]:






            '''simple docstring'''





            __snake_case			:str          =						self.get_tokenizers()
            for tokenizer in tokenizers:
                 with self.subTest(F'''{tokenizer.__class__.__name__}'''    ):
                      self.assertNotEqual(tokenizer.model_max_length			,						42    )

        # Now let's start the test
            __snake_case			:Dict          =						self.get_tokenizers()
            for tokenizer in tokenizers:
                 with self.subTest(F'''{tokenizer.__class__.__name__}'''    ):
                      # Isolate this from the other tests because we save additional tokens/etc
                      __snake_case			:int          =						tempfile.mkdtemp()

                      __snake_case			:int          =						''' He is very happy, UNwant\u00E9d,running'''
                      __snake_case			:Optional[int]          =						tokenizer.encode(UpperCamelCase__			,						add_special_tokens=UpperCamelCase__    )
                      tokenizer.save_pretrained(UpperCamelCase__    )

                      __snake_case			:str          =						tokenizer.__class__.from_pretrained(UpperCamelCase__    )
                      __snake_case			:Tuple          =						after_tokenizer.encode(UpperCamelCase__			,						add_special_tokens=UpperCamelCase__    )
                      self.assertListEqual(UpperCamelCase__			,						UpperCamelCase__    )

                      shutil.rmtree(UpperCamelCase__    )

            __snake_case			:Union[str, Any]          =						self.get_tokenizers(model_max_length=42    )
            for tokenizer in tokenizers:
                 with self.subTest(F'''{tokenizer.__class__.__name__}'''    ):
                      # Isolate this from the other tests because we save additional tokens/etc
                      __snake_case			:Tuple          =						tempfile.mkdtemp()

                      __snake_case			:Dict          =						''' He is very happy, UNwant\u00E9d,running'''
                      tokenizer.add_tokens(["""bim""", """bambam"""]    )
                      __snake_case			:Optional[int]          =						tokenizer.additional_special_tokens
                      additional_special_tokens.append("""new_additional_special_token"""    )
                      tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}    )
                      __snake_case			:List[str]          =						tokenizer.encode(UpperCamelCase__			,						add_special_tokens=UpperCamelCase__    )
                      tokenizer.save_pretrained(UpperCamelCase__    )

                      __snake_case			:List[str]          =						tokenizer.__class__.from_pretrained(UpperCamelCase__    )
                      __snake_case			:Tuple          =						after_tokenizer.encode(UpperCamelCase__			,						add_special_tokens=UpperCamelCase__    )
                      self.assertListEqual(UpperCamelCase__			,						UpperCamelCase__    )
                      self.assertIn("""new_additional_special_token"""			,						after_tokenizer.additional_special_tokens    )
                      self.assertEqual(after_tokenizer.model_max_length			,						42    )

                      __snake_case			:Tuple          =						tokenizer.__class__.from_pretrained(UpperCamelCase__			,						model_max_length=43    )
                      self.assertEqual(tokenizer.model_max_length			,						43    )

                      shutil.rmtree(UpperCamelCase__    )



       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            __snake_case			:List[str]          =						[]
            if self.test_slow_tokenizer:
                 tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())    )

            if self.test_rust_tokenizer:
                 tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())    )

            for tokenizer_class, tokenizer_utils in tokenizer_list:
                 with tempfile.TemporaryDirectory() as tmp_dir:
                      tokenizer_utils.save_pretrained(UpperCamelCase__    )

                      with open(os.path.join(UpperCamelCase__			,						"""special_tokens_map.json"""    )			,						encoding="""utf-8"""    ) as json_file:
                           __snake_case			:Tuple          =						json.load(UpperCamelCase__    )

                      with open(os.path.join(UpperCamelCase__			,						"""tokenizer_config.json"""    )			,						encoding="""utf-8"""    ) as json_file:
                           __snake_case			:List[Any]          =						json.load(UpperCamelCase__    )

                      __snake_case			:Tuple          =						[F'''<extra_id_{i}>''' for i in range(1_25    )]

                      __snake_case			:int          =						added_tokens_extra_ids + [
                          '''an_additional_special_token'''
                      ]
                      __snake_case			:Optional[Any]          =						added_tokens_extra_ids + [
                          '''an_additional_special_token'''
                      ]

                      with open(os.path.join(UpperCamelCase__			,						"""special_tokens_map.json"""    )			,						"""w"""			,						encoding="""utf-8"""    ) as outfile:
                           json.dump(UpperCamelCase__			,						UpperCamelCase__    )
                      with open(os.path.join(UpperCamelCase__			,						"""tokenizer_config.json"""    )			,						"""w"""			,						encoding="""utf-8"""    ) as outfile:
                           json.dump(UpperCamelCase__			,						UpperCamelCase__    )

                      # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                      # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                      # "special_tokens_map.json" files
                      __snake_case			:Any          =						tokenizer_class.from_pretrained(
                          UpperCamelCase__			,						)
                      self.assertIn(
                          """an_additional_special_token"""			,						tokenizer_without_change_in_init.additional_special_tokens    )
                      self.assertEqual(
                          ["""an_additional_special_token"""]			,						tokenizer_without_change_in_init.convert_ids_to_tokens(
                              tokenizer_without_change_in_init.convert_tokens_to_ids(["""an_additional_special_token"""]    )    )			,						)

