
import math
import os

import cva
import numpy as np

lowerCamelCase_  :       Optional[Any]					      =     255.0







def 				A__    (  lowerCamelCase   ,			lowerCamelCase  )     ->		float:
				UpperCamelCase_:							Optional[int]		=  np.mean((original - contrast) ** 2  )
				if mse == 0:
								return 1_00

				return 20 * math.logaa(PIXEL_MAX / math.sqrt(lowerCamelCase  )  )







def 				A__    (  )     ->		None:
				UpperCamelCase_:							int		=  os.path.dirname(os.path.realpath(lowerCamelCase  )  )
				# Loading images (original image and compressed image)
				UpperCamelCase_:							Optional[Any]		=  cva.imread(os.path.join(lowerCamelCase   ,			"""image_data/original_image.png"""  )  )
				UpperCamelCase_:							Dict		=  cva.imread(os.path.join(lowerCamelCase   ,			"""image_data/compressed_image.png"""  )   ,			1  )

				UpperCamelCase_:							Tuple		=  cva.imread(os.path.join(lowerCamelCase   ,			"""image_data/PSNR-example-base.png"""  )  )
				UpperCamelCase_:							Optional[Any]		=  cva.imread(
				    os.path.join(lowerCamelCase   ,			"""image_data/PSNR-example-comp-10.jpg"""  )   ,			1  )

				# Value expected: 29.73dB
				print("""-- First Test --"""  )
				print(F'''PSNR value is {peak_signal_to_noise_ratio(lowerCamelCase   ,			lowerCamelCase  )} dB'''  )

				# # Value expected: 31.53dB (Wikipedia Example)
				print("""\n-- Second Test --"""  )
				print(F'''PSNR value is {peak_signal_to_noise_ratio(lowerCamelCase   ,			lowerCamelCase  )} dB'''  )


if __name__ == "__main__":
							main()








import torch

from transformers import AutoTokenizer

class 				_UpperCamelCase (    _A     ):






			'''simple docstring'''

			def __init__(				self       :				List[str]  ,		snake_case_       :				Dict					):
							UpperCamelCase_:							int		=  AutoTokenizer.from_pretrained(snake_case_					)

			def 					lowerCAmelCase__		(				self       :				Union[str, Any]  ,		snake_case_       :				Optional[Any]					):

							if isinstance(snake_case_  ,		snake_case_					) and all(isinstance(_x  ,		snake_case_					) for _x in x					):
											UpperCamelCase_:							Tuple		=  None
											for l in x:
															UpperCamelCase_:							Optional[Any]		=  self.tokenizer(
															    snake_case_  ,		padding="""max_length"""  ,		max_length=384  ,		truncation=snake_case_  ,		return_tensors="""pt"""  ,		)
															UpperCamelCase_:							str		=  torch.tensor([len(snake_case_					)]					)
															if d is not None:
																			for k in d.keys():
																							UpperCamelCase_:							List[Any]		=  torch.cat((d[k], t[k])  ,		0					)
															else:
																			UpperCamelCase_:							Optional[int]		=  t

											UpperCamelCase_:							Union[str, Any]		=  torch.tensor(self.tokenizer.convert_tokens_to_ids("""[E]"""					)					)
											UpperCamelCase_:							List[str]		=  torch.tensor(self.tokenizer.convert_tokens_to_ids("""[/E]"""					)					)

							elif isinstance(snake_case_  ,		snake_case_					) and all(isinstance(_x  ,		snake_case_					) for _x in x					):
											UpperCamelCase_:							List[str]		=  self.tokenizer(
											    snake_case_  ,		padding="""max_length"""  ,		max_length=384  ,		truncation=snake_case_  ,		return_tensors="""pt"""  ,		)

							else:
											raise Exception(
											    """Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"""
											    """ strings` for supports are supported."""					)

							return d




			def 					lowerCAmelCase__		(				self       :				int  ,		snake_case_       :				List[str]  ,		snake_case_       :				Optional[int]  ,		snake_case_       :				Optional[Any]  ,		snake_case_       :				Optional[int]  ,		snake_case_       :				List[Any]=0.70					):
							UpperCamelCase_:							int		=  []
							for idx in range(len(W_query["""input_ids"""]					)					):
											UpperCamelCase_:							List[str]		=  range(p_start.shape[1]					)

											UpperCamelCase_:							str		=  []
											for start_id in start_indexes:
															for end_id in end_indexes:
																			if start_id < end_id:
																							output.append(
																							    (
																							        start_id,
																							        end_id,
																							        p_start[idx][start_id].item(),
																							        p_end[idx][end_id].item(),
																							    )					)

											output.sort(key=lambda snake_case_					: (tup[2] * tup[3])  ,		reverse=snake_case_					)
											UpperCamelCase_:							Optional[int]		=  []
											for k in range(len(snake_case_					)					):
															if output[k][2] * output[k][3] >= thresh:
																			UpperCamelCase_,      UpperCamelCase_:							Tuple		=  output[k][0], output[k][1]
																			UpperCamelCase_:							Any		=  self.tokenizer.decode(W_query["""input_ids"""][idx][c_start_pos:c_end_pos]					)
																			temp.append((decoded, output[k][2] * output[k][3])					)

											final_outputs.append(snake_case_					)

							return final_outputs






