

'''simple docstring'''
import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 						__magic_name__   (							_UpperCamelCase ,				unittest.TestCase ):
 UpperCamelCase__     =       GPTSanJapaneseTokenizer
 UpperCamelCase__     =       False
 UpperCamelCase__     =       {"""do_clean_text""": False, """add_prefix_space""": False}







 def 						_A(       self   ):
   super().setUp()

   # fmt: off
   lowercase									=["こん", "こんに", "にちは", "ばんは", "世界,㔺界", "、", "。", "<BR>", "<SP>", "<TAB>", "<URL>", "<EMAIL>", "<TEL>", "<DATE>", "<PRICE>", "<BLOCK>", "<KIGOU>", "<U2000U2BFF>", "<|emoji1|>", "<unk>", "<|bagoftoken|>", "<|endoftext|>"]
   # fmt: on
   lowercase									={"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}}  # 😀
   lowercase									={"unk_token": "<unk>"}

   lowercase									=os.path.join(self.tmpdirname  , VOCAB_FILES_NAMES['''vocab_file''']   )
   lowercase									=os.path.join(self.tmpdirname  , VOCAB_FILES_NAMES['''emoji_file''']   )
   with open(self.vocab_file  , '''w'''  , encoding='''utf-8'''   ) as vocab_writer:
     vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]   )   )
   with open(self.emoji_file  , '''w'''   ) as emoji_writer:
     emoji_writer.write(json.dumps(snake_case_   )   )







 def 						_A(       self  , **snake_case_   ):
   kwargs.update(self.special_tokens_map   )
   return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname  , **snake_case_   )







 def 						_A(       self  , snake_case_   ):
   lowercase									="こんにちは、世界。 \nこんばんは、㔺界。😀"
   lowercase									="こんにちは、世界。 \nこんばんは、世界。😀"
   return input_text, output_text







 def 						_A(       self  , snake_case_   ):
   lowercase									=self.get_input_output_texts(snake_case_   )
   lowercase									=tokenizer.encode(snake_case_  , add_special_tokens=snake_case_   )
   lowercase									=tokenizer.decode(snake_case_  , clean_up_tokenization_spaces=snake_case_   )
   return text, ids







 def 						_A(       self   ):
   pass  # TODO add if relevant







 def 						_A(       self   ):
   pass  # TODO add if relevant







 def 						_A(       self   ):
   pass  # TODO add if relevant







 def 						_A(       self   ):
   lowercase									=self.get_tokenizer()

   # Testing tokenization
   lowercase									="こんにちは、世界。　こんばんは、㔺界。"
   lowercase									=["こん", "にちは", "、", "世界", "。", "<SP>", "こん", "ばんは", "、", "㔺界", "。"]
   lowercase									=tokenizer.tokenize(snake_case_   )
   self.assertListEqual(snake_case_  , snake_case_   )

   # Testing conversion to ids without special tokens
   lowercase									=[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
   lowercase									=tokenizer.convert_tokens_to_ids(snake_case_   )
   self.assertListEqual(snake_case_  , snake_case_   )

   # Testing conversion to ids with special tokens
   lowercase									=tokens + [tokenizer.unk_token]
   lowercase									=[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
   lowercase									=tokenizer.convert_tokens_to_ids(snake_case_   )
   self.assertListEqual(snake_case_  , snake_case_   )







 def 						_A(       self   ):
   lowercase									=self.get_tokenizer()

   # Testing tokenization
   lowercase									="こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。"
   lowercase									="こんにちは、、、、世界。こんばんは、、、、世界。"
   lowercase									=tokenizer.encode(snake_case_   )
   lowercase									=tokenizer.decode(snake_case_   )
   self.assertEqual(snake_case_  , snake_case_   )







 @slow
 def 						_A(       self   ):
   lowercase									=self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese'''   )

   # Testing tokenization
   lowercase									="こんにちは、世界。"
   lowercase									="こんばんは、㔺界。😀"
   lowercase									="こんにちは、世界。こんばんは、世界。😀"
   lowercase									=tokenizer.encode(prefix_text + input_text   )
   lowercase									=tokenizer.encode(''''''  , prefix_text=prefix_text + input_text   )
   lowercase									=tokenizer.encode(snake_case_  , prefix_text=snake_case_   )
   lowercase									=tokenizer.decode(snake_case_   )
   lowercase									=tokenizer.decode(snake_case_   )
   lowercase									=tokenizer.decode(snake_case_   )
   self.assertEqual(snake_case_  , snake_case_   )
   self.assertEqual(snake_case_  , snake_case_   )
   self.assertEqual(snake_case_  , snake_case_   )







 @slow
 def 						_A(       self   ):
   lowercase									=self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese'''   )

