





'''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 			SCREAMING_SNAKE_CASE   (tf.keras.layers.Layer							):


     def __init__(						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase = None						,     _UpperCAmelCase = None):

          '''simple docstring'''






          super().__init__()
          __A						:			int								=	pad_token_id
          __A						:			List[str]								=	max_length
          __A						:			Any								=	vocab
          __A						:			List[str]								=	merges
          __A						:			Tuple								=	BytePairTokenizer(_UpperCAmelCase						,     _UpperCAmelCase						,     sequence_length=_UpperCAmelCase)


     @classmethod
     def 							SCREAMING_SNAKE_CASE  (						cls						,     _UpperCAmelCase						,     *_UpperCAmelCase						,     **_UpperCAmelCase):

          '''simple docstring'''






          __A						:			int								=	[' '.join(_UpperCAmelCase) for m in tokenizer.bpe_ranks.keys()]
          __A						:			str								=	tokenizer.get_vocab()
          return cls(_UpperCAmelCase						,     _UpperCAmelCase						,     *_UpperCAmelCase						,     **_UpperCAmelCase)


     @classmethod
     def 							SCREAMING_SNAKE_CASE  (						cls						,     _UpperCAmelCase						,     *_UpperCAmelCase						,     **_UpperCAmelCase):

          '''simple docstring'''






          __A						:			Dict								=	GPTaTokenizer.from_pretrained(_UpperCAmelCase						,     *_UpperCAmelCase						,     **_UpperCAmelCase)
          return cls.from_tokenizer(_UpperCAmelCase						,     *_UpperCAmelCase						,     **_UpperCAmelCase)


     @classmethod
     def 							SCREAMING_SNAKE_CASE  (						cls						,     _UpperCAmelCase):

          '''simple docstring'''






          return cls(**_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase = None):

          '''simple docstring'''






          __A						:			str								=	self.tf_tokenizer(_UpperCAmelCase)
          __A						:			Tuple								=	tf.ones_like(_UpperCAmelCase)

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

               if max_length is not None:
                    __A						:			Optional[int]								=	pad_model_inputs(
                        _UpperCAmelCase						,     max_seq_length=_UpperCAmelCase						,     pad_value=self.pad_token_id)

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






'''simple docstring'''




from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 			SCREAMING_SNAKE_CASE   (a__							):
     lowerCAmelCase		      =	['''image_processor''', '''tokenizer''']
     lowerCAmelCase		      =	'''AutoImageProcessor'''
     lowerCAmelCase		      =	'''AutoTokenizer'''


     def __init__(						self						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          super().__init__(_UpperCAmelCase						,     _UpperCAmelCase)
          __A						:			Tuple								=	self.image_processor


     def __call__(						self						,     _UpperCAmelCase=None						,     _UpperCAmelCase=None						,     _UpperCAmelCase=None						,     **_UpperCAmelCase):

          '''simple docstring'''






          if text is None and images is None:
               raise ValueError('You have to specify either text or images. Both cannot be none.')

          if text is not None:
               __A						:			Any								=	self.tokenizer(_UpperCAmelCase						,     return_tensors=_UpperCAmelCase						,     **_UpperCAmelCase)

          if images is not None:
               __A						:			Tuple								=	self.image_processor(_UpperCAmelCase						,     return_tensors=_UpperCAmelCase						,     **_UpperCAmelCase)

          if text is not None and images is not None:
               __A						:			Optional[int]								=	image_features.pixel_values
               return encoding
          elif text is not None:
               return encoding
          else:
               return BatchEncoding(data=dict(**_UpperCAmelCase)						,     tensor_type=_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,     *_UpperCAmelCase						,     **_UpperCAmelCase):

          '''simple docstring'''






          return self.tokenizer.batch_decode(*_UpperCAmelCase						,     **_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,     *_UpperCAmelCase						,     **_UpperCAmelCase):

          '''simple docstring'''






          return self.tokenizer.decode(*_UpperCAmelCase						,     **_UpperCAmelCase)


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return ["input_ids", "attention_mask", "pixel_values"]