






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


a						:			Tuple     	=					logging.get_logger(__name__)

a						:			List[str]     	=					{"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}
class        a       (					lowercase__  ):



      """simple docstring"""


      a    :					Optional[Any]							=      'openai-gpt'
      a    :					List[Any]							=      {
          'max_position_embeddings': 'n_positions',
          'hidden_size': 'n_embd',
          'num_attention_heads': 'n_head',
          'num_hidden_layers': 'n_layer',
      }
      def __init__( self   :     Union[str, Any]						, __lowercase   :     Tuple=40478						, __lowercase   :     Tuple=512						, __lowercase   :     int=768						, __lowercase   :     Dict=12						, __lowercase   :     Union[str, Any]=12						, __lowercase   :     Optional[Any]="gelu"						, __lowercase   :     Optional[Any]=0.1						, __lowercase   :     Optional[Any]=0.1						, __lowercase   :     Tuple=0.1						, __lowercase   :     Dict=1e-5						, __lowercase   :     Any=0.02						, __lowercase   :     List[str]="cls_index"						, __lowercase   :     str=True						, __lowercase   :     Dict=None						, __lowercase   :     str=True						, __lowercase   :     List[str]=0.1						, **__lowercase   :     List[Any]						, )				-> List[Any]:
        __UpperCAmelCase				:      Optional[int]	 =    vocab_size
        __UpperCAmelCase				:      Optional[Any]	 =    n_positions
        __UpperCAmelCase				:      Optional[int]	 =    n_embd
        __UpperCAmelCase				:      str	 =    n_layer
        __UpperCAmelCase				:      Any	 =    n_head
        __UpperCAmelCase				:      Tuple	 =    afn
        __UpperCAmelCase				:      Any	 =    resid_pdrop
        __UpperCAmelCase				:      Union[str, Any]	 =    embd_pdrop
        __UpperCAmelCase				:      str	 =    attn_pdrop
        __UpperCAmelCase				:      str	 =    layer_norm_epsilon
        __UpperCAmelCase				:      Dict	 =    initializer_range
        __UpperCAmelCase				:      Optional[int]	 =    summary_type
        __UpperCAmelCase				:      Optional[Any]	 =    summary_use_proj
        __UpperCAmelCase				:      List[Any]	 =    summary_activation
        __UpperCAmelCase				:      Union[str, Any]	 =    summary_first_dropout
        __UpperCAmelCase				:      Dict	 =    summary_proj_to_labels
        super().__init__(**__lowercase   )



'''simple docstring'''


import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration

def   A_      (       SCREAMING_SNAKE_CASE_						)			->List[Any]:
  lowercase_     =       [
      """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(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Optional[int]:
  lowercase_     =       list(s_dict.keys()						)
  for key in keys:
    if "transformer_layers" in key:
      lowercase_     =       s_dict.pop(SCREAMING_SNAKE_CASE_						)
    elif "subsample" in key:
      lowercase_     =       s_dict.pop(SCREAMING_SNAKE_CASE_						)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Dict:
  lowercase_     ,	lowercase_     =       emb.weight.shape
  lowercase_     =       nn.Linear(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	bias=SCREAMING_SNAKE_CASE_						)
  lowercase_     =       emb.weight.data
  return lin_layer

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Any:
  lowercase_     =       torch.load(SCREAMING_SNAKE_CASE_ ,	map_location="""cpu"""						)
  lowercase_     =       mam_aaa["""args"""]
  lowercase_     =       mam_aaa["""model"""]
  lowercase_     =       state_dict["""decoder.output_projection.weight"""]

  remove_ignore_keys_(SCREAMING_SNAKE_CASE_						)
  rename_keys(SCREAMING_SNAKE_CASE_						)

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

  lowercase_     =       args.share_decoder_input_output_embed

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

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

  model.save_pretrained(SCREAMING_SNAKE_CASE_						)


if __name__ == "__main__":
       __snake_case						  =		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.""")
       __snake_case						  =		parser.parse_args()
       convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)

