






from typing import TYPE_CHECKING

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


_UpperCAmelCase			:		Union[str, Any]   				=    {"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Dict   				=    ["XGLMTokenizer"]

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Tuple   				=    ["XGLMTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Union[str, Any]   				=    [
			    "XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "XGLMForCausalLM",
			    "XGLMModel",
			    "XGLMPreTrainedModel",
			]


try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		str   				=    [
			    "FlaxXGLMForCausalLM",
			    "FlaxXGLMModel",
			    "FlaxXGLMPreTrainedModel",
			]


try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		int   				=    [
			    "TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFXGLMForCausalLM",
			    "TFXGLMModel",
			    "TFXGLMPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xglm import XGLMTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xglm_fast import XGLMTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xglm import (
						    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXGLMForCausalLM,
						    TFXGLMModel,
						    TFXGLMPreTrainedModel,
						)


else:
			import sys

			_UpperCAmelCase			:		Dict   				=    _LazyModule(__name__, globals()["__file__"], _import_structure)













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




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

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

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

											lowerCAmelCase__      			=						lowerCAmelCase__      			=						(
											    np.ones((self.dst_h, self.dst_w, 3) ,							np.uinta	) * 255
											)




							def 						__snake_case					(   self     :   List[str]	):
											for i in range(self.dst_h	):
															for j in range(self.dst_w	):
																			lowerCAmelCase__      			=						self.img[self.get_y(SCREAMING_SNAKE_CASE_	)][self.get_x(SCREAMING_SNAKE_CASE_	)]




							def 						__snake_case					(   self     :   Any ,							SCREAMING_SNAKE_CASE_     :   int	):
											return int(self.ratio_x * x	)




							def 						__snake_case					(   self     :   List[str] ,							SCREAMING_SNAKE_CASE_     :   int	):
											return int(self.ratio_y * y	)


if __name__ == "__main__":
			_UpperCAmelCase				,   _UpperCAmelCase			:		List[str]   				=    800, 600
			_UpperCAmelCase			:		Tuple   				=    imread("image_data/lena.jpg", 1)
			_UpperCAmelCase			:		str   				=    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()





