



from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION

class 		UpperCAmelCase__      (      __UpperCamelCase						,__UpperCamelCase       ):




			'''simple docstring'''






			def __init__(       self							:			Tuple			):
					'''simple docstring'''





					super().__init__()

					self.register_buffer('''watermark_image'''	,	torch.zeros((62, 62, 4)			)			)
					__UpperCAmelCase   :					Union[str, Any]													=     None




			def 			snake_case__					(       self							:			List[str]	,	a_							:			List[PIL.Image.Image]	,	a_							:			Dict=None			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[str]													=     images[0].height
					__UpperCAmelCase   :					Union[str, Any]													=     images[0].width

					__UpperCAmelCase   :					Union[str, Any]													=     sample_size or h

					__UpperCAmelCase   :					Optional[Any]													=     min(h / sample_size	,	w / sample_size			)
					__UpperCAmelCase							, __UpperCAmelCase   :					List[Any]													=     (int(h / coef			), int(w / coef			)) if coef < 1 else (h, w)

					__UpperCAmelCase							, __UpperCAmelCase   :					Optional[int]													=     10_24**2, img_w * img_h
					__UpperCAmelCase   :					int													=     (Sa / Sa) ** 0.5
					__UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase   :					Optional[Any]													=     int(K * 62			), img_w - int(14 * K			), img_h - int(14 * K			)

					if self.watermark_image_as_pil is None:
							__UpperCAmelCase   :					str													=     self.watermark_image.to(torch.uinta			).cpu().numpy()
							__UpperCAmelCase   :					List[Any]													=     Image.fromarray(a_	,	mode='''RGBA'''			)
							__UpperCAmelCase   :					str													=     watermark_image

					__UpperCAmelCase   :					int													=     self.watermark_image_as_pil.resize(
					    (wm_size, wm_size)	,	PIL_INTERPOLATION['''bicubic''']	,	reducing_gap=a_			)

					for pil_img in images:
							pil_img.paste(a_	,	box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)	,	mask=wm_img.split()[-1]			)

					return images










def 	a						(				_UpperCAmelCase							: Optional[int]			,       _UpperCAmelCase							: List[Any]						):







		'''simple docstring'''





		__UpperCAmelCase   :					int													=     len(_UpperCAmelCase						)
		# Create a stack and push index of fist element to it
		__UpperCAmelCase   :					List[Any]													=     []
		st.append(0						)

		# Span value of first element is always 1
		__UpperCAmelCase   :					Any													=     1

		# Calculate span values for rest of the elements
		for i in range(1			,       _UpperCAmelCase						):
				# Pop elements from stack while stack is not
				# empty and top of stack is smaller than price[i]
				while len(_UpperCAmelCase						) > 0 and price[st[0]] <= price[i]:
						st.pop()

				# If stack becomes empty, then price[i] is greater
				# than all elements on left of it, i.e. price[0],
				# price[1], ..price[i-1]. Else the price[i]  is
				# greater than elements after top of stack
				__UpperCAmelCase   :					int													=     i + 1 if len(_UpperCAmelCase						) <= 0 else (i - st[0])

				# Push this element to stack
				st.append(_UpperCAmelCase						)

def 	a						(				_UpperCAmelCase							: Dict			,       _UpperCAmelCase							: List[str]						):







		'''simple docstring'''





		for i in range(0			,       _UpperCAmelCase						):
				print(arr[i]			,       end=''' '''						)


# Driver program to test above function
__A             =[1_0, 4, 5, 9_0, 1_2_0, 8_0]
__A             =[0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)
print_array(S, len(price))





