


'''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 		a__    (							a__  ):





							'''simple docstring'''






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







							def __init__(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							Dict:
														lowerCAmelCase__	       =     False
														super().__init__(lowerCamelCase_ ,   lowerCamelCase_       )
														lowerCAmelCase__	       =     self.image_processor







							def __call__(		self ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = True ,   lowerCamelCase_ = False ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = 0 ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = False ,   lowerCamelCase_ = False ,   lowerCamelCase_ = False ,   lowerCamelCase_ = False ,   lowerCamelCase_ = False ,   lowerCamelCase_ = True ,   lowerCamelCase_ = None ,   **lowerCamelCase_ ,   )	->							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   __SCREAMING_SNAKE_CASE							(		self ,   *lowerCamelCase_ ,   **lowerCamelCase_       )	->							Any:
														return self.tokenizer.batch_decode(*lowerCamelCase_ ,   **lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   *lowerCamelCase_ ,   **lowerCamelCase_       )	->							Any:
														return self.tokenizer.decode(*lowerCamelCase_ ,   **lowerCamelCase_       )







							@property
							# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							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       )       )



'''simple docstring'''






from __future__ import annotations

import math
from collections.abc import Callable


def  _snake_case       (					A			,   A			,   A			,   A = 100			,   )						->   float:
							lowerCAmelCase__	       =     x_start
							lowerCAmelCase__	       =     fnc(A	)
							lowerCAmelCase__	       =     0.0

							for _ in range(A	):
														# Approximates curve as a sequence of linear lines and sums their length
														lowerCAmelCase__	       =     (x_end - x_start) / steps + xa
														lowerCAmelCase__	       =     fnc(A	)
														length += math.hypot(xa - xa			,   fxa - fxa	)

														# Increment step
														lowerCAmelCase__	       =     xa
														lowerCAmelCase__	       =     fxa

							return length


if __name__ == "__main__":


		def  _snake_case       (					A	)						->   List[Any]:
									return math.sin(10 * x	)

		print('''f(x) = sin(10 * x)''')
		print('''The length of the curve from x = -10 to x = 10 is:''')
		__UpperCAmelCase       =							10
		while i <= 100_000:
				print(f"""With {i} steps: {line_length(f, -10, 10, i)}""")
				i *= 10