




"""simple docstring"""






def      _snake_case			(				lowercase__							,    lowercase__		):
				# "extended trapezoidal rule"
				# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
				_lowerCamelCase     :			List[Any]    =    (boundary[1] - boundary[0]) / steps
				_lowerCamelCase     :			Tuple    =    boundary[0]
				_lowerCamelCase     :			Dict    =    boundary[1]
				_lowerCamelCase     :			List[Any]    =    make_points(lowercase__							,    lowercase__							,    lowercase__		)
				_lowerCamelCase     :			List[Any]    =    0.0
				y += (h / 2.0) * f(lowercase__		)
				for i in x_i:
								# print(i)
								y += h * f(lowercase__		)
				y += (h / 2.0) * f(lowercase__		)
				return y







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__		):
				_lowerCamelCase     :			str    =    a + h
				while x < (b - h):
								yield x
								_lowerCamelCase     :			int    =    x + h







def      _snake_case			(				lowercase__		):  # enter your function here
				_lowerCamelCase     :			Optional[Any]    =    (x - 0) * (x - 0)
				return y







def      _snake_case			(				):
				_lowerCamelCase     :			int    =    0.0  # Lower bound of integration
				_lowerCamelCase     :			Optional[int]    =    1.0  # Upper bound of integration
				_lowerCamelCase     :			List[str]    =    1_0.0  # define number of steps or resolution
				_lowerCamelCase     :			List[Any]    =    [a, b]  # define boundary of integration
				_lowerCamelCase     :			Optional[Any]    =    method_a(lowercase__							,    lowercase__		)
				print(f'''y = {y}'''		)


if __name__ == "__main__":
			main()





"""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 lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			lowerCamelCase__      =			["""image_processor""", """tokenizer"""]
			lowerCamelCase__      =			"""BlipImageProcessor"""
			lowerCamelCase__      =			"""AutoTokenizer"""
			def __init__(  self  ,      lowercase  ,      lowercase  ,      lowercase  ):
							super().__init__(lowercase  ,      lowercase  )

							# add QFormer tokenizer
							_lowerCamelCase     :			int    =    qformer_tokenizer
			def __call__(  self  ,      lowercase = None  ,      lowercase = None  ,      lowercase = True  ,      lowercase = False  ,      lowercase = None  ,      lowercase = None  ,      lowercase = 0  ,      lowercase = None  ,      lowercase = None  ,      lowercase = False  ,      lowercase = False  ,      lowercase = False  ,      lowercase = False  ,      lowercase = False  ,      lowercase = True  ,      lowercase = None  ,      **lowercase  ,      ):
							if images is None and text is None:
											raise ValueError('You have to specify at least images or text.'  )

							_lowerCamelCase     :			int    =    BatchFeature()

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

							if images is not None:
											_lowerCamelCase     :			int    =    self.image_processor(lowercase  ,      return_tensors=lowercase  )
											encoding.update(lowercase  )

							return encoding
			def 					A_    (  self  ,      *lowercase  ,      **lowercase  ):
							return self.tokenizer.batch_decode(*lowercase  ,      **lowercase  )
			def 					A_    (  self  ,      *lowercase  ,      **lowercase  ):
							return self.tokenizer.decode(*lowercase  ,      **lowercase  )
			@property
			# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
			def 					A_    (  self  ):
							_lowerCamelCase     :			Union[str, Any]    =    self.tokenizer.model_input_names
							_lowerCamelCase     :			Any    =    self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )
			def 					A_    (  self  ,      lowercase  ,      **lowercase  ):
							if os.path.isfile(lowercase  ):
											raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file'''  )
							os.makedirs(lowercase  ,      exist_ok=lowercase  )
							_lowerCamelCase     :			Optional[Any]    =    os.path.join(lowercase  ,      'qformer_tokenizer'  )
							self.qformer_tokenizer.save_pretrained(lowercase  )
							return super().save_pretrained(lowercase  ,      **lowercase  )






			@classmethod
			def 					A_    (  cls  ,      lowercase  ,      **lowercase  ):
							_lowerCamelCase     :			Union[str, Any]    =    AutoTokenizer.from_pretrained(lowercase  ,      subfolder='qformer_tokenizer'  )
							_lowerCamelCase     :			Dict    =    cls._get_arguments_from_pretrained(lowercase  ,      **lowercase  )
							args.append(lowercase  )
							return cls(*lowercase  )