

import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration


def        __lowerCAmelCase    (       _UpperCamelCase		:  str	)		->							List[str]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			[
     '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        __lowerCAmelCase    (       _UpperCamelCase		:  Optional[int]	)		->							Any:
 '''simple docstring'''


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


def        __lowerCAmelCase    (       _UpperCamelCase		:  List[str]	)		->							List[str]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			emb.weight.shape
 SCREAMING_SNAKE_CASE								=			nn.Linear(_UpperCamelCase		,							_UpperCamelCase		,							bias=_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			emb.weight.data
 return lin_layer


def        __lowerCAmelCase    (       _UpperCamelCase		:  str		,							_UpperCamelCase		:  List[str]	)		->							Union[str, Any]:
 '''simple docstring'''


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

 remove_ignore_keys_(_UpperCamelCase	)
 rename_keys(_UpperCamelCase	)

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

 SCREAMING_SNAKE_CASE								=			args.share_decoder_input_output_embed

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

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

 model.save_pretrained(_UpperCamelCase	)


if __name__ == "__main__":
      a_    :						Optional[int]    		=      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)




from ....configuration_utils import PretrainedConfig
from ....utils import logging


a_    :						Any    		=      logging.get_logger(__name__)

a_    :						Dict    		=      {
    "Visual-Attention-Network/van-base": (
        "https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
    ),
}




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         ="van"

    def __init__(			self :      Optional[Any]							,    snake_case__ :      Tuple=2_2_4							,    snake_case__ :      Dict=3							,    snake_case__ :      Union[str, Any]=[7, 3, 3, 3]							,    snake_case__ :      str=[4, 2, 2, 2]							,    snake_case__ :      Optional[Any]=[6_4, 1_2_8, 3_2_0, 5_1_2]							,    snake_case__ :      Optional[Any]=[3, 3, 1_2, 3]							,    snake_case__ :      Tuple=[8, 8, 4, 4]							,    snake_case__ :      Any="gelu"							,    snake_case__ :      Dict=0.02							,    snake_case__ :      List[str]=1E-6							,    snake_case__ :      int=1E-2							,    snake_case__ :      Any=0.0							,    snake_case__ :      Tuple=0.0							,    **snake_case__ :      Any							,    ):

     """simple docstring"""






     super().__init__(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			image_size
     SCREAMING_SNAKE_CASE								=			num_channels
     SCREAMING_SNAKE_CASE								=			patch_sizes
     SCREAMING_SNAKE_CASE								=			strides
     SCREAMING_SNAKE_CASE								=			hidden_sizes
     SCREAMING_SNAKE_CASE								=			depths
     SCREAMING_SNAKE_CASE								=			mlp_ratios
     SCREAMING_SNAKE_CASE								=			hidden_act
     SCREAMING_SNAKE_CASE								=			initializer_range
     SCREAMING_SNAKE_CASE								=			layer_norm_eps
     SCREAMING_SNAKE_CASE								=			layer_scale_init_value
     SCREAMING_SNAKE_CASE								=			drop_path_rate
     SCREAMING_SNAKE_CASE								=			dropout_rate