                      # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                      __snake_case			:Optional[int]          =						added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""			,						lstrip=UpperCamelCase__    )]
                      __snake_case			:Optional[Any]          =						tokenizer_class.from_pretrained(
                          UpperCamelCase__			,						additional_special_tokens=UpperCamelCase__			,						)

                      self.assertIn("""a_new_additional_special_token"""			,						tokenizer.additional_special_tokens    )
                      self.assertEqual(
                          ["""a_new_additional_special_token"""]			,						tokenizer.convert_ids_to_tokens(
                              tokenizer.convert_tokens_to_ids(["""a_new_additional_special_token"""]    )    )			,						)



       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            __snake_case			:Union[str, Any]          =						self.perceiver_tokenizer
            self.assertEqual(tokenizer.decode([1_78]    )			,						"""�"""    )



       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            pass



       def 					__lowercase						(							self    ) ->      Union[str, Any]:






            '''simple docstring'''





            pass



       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            pass



       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            pass



       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:Tuple          =						self.get_tokenizers(fast=UpperCamelCase__			,						do_lower_case=UpperCamelCase__    )
            for tokenizer in tokenizers:
                 with self.subTest(F'''{tokenizer.__class__.__name__}'''    ):
                      __snake_case			:int          =						['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
                      __snake_case			:Dict          =						tokenizer.convert_tokens_to_string(UpperCamelCase__    )

                      self.assertIsInstance(UpperCamelCase__			,						UpperCamelCase__    )









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


if TYPE_CHECKING:
     pass

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


lowerCamelCase__							=     logging.get_logger(__name__)

lowerCamelCase__							=     {
    """kakaobrain/align-base""": """https://huggingface.co/kakaobrain/align-base/resolve/main/config.json""",
}




class 			snake_case__   (							lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				Union[str, Any] 				= "align_text_model"



       def __init__(							self			,						a__=3_05_22			,						a__=7_68			,						a__=12			,						a__=12			,						a__=30_72			,						a__="gelu"			,						a__=0.1			,						a__=0.1			,						a__=5_12			,						a__=2			,						a__=0.02			,						a__=1e-12			,						a__=0			,						a__="absolute"			,						a__=True			,						**a__			,						) ->      List[str]:






            '''simple docstring'''





            super().__init__(**a__    )

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



       @classmethod
       def 					__lowercase						(							cls			,						a__			,						**a__    ) ->      "PretrainedConfig":






            '''simple docstring'''





            cls._set_token_in_kwargs(a__    )

            __snake_case       , __snake_case			:Tuple          =						cls.get_config_dict(a__			,						**a__    )

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




class 			snake_case__   (							lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				Optional[int] 				= "align_vision_model"



       def __init__(							self			,						a__ = 3			,						a__ = 6_00			,						a__ = 2.0			,						a__ = 3.1			,						a__ = 8			,						a__ = [3, 3, 5, 3, 5, 5, 3]			,						a__ = [32, 16, 24, 40, 80, 1_12, 1_92]			,						a__ = [16, 24, 40, 80, 1_12, 1_92, 3_20]			,						a__ = []			,						a__ = [1, 2, 2, 2, 1, 2, 1]			,						a__ = [1, 2, 2, 3, 3, 4, 1]			,						a__ = [1, 6, 6, 6, 6, 6, 6]			,						a__ = 0.25			,						a__ = "swish"			,						a__ = 25_60			,						a__ = "mean"			,						a__ = 0.02			,						a__ = 0.0_01			,						a__ = 0.99			,						a__ = 0.2			,						**a__			,						) ->      List[Any]:






            '''simple docstring'''





            super().__init__(**a__    )

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



       @classmethod
       def 					__lowercase						(							cls			,						a__			,						**a__    ) ->      "PretrainedConfig":






            '''simple docstring'''





            cls._set_token_in_kwargs(a__    )

            __snake_case       , __snake_case			:int          =						cls.get_config_dict(a__			,						**a__    )

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




class 			snake_case__   (							lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				str 				= "align"
       lowerCamelCase							:				Union[str, Any] 				= True



       def __init__(							self			,						a__=None			,						a__=None			,						a__=6_40			,						a__=1.0			,						a__=0.02			,						**a__			,						) ->      Dict:






            '''simple docstring'''





            super().__init__(**a__    )

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

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

            __snake_case			:List[Any]          =						AlignTextConfig(**a__    )
            __snake_case			:Tuple          =						AlignVisionConfig(**a__    )

            __snake_case			:Tuple          =						projection_dim
            __snake_case			:int          =						temperature_init_value
            __snake_case			:Any          =						initializer_range



       @classmethod
       def 					__lowercase						(							cls			,						a__			,						a__			,						**a__    ) ->      str:






            '''simple docstring'''





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



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						copy.deepcopy(self.__dict__    )
            __snake_case			:Dict          =						self.text_config.to_dict()
            __snake_case			:Union[str, Any]          =						self.vision_config.to_dict()
            __snake_case			:List[Any]          =						self.__class__.model_type
            return output


