

"""simple docstring"""


import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer





class        snake_case      (							__snake_case    ):
				SCREAMING_SNAKE_CASE_  : Any   =    ["image_processor", "tokenizer"]
				SCREAMING_SNAKE_CASE_  : Tuple   =    "BlipImageProcessor"
				SCREAMING_SNAKE_CASE_  : Any   =    "AutoTokenizer"

				def __init__(      self				:							Optional[int]	,    UpperCamelCase__				:							Tuple	,    UpperCamelCase__				:							Any	,    UpperCamelCase__				:							int)->  Dict:



						'''simple docstring'''


						super().__init__(UpperCamelCase__	,    UpperCamelCase__)

						# add QFormer tokenizer
						__lowerCAmelCase:			Union[str, Any]        =     qformer_tokenizer

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



						'''simple docstring'''


						if images is None and text is None:
								raise ValueError("You have to specify at least images or text.")

						__lowerCAmelCase:			Union[str, Any]        =     BatchFeature()

						if text is not None:
								__lowerCAmelCase:			Dict        =     self.tokenizer(
								    text=UpperCamelCase__	,    add_special_tokens=UpperCamelCase__	,    padding=UpperCamelCase__	,    truncation=UpperCamelCase__	,    max_length=UpperCamelCase__	,    stride=UpperCamelCase__	,    pad_to_multiple_of=UpperCamelCase__	,    return_attention_mask=UpperCamelCase__	,    return_overflowing_tokens=UpperCamelCase__	,    return_special_tokens_mask=UpperCamelCase__	,    return_offsets_mapping=UpperCamelCase__	,    return_token_type_ids=UpperCamelCase__	,    return_length=UpperCamelCase__	,    verbose=UpperCamelCase__	,    return_tensors=UpperCamelCase__	,    **UpperCamelCase__	,    )
								encoding.update(UpperCamelCase__)
								__lowerCAmelCase:			List[str]        =     self.qformer_tokenizer(
								    text=UpperCamelCase__	,    add_special_tokens=UpperCamelCase__	,    padding=UpperCamelCase__	,    truncation=UpperCamelCase__	,    max_length=UpperCamelCase__	,    stride=UpperCamelCase__	,    pad_to_multiple_of=UpperCamelCase__	,    return_attention_mask=UpperCamelCase__	,    return_overflowing_tokens=UpperCamelCase__	,    return_special_tokens_mask=UpperCamelCase__	,    return_offsets_mapping=UpperCamelCase__	,    return_token_type_ids=UpperCamelCase__	,    return_length=UpperCamelCase__	,    verbose=UpperCamelCase__	,    return_tensors=UpperCamelCase__	,    **UpperCamelCase__	,    )
								__lowerCAmelCase:			List[str]        =     qformer_text_encoding.pop("input_ids")
								__lowerCAmelCase:			Any        =     qformer_text_encoding.pop("attention_mask")

						if images is not None:
								__lowerCAmelCase:			List[str]        =     self.image_processor(UpperCamelCase__	,    return_tensors=UpperCamelCase__)
								encoding.update(UpperCamelCase__)

						return encoding

				def 				lowercase_	(      self				:							Dict	,    *UpperCamelCase__				:							List[Any]	,    **UpperCamelCase__				:							str)->  List[str]:



						'''simple docstring'''


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

				def 				lowercase_	(      self				:							Any	,    *UpperCamelCase__				:							int	,    **UpperCamelCase__				:							List[Any])->  Any:



						'''simple docstring'''


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

				@property
				# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
				def 				lowercase_	(      self				:							Optional[int])->  Optional[int]:



						'''simple docstring'''


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

				def 				lowercase_	(      self				:							Tuple	,    UpperCamelCase__				:							Any	,    **UpperCamelCase__				:							Optional[int])->  Any:



						'''simple docstring'''


						if os.path.isfile(UpperCamelCase__):
								raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file")
						os.makedirs(UpperCamelCase__	,    exist_ok=UpperCamelCase__)
						__lowerCAmelCase:			str        =     os.path.join(UpperCamelCase__	,    "qformer_tokenizer")
						self.qformer_tokenizer.save_pretrained(UpperCamelCase__)
						return super().save_pretrained(UpperCamelCase__	,    **UpperCamelCase__)




				@classmethod
				def 				lowercase_	(      cls				:							Tuple	,    UpperCamelCase__				:							Any	,    **UpperCamelCase__				:							List[Any])->  List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:			int        =     AutoTokenizer.from_pretrained(UpperCamelCase__	,    subfolder="qformer_tokenizer")
						__lowerCAmelCase:			Dict        =     cls._get_arguments_from_pretrained(UpperCamelCase__	,    **UpperCamelCase__)
						args.append(UpperCamelCase__)
						return cls(*UpperCamelCase__)







from __future__ import annotations

def 	_lowerCAmelCase (    A__    ,	A__ = None						):
      lowercase__  =	word_bank or []
      # create a table
      lowercase__  =	len(A__						) + 1

      lowercase__  =	[]
      for _ in range(A__						):
            table.append([]						)
      # seed value
      lowercase__  =	[[]]  # because empty string has empty combination

      # iterate through the indices
      for i in range(A__						):
            # condition
            if table[i] != []:
                  for word in word_bank:
                        # slice condition
                        if target[i : i + len(A__						)] == word:
                              lowercase__  =	[
                                  [word, *way] for way in table[i]
                              ]
                              # adds the word to every combination the current position holds
                              # now,push that combination to the table[i+len(word)]
                              table[i + len(A__						)] += new_combinations

    # combinations are in reverse order so reverse for better output
      for combination in table[len(A__						)]:
            combination.reverse()

      return table[len(A__						)]


if __name__ == "__main__":
       print(all_construct("jwajalapa", ["jwa", "j", "w", "a", "la", "lapa"]))
       print(all_construct("rajamati", ["s", "raj", "amat", "raja", "ma", "i", "t"]))
       print(
           all_construct(
               "hexagonosaurus",
               ["h", "ex", "hex", "ag", "ago", "ru", "auru", "rus", "go", "no", "o", "s"],
           )
       )
