



"""simple docstring"""




import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[Any]							)    ->  Dict:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[int]														=       [
         "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 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str							)    ->  Dict:





     '''simple docstring'''

     _UpperCAmelCase     :      Any														=       list(s_dict.keys()							)
     for key in keys:
          if "transformer_layers" in key:
               _UpperCAmelCase     :      Optional[Any]														=       s_dict.pop(SCREAMING_SNAKE_CASE__							)
          elif "subsample" in key:
               _UpperCAmelCase     :      Dict														=       s_dict.pop(SCREAMING_SNAKE_CASE__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Any							)    ->  Any:





     '''simple docstring'''

     _UpperCAmelCase							,			_UpperCAmelCase     :      List[str]														=       emb.weight.shape
     _UpperCAmelCase     :      str														=       nn.Linear(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    bias=SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      Optional[Any]														=       emb.weight.data
     return lin_layer

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[Any]		,    SCREAMING_SNAKE_CASE__      :		Tuple							)    ->  Tuple:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[int]														=       torch.load(SCREAMING_SNAKE_CASE__		,    map_location="cpu"							)
     _UpperCAmelCase     :      Tuple														=       mam_aaa["args"]
     _UpperCAmelCase     :      Optional[Any]														=       mam_aaa["model"]
     _UpperCAmelCase     :      str														=       state_dict["decoder.output_projection.weight"]

     remove_ignore_keys_(SCREAMING_SNAKE_CASE__							)
     rename_keys(SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase     :      Tuple														=       state_dict["decoder.embed_tokens.weight"].shape[0]

     _UpperCAmelCase     :      str														=       args.share_decoder_input_output_embed

     _UpperCAmelCase     :      Union[str, Any]														=       [int(SCREAMING_SNAKE_CASE__							) for i in args.conv_kernel_sizes.split(","							)]
     _UpperCAmelCase     :      Union[str, Any]														=       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=200		,    use_cache=SCREAMING_SNAKE_CASE__		,    decoder_start_token_id=2		,    early_stopping=SCREAMING_SNAKE_CASE__		,    )

     _UpperCAmelCase     :      Union[str, Any]														=       SpeechaTextForConditionalGeneration(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase							,			_UpperCAmelCase     :      Union[str, Any]														=       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:
          _UpperCAmelCase     :      str														=       make_linear_from_emb(model.model.decoder.embed_tokens							)
     else:
          _UpperCAmelCase     :      Union[str, Any]														=       lm_head_weights

     model.save_pretrained(SCREAMING_SNAKE_CASE__							)


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





"""simple docstring"""




# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")






class       UpperCAmelCase_    :



      def __init__(	self	:      Optional[Any]	,      A	:      List[str]	,      A	:      List[str]	,      A	:      bool = True	,      A	:      bool = False		):
           _UpperCAmelCase     :      Dict														=       scheduler
           _UpperCAmelCase     :      str														=       optimizers if isinstance(A	,      (list, tuple)		) else [optimizers]
           _UpperCAmelCase     :      List[str]														=       split_batches
           _UpperCAmelCase     :      Dict														=       step_with_optimizer
           _UpperCAmelCase     :      Tuple														=       GradientState()



      def 		snake_case_   (	self	:      Optional[int]	,      *A	:      List[Any]	,      **A	:      Optional[int]		):
           if not self.step_with_optimizer:
                # No link between scheduler and optimizer -> just step
                self.scheduler.step(*A	,      **A		)
                return

           # Otherwise, first make sure the optimizer was stepped.
           if not self.gradient_state.sync_gradients:
                if self.gradient_state.adjust_scheduler:
                     self.scheduler._step_count += 1
                return

           for opt in self.optimizers:
                if opt.step_was_skipped:
                     return
           if self.split_batches:
                # Split batches -> the training dataloader batch size is not changed so one step per training step
                self.scheduler.step(*A	,      **A		)
           else:
                # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
                # num_processes steps per training step
                _UpperCAmelCase     :      int														=       AcceleratorState().num_processes
                for _ in range(A		):
                     # Special case when using OneCycle and `drop_last` was not used
                     if hasattr(self.scheduler	,      "total_steps"		):
                          if self.scheduler._step_count <= self.scheduler.total_steps:
                               self.scheduler.step(*A	,      **A		)
                     else:
                          self.scheduler.step(*A	,      **A		)



      def 		snake_case_   (	self	:      Union[str, Any]		):
           return self.scheduler.get_last_lr()



      def 		snake_case_   (	self	:      str		):
           return self.scheduler.state_dict()



      def 		snake_case_   (	self	:      str	,      A	:      Optional[Any]		):
           self.scheduler.load_state_dict(A		)



      def 		snake_case_   (	self	:      Tuple		):
           return self.scheduler.get_lr()



      def 		snake_case_   (	self	:      Optional[int]	,      *A	:      List[str]	,      **A	:      Optional[Any]		):
           return self.scheduler.print_lr(*A	,      **A		)
