
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class 	A__   :





							def __init__( self  :   Dict     , _UpperCAmelCase  :   int     , _UpperCAmelCase  :   int     , _UpperCAmelCase  :   int				)						->		Optional[int]:



											"""simple docstring"""


											if dst_width < 0 or dst_height < 0:
															raise ValueError('Destination width/height should be > 0'				)

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

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

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





							def 			a__ ( self  :   Optional[int]				)						->		List[Any]:



											"""simple docstring"""


											for i in range(self.dst_h				):
															for j in range(self.dst_w				):
																			__lowercase								=					self.img[self.get_y(_SCREAMING_SNAKE_CASE				)][self.get_x(_SCREAMING_SNAKE_CASE				)]





							def 			a__ ( self  :   Union[str, Any]     , _UpperCAmelCase  :   int				)						->		int:



											"""simple docstring"""


											return int(self.ratio_x * x				)





							def 			a__ ( self  :   str     , _UpperCAmelCase  :   int				)						->		int:



											"""simple docstring"""


											return int(self.ratio_y * y				)


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__							=				800, 600
							SCREAMING_SNAKE_CASE__							=				imread("""image_data/lena.jpg""", 1)
							SCREAMING_SNAKE_CASE__							=				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()










"""simple docstring"""






import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
	import optax
	from flax.training.common_utils import onehot

	from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
	from transformers.models.ta.modeling_flax_ta import shift_tokens_right



@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	@slow
	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       str:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small")
				__lowerCAmelCase      :  Optional[Any]												=       AutoTokenizer.from_pretrained("google/mt5-small")

				__lowerCAmelCase      :  Tuple												=       tokenizer("Hello there"       ,			return_tensors="np").input_ids
				__lowerCAmelCase      :  Dict												=       tokenizer("Hi I am"       ,			return_tensors="np").input_ids

				__lowerCAmelCase      :  str												=       shift_tokens_right(_SCREAMING_SNAKE_CASE       ,			model.config.pad_token_id       ,			model.config.decoder_start_token_id)

				__lowerCAmelCase      :  Optional[int]												=       model(_SCREAMING_SNAKE_CASE       ,			decoder_input_ids=_SCREAMING_SNAKE_CASE).logits
				__lowerCAmelCase      :  int												=       optax.softmax_cross_entropy(_SCREAMING_SNAKE_CASE       ,			onehot(_SCREAMING_SNAKE_CASE       ,			logits.shape[-1])).mean()

				__lowerCAmelCase      :  List[str]												=       -(labels.shape[-1] * loss.item())

				__lowerCAmelCase      :  str												=       -84.9127
				self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)