

import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   [
         '''decoder.version''',
         '''decoder.output_projection.weight''',
         '''_float_tensor''',
         '''decoder.embed_positions._float_tensor''',
     ]
     for k in ignore_keys:
          state_dict.pop(_A				,		_A    )
def     UpperCAmelCase_       (						_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     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   torch.load(_A				,		map_location='''cpu'''    )
     SCREAMING_SNAKE_CASE__   =   Namespace(**checkpoint['''cfg''']['''model''']    )
     SCREAMING_SNAKE_CASE__   =   checkpoint['''model''']
     remove_ignore_keys_(_A    )
     SCREAMING_SNAKE_CASE__   =   state_dict['''decoder.embed_tokens.weight'''].shape[0]

     SCREAMING_SNAKE_CASE__   =   {key.replace('''decoder'''				,		'''model'''    ): val for key, val in state_dict.items()}

     SCREAMING_SNAKE_CASE__   =   XGLMConfig(
         vocab_size=_A				,		max_position_embeddings=args.max_target_positions				,		num_layers=args.decoder_layers				,		attention_heads=args.decoder_attention_heads				,		ffn_dim=args.decoder_ffn_embed_dim				,		d_model=args.decoder_embed_dim				,		layerdrop=args.decoder_layerdrop				,		dropout=args.dropout				,		attention_dropout=args.attention_dropout				,		activation_dropout=args.activation_dropout				,		activation_function='''gelu'''				,		scale_embedding=not args.no_scale_embedding				,		tie_word_embeddings=args.share_decoder_input_output_embed				,		)

     SCREAMING_SNAKE_CASE__   =   XGLMForCausalLM(_A    )
     SCREAMING_SNAKE_CASE__   =   model.load_state_dict(_A				,		strict=_A    )
     print(_A    )
     SCREAMING_SNAKE_CASE__   =   make_linear_from_emb(model.model.embed_tokens    )

     return model


if __name__ == "__main__":
   _SCREAMING_SNAKE_CASE					:  Optional[Any]             =      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.''')
   _SCREAMING_SNAKE_CASE					:  int             =      parser.parse_args()
   _SCREAMING_SNAKE_CASE					:  str             =      convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
   model.save_pretrained(args.pytorch_dump_folder_path)





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


_SCREAMING_SNAKE_CASE					:  Any             =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:  Optional[Any]             =      {
    '''google/vivit-b-16x2-kinetics400''': (
        '''https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'''
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= "vivit"






  def __init__(							self						:		str     ,       __lowerCamelCase						:		List[Any]=224     ,       __lowerCamelCase						:		Optional[int]=32     ,       __lowerCamelCase						:		Tuple=[2, 16, 16]     ,       __lowerCamelCase						:		Union[str, Any]=3     ,       __lowerCamelCase						:		Optional[Any]=768     ,       __lowerCamelCase						:		Any=12     ,       __lowerCamelCase						:		Optional[Any]=12     ,       __lowerCamelCase						:		List[str]=3072     ,       __lowerCamelCase						:		Any="gelu_fast"     ,       __lowerCamelCase						:		Union[str, Any]=0.0     ,       __lowerCamelCase						:		int=0.0     ,       __lowerCamelCase						:		str=0.02     ,       __lowerCamelCase						:		Any=1e-06     ,       __lowerCamelCase						:		Dict=True     ,       **__lowerCamelCase						:		Any     ,       )		->							List[str]:
       SCREAMING_SNAKE_CASE__   =   hidden_size
       SCREAMING_SNAKE_CASE__   =   num_hidden_layers
       SCREAMING_SNAKE_CASE__   =   num_attention_heads
       SCREAMING_SNAKE_CASE__   =   intermediate_size
       SCREAMING_SNAKE_CASE__   =   hidden_act
       SCREAMING_SNAKE_CASE__   =   hidden_dropout_prob
       SCREAMING_SNAKE_CASE__   =   attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__   =   initializer_range
       SCREAMING_SNAKE_CASE__   =   layer_norm_eps

       SCREAMING_SNAKE_CASE__   =   image_size
       SCREAMING_SNAKE_CASE__   =   num_frames
       SCREAMING_SNAKE_CASE__   =   tubelet_size
       SCREAMING_SNAKE_CASE__   =   num_channels
       SCREAMING_SNAKE_CASE__   =   qkv_bias

       super().__init__(**__lowerCamelCase )


