"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_     =	{
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_     =	['''RoFormerTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_     =	[
			    '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''RoFormerForCausalLM''',
			    '''RoFormerForMaskedLM''',
			    '''RoFormerForMultipleChoice''',
			    '''RoFormerForQuestionAnswering''',
			    '''RoFormerForSequenceClassification''',
			    '''RoFormerForTokenClassification''',
			    '''RoFormerLayer''',
			    '''RoFormerModel''',
			    '''RoFormerPreTrainedModel''',
			    '''load_tf_weights_in_roformer''',
			]


try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_     =	[
			    '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFRoFormerForCausalLM''',
			    '''TFRoFormerForMaskedLM''',
			    '''TFRoFormerForMultipleChoice''',
			    '''TFRoFormerForQuestionAnswering''',
			    '''TFRoFormerForSequenceClassification''',
			    '''TFRoFormerForTokenClassification''',
			    '''TFRoFormerLayer''',
			    '''TFRoFormerModel''',
			    '''TFRoFormerPreTrainedModel''',
			]


try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_     =	[
			    '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''FlaxRoFormerForMaskedLM''',
			    '''FlaxRoFormerForMultipleChoice''',
			    '''FlaxRoFormerForQuestionAnswering''',
			    '''FlaxRoFormerForSequenceClassification''',
			    '''FlaxRoFormerForTokenClassification''',
			    '''FlaxRoFormerModel''',
			    '''FlaxRoFormerPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
			from .tokenization_roformer import RoFormerTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_roformer_fast import RoFormerTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_roformer import (
						    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    RoFormerForCausalLM,
						    RoFormerForMaskedLM,
						    RoFormerForMultipleChoice,
						    RoFormerForQuestionAnswering,
						    RoFormerForSequenceClassification,
						    RoFormerForTokenClassification,
						    RoFormerLayer,
						    RoFormerModel,
						    RoFormerPreTrainedModel,
						    load_tf_weights_in_roformer,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_roformer import (
						    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFRoFormerForCausalLM,
						    TFRoFormerForMaskedLM,
						    TFRoFormerForMultipleChoice,
						    TFRoFormerForQuestionAnswering,
						    TFRoFormerForSequenceClassification,
						    TFRoFormerForTokenClassification,
						    TFRoFormerLayer,
						    TFRoFormerModel,
						    TFRoFormerPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_roformer import (
						    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FlaxRoFormerForMaskedLM,
						    FlaxRoFormerForMultipleChoice,
						    FlaxRoFormerForQuestionAnswering,
						    FlaxRoFormerForSequenceClassification,
						    FlaxRoFormerForTokenClassification,
						    FlaxRoFormerModel,
						    FlaxRoFormerPreTrainedModel,
						)


else:
			import sys

			lowerCAmelCase_     =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



"""simple docstring"""





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




class  _snake_case		:







				"""simple docstring"""






				def __init__(      self							:							int      ,    _A							:							List[Any]      ,    _A							:							int      ,    _A							:							int):



											"""simple docstring"""







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

											_SCREAMING_SNAKE_CASE		:						str			  =    img
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    img.shape[1]
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    img.shape[0]
											_SCREAMING_SNAKE_CASE		:						Any			  =    dst_width
											_SCREAMING_SNAKE_CASE		:						Any			  =    dst_height

											_SCREAMING_SNAKE_CASE		:						Any			  =    self.src_w / self.dst_w
											_SCREAMING_SNAKE_CASE		:						Dict			  =    self.src_h / self.dst_h

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    (
											    np.ones((self.dst_h, self.dst_w, 3)      ,    np.uinta) * 2_5_5
											)





				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







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





				def 		_lowerCAmelCase		(      self							:							int      ,    _A							:							int):



											"""simple docstring"""







											return int(self.ratio_x * x)





				def 		_lowerCAmelCase		(      self							:							str      ,    _A							:							int):



											"""simple docstring"""







											return int(self.ratio_y * y)


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


