



"""simple docstring"""







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







		'''simple docstring'''





		def __init__(	self						:  List[Any]			,_snake_case						:  Optional[int]			,_snake_case						:  int			,_snake_case						:  int						)					->		Tuple:






				"""simple docstring"""



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

				lowercase__   :    Dict				=			img
				lowercase__   :    List[Any]				=			img.shape[1]
				lowercase__   :    Optional[Any]				=			img.shape[0]
				lowercase__   :    List[Any]				=			dst_width
				lowercase__   :    Any				=			dst_height

				lowercase__   :    Union[str, Any]				=			self.src_w / self.dst_w
				lowercase__   :    str				=			self.src_h / self.dst_h

				lowercase__   :    Any				=			(
				    np.ones((self.dst_h, self.dst_w, 3)			,np.uinta						) * 255
				)







		def 					UpperCAmelCase    (	self						:  Union[str, Any]						)					->		Optional[int]:






				"""simple docstring"""



				for i in range(self.dst_h						):
						for j in range(self.dst_w						):
								lowercase__   :    Optional[int]				=			self.img[self.get_y(_snake_case						)][self.get_x(_snake_case						)]







		def 					UpperCAmelCase    (	self						:  Dict			,_snake_case						:  int						)					->		int:






				"""simple docstring"""



				return int(self.ratio_x * x						)







		def 					UpperCAmelCase    (	self						:  Optional[int]			,_snake_case						:  int						)					->		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()


import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM

def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       [
      """decoder.version""",
      """decoder.output_projection.weight""",
      """_float_tensor""",
      """decoder.embed_positions._float_tensor""",
  ]
  for k in ignore_keys:
    state_dict.pop(A_,     A_					)

def a__    (       A_					):






  '''simple docstring'''





  __magic_name__     ,    __magic_name__				     =       emb.weight.shape
  __magic_name__				     =       nn.Linear(A_,     A_,     bias=A_					)
  __magic_name__				     =       emb.weight.data
  return lin_layer

def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       torch.load(A_,     map_location="""cpu"""					)
  __magic_name__				     =       Namespace(**checkpoint["""cfg"""]["""model"""]					)
  __magic_name__				     =       checkpoint["""model"""]
  remove_ignore_keys_(A_					)
  __magic_name__				     =       state_dict["""decoder.embed_tokens.weight"""].shape[0]

  __magic_name__				     =       {key.replace("""decoder""",     """model"""					): val for key, val in state_dict.items()}

  __magic_name__				     =       XGLMConfig(
      vocab_size=A_,     max_position_embeddings=args.max_target_positions,     num_layers=args.decoder_layers,     attention_heads=args.decoder_attention_heads,     ffn_dim=args.decoder_ffn_embed_dim,     d_model=args.decoder_embed_dim,     layerdrop=args.decoder_layerdrop,     dropout=args.dropout,     attention_dropout=args.attention_dropout,     activation_dropout=args.activation_dropout,     activation_function="""gelu""",     scale_embedding=not args.no_scale_embedding,     tie_word_embeddings=args.share_decoder_input_output_embed,     )

  __magic_name__				     =       XGLMForCausalLM(A_					)
  __magic_name__				     =       model.load_state_dict(A_,     strict=A_					)
  print(A_					)
  __magic_name__				     =       make_linear_from_emb(model.model.embed_tokens					)

  return model


if __name__ == "__main__":
    __lowerCAmelCase			:     Dict           =   argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
    parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
    __lowerCAmelCase			:     List[str]           =   parser.parse_args()
    __lowerCAmelCase			:     str           =   convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
    model.save_pretrained(args.pytorch_dump_folder_path)

