




import os
import sys
import unittest


_snake_case     :   Dict            = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


_snake_case     :   Optional[int]            = os.path.join("tests", "models", "bert", "test_modeling_bert.py")
_snake_case     :   List[Any]            = os.path.join("tests", "models", "blip", "test_modeling_blip.py")







class 		lowercase							(     unittest.TestCase	):

     def       a__			(						self       )		->							Tuple:
           _A							:  Optional[int]    =		get_test_to_tester_mapping(A_       )
           _A							:  List[str]    =		get_test_to_tester_mapping(A_       )

           _A							:  int    =		{"BertModelTest": "BertModelTester"}

           _A							:  List[Any]    =		{
               "BlipModelTest": "BlipModelTester",
               "BlipTextImageModelTest": "BlipTextImageModelsModelTester",
               "BlipTextModelTest": "BlipTextModelTester",
               "BlipTextRetrievalModelTest": "BlipTextRetrievalModelTester",
               "BlipVQAModelTest": "BlipVQAModelTester",
               "BlipVisionModelTest": "BlipVisionModelTester",
           }

           self.assertEqual(get_test_info.to_json(A_       )	,  A_       )
           self.assertEqual(get_test_info.to_json(A_       )	,  A_       )

     def       a__			(						self       )		->							int:
           _A							:  List[Any]    =		get_model_to_test_mapping(A_       )
           _A							:  List[str]    =		get_model_to_test_mapping(A_       )

           _A							:  List[str]    =		{
               "BertForMaskedLM": ["BertModelTest"],
               "BertForMultipleChoice": ["BertModelTest"],
               "BertForNextSentencePrediction": ["BertModelTest"],
               "BertForPreTraining": ["BertModelTest"],
               "BertForQuestionAnswering": ["BertModelTest"],
               "BertForSequenceClassification": ["BertModelTest"],
               "BertForTokenClassification": ["BertModelTest"],
               "BertLMHeadModel": ["BertModelTest"],
               "BertModel": ["BertModelTest"],
           }

           _A							:  int    =		{
               "BlipForConditionalGeneration": ["BlipTextImageModelTest"],
               "BlipForImageTextRetrieval": ["BlipTextRetrievalModelTest"],
               "BlipForQuestionAnswering": ["BlipVQAModelTest"],
               "BlipModel": ["BlipModelTest"],
               "BlipTextModel": ["BlipTextModelTest"],
               "BlipVisionModel": ["BlipVisionModelTest"],
           }

           self.assertEqual(get_test_info.to_json(A_       )	,  A_       )
           self.assertEqual(get_test_info.to_json(A_       )	,  A_       )



     def       a__			(						self       )		->							Dict:
           _A							:  List[str]    =		get_model_to_tester_mapping(A_       )
           _A							:  Tuple    =		get_model_to_tester_mapping(A_       )

           _A							:  Union[str, Any]    =		{
               "BertForMaskedLM": ["BertModelTester"],
               "BertForMultipleChoice": ["BertModelTester"],
               "BertForNextSentencePrediction": ["BertModelTester"],
               "BertForPreTraining": ["BertModelTester"],
               "BertForQuestionAnswering": ["BertModelTester"],
               "BertForSequenceClassification": ["BertModelTester"],
               "BertForTokenClassification": ["BertModelTester"],
               "BertLMHeadModel": ["BertModelTester"],
               "BertModel": ["BertModelTester"],
           }

           _A							:  Tuple    =		{
               "BlipForConditionalGeneration": ["BlipTextImageModelsModelTester"],
               "BlipForImageTextRetrieval": ["BlipTextRetrievalModelTester"],
               "BlipForQuestionAnswering": ["BlipVQAModelTester"],
               "BlipModel": ["BlipModelTester"],
               "BlipTextModel": ["BlipTextModelTester"],
               "BlipVisionModel": ["BlipVisionModelTester"],
           }

           self.assertEqual(get_test_info.to_json(A_       )	,  A_       )
           self.assertEqual(get_test_info.to_json(A_       )	,  A_       )







import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer







class 		lowercase							(     tf.keras.layers.Layer	):

     def __init__(						self	,  _a	,  _a	,  _a = None	,  _a = None       )		->							Any:
           super().__init__()
           _A							:  Dict    =		pad_token_id
           _A							:  List[Any]    =		max_length
           _A							:  Optional[int]    =		vocab
           _A							:  Optional[int]    =		merges
           _A							:  Optional[int]    =		BytePairTokenizer(_a	,  _a	,  sequence_length=_a       )

     @classmethod
     def       a__			(						cls	,  _a	,  *_a	,  **_a       )		->							str:
           _A							:  Any    =		[""" """.join(_a       ) for m in tokenizer.bpe_ranks.keys()]
           _A							:  str    =		tokenizer.get_vocab()
           return cls(_a	,  _a	,  *_a	,  **_a       )

     @classmethod
     def       a__			(						cls	,  _a	,  *_a	,  **_a       )		->							List[Any]:
           _A							:  Union[str, Any]    =		GPTaTokenizer.from_pretrained(_a	,  *_a	,  **_a       )
           return cls.from_tokenizer(_a	,  *_a	,  **_a       )

     @classmethod
     def       a__			(						cls	,  _a       )		->							Union[str, Any]:
           return cls(**_a       )

     def       a__			(						self       )		->							Union[str, Any]:
           return {
               "vocab": self.vocab,
               "merges": self.merges,
               "max_length": self.max_length,
               "pad_token_id": self.pad_token_id,
           }



     def       a__			(						self	,  _a	,  _a = None       )		->							int:
           _A							:  Optional[int]    =		self.tf_tokenizer(_a       )
           _A							:  Tuple    =		tf.ones_like(_a       )

           if self.pad_token_id is not None:
                 # pad the tokens up to max length
                 _A							:  Dict    =		max_length if max_length is not None else self.max_length

                 if max_length is not None:
                       _A    ,     _A							:  Dict    =		pad_model_inputs(
                           _a	,  max_seq_length=_a	,  pad_value=self.pad_token_id       )

           return {"attention_mask": attention_mask, "input_ids": input_ids}

