


'''simple docstring'''







import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    ["image_processor", "tokenizer"]
     __magic_name__    			=    "FlavaImageProcessor"
     __magic_name__    			=    ("BertTokenizer", "BertTokenizerFast")







     def __init__( self      ,	snake_case__=None      ,	snake_case__=None      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       None
            if "feature_extractor" in kwargs:
                   warnings.warn(
                       'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                       ' instead.'      ,	snake_case__      ,	)
                   _lowerCAmelCase    :							Optional[int]   			=       kwargs.pop('feature_extractor'   )

            _lowerCAmelCase    :							str   			=       image_processor if image_processor is not None else feature_extractor
            if image_processor is None:
                   raise ValueError('You need to specify an `image_processor`.'   )
            if tokenizer is None:
                   raise ValueError('You need to specify a `tokenizer`.'   )

            super().__init__(snake_case__      ,	snake_case__   )
            _lowerCAmelCase    :							str   			=       self.image_processor







     def __call__( self      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = True      ,	snake_case__ = False      ,	snake_case__ = False      ,	snake_case__ = None      ,	snake_case__ = 0      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = False      ,	snake_case__ = False      ,	snake_case__ = False      ,	snake_case__ = False      ,	snake_case__ = True      ,	snake_case__ = None      ,	**snake_case__      ,	):

            '''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:
                   _lowerCAmelCase    :							List[Any]   			=       self.tokenizer(
                       text=snake_case__      ,	add_special_tokens=snake_case__      ,	padding=snake_case__      ,	truncation=snake_case__      ,	max_length=snake_case__      ,	stride=snake_case__      ,	pad_to_multiple_of=snake_case__      ,	return_token_type_ids=snake_case__      ,	return_attention_mask=snake_case__      ,	return_overflowing_tokens=snake_case__      ,	return_special_tokens_mask=snake_case__      ,	return_offsets_mapping=snake_case__      ,	return_length=snake_case__      ,	verbose=snake_case__      ,	return_tensors=snake_case__      ,	**snake_case__      ,	)
            if images is not None:
                   _lowerCAmelCase    :							Dict   			=       self.image_processor(
                       snake_case__      ,	return_image_mask=snake_case__      ,	return_codebook_pixels=snake_case__      ,	return_tensors=snake_case__      ,	**snake_case__      ,	)

            if text is not None and images is not None:
                   encoding.update(snake_case__   )
                   return encoding
            elif text is not None:
                   return encoding
            else:
                   return BatchEncoding(data=dict(**snake_case__   )      ,	tensor_type=snake_case__   )







     def a	( self      ,	*snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

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







     def a	( self      ,	*snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

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







     @property
     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       self.tokenizer.model_input_names
            _lowerCAmelCase    :							Optional[Any]   			=       self.image_processor.model_input_names
            return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )







     @property
     def a	( self   ):

            '''simple docstring'''

            warnings.warn(
                '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'      ,	snake_case__      ,	)
            return self.image_processor_class







     @property
     def a	( self   ):

            '''simple docstring'''

            warnings.warn(
                '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'      ,	snake_case__      ,	)
            return self.image_processor





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



     """simple docstring"""






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

            '''simple docstring'''

            super().__init__()
            _lowerCAmelCase    :							Union[str, Any]   			=       pad_token_id
            _lowerCAmelCase    :							List[Any]   			=       max_length
            _lowerCAmelCase    :							Tuple   			=       vocab
            _lowerCAmelCase    :							str   			=       merges
            _lowerCAmelCase    :							List[str]   			=       BytePairTokenizer(snake_case__      ,	snake_case__      ,	sequence_length=snake_case__   )







     @classmethod
     def a	( cls      ,	snake_case__      ,	*snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=       [' '.join(snake_case__   ) for m in tokenizer.bpe_ranks.keys()]
            _lowerCAmelCase    :							Any   			=       tokenizer.get_vocab()
            return cls(snake_case__      ,	snake_case__      ,	*snake_case__      ,	**snake_case__   )







     @classmethod
     def a	( cls      ,	snake_case__      ,	*snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       GPTaTokenizer.from_pretrained(snake_case__      ,	*snake_case__      ,	**snake_case__   )
            return cls.from_tokenizer(snake_case__      ,	*snake_case__      ,	**snake_case__   )







     @classmethod
     def a	( cls      ,	snake_case__   ):

            '''simple docstring'''

            return cls(**snake_case__   )







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self      ,	snake_case__      ,	snake_case__ = None   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       self.tf_tokenizer(snake_case__   )
            _lowerCAmelCase    :							str   			=       tf.ones_like(snake_case__   )

            if self.pad_token_id is not None:
                   # pad the tokens up to max length
                   _lowerCAmelCase    :							Optional[int]   			=       max_length if max_length is not None else self.max_length

                   if max_length is not None:
                          _lowerCAmelCase			,     _lowerCAmelCase    :							str   			=       pad_model_inputs(
                              snake_case__      ,	max_seq_length=snake_case__      ,	pad_value=self.pad_token_id   )

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

