






"""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       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				["image_processor", "tokenizer"]
					UpperCAmelCase__								=				"FlavaImageProcessor"
					UpperCAmelCase__								=				("BertTokenizer", "BertTokenizerFast")

					def __init__(			self  :      Optional[Any]     ,		__snake_case  :      Dict=None     ,		__snake_case  :      List[str]=None     ,		**__snake_case  :      int					)			->							Any:
											__magic_name__:	str							=       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     ,		)
																	__magic_name__:	List[str]							=       kwargs.pop("""feature_extractor"""					)

											__magic_name__:	Optional[int]							=       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					)
											__magic_name__:	Any							=       self.image_processor

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

											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:
																	__magic_name__:	Optional[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:
																	__magic_name__:	List[str]							=       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   lowerCamelCase__   (			self  :      Union[str, Any]     ,		*__snake_case  :      Optional[int]     ,		**__snake_case  :      Optional[int]					)			->							Dict:
											return self.tokenizer.batch_decode(*__snake_case     ,		**__snake_case					)

					def   lowerCamelCase__   (			self  :      Dict     ,		*__snake_case  :      List[Any]     ,		**__snake_case  :      Optional[int]					)			->							int:
											return self.tokenizer.decode(*__snake_case     ,		**__snake_case					)

					@property
					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							str:
											__magic_name__:	List[str]							=       self.tokenizer.model_input_names
											__magic_name__:	List[Any]							=       self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)

					@property
					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							Union[str, Any]:
											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   lowerCamelCase__   (			self  :      Optional[Any]					)			->							List[str]:
											warnings.warn(
											    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""     ,		__snake_case     ,		)
											return self.image_processor






import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text





def      A__					(			__A  :						List[str]			)					->str:
      __A    =[]
      for line in lines:
            __A    =re.sub(r'''#.*'''     ,				''''''     ,				__A			)  # remove comments
            if line:
                  filtered_lines.append(__A			)
      __A    ='''\n'''.join(__A			)

      # Make a hash from all this code
      __A    =full_str.encode('''utf-8'''			)
      return shaaaa(__A			).hexdigest()


# get importable module names and hash for caching
_lowerCamelCase      :					Dict					    =      {
    '''csv''': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    '''json''': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    '''pandas''': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    '''parquet''': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    '''arrow''': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    '''text''': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    '''imagefolder''': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    '''audiofolder''': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
_lowerCamelCase      :					int					    =      {
    '''.csv''': ('''csv''', {}),
    '''.tsv''': ('''csv''', {'''sep''': '''\t'''}),
    '''.json''': ('''json''', {}),
    '''.jsonl''': ('''json''', {}),
    '''.parquet''': ('''parquet''', {}),
    '''.arrow''': ('''arrow''', {}),
    '''.txt''': ('''text''', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('''imagefolder''', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('''imagefolder''', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('''audiofolder''', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('''audiofolder''', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_lowerCamelCase      :					List[str]					    =      {'''imagefolder''', '''audiofolder'''}

# Used to filter data files based on extensions given a module name
_lowerCamelCase      :					Dict[str, List[str]]					    =      {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
   _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append('''.zip''')


_MODULE_TO_EXTENSIONS["audiofolder"].append('''.zip''')


