


import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def      snake_case_						( __lowercase       ):
   UpperCAmelCase_							:      Any						=   [
       '''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(__lowercase			,       __lowercase       )
def      snake_case_						( __lowercase       ):
   UpperCAmelCase_							:      Union[str, Any]						=   list(s_dict.keys()       )
   for key in keys:
      if "transformer_layers" in key:
         UpperCAmelCase_							:      Union[str, Any]						=   s_dict.pop(__lowercase       )
      elif "subsample" in key:
         UpperCAmelCase_							:      List[Any]						=   s_dict.pop(__lowercase       )
def      snake_case_						( __lowercase       ):
   UpperCAmelCase_		,      UpperCAmelCase_							:      Dict						=   emb.weight.shape
   UpperCAmelCase_							:      Union[str, Any]						=   nn.Linear(__lowercase			,       __lowercase			,       bias=__lowercase       )
   UpperCAmelCase_							:      List[Any]						=   emb.weight.data
   return lin_layer
def      snake_case_						( __lowercase			,       __lowercase       ):
   UpperCAmelCase_							:      str						=   torch.load(__lowercase			,       map_location='''cpu'''       )
   UpperCAmelCase_							:      int						=   mam_aaa['''args''']
   UpperCAmelCase_							:      Dict						=   mam_aaa['''model''']
   UpperCAmelCase_							:      Optional[Any]						=   state_dict['''decoder.output_projection.weight''']

   remove_ignore_keys_(__lowercase       )
   rename_keys(__lowercase       )

   UpperCAmelCase_							:      int						=   state_dict['''decoder.embed_tokens.weight'''].shape[0]

   UpperCAmelCase_							:      Optional[Any]						=   args.share_decoder_input_output_embed

   UpperCAmelCase_							:      str						=   [int(__lowercase       ) for i in args.conv_kernel_sizes.split(''','''       )]
   UpperCAmelCase_							:      List[Any]						=   SpeechaTextConfig(
       vocab_size=__lowercase			,       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(__lowercase       )			,       conv_channels=args.conv_channels			,       conv_kernel_sizes=__lowercase			,       input_feat_per_channel=args.input_feat_per_channel			,       input_channels=args.input_channels			,       tie_word_embeddings=__lowercase			,       num_beams=5			,       max_length=2_0_0			,       use_cache=__lowercase			,       decoder_start_token_id=2			,       early_stopping=__lowercase			,       )

   UpperCAmelCase_							:      Tuple						=   SpeechaTextForConditionalGeneration(__lowercase       )
   UpperCAmelCase_		,      UpperCAmelCase_							:      List[Any]						=   model.model.load_state_dict(__lowercase			,       strict=__lowercase       )
   if len(__lowercase       ) > 0 and not set(__lowercase       ) <= {
       "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:
      UpperCAmelCase_							:      List[str]						=   make_linear_from_emb(model.model.decoder.embed_tokens       )
   else:
      UpperCAmelCase_							:      List[Any]						=   lm_head_weights

   model.save_pretrained(__lowercase       )


if __name__ == "__main__":
       __UpperCamelCase				:  List[str]      					=							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				:  Tuple      					=							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


__UpperCamelCase				:  int      					=							logging.get_logger(__name__)

__UpperCamelCase				:  Union[str, Any]      					=							{
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}





class  lowerCAmelCase__(    snake_case__						):






    '''simple docstring'''





    A_  :     str			= 'timesformer'





    def __init__(	self				:   int       ,   __snake_case				:   Any=224       ,   __snake_case				:   str=16       ,   __snake_case				:   Any=3       ,   __snake_case				:   List[Any]=8       ,   __snake_case				:   Dict=768       ,   __snake_case				:   Dict=12       ,   __snake_case				:   Tuple=12       ,   __snake_case				:   Dict=3_072       ,   __snake_case				:   str="gelu"       ,   __snake_case				:   Union[str, Any]=0.0       ,   __snake_case				:   List[Any]=0.0       ,   __snake_case				:   Tuple=0.02       ,   __snake_case				:   Optional[Any]=1E-6       ,   __snake_case				:   List[Any]=True       ,   __snake_case				:   List[str]="divided_space_time"       ,   __snake_case				:   Optional[int]=0       ,   **__snake_case				:   Dict       ,   ):





       '''simple docstring'''
       super().__init__(**__snake_case	)

       UpperCAmelCase_							:      Optional[int]						=   image_size
       UpperCAmelCase_							:      Optional[Any]						=   patch_size
       UpperCAmelCase_							:      Any						=   num_channels
       UpperCAmelCase_							:      int						=   num_frames

       UpperCAmelCase_							:      List[Any]						=   hidden_size
       UpperCAmelCase_							:      Any						=   num_hidden_layers
       UpperCAmelCase_							:      Dict						=   num_attention_heads
       UpperCAmelCase_							:      List[Any]						=   intermediate_size
       UpperCAmelCase_							:      str						=   hidden_act
       UpperCAmelCase_							:      List[str]						=   hidden_dropout_prob
       UpperCAmelCase_							:      List[str]						=   attention_probs_dropout_prob
       UpperCAmelCase_							:      Optional[int]						=   initializer_range
       UpperCAmelCase_							:      List[str]						=   layer_norm_eps
       UpperCAmelCase_							:      str						=   qkv_bias

       UpperCAmelCase_							:      Dict						=   attention_type
       UpperCAmelCase_							:      str						=   drop_path_rate