



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 	lowerCamelCase__	(       tf.keras.layers.Layer):



 """simple docstring"""

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

 @classmethod
 def     snake_case_      (    cls					:					str			,						__lowerCAmelCase					:					GPTaTokenizer			,						*__lowerCAmelCase					:					Union[str, Any]			,						**__lowerCAmelCase					:					Dict      )   ->	str:
    _A      =       [''' '''.join(__lowerCAmelCase      ) for m in tokenizer.bpe_ranks.keys()]
    _A      =       tokenizer.get_vocab()
    return cls(__lowerCAmelCase			,						__lowerCAmelCase			,						*__lowerCAmelCase			,						**__lowerCAmelCase      )

 @classmethod
 def     snake_case_      (    cls					:					Tuple			,						__lowerCAmelCase					:					Union[str, os.PathLike]			,						*__lowerCAmelCase					:					str			,						**__lowerCAmelCase					:					Any      )   ->	Any:
    _A      =       GPTaTokenizer.from_pretrained(__lowerCAmelCase			,						*__lowerCAmelCase			,						**__lowerCAmelCase      )
    return cls.from_tokenizer(__lowerCAmelCase			,						*__lowerCAmelCase			,						**__lowerCAmelCase      )

 @classmethod
 def     snake_case_      (    cls					:					Union[str, Any]			,						__lowerCAmelCase					:					Tuple      )   ->	Dict:
    return cls(**__lowerCAmelCase      )

 def     snake_case_      (    self					:					Tuple      )   ->	Tuple:
    return {
        "vocab": self.vocab,
        "merges": self.merges,
        "max_length": self.max_length,
        "pad_token_id": self.pad_token_id,
    }

 def     snake_case_      (    self					:					List[Any]			,						__lowerCAmelCase					:					Dict			,						__lowerCAmelCase					:					int = None      )   ->	Any:
    _A      =       self.tf_tokenizer(__lowerCAmelCase      )
    _A      =       tf.ones_like(__lowerCAmelCase      )

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

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

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











"""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_			(__A	):
	__magic_name__       =							['''image_processor''', '''tokenizer''']
	__magic_name__       =							'''FlavaImageProcessor'''
	__magic_name__       =							('''BertTokenizer''', '''BertTokenizerFast''')





	def __init__( self   :					Optional[int]      ,      lowerCAmelCase_   :					Tuple=None      ,      lowerCAmelCase_   :					List[str]=None      ,      **lowerCAmelCase_   :					Union[str, Any]		)		->     List[Any]:
				UpperCAmelCase_	:					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."      ,      lowerCAmelCase_      ,      )
							UpperCAmelCase_	:					Tuple     	=   kwargs.pop("feature_extractor"		)

				UpperCAmelCase_	:					Optional[Any]     	=   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__(lowerCAmelCase_      ,      lowerCAmelCase_		)
				UpperCAmelCase_	:					List[str]     	=   self.image_processor





	def __call__( self   :					List[Any]      ,      lowerCAmelCase_   :					Optional[ImageInput] = None      ,      lowerCAmelCase_   :					Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None      ,      lowerCAmelCase_   :					bool = True      ,      lowerCAmelCase_   :					Union[bool, str, PaddingStrategy] = False      ,      lowerCAmelCase_   :					Union[bool, str, TruncationStrategy] = False      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					int = 0      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					Optional[bool] = None      ,      lowerCAmelCase_   :					Optional[bool] = None      ,      lowerCAmelCase_   :					Optional[bool] = None      ,      lowerCAmelCase_   :					Optional[bool] = None      ,      lowerCAmelCase_   :					bool = False      ,      lowerCAmelCase_   :					bool = False      ,      lowerCAmelCase_   :					bool = False      ,      lowerCAmelCase_   :					bool = False      ,      lowerCAmelCase_   :					bool = True      ,      lowerCAmelCase_   :					Optional[Union[str, TensorType]] = None      ,      **lowerCAmelCase_   :					Optional[int]      ,      )		->     List[Any]:

				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:
							UpperCAmelCase_	:					Union[str, Any]     	=   self.tokenizer(
							    text=lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_      ,      padding=lowerCAmelCase_      ,      truncation=lowerCAmelCase_      ,      max_length=lowerCAmelCase_      ,      stride=lowerCAmelCase_      ,      pad_to_multiple_of=lowerCAmelCase_      ,      return_token_type_ids=lowerCAmelCase_      ,      return_attention_mask=lowerCAmelCase_      ,      return_overflowing_tokens=lowerCAmelCase_      ,      return_special_tokens_mask=lowerCAmelCase_      ,      return_offsets_mapping=lowerCAmelCase_      ,      return_length=lowerCAmelCase_      ,      verbose=lowerCAmelCase_      ,      return_tensors=lowerCAmelCase_      ,      **lowerCAmelCase_      ,      )
				if images is not None:
							UpperCAmelCase_	:					Optional[Any]     	=   self.image_processor(
							    lowerCAmelCase_      ,      return_image_mask=lowerCAmelCase_      ,      return_codebook_pixels=lowerCAmelCase_      ,      return_tensors=lowerCAmelCase_      ,      **lowerCAmelCase_      ,      )

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





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      *lowerCAmelCase_   :					int      ,      **lowerCAmelCase_   :					List[str]		)		->     Any:
				return self.tokenizer.batch_decode(*lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      *lowerCAmelCase_   :					List[str]      ,      **lowerCAmelCase_   :					Optional[int]		)		->     List[Any]:
				return self.tokenizer.decode(*lowerCAmelCase_      ,      **lowerCAmelCase_		)





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     int:
				UpperCAmelCase_	:					List[Any]     	=   self.tokenizer.model_input_names
				UpperCAmelCase_	:					Any     	=   self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     Optional[Any]:
				warnings.warn(
				    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."      ,      lowerCAmelCase_      ,      )
				return self.image_processor_class





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     int:
				warnings.warn(
				    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."      ,      lowerCAmelCase_      ,      )
				return self.image_processor



