

'''simple docstring'''






import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration






def 	_A				(lowerCAmelCase__  :Any		)   ->  Optional[Any]:




     '''simple docstring'''



     _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(lowerCAmelCase__				,						lowerCAmelCase__		)






def 	_A				(lowerCAmelCase__  :List[Any]		)   ->  Tuple:




     '''simple docstring'''



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






def 	_A				(lowerCAmelCase__  :int		)   ->  Any:




     '''simple docstring'''



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






def 	_A				(lowerCAmelCase__  :Union[str, Any]				,						lowerCAmelCase__  :Optional[int]		)   ->  str:




     '''simple docstring'''



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

     remove_ignore_keys_(lowerCAmelCase__		)
     rename_keys(lowerCAmelCase__		)

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

     _a			      =							args.share_decoder_input_output_embed

     _a			      =							[int(lowerCAmelCase__		) for i in args.conv_kernel_sizes.split(','		)]
     _a			      =							SpeechaTextConfig(
         vocab_size=lowerCAmelCase__				,						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(lowerCAmelCase__		)				,						conv_channels=args.conv_channels				,						conv_kernel_sizes=lowerCAmelCase__				,						input_feat_per_channel=args.input_feat_per_channel				,						input_channels=args.input_channels				,						tie_word_embeddings=lowerCAmelCase__				,						num_beams=5				,						max_length=2_00				,						use_cache=lowerCAmelCase__				,						decoder_start_token_id=2				,						early_stopping=lowerCAmelCase__				,						)

     _a			      =							SpeechaTextForConditionalGeneration(lowerCAmelCase__		)
     _a      ,   _a			      =							model.model.load_state_dict(lowerCAmelCase__				,						strict=lowerCAmelCase__		)
     if len(lowerCAmelCase__		) > 0 and not set(lowerCAmelCase__		) <= {
         "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:
          _a			      =							make_linear_from_emb(model.model.decoder.embed_tokens		)
     else:
          _a			      =							lm_head_weights

     model.save_pretrained(lowerCAmelCase__		)


if __name__ == "__main__":
 a_					:    List[Any]          =    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.")
 a_					:    str          =    parser.parse_args()
 convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)





'''simple docstring'''






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
 from PIL import Image




class  a     (     _SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	"""dandelin/vilt-b32-finetuned-vqa"""
 _lowerCAmelCase 					=	(
     """This is a tool that answers a question about an image. It takes an input named `image` which should be the """
     """image containing the information, as well as a `question` which should be the question in English. It """
     """returns a text that is the answer to the question."""
 )
 _lowerCAmelCase 					=	"""image_qa"""
 _lowerCAmelCase 					=	AutoProcessor
 _lowerCAmelCase 					=	AutoModelForVisualQuestionAnswering

 _lowerCAmelCase 					=	["""image""", """text"""]
 _lowerCAmelCase 					=	["""text"""]
 def __init__( self      ,				*__magic_name__      ,				**__magic_name__						)					->							Tuple:
      requires_backends(self      ,				['vision']						)
      super().__init__(*__magic_name__      ,				**__magic_name__						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							Tuple:
      return self.pre_processor(__magic_name__      ,				__magic_name__      ,				return_tensors='pt'						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Any:
      with torch.no_grad():
           return self.model(**__magic_name__						).logits




 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Optional[int]:
      _a			      =							outputs.argmax(-1						).item()
      return self.model.config.idalabel[idx]

