





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       (       _UpperCamelCase       ):

  """simple docstring"""


  a_              =					["""image_processor""", """tokenizer"""]
  a_              =					"""BlipImageProcessor"""
  a_              =					"""AutoTokenizer"""






  def __init__(							self					:						Optional[int]   ,    lowerCAmelCase_					:						Optional[int]   ,    lowerCAmelCase_					:						str	)				->		Optional[Any]:
     __lowerCAmelCase				      =      False
     super().__init__(lowerCAmelCase_   ,    lowerCAmelCase_	)
     __lowerCAmelCase				      =      self.image_processor






  def __call__(							self					:						List[str]   ,    lowerCAmelCase_					:						ImageInput = None   ,    lowerCAmelCase_					:						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,    lowerCAmelCase_					:						bool = True   ,    lowerCAmelCase_					:						Union[bool, str, PaddingStrategy] = False   ,    lowerCAmelCase_					:						Union[bool, str, TruncationStrategy] = None   ,    lowerCAmelCase_					:						Optional[int] = None   ,    lowerCAmelCase_					:						int = 0   ,    lowerCAmelCase_					:						Optional[int] = None   ,    lowerCAmelCase_					:						Optional[bool] = None   ,    lowerCAmelCase_					:						bool = False   ,    lowerCAmelCase_					:						bool = False   ,    lowerCAmelCase_					:						bool = False   ,    lowerCAmelCase_					:						bool = False   ,    lowerCAmelCase_					:						bool = False   ,    lowerCAmelCase_					:						bool = True   ,    lowerCAmelCase_					:						Optional[Union[str, TensorType]] = None   ,    **lowerCAmelCase_					:						Optional[Any]   ,    )				->		BatchEncoding:
     if images is None and text is None:
        raise ValueError('You have to specify either images or text.'	)

     # Get only text
     if images is None:
        __lowerCAmelCase				      =      self.tokenizer
        __lowerCAmelCase				      =      self.tokenizer(
            text=lowerCAmelCase_   ,    add_special_tokens=lowerCAmelCase_   ,    padding=lowerCAmelCase_   ,    truncation=lowerCAmelCase_   ,    max_length=lowerCAmelCase_   ,    stride=lowerCAmelCase_   ,    pad_to_multiple_of=lowerCAmelCase_   ,    return_attention_mask=lowerCAmelCase_   ,    return_overflowing_tokens=lowerCAmelCase_   ,    return_special_tokens_mask=lowerCAmelCase_   ,    return_offsets_mapping=lowerCAmelCase_   ,    return_token_type_ids=lowerCAmelCase_   ,    return_length=lowerCAmelCase_   ,    verbose=lowerCAmelCase_   ,    return_tensors=lowerCAmelCase_   ,    **lowerCAmelCase_   ,    )
        return text_encoding

     # add pixel_values
     __lowerCAmelCase				      =      self.image_processor(lowerCAmelCase_   ,    return_tensors=lowerCAmelCase_	)

     if text is not None:
        __lowerCAmelCase				      =      self.tokenizer(
            text=lowerCAmelCase_   ,    add_special_tokens=lowerCAmelCase_   ,    padding=lowerCAmelCase_   ,    truncation=lowerCAmelCase_   ,    max_length=lowerCAmelCase_   ,    stride=lowerCAmelCase_   ,    pad_to_multiple_of=lowerCAmelCase_   ,    return_attention_mask=lowerCAmelCase_   ,    return_overflowing_tokens=lowerCAmelCase_   ,    return_special_tokens_mask=lowerCAmelCase_   ,    return_offsets_mapping=lowerCAmelCase_   ,    return_token_type_ids=lowerCAmelCase_   ,    return_length=lowerCAmelCase_   ,    verbose=lowerCAmelCase_   ,    return_tensors=lowerCAmelCase_   ,    **lowerCAmelCase_   ,    )
     else:
        __lowerCAmelCase				      =      None

     if text_encoding is not None:
        encoding_image_processor.update(lowerCAmelCase_	)

     return encoding_image_processor






  def 							lowercase						(							self					:						Dict   ,    *lowerCAmelCase_					:						str   ,    **lowerCAmelCase_					:						Any	)				->		Optional[Any]:
     return self.tokenizer.batch_decode(*lowerCAmelCase_   ,    **lowerCAmelCase_	)






  def 							lowercase						(							self					:						Optional[Any]   ,    *lowerCAmelCase_					:						Optional[int]   ,    **lowerCAmelCase_					:						Dict	)				->		int:
     return self.tokenizer.decode(*lowerCAmelCase_   ,    **lowerCAmelCase_	)






  @property
  # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
  def 							lowercase						(							self					:						int	)				->		Optional[int]:
     __lowerCAmelCase				      =      self.tokenizer.model_input_names
     __lowerCAmelCase				      =      self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)





import os

try:
 from .build_directory_md import good_file_paths
except ImportError:
 from build_directory_md import good_file_paths  # type: ignore

SCREAMING_SNAKE_CASE			: int				   =					list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

SCREAMING_SNAKE_CASE			: Dict				   =					[file for file in filepaths if file != file.lower()]
if upper_files:
 print(F"{len(upper_files)} files contain uppercase characters:")
 print("\n".join(upper_files) + "\n")

SCREAMING_SNAKE_CASE			: List[Any]				   =					[file for file in filepaths if " " in file]
if space_files:
 print(F"{len(space_files)} files contain space characters:")
 print("\n".join(space_files) + "\n")

SCREAMING_SNAKE_CASE			: Any				   =					[file for file in filepaths if "-" in file]
if hyphen_files:
 print(F"{len(hyphen_files)} files contain hyphen characters:")
 print("\n".join(hyphen_files) + "\n")

SCREAMING_SNAKE_CASE			: str				   =					[file for file in filepaths if os.sep not in file]
if nodir_files:
 print(F"{len(nodir_files)} files are not in a directory:")
 print("\n".join(nodir_files) + "\n")

SCREAMING_SNAKE_CASE			: Tuple				   =					len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
 import sys

 sys.exit(bad_files)


