






import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration





def 	lowerCAmelCase_		(lowercase__						:       Any       )		->    List[str]:


				'''simple docstring'''




				lowerCAmelCase__      			=						[
				    '''encoder.version''',
				    '''decoder.version''',
				    '''model.encoder.version''',
				    '''model.decoder.version''',
				    '''decoder.output_projection.weight''',
				    '''_float_tensor''',
				    '''encoder.embed_positions._float_tensor''',
				    '''decoder.embed_positions._float_tensor''',
				]
				for k in ignore_keys:
								state_dict.pop(lowercase__     ,					lowercase__       )





def 	lowerCAmelCase_		(lowercase__						:       Optional[int]       )		->    str:


				'''simple docstring'''




				lowerCAmelCase__      			=						list(s_dict.keys()       )
				for key in keys:
								if "transformer_layers" in key:
												lowerCAmelCase__      			=						s_dict.pop(lowercase__       )
								elif "subsample" in key:
												lowerCAmelCase__      			=						s_dict.pop(lowercase__       )





def 	lowerCAmelCase_		(lowercase__						:       Any       )		->    Optional[Any]:


				'''simple docstring'''




				lowerCAmelCase__			,		lowerCAmelCase__      			=						emb.weight.shape
				lowerCAmelCase__      			=						nn.Linear(lowercase__     ,					lowercase__     ,					bias=lowercase__       )
				lowerCAmelCase__      			=						emb.weight.data
				return lin_layer





def 	lowerCAmelCase_		(lowercase__						:       Optional[Any]     ,					lowercase__						:       Dict       )		->    Any:


				'''simple docstring'''




				lowerCAmelCase__      			=						torch.load(lowercase__     ,					map_location='''cpu'''       )
				lowerCAmelCase__      			=						mam_aaa['''args''']
				lowerCAmelCase__      			=						mam_aaa['''model''']
				lowerCAmelCase__      			=						state_dict['''decoder.output_projection.weight''']

				remove_ignore_keys_(lowercase__       )
				rename_keys(lowercase__       )

				lowerCAmelCase__      			=						state_dict['''decoder.embed_tokens.weight'''].shape[0]

				lowerCAmelCase__      			=						args.share_decoder_input_output_embed

				lowerCAmelCase__      			=						[int(lowercase__       ) for i in args.conv_kernel_sizes.split(''','''       )]
				lowerCAmelCase__      			=						SpeechaTextConfig(
				    vocab_size=lowercase__     ,					max_source_positions=args.max_source_positions     ,					max_target_positions=args.max_target_positions     ,					encoder_layers=args.encoder_layers     ,					decoder_layers=args.decoder_layers     ,					encoder_attention_heads=args.encoder_attention_heads     ,					decoder_attention_heads=args.decoder_attention_heads     ,					encoder_ffn_dim=args.encoder_ffn_embed_dim     ,					decoder_ffn_dim=args.decoder_ffn_embed_dim     ,					d_model=args.encoder_embed_dim     ,					dropout=args.dropout     ,					attention_dropout=args.attention_dropout     ,					activation_dropout=args.activation_dropout     ,					activation_function='''relu'''     ,					num_conv_layers=len(lowercase__       )     ,					conv_channels=args.conv_channels     ,					conv_kernel_sizes=lowercase__     ,					input_feat_per_channel=args.input_feat_per_channel     ,					input_channels=args.input_channels     ,					tie_word_embeddings=lowercase__     ,					num_beams=5     ,					max_length=2_00     ,					use_cache=lowercase__     ,					decoder_start_token_id=2     ,					early_stopping=lowercase__     ,					)

				lowerCAmelCase__      			=						SpeechaTextForConditionalGeneration(lowercase__       )
				lowerCAmelCase__			,		lowerCAmelCase__      			=						model.model.load_state_dict(lowercase__     ,					strict=lowercase__       )
				if len(lowercase__       ) > 0 and not set(lowercase__       ) <= {
				    "encoder.embed_positions.weights",
				    "decoder.embed_positions.weights",
				}:
								raise ValueError(
								    '''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'''
								    f' but all the following weights are missing {missing}'       )

				if tie_embeds:
								lowerCAmelCase__      			=						make_linear_from_emb(model.model.decoder.embed_tokens       )
				else:
								lowerCAmelCase__      			=						lm_head_weights

				model.save_pretrained(lowercase__       )


if __name__ == "__main__":
			_UpperCAmelCase			:		Dict   				=    argparse.ArgumentParser()
			# Required parameters
			parser.add_argument("--fairseq_path", type=str, help="Path to the fairseq model (.pt) file.")
			parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
			_UpperCAmelCase			:		Dict   				=    parser.parse_args()
			convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)













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()