   # Testing tokenization
   lowercase									="こんにちは、世界。"
   lowercase									="こんばんは、㔺界。😀"

   lowercase									=len(tokenizer.encode(snake_case_   )   ) - 2
   lowercase									=len(tokenizer.encode(snake_case_   )   ) - 2

   lowercase									=[1] + [0] * (len_prefix + len_text + 1)
   lowercase									=[1] * (len_prefix + len_text + 1) + [0]
   lowercase									=[1] + [1] * (len_prefix) + [0] * (len_text + 1)

   lowercase									=tokenizer(prefix_text + input_text   ).token_type_ids
   lowercase									=tokenizer(''''''  , prefix_text=prefix_text + input_text   ).token_type_ids
   lowercase									=tokenizer(snake_case_  , prefix_text=snake_case_   ).token_type_ids
   self.assertListEqual(snake_case_  , snake_case_   )
   self.assertListEqual(snake_case_  , snake_case_   )
   self.assertListEqual(snake_case_  , snake_case_   )







 @slow
 def 						_A(       self   ):
   lowercase									=self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese'''   )

   lowercase									=tokenizer.encode('''あンいワ'''   )
   lowercase									=tokenizer.encode(''''''  , prefix_text='''あンいワ'''   )
   lowercase									=tokenizer.encode('''いワ'''  , prefix_text='''あン'''   )

   self.assertEqual(tokenizer.decode(snake_case_   )  , tokenizer.decode(snake_case_   )   )
   self.assertEqual(tokenizer.decode(snake_case_   )  , tokenizer.decode(snake_case_   )   )
   self.assertNotEqual(snake_case_  , snake_case_   )
   self.assertNotEqual(snake_case_  , snake_case_   )
   self.assertEqual(x_token_a[1]  , x_token_a[-1]   )  # SEG token
   self.assertEqual(x_token_a[1]  , x_token_a[3]   )  # SEG token







 @slow
 def 						_A(       self   ):
   lowercase									=self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese'''   )

   lowercase									=[["武田信玄", "は、"], ["織田信長", "の配下の、"]]
   lowercase									=tokenizer(snake_case_  , padding=snake_case_   )
   lowercase									=tokenizer.batch_encode_plus(snake_case_  , padding=snake_case_   )

   # fmt: off
   lowercase									=[[3_59_93, 86_40, 2_59_48, 3_59_98, 3_06_47, 3_56_75, 3_59_99, 3_59_99], [3_59_93, 1_03_82, 98_68, 3_59_98, 3_06_46, 94_59, 3_06_46, 3_56_75]]
   lowercase									=[[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
   lowercase									=[[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
   # fmt: on
   self.assertListEqual(x_token.input_ids  , snake_case_   )
   self.assertListEqual(x_token.token_type_ids  , snake_case_   )
   self.assertListEqual(x_token.attention_mask  , snake_case_   )
   self.assertListEqual(x_token_a.input_ids  , snake_case_   )
   self.assertListEqual(x_token_a.token_type_ids  , snake_case_   )
   self.assertListEqual(x_token_a.attention_mask  , snake_case_   )







 def 						_A(       self   ):
   pass







 def 						_A(       self   ):
   pass







import copy
import os
from typing import Union

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


SCREAMING_SNAKE_CASE__							:      Tuple         =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__							:      int         =    {
    """google/pix2struct-textcaps-base""": (
        """https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"""
    ),
}







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    Optional[int]  		=					"""pix2struct_text_model"""
   _UpperCamelCase			:    Dict  		=					["""past_key_values"""]
   _UpperCamelCase			:    Union[str, Any]  		=					{
       """hidden_size""": """hidden_size""",
       """num_attention_heads""": """num_heads""",
       """num_hidden_layers""": """num_layers""",
   }





   def __init__(			self       ,      snake_case=50_244       ,      snake_case=768       ,      snake_case=64       ,      snake_case=2_048       ,      snake_case=12       ,      snake_case=12       ,      snake_case=32       ,      snake_case=128       ,      snake_case=0.1       ,      snake_case=1E-6       ,      snake_case=1.0       ,      snake_case="gelu_new"       ,      snake_case=0       ,      snake_case=False       ,      snake_case=0       ,      snake_case=1       ,      snake_case=False       ,      snake_case=True       ,      **snake_case       ,      )					->	Dict:
      """simple docstring"""





      a__						:    Tuple   		=	vocab_size
      a__						:    List[str]   		=	hidden_size
      a__						:    Optional[Any]   		=	d_kv
      a__						:    List[str]   		=	d_ff
      a__						:    Optional[int]   		=	num_layers
      a__						:    Tuple   		=	num_heads
      a__						:    List[Any]   		=	relative_attention_num_buckets
      a__						:    Optional[int]   		=	relative_attention_max_distance
      a__						:    Optional[int]   		=	dropout_rate
      a__						:    List[Any]   		=	layer_norm_epsilon
      a__						:    List[str]   		=	initializer_factor
      a__						:    Any   		=	use_cache

      a__						:    Any   		=	eos_token_id
      a__						:    List[Any]   		=	decoder_start_token_id

