



import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
a__      :				Optional[Any]   	=			logging.get_logger(__name__)

def   _lowerCAmelCase   (						A__	):
      lowercase__     =  torch.load(A__						,  map_location='cpu'	)
      if "model" in sd.keys():
            lowercase__     =  torch.load(A__						,  map_location='cpu'	)["model"]

      # pop unnecessary weights
      lowercase__     =  [
          "decoder.version",
          "decoder.output_projection.weight",
      ]
      for key in keys_to_delete:
            if key in sd:
                  sd.pop(A__	)

      lowercase__     =  {
          "decoder.project_in_dim.weight": "decoder.project_in.weight",
          "decoder.project_out_dim.weight": "decoder.project_out.weight",
          "decoder.layer_norm.weight": "decoder.final_layer_norm.weight",
          "decoder.layer_norm.bias": "decoder.final_layer_norm.bias",
      }
      for old_key, new_key in keys_to_rename.items():
            if old_key in sd:
                  lowercase__     =  sd.pop(A__	)

      lowercase__     =  list(sd.keys()	)
      for key in keys:
            if ".qkv_proj." in key:
                  lowercase__     =  sd[key]
                  # We split QKV in separate Q,K,V

                  lowercase__     =  key.replace('.qkv_proj.'						,  '.q_proj.'	)
                  lowercase__     =  key.replace('.qkv_proj.'						,  '.k_proj.'	)
                  lowercase__     =  key.replace('.qkv_proj.'						,  '.v_proj.'	)

                  lowercase__     =  value.shape[0]
                  assert depth % 3 == 0
                  # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
                  # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
                  lowercase__     =  torch.split(A__						,  depth // 3						,  dim=0	)

                  lowercase__     =  q
                  lowercase__     =  k
                  lowercase__     =  v
                  del sd[key]

      return sd

@torch.no_grad()
def   _lowerCAmelCase   (						A__						,  A__						,  A__=None	):
      lowercase__     =  load_checkpoint(A__	)

      if config is not None:
            lowercase__     =  OPTConfig.from_pretrained(A__	)
      else:
            lowercase__     =  OPTConfig()

      lowercase__     =  OPTModel(A__	).half().eval()
      model.load_state_dict(A__	)

      # Check results
      Path(A__	).mkdir(exist_ok=A__	)
      model.save_pretrained(A__	)


if __name__ == "__main__":
       a__      :				Any   	=			argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--fairseq_path",
           type=str,
           help=(
               "path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"
               " https://huggingface.co/models?other=opt_metasq"
           ),
       )
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.")
       a__      :				Optional[Any]   	=			parser.parse_args()
       convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)





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


a__      :				Dict   	=			logging.get_logger(__name__)

a__      :				List[Any]   	=			{
    "facebook/s2t-small-librispeech-asr": (
        "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  int       				=    "speech_to_text"
   A    :  Optional[Any]       				=    ["past_key_values"]
   A    :  Optional[int]       				=    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


   def __init__(						self  :			Optional[int]			,				lowerCAmelCase  :			Tuple=1_00_00			,				lowerCAmelCase  :			int=12			,				lowerCAmelCase  :			int=20_48			,				lowerCAmelCase  :			Union[str, Any]=4			,				lowerCAmelCase  :			str=6			,				lowerCAmelCase  :			Dict=20_48			,				lowerCAmelCase  :			Dict=4			,				lowerCAmelCase  :			Optional[int]=0.0			,				lowerCAmelCase  :			Union[str, Any]=0.0			,				lowerCAmelCase  :			int=True			,				lowerCAmelCase  :			Optional[Any]=True			,				lowerCAmelCase  :			Dict="relu"			,				lowerCAmelCase  :			Tuple=2_56			,				lowerCAmelCase  :			Dict=0.1			,				lowerCAmelCase  :			Optional[Any]=0.0			,				lowerCAmelCase  :			List[Any]=0.0			,				lowerCAmelCase  :			Any=0.02			,				lowerCAmelCase  :			List[Any]=2			,				lowerCAmelCase  :			Tuple=True			,				lowerCAmelCase  :			Tuple=1			,				lowerCAmelCase  :			List[str]=0			,				lowerCAmelCase  :			Union[str, Any]=2			,				lowerCAmelCase  :			Any=60_00			,				lowerCAmelCase  :			Optional[int]=10_24			,				lowerCAmelCase  :			Optional[Any]=2			,				lowerCAmelCase  :			Optional[Any]=(5, 5)			,				lowerCAmelCase  :			Union[str, Any]=10_24			,				lowerCAmelCase  :			List[Any]=80			,				lowerCAmelCase  :			List[str]=1			,				**lowerCAmelCase  :			List[str]			,				)	->				Dict:




         """simple docstring"""

         lowercase__     =  vocab_size
         lowercase__     =  d_model
         lowercase__     =  encoder_ffn_dim
         lowercase__     =  encoder_layers
         lowercase__     =  encoder_attention_heads
         lowercase__     =  decoder_ffn_dim
         lowercase__     =  decoder_layers
         lowercase__     =  decoder_attention_heads
         lowercase__     =  dropout
         lowercase__     =  attention_dropout
         lowercase__     =  activation_dropout
         lowercase__     =  activation_function
         lowercase__     =  init_std
         lowercase__     =  encoder_layerdrop
         lowercase__     =  decoder_layerdrop
         lowercase__     =  use_cache
         lowercase__     =  encoder_layers
         lowercase__     =  scale_embedding  # scale factor will be sqrt(d_model) if True
         lowercase__     =  max_source_positions
         lowercase__     =  max_target_positions
         lowercase__     =  num_conv_layers
         lowercase__     =  list(lowerCAmelCase)
         lowercase__     =  conv_channels
         lowercase__     =  input_feat_per_channel
         lowercase__     =  input_channels

         if len(self.conv_kernel_sizes) != self.num_conv_layers:
               raise ValueError(
                   'Configuration for convolutional module is incorrect. '
                   'It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '
                   f'''but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes)}`, '''
                   f'''`config.num_conv_layers = {self.num_conv_layers}`.''')

         super().__init__(
             pad_token_id=lowerCAmelCase			,				bos_token_id=lowerCAmelCase			,				eos_token_id=lowerCAmelCase			,				is_encoder_decoder=lowerCAmelCase			,				decoder_start_token_id=lowerCAmelCase			,				**lowerCAmelCase			,				)
