






"""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_       (    _lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Dict   			=					["""image_processor""", """tokenizer"""]
	SCREAMING_SNAKE_CASE_:							Tuple   			=					"""BlipImageProcessor"""
	SCREAMING_SNAKE_CASE_:							List[Any]   			=					"""AutoTokenizer"""


	def __init__(      self  ,					__a  ,					__a  ,					__a						):


					"""simple docstring"""

					super().__init__(__a  ,					__a						)

					# add QFormer tokenizer
					A__							 =       qformer_tokenizer


	def __call__(      self  ,					__a = None  ,					__a = None  ,					__a = True  ,					__a = False  ,					__a = None  ,					__a = None  ,					__a = 0  ,					__a = None  ,					__a = None  ,					__a = False  ,					__a = False  ,					__a = False  ,					__a = False  ,					__a = False  ,					__a = True  ,					__a = None  ,					**__a  ,					):


					"""simple docstring"""

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

					A__							 =       BatchFeature()

					if text is not None:
									A__							 =       self.tokenizer(
									    text=__a  ,					add_special_tokens=__a  ,					padding=__a  ,					truncation=__a  ,					max_length=__a  ,					stride=__a  ,					pad_to_multiple_of=__a  ,					return_attention_mask=__a  ,					return_overflowing_tokens=__a  ,					return_special_tokens_mask=__a  ,					return_offsets_mapping=__a  ,					return_token_type_ids=__a  ,					return_length=__a  ,					verbose=__a  ,					return_tensors=__a  ,					**__a  ,					)
									encoding.update(__a						)
									A__							 =       self.qformer_tokenizer(
									    text=__a  ,					add_special_tokens=__a  ,					padding=__a  ,					truncation=__a  ,					max_length=__a  ,					stride=__a  ,					pad_to_multiple_of=__a  ,					return_attention_mask=__a  ,					return_overflowing_tokens=__a  ,					return_special_tokens_mask=__a  ,					return_offsets_mapping=__a  ,					return_token_type_ids=__a  ,					return_length=__a  ,					verbose=__a  ,					return_tensors=__a  ,					**__a  ,					)
									A__							 =       qformer_text_encoding.pop('input_ids'						)
									A__							 =       qformer_text_encoding.pop('attention_mask'						)

					if images is not None:
									A__							 =       self.image_processor(__a  ,					return_tensors=__a						)
									encoding.update(__a						)

					return encoding


	def       _UpperCAmelCase						(      self  ,					*__a  ,					**__a						):


					"""simple docstring"""

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


	def       _UpperCAmelCase						(      self  ,					*__a  ,					**__a						):


					"""simple docstring"""

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


	@property
	# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       self.tokenizer.model_input_names
					A__							 =       self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)


	def       _UpperCAmelCase						(      self  ,					__a  ,					**__a						):


					"""simple docstring"""

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






	@classmethod
	def       _UpperCAmelCase						(      cls  ,					__a  ,					**__a						):


					"""simple docstring"""

					A__							 =       AutoTokenizer.from_pretrained(__a  ,					subfolder='qformer_tokenizer'						)
					A__							 =       cls._get_arguments_from_pretrained(__a  ,					**__a						)
					args.append(__a						)
					return cls(*__a						)










"""simple docstring"""






from math import factorial, pi



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__ = 30				):

				if not isinstance(lowerCAmelCase__       ,(int, float)				):
								raise ValueError('maclaurin_sin() requires either an int or float for theta'				)

				if not isinstance(lowerCAmelCase__       ,lowerCAmelCase__				) or accuracy <= 0:
								raise ValueError('maclaurin_sin() requires a positive int for accuracy'				)

				A__							 =       float(lowerCAmelCase__				)
				A__							 =       theta // (2 * pi)
				theta -= 2 * div * pi
				return sum(
				    (-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1				) for r in range(lowerCAmelCase__				)				)



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__ = 30				):

				if not isinstance(lowerCAmelCase__       ,(int, float)				):
								raise ValueError('maclaurin_cos() requires either an int or float for theta'				)

				if not isinstance(lowerCAmelCase__       ,lowerCAmelCase__				) or accuracy <= 0:
								raise ValueError('maclaurin_cos() requires a positive int for accuracy'				)

				A__							 =       float(lowerCAmelCase__				)
				A__							 =       theta // (2 * pi)
				theta -= 2 * div * pi
				return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r				) for r in range(lowerCAmelCase__				)				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					print(maclaurin_sin(10))
					print(maclaurin_sin(-10))
					print(maclaurin_sin(10, 15))
					print(maclaurin_sin(-10, 15))

					print(maclaurin_cos(5))
					print(maclaurin_cos(-5))
					print(maclaurin_cos(10, 15))
					print(maclaurin_cos(-10, 15))


