






'''simple docstring'''





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 					_snake_case						(			_A ):
	_A          =   ['image_processor', 'tokenizer']
	_A          =   'BlipImageProcessor'
	_A          =   'AutoTokenizer'






	def __init__(		self					,UpperCamelCase					,UpperCamelCase       )						->    Optional[Any]:
					snake_case__      :List[Any]         =   False
					super().__init__(__UpperCamelCase					,__UpperCamelCase       )
					snake_case__      :Union[str, Any]         =   self.image_processor






	def __call__(		self					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = True					,UpperCamelCase = False					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = 0					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = False					,UpperCamelCase = False					,UpperCamelCase = False					,UpperCamelCase = False					,UpperCamelCase = False					,UpperCamelCase = True					,UpperCamelCase = None					,**UpperCamelCase					,)						->    Optional[Any]:
					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:
									snake_case__      :Dict         =   self.tokenizer
									snake_case__      :int         =   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					,)
									return text_encoding

					# add pixel_values
					snake_case__      :Union[str, Any]         =   self.image_processor(__UpperCamelCase					,return_tensors=__UpperCamelCase       )

					if text is not None:
									snake_case__      :int         =   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					,)
					else:
									snake_case__      :Optional[Any]         =   None

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

					return encoding_image_processor






	def    lowerCAmelCase_							(		self					,*UpperCamelCase					,**UpperCamelCase       )						->    int:
					return self.tokenizer.batch_decode(*__UpperCamelCase					,**__UpperCamelCase       )






	def    lowerCAmelCase_							(		self					,*UpperCamelCase					,**UpperCamelCase       )						->    List[Any]:
					return self.tokenizer.decode(*__UpperCamelCase					,**__UpperCamelCase       )






	@property
	# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					snake_case__      :int         =   self.tokenizer.model_input_names
					snake_case__      :Dict         =   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

__UpperCAmelCase							:   List[Any]							=  list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

__UpperCAmelCase							:   int							=  [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")

__UpperCAmelCase							:   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")

__UpperCAmelCase							:   str							=  [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")

__UpperCAmelCase							:   Dict							=  [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")

__UpperCAmelCase							:   int							=  len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
	import sys

	sys.exit(bad_files)