

'''simple docstring'''



import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration







def    a__       (  a__						):




     """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(a__      ,    a__						)







def    a__       (  a__						):




     """simple docstring"""


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







def    a__       (  a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      emb.weight.shape
     __SCREAMING_SNAKE_CASE     =      nn.Linear(a__      ,    a__      ,    bias=a__						)
     __SCREAMING_SNAKE_CASE     =      emb.weight.data
     return lin_layer







def    a__       (  a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      torch.load(a__      ,    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_(a__						)
     rename_keys(a__						)

     __SCREAMING_SNAKE_CASE     =      state_dict["""decoder.embed_tokens.weight"""].shape[0]

     __SCREAMING_SNAKE_CASE     =      args.share_decoder_input_output_embed

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

     __SCREAMING_SNAKE_CASE     =      SpeechaTextForConditionalGeneration(a__						)
     __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      model.model.load_state_dict(a__      ,    strict=a__						)
     if len(a__						) > 0 and not set(a__						) <= {
         "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(a__						)


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









'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase :       Optional[int]			   =     logging.get_logger(__name__)

UpperCAmelCase :       str			   =     {
    'facebook/convnextv2-tiny-1k-224': 'https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json',
}







class 						lowerCAmelCase__   (      a ,					a					):



 """simple docstring"""



 lowerCAmelCase__  					=      "convnextv2"
 def __init__(		self					:       Any					,							__SCREAMING_SNAKE_CASE					:       int=3					,							__SCREAMING_SNAKE_CASE					:       Dict=4					,							__SCREAMING_SNAKE_CASE					:       List[Any]=4					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=None					,							__SCREAMING_SNAKE_CASE					:       Dict=None					,							__SCREAMING_SNAKE_CASE					:       Optional[int]="gelu"					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]=0.02					,							__SCREAMING_SNAKE_CASE					:       Dict=1E-12					,							__SCREAMING_SNAKE_CASE					:       List[str]=0.0					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=224					,							__SCREAMING_SNAKE_CASE					:       Tuple=None					,							__SCREAMING_SNAKE_CASE					:       List[str]=None					,							**__SCREAMING_SNAKE_CASE					:       Union[str, Any]					,							)  -> Union[str, Any]:


      """simple docstring"""




      super().__init__(**__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      num_channels
      __SCREAMING_SNAKE_CASE     =      patch_size
      __SCREAMING_SNAKE_CASE     =      num_stages
      __SCREAMING_SNAKE_CASE     =      [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
      __SCREAMING_SNAKE_CASE     =      [3, 3, 9, 3] if depths is None else depths
      __SCREAMING_SNAKE_CASE     =      hidden_act
      __SCREAMING_SNAKE_CASE     =      initializer_range
      __SCREAMING_SNAKE_CASE     =      layer_norm_eps
      __SCREAMING_SNAKE_CASE     =      drop_path_rate
      __SCREAMING_SNAKE_CASE     =      image_size
      __SCREAMING_SNAKE_CASE     =      ["""stem"""] + [f'stage{idx}' for idx in range(1					,							len(self.depths			) + 1			)]
      __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE					,							out_indices=__SCREAMING_SNAKE_CASE					,							stage_names=self.stage_names			)






