






import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey





class 						SCREAMING_SNAKE_CASE				:





							"""simple docstring"""

							def __init__(  self:   int		,     __A:   Union[str, Any]		,     __A:   int		,     __A:   int   )    ->				Optional[int]:
												if dst_width < 0 or dst_height < 0:
																	raise ValueError('''Destination width/height should be > 0'''   )

												_A								= img
												_A								= img.shape[1]
												_A								= img.shape[0]
												_A								= dst_width
												_A								= dst_height

												_A								= self.src_w / self.dst_w
												_A								= self.src_h / self.dst_h

												_A								= _A								= (
												    np.ones((self.dst_h, self.dst_w, 3)		,     np.uinta   ) * 2_55
												)

							def       __A (  self:   Optional[int]   )    ->				List[str]:
												for i in range(self.dst_h   ):
																	for j in range(self.dst_w   ):
																						_A								= self.img[self.get_y(__A   )][self.get_x(__A   )]

							def       __A (  self:   List[str]		,     __A:   int   )    ->				int:
												return int(self.ratio_x * x   )



							def       __A (  self:   Dict		,     __A:   int   )    ->				int:
												return int(self.ratio_y * y   )


if __name__ == "__main__":
				__A ,  __A      =				800, 600
				__A      =				imread('image_data/lena.jpg', 1)
				__A      =				NearestNeighbour(im, dst_w, dst_h)
				n.process()

				imshow(
				    f'Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}', n.output
				)
				waitKey(0)
				destroyAllWindows()











import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


__A      =				logging.get_logger(__name__)





class 						SCREAMING_SNAKE_CASE				:





							"""simple docstring"""

							def __init__(  self:   int		,     __A:   Optional[int]		,     __A:   Optional[Any]   )    ->				str:
												_A								= question_encoder
												_A								= generator
												_A								= self.question_encoder

							def       __A (  self:   Optional[int]		,     __A:   Union[str, Any]   )    ->				Dict:
												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		,     '''question_encoder_tokenizer'''   )
												_A								= os.path.join(__A		,     '''generator_tokenizer'''   )
												self.question_encoder.save_pretrained(__A   )
												self.generator.save_pretrained(__A   )

							@classmethod
							def       __A (  cls:   Optional[Any]		,     __A:   List[str]		,     **__A:   int   )    ->				Any:
												# dynamically import AutoTokenizer
												from ..auto.tokenization_auto import AutoTokenizer

												_A								= kwargs.pop('''config'''		,     __A   )

												if config is None:
																	_A								= RagConfig.from_pretrained(__A   )

												_A								= AutoTokenizer.from_pretrained(
												    __A		,     config=config.question_encoder		,     subfolder='''question_encoder_tokenizer'''   )
												_A								= AutoTokenizer.from_pretrained(
												    __A		,     config=config.generator		,     subfolder='''generator_tokenizer'''   )
												return cls(question_encoder=__A		,     generator=__A   )

							def __call__(  self:   int		,     *__A:   Optional[int]		,     **__A:   List[str]   )    ->				int:
												return self.current_tokenizer(*__A		,     **__A   )

							def       __A (  self:   Dict		,     *__A:   List[str]		,     **__A:   List[str]   )    ->				Dict:
												return self.generator.batch_decode(*__A		,     **__A   )

							def       __A (  self:   Union[str, Any]		,     *__A:   Tuple		,     **__A:   List[str]   )    ->				Tuple:
												return self.generator.decode(*__A		,     **__A   )

							def       __A (  self:   Dict   )    ->				List[str]:
												_A								= self.question_encoder

							def       __A (  self:   Union[str, Any]   )    ->				int:
												_A								= self.generator



							def       __A (  self:   Dict		,     __A:   List[str]		,     __A:   Optional[List[str]] = None		,     __A:   Optional[int] = None		,     __A:   Optional[int] = None		,     __A:   str = "longest"		,     __A:   str = None		,     __A:   bool = True		,     **__A:   Tuple		,     )    ->				BatchEncoding:
												warnings.warn(
												    '''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
												    '''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
												    '''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
												    '''details'''		,     __A		,     )
												if max_length is None:
																	_A								= self.current_tokenizer.model_max_length
												_A								= self(
												    __A		,     add_special_tokens=__A		,     return_tensors=__A		,     max_length=__A		,     padding=__A		,     truncation=__A		,     **__A		,     )
												if tgt_texts is None:
																	return model_inputs
												# Process tgt_texts
												if max_target_length is None:
																	_A								= self.current_tokenizer.model_max_length
												_A								= self(
												    text_target=__A		,     add_special_tokens=__A		,     return_tensors=__A		,     padding=__A		,     max_length=__A		,     truncation=__A		,     **__A		,     )
												_A								= labels['''input_ids''']
												return model_inputs



