

'''simple docstring'''
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 							A__   (	tf.keras.layers.Layer  ):





  def __init__(		self				:     Tuple							, _a				:     Dict[str, int]							, _a				:     List[str]							, _a				:     int = None							, _a				:     int = None			)     ->   str:





       '''simple docstring'''






       super().__init__()
       _SCREAMING_SNAKE_CASE							   =pad_token_id
       _SCREAMING_SNAKE_CASE							   =max_length
       _SCREAMING_SNAKE_CASE							   =vocab
       _SCREAMING_SNAKE_CASE							   =merges
       _SCREAMING_SNAKE_CASE							   =BytePairTokenizer(_a							, _a							, sequence_length=_a			)





  @classmethod
  def 						A  (		cls				:     Any							, _a				:     GPTaTokenizer							, *_a				:     List[str]							, **_a				:     Optional[int]			)     ->   Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =[' '.join(_a			) for m in tokenizer.bpe_ranks.keys()]
       _SCREAMING_SNAKE_CASE							   =tokenizer.get_vocab()
       return cls(_a							, _a							, *_a							, **_a			)





  @classmethod
  def 						A  (		cls				:     Tuple							, _a				:     Union[str, os.PathLike]							, *_a				:     List[str]							, **_a				:     Union[str, Any]			)     ->   Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =GPTaTokenizer.from_pretrained(_a							, *_a							, **_a			)
       return cls.from_tokenizer(_a							, *_a							, **_a			)





  @classmethod
  def 						A  (		cls				:     List[str]							, _a				:     List[str]			)     ->   Tuple:





       '''simple docstring'''






       return cls(**_a			)





  def 						A  (		self				:     Tuple			)     ->   List[str]:





       '''simple docstring'''






       return {
           "vocab": self.vocab,
           "merges": self.merges,
           "max_length": self.max_length,
           "pad_token_id": self.pad_token_id,
       }





  def 						A  (		self				:     List[Any]							, _a				:     List[Any]							, _a				:     int = None			)     ->   str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =self.tf_tokenizer(_a			)
       _SCREAMING_SNAKE_CASE							   =tf.ones_like(_a			)

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

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

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



'''simple docstring'''
import os
import sys
import unittest


lowerCamelCase			:				Optional[Any] 			=					os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
lowerCamelCase			:				Optional[int] 			=					os.path.join(git_repo_path, "src", "transformers")

lowerCamelCase			:				Union[str, Any] 			=					"\n{0} = None\n"

lowerCamelCase			:				Optional[Any] 			=					"\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


lowerCamelCase			:				List[Any] 			=					"\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"







class 							A__   (	unittest.TestCase  ):





  def 						A  (		self				:     Optional[int]			)     ->   Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =find_backend('    _import_structure["models.albert"].append("AlbertTokenizerFast")'			)
       self.assertIsNone(_a			)

       _SCREAMING_SNAKE_CASE							   =find_backend('    if not is_tokenizers_available():'			)
       self.assertEqual(_a							, 'tokenizers'			)

       _SCREAMING_SNAKE_CASE							   =find_backend('    if not is_tensorflow_text_available():'			)
       self.assertEqual(_a							, 'tensorflow_text'			)

       _SCREAMING_SNAKE_CASE							   =find_backend('    if not (is_sentencepiece_available() and is_tokenizers_available()):'			)
       self.assertEqual(_a							, 'sentencepiece_and_tokenizers'			)

       _SCREAMING_SNAKE_CASE							   =find_backend(
           '    if not (is_sentencepiece_available() and is_tensorflow_text_available()):'			)
       self.assertEqual(_a							, 'sentencepiece_and_tensorflow_text'			)

       _SCREAMING_SNAKE_CASE							   =find_backend(
           '    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'			)
       self.assertEqual(_a							, 'sentencepiece_and_tokenizers_and_vision'			)





  def 						A  (		self				:     Optional[int]			)     ->   Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =read_init()
       # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
       self.assertIn('torch'							, _a			)
       self.assertIn('tensorflow_text'							, _a			)
       self.assertIn('sentencepiece_and_tokenizers'							, _a			)

       # Likewise, we can't assert on the exact content of a key
       self.assertIn('BertModel'							, objects['torch']			)
       self.assertIn('TFBertModel'							, objects['tf']			)
       self.assertIn('FlaxBertModel'							, objects['flax']			)
       self.assertIn('BertModel'							, objects['torch']			)
       self.assertIn('TFBertTokenizer'							, objects['tensorflow_text']			)
       self.assertIn('convert_slow_tokenizer'							, objects['sentencepiece_and_tokenizers']			)





  def 						A  (		self				:     Optional[int]			)     ->   List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =create_dummy_object('CONSTANT'							, '\'torch\''			)
       self.assertEqual(_a							, '\nCONSTANT = None\n'			)

       _SCREAMING_SNAKE_CASE							   =create_dummy_object('function'							, '\'torch\''			)
       self.assertEqual(
           _a							, '\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'			)

       _SCREAMING_SNAKE_CASE							   ='\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'
       _SCREAMING_SNAKE_CASE							   =create_dummy_object('FakeClass'							, '\'torch\''			)
       self.assertEqual(_a							, _a			)





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   ='# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n'
       _SCREAMING_SNAKE_CASE							   =create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']}			)
       self.assertEqual(dummy_files['torch']							, _a			)
