



import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def A    (							__UpperCamelCase       )    ->   Optional[Any]:
			A__	  =     [
			    '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(__UpperCamelCase			,						__UpperCamelCase       )


def A    (							__UpperCamelCase       )    ->   int:
			A__  ,			A__	  =     emb.weight.shape
			A__	  =     nn.Linear(__UpperCamelCase			,						__UpperCamelCase			,						bias=__UpperCamelCase       )
			A__	  =     emb.weight.data
			return lin_layer


def A    (							__UpperCamelCase       )    ->   Optional[int]:
			A__	  =     torch.load(__UpperCamelCase			,						map_location='cpu'       )
			A__	  =     mam_aaa['args'] or mam_aaa['cfg']['model']
			A__	  =     mam_aaa['model']
			remove_ignore_keys_(__UpperCamelCase       )
			A__	  =     state_dict['encoder.embed_tokens.weight'].shape[0]

			A__	  =     MaMaaaConfig(
			    vocab_size=__UpperCamelCase			,						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'			,						)

			A__	  =     state_dict['decoder.embed_tokens.weight']
			A__	  =     MaMaaaForConditionalGeneration(__UpperCamelCase       )
			model.model.load_state_dict(__UpperCamelCase			,						strict=__UpperCamelCase       )
			A__	  =     make_linear_from_emb(model.model.shared       )

			return model


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__   			=   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.''')
							SCREAMING_SNAKE_CASE__   			=   parser.parse_args()
							SCREAMING_SNAKE_CASE__   			=   convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
							model.save_pretrained(args.pytorch_dump_folder_path)









'''simple docstring'''





import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			) -> int:
  __lowerCamelCase			:			Optional[Any]       			=					1.5
  __lowerCamelCase			:			Tuple       			=					int(factor * num_class_images			)
  __lowerCamelCase			:			Tuple       			=					ClipClient(
      url='https://knn.laion.ai/knn-service'			,indice_name='laion_400m'			,num_images=_lowerCAmelCase			,aesthetic_weight=0.1			)

  os.makedirs(F'{class_data_dir}/images'			,exist_ok=_lowerCAmelCase			)
  if len(list(Path(F'{class_data_dir}/images'			).iterdir()			)			) >= num_class_images:
    return

  while True:
    __lowerCamelCase			:			Dict       			=					client.query(text=_lowerCAmelCase			)
    if len(_lowerCAmelCase			) >= factor * num_class_images or num_images > 1E4:
      break
    else:
      __lowerCamelCase			:			Union[str, Any]       			=					int(factor * num_images			)
      __lowerCamelCase			:			List[str]       			=					ClipClient(
          url='https://knn.laion.ai/knn-service'			,indice_name='laion_400m'			,num_images=_lowerCAmelCase			,aesthetic_weight=0.1			,)

  __lowerCamelCase			:			List[Any]       			=					0
  __lowerCamelCase			:			Tuple       			=					0
  __lowerCamelCase			:			int       			=					tqdm(desc='downloading real regularization images'			,total=_lowerCAmelCase			)

  with open(F'{class_data_dir}/caption.txt'			,'w'			) as fa, open(F'{class_data_dir}/urls.txt'			,'w'			) as fa, open(
      F'{class_data_dir}/images.txt'			,'w'			) as fa:
    while total < num_class_images:
      __lowerCamelCase			:			List[str]       			=					class_images[count]
      count += 1
      try:
        __lowerCamelCase			:			int       			=					requests.get(images['url']			)
        if img.status_code == 200:
          __lowerCamelCase			:			str       			=					Image.open(BytesIO(img.content			)			)
          with open(F'{class_data_dir}/images/{total}.jpg'			,'wb'			) as f:
            f.write(img.content			)
          fa.write(images['caption'] + '\n'			)
          fa.write(images['url'] + '\n'			)
          fa.write(F'{class_data_dir}/images/{total}.jpg' + '\n'			)
          total += 1
          pbar.update(1			)
        else:
          continue
      except Exception:
        continue
  return

def 			a_	(				) -> Dict:
  __lowerCamelCase			:			Any       			=					argparse.ArgumentParser(''			,add_help=_lowerCAmelCase			)
  parser.add_argument('--class_prompt'			,help='text prompt to retrieve images'			,required=_lowerCAmelCase			,type=_lowerCAmelCase			)
  parser.add_argument('--class_data_dir'			,help='path to save images'			,required=_lowerCAmelCase			,type=_lowerCAmelCase			)
  parser.add_argument('--num_class_images'			,help='number of images to download'			,default=200			,type=_lowerCAmelCase			)
  return parser.parse_args()


if __name__ == "__main__":
   _UpperCamelCase											=		parse_args()
   retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)






