
import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Optional[Any])					->    List[str]:






							'''simple docstring'''





							__UpperCamelCase	:   Tuple			   =					[
							    "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(_lowerCamelCase						,  _lowerCamelCase)




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str)					->    int:






							'''simple docstring'''





							__UpperCamelCase   ,						__UpperCamelCase	:   Union[str, Any]			   =					emb.weight.shape
							__UpperCamelCase	:   Tuple			   =					nn.Linear(_lowerCamelCase						,  _lowerCamelCase						,  bias=_lowerCamelCase)
							__UpperCamelCase	:   Dict			   =					emb.weight.data
							return lin_layer




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			int)					->    List[Any]:






							'''simple docstring'''





							__UpperCamelCase	:   str			   =					torch.load(_lowerCamelCase						,  map_location="cpu")
							__UpperCamelCase	:   Union[str, Any]			   =					mam_aaa["args"] or mam_aaa["cfg"]["model"]
							__UpperCamelCase	:   Tuple			   =					mam_aaa["model"]
							remove_ignore_keys_(_lowerCamelCase)
							__UpperCamelCase	:   List[str]			   =					state_dict["encoder.embed_tokens.weight"].shape[0]

							__UpperCamelCase	:   List[Any]			   =					MaMaaaConfig(
							    vocab_size=_lowerCamelCase						,  max_position_embeddings=1_024						,  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						,  encoder_layerdrop=args.encoder_layerdrop						,  decoder_layerdrop=args.decoder_layerdrop						,  dropout=args.dropout						,  attention_dropout=args.attention_dropout						,  activation_dropout=args.activation_dropout						,  activation_function="relu"						,  )

							__UpperCamelCase	:   Union[str, Any]			   =					state_dict["decoder.embed_tokens.weight"]
							__UpperCamelCase	:   int			   =					MaMaaaForConditionalGeneration(_lowerCamelCase)
							model.model.load_state_dict(_lowerCamelCase						,  strict=_lowerCamelCase)
							__UpperCamelCase	:   int			   =					make_linear_from_emb(model.model.shared)

							return model


if __name__ == "__main__":
		lowercase  :					int													=       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.')
		lowercase  :					List[str]													=       parser.parse_args()
		lowercase  :					List[str]													=       convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
		model.save_pretrained(args.pytorch_dump_folder_path)

from PIL import Image




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Image						,  _lowerCamelCase :			int)					->    Image:






							'''simple docstring'''





							__UpperCamelCase	:   str			   =					(259 * (level + 255)) / (255 * (259 - level))

							def contrast(_lowerCamelCase :			int) -> int:
														return int(128 + factor * (c - 128))

							return img.point(_lowerCamelCase)


if __name__ == "__main__":
		# Load image
		with Image.open('image_data/lena.jpg') as img:
				# Change contrast to 170
				lowercase  :					Tuple													=       change_contrast(img, 170)
				cont_img.save('image_data/lena_high_contrast.png', format='png')