"""simple docstring"""






import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration


def      UpperCAmelCase__			(							lowerCAmelCase__	:str						)					->       Optional[Any]:
      '''simple docstring'''


      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(lowerCAmelCase__   ,						lowerCAmelCase__						)


def      UpperCAmelCase__			(							lowerCAmelCase__	:str						)					->       Any:
      '''simple docstring'''


      lowercase								=      list(s_dict.keys()						)
      for key in keys:
            if "transformer_layers" in key:
                  lowercase								=      s_dict.pop(lowerCAmelCase__						)
            elif "subsample" in key:
                  lowercase								=      s_dict.pop(lowerCAmelCase__						)


def      UpperCAmelCase__			(							lowerCAmelCase__	:Optional[int]						)					->       int:
      '''simple docstring'''


      lowercase				,				lowercase								=      emb.weight.shape
      lowercase								=      nn.Linear(lowerCAmelCase__   ,						lowerCAmelCase__   ,						bias=lowerCAmelCase__						)
      lowercase								=      emb.weight.data
      return lin_layer


def      UpperCAmelCase__			(							lowerCAmelCase__	:Optional[Any]   ,						lowerCAmelCase__	:int						)					->       Optional[Any]:
      '''simple docstring'''


      lowercase								=      torch.load(lowerCAmelCase__   ,						map_location="""cpu"""						)
      lowercase								=      mam_aaa["""args"""]
      lowercase								=      mam_aaa["""model"""]
      lowercase								=      state_dict["""decoder.output_projection.weight"""]

      remove_ignore_keys_(lowerCAmelCase__						)
      rename_keys(lowerCAmelCase__						)

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

      lowercase								=      args.share_decoder_input_output_embed

      lowercase								=      [int(lowerCAmelCase__						) for i in args.conv_kernel_sizes.split(""","""						)]
      lowercase								=      SpeechaTextConfig(
          vocab_size=lowerCAmelCase__   ,						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(lowerCAmelCase__						)   ,						conv_channels=args.conv_channels   ,						conv_kernel_sizes=lowerCAmelCase__   ,						input_feat_per_channel=args.input_feat_per_channel   ,						input_channels=args.input_channels   ,						tie_word_embeddings=lowerCAmelCase__   ,						num_beams=5   ,						max_length=2_0_0   ,						use_cache=lowerCAmelCase__   ,						decoder_start_token_id=2   ,						early_stopping=lowerCAmelCase__   ,						)

      lowercase								=      SpeechaTextForConditionalGeneration(lowerCAmelCase__						)
      lowercase				,				lowercase								=      model.model.load_state_dict(lowerCAmelCase__   ,						strict=lowerCAmelCase__						)
      if len(lowerCAmelCase__						) > 0 and not set(lowerCAmelCase__						) <= {
          "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(lowerCAmelCase__						)


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       :			Tuple   	=parser.parse_args()
       convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)




"""simple docstring"""






from math import pow, sqrt


def      UpperCAmelCase__			(							*lowerCAmelCase__	:float						)					->       bool:
      '''simple docstring'''


      lowercase								=      len(lowerCAmelCase__						) > 0 and all(value > 0.0 for value in values						)
      return result


def      UpperCAmelCase__			(							lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float						)					->       float | ValueError:
      '''simple docstring'''


      return (
          round(sqrt(molar_mass_a / molar_mass_a						)   ,						6						)
          if validate(lowerCAmelCase__   ,						lowerCAmelCase__						)
          else ValueError("""Input Error: Molar mass values must greater than 0."""						)
      )


def      UpperCAmelCase__			(							lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float						)					->       float | ValueError:
      '''simple docstring'''


      return (
          round(effusion_rate * sqrt(molar_mass_a / molar_mass_a						)   ,						6						)
          if validate(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__						)
          else ValueError(
              """Input Error: Molar mass and effusion rate values must greater than 0."""						)
      )


def      UpperCAmelCase__			(							lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float						)					->       float | ValueError:
      '''simple docstring'''


      return (
          round(effusion_rate / sqrt(molar_mass_a / molar_mass_a						)   ,						6						)
          if validate(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__						)
          else ValueError(
              """Input Error: Molar mass and effusion rate values must greater than 0."""						)
      )


def      UpperCAmelCase__			(							lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float						)					->       float | ValueError:
      '''simple docstring'''


      return (
          round(molar_mass / pow(effusion_rate_a / effusion_rate_a   ,						2						)   ,						6						)
          if validate(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__						)
          else ValueError(
              """Input Error: Molar mass and effusion rate values must greater than 0."""						)
      )






def      UpperCAmelCase__			(							lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float						)					->       float | ValueError:
      '''simple docstring'''


      return (
          round(pow(effusion_rate_a / effusion_rate_a   ,						2						) / molar_mass   ,						6						)
          if validate(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__						)
          else ValueError(
              """Input Error: Molar mass and effusion rate values must greater than 0."""						)
      )



