




'''simple docstring'''







import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration





def 		snake_case_							(_a			:							str  ):
    UpperCAmelCase     =	[
        '''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 		snake_case_							(_a			:							Optional[int]  ):
    UpperCAmelCase ,  UpperCAmelCase     =	emb.weight.shape
    UpperCAmelCase     =	nn.Linear(UpperCAmelCase__					,     UpperCAmelCase__					,     bias=UpperCAmelCase__  )
    UpperCAmelCase     =	emb.weight.data
    return lin_layer





def 		snake_case_							(_a			:							int  ):
    UpperCAmelCase     =	torch.load(UpperCAmelCase__					,     map_location='''cpu'''  )
    UpperCAmelCase     =	mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
    UpperCAmelCase     =	mam_aaa['''model''']
    remove_ignore_keys_(UpperCAmelCase__  )
    UpperCAmelCase     =	state_dict['''encoder.embed_tokens.weight'''].shape[0]

    UpperCAmelCase     =	MaMaaaConfig(
        vocab_size=UpperCAmelCase__					,     max_position_embeddings=1_0_2_4					,     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     =	state_dict['''decoder.embed_tokens.weight''']
    UpperCAmelCase     =	MaMaaaForConditionalGeneration(UpperCAmelCase__  )
    model.model.load_state_dict(UpperCAmelCase__					,     strict=UpperCAmelCase__  )
    UpperCAmelCase     =	make_linear_from_emb(model.model.shared  )

    return model


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






'''simple docstring'''







from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
   from PIL import Image

   from ..image_utils import load_image

if is_torch_available():
   from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

A									=logging.get_logger(__name__)


@add_end_docstrings(__a				)
class     _a						(			__a				):
       def __init__(			self		:			Optional[int]     ,       *lowercase		:			Any     ,       **lowercase		:			Optional[int]       ):



           '''simple docstring'''

           super().__init__(*lowercase     ,       **lowercase       )
           self.check_model_type(lowercase       )
       def 					A   (			self		:			List[str]     ,       lowercase		:			str=None     ,       lowercase		:			List[str]=None     ,       lowercase		:			List[str]=None     ,       **lowercase		:			List[str]       ):



           '''simple docstring'''

           UpperCAmelCase ,  UpperCAmelCase     =	{}, {}
           if padding is not None:
               UpperCAmelCase     =	padding
           if truncation is not None:
               UpperCAmelCase     =	truncation
           if top_k is not None:
               UpperCAmelCase     =	top_k
           return preprocess_params, {}, postprocess_params
       def __call__(			self		:			Optional[Any]     ,       lowercase		:			Union["Image.Image", str]     ,       lowercase		:			str = None     ,       **lowercase		:			Optional[int]       ):



           '''simple docstring'''

           if isinstance(lowercase     ,       (Image.Image, str)       ) and isinstance(lowercase     ,       lowercase       ):
               UpperCAmelCase     =	{'''image''': image, '''question''': question}
           else:
               UpperCAmelCase     =	image
           UpperCAmelCase     =	super().__call__(lowercase     ,       **lowercase       )
           return results
       def 					A   (			self		:			List[Any]     ,       lowercase		:			List[str]     ,       lowercase		:			Any=False     ,       lowercase		:			Any=False       ):



           '''simple docstring'''

           UpperCAmelCase     =	load_image(inputs['''image''']       )
           UpperCAmelCase     =	self.tokenizer(
               inputs['''question''']     ,       return_tensors=self.framework     ,       padding=lowercase     ,       truncation=lowercase       )
           UpperCAmelCase     =	self.image_processor(images=lowercase     ,       return_tensors=self.framework       )
           model_inputs.update(lowercase       )
           return model_inputs
       def 					A   (			self		:			Dict     ,       lowercase		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model(**lowercase       )
           return model_outputs







       def 					A   (			self		:			Union[str, Any]     ,       lowercase		:			int     ,       lowercase		:			Optional[Any]=5       ):



           '''simple docstring'''

           if top_k > self.model.config.num_labels:
               UpperCAmelCase     =	self.model.config.num_labels

           if self.framework == "pt":
               UpperCAmelCase     =	model_outputs.logits.sigmoid()[0]
               UpperCAmelCase ,  UpperCAmelCase     =	probs.topk(lowercase       )
           else:
               raise ValueError(f"Unsupported framework: {self.framework}"       )

           UpperCAmelCase     =	scores.tolist()
           UpperCAmelCase     =	ids.tolist()
           return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(lowercase     ,       lowercase       )]