      # for backwards compatibility
      a__						:    Tuple   		=	dense_act_fn

      super().__init__(
          pad_token_id=snake_case       ,      eos_token_id=snake_case       ,      decoder_start_token_id=snake_case       ,      tie_word_embeddings=snake_case       ,      is_decoder=snake_case       ,      **snake_case       ,      )





   @classmethod
   def 						_snake_case			(			cls       ,      snake_case       ,      **snake_case   )					->	"PretrainedConfig":
      """simple docstring"""





      cls._set_token_in_kwargs(snake_case   )

      a__		,  a__						:    Optional[Any]   		=	cls.get_config_dict(snake_case       ,      **snake_case   )

      # get the text config dict if we are loading from Pix2StructConfig
      if config_dict.get("model_type"   ) == "pix2struct":
         a__						:    Optional[int]   		=	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(snake_case       ,      **snake_case   )







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    int  		=					"""pix2struct_vision_model"""





   def __init__(			self       ,      snake_case=768       ,      snake_case=768       ,      snake_case=2_048       ,      snake_case=64       ,      snake_case=12       ,      snake_case=12       ,      snake_case="gelu_new"       ,      snake_case=1E-6       ,      snake_case=0.0       ,      snake_case=0.0       ,      snake_case=1E-10       ,      snake_case=1.0       ,      snake_case=4_096       ,      snake_case=32       ,      snake_case=128       ,      **snake_case       ,      )					->	Optional[Any]:
      """simple docstring"""





      super().__init__(**snake_case   )

      a__						:    Optional[Any]   		=	hidden_size
      a__						:    Tuple   		=	patch_embed_hidden_size
      a__						:    Tuple   		=	d_ff
      a__						:    List[Any]   		=	dropout_rate
      a__						:    Optional[Any]   		=	num_hidden_layers
      a__						:    Optional[Any]   		=	num_attention_heads
      a__						:    List[str]   		=	initializer_range
      a__						:    List[str]   		=	initializer_factor
      a__						:    List[Any]   		=	attention_dropout
      a__						:    Optional[int]   		=	layer_norm_eps
      a__						:    int   		=	dense_act_fn
      a__						:    Optional[Any]   		=	seq_len
      a__						:    List[Any]   		=	relative_attention_num_buckets
      a__						:    int   		=	relative_attention_max_distance
      a__						:    int   		=	d_kv





   @classmethod
   def 						_snake_case			(			cls       ,      snake_case       ,      **snake_case   )					->	"PretrainedConfig":
      """simple docstring"""





      cls._set_token_in_kwargs(snake_case   )

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

      # get the vision config dict if we are loading from Pix2StructConfig
      if config_dict.get("model_type"   ) == "pix2struct":
         a__						:    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(snake_case       ,      **snake_case   )







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    List[Any]  		=					"""pix2struct"""
   _UpperCamelCase			:    Dict  		=					True





   def __init__(			self       ,      snake_case=None       ,      snake_case=None       ,      snake_case=1.0       ,      snake_case=0.02       ,      snake_case=False       ,      snake_case=False       ,      snake_case=True       ,      **snake_case       ,      )					->	Optional[int]:
      """simple docstring"""





      super().__init__(tie_word_embeddings=snake_case       ,      is_encoder_decoder=snake_case       ,      **snake_case   )

      if text_config is None:
         a__						:    Optional[Any]   		=	{}
         logger.info("text_config is None. Initializing the Pix2StructTextConfig with default values."   )

      if vision_config is None:
         a__						:    List[Any]   		=	{}
         logger.info("vision_config is None. Initializing the Pix2StructVisionConfig with default values."   )

      a__						:    List[str]   		=	PixaStructTextConfig(**snake_case   )
      a__						:    List[str]   		=	PixaStructVisionConfig(**snake_case   )

      a__						:    Tuple   		=	self.text_config.decoder_start_token_id
      a__						:    int   		=	self.text_config.pad_token_id
      a__						:    List[Any]   		=	self.text_config.eos_token_id

      a__						:    str   		=	initializer_factor
      a__						:    Dict   		=	initializer_range

      a__						:    Optional[int]   		=	self.initializer_range
      a__						:    Union[str, Any]   		=	self.initializer_range

      a__						:    Union[str, Any]   		=	is_vqa





   @classmethod
   def 						_snake_case			(			cls       ,      snake_case       ,      snake_case       ,      **snake_case   )					->	Dict:
      """simple docstring"""





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





   def 						_snake_case			(			self   )					->	int:
      """simple docstring"""





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

