from typing import List, Optional

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


UpperCamelCase__											=					logging.get_logger(__name__)

UpperCamelCase__											=					{
    'huggingface/autoformer-tourism-monthly': 'https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json',
}





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : Optional[Any]      =			'autoformer'
   __UpperCAmelCase : Optional[int]      =			{
       'hidden_size': 'd_model',
       'num_attention_heads': 'encoder_attention_heads',
       'num_hidden_layers': 'encoder_layers',
   }


   def __init__(self     :						List[str]       ,  __UpperCAmelCase     :						Optional[int] = None       ,  __UpperCAmelCase     :						Optional[int] = None       ,  __UpperCAmelCase     :						str = "student_t"       ,  __UpperCAmelCase     :						str = "nll"       ,  __UpperCAmelCase     :						int = 1       ,  __UpperCAmelCase     :						List[int] = [1, 2, 3, 4, 5, 6, 7]       ,  __UpperCAmelCase     :						bool = True       ,  __UpperCAmelCase     :						int = 0       ,  __UpperCAmelCase     :						int = 0       ,  __UpperCAmelCase     :						int = 0       ,  __UpperCAmelCase     :						int = 0       ,  __UpperCAmelCase     :						Optional[List[int]] = None       ,  __UpperCAmelCase     :						Optional[List[int]] = None       ,  __UpperCAmelCase     :						int = 6_4       ,  __UpperCAmelCase     :						int = 2       ,  __UpperCAmelCase     :						int = 2       ,  __UpperCAmelCase     :						int = 2       ,  __UpperCAmelCase     :						int = 2       ,  __UpperCAmelCase     :						int = 3_2       ,  __UpperCAmelCase     :						int = 3_2       ,  __UpperCAmelCase     :						str = "gelu"       ,  __UpperCAmelCase     :						float = 0.1       ,  __UpperCAmelCase     :						float = 0.1       ,  __UpperCAmelCase     :						float = 0.1       ,  __UpperCAmelCase     :						float = 0.1       ,  __UpperCAmelCase     :						float = 0.1       ,  __UpperCAmelCase     :						int = 1_0_0       ,  __UpperCAmelCase     :						float = 0.02       ,  __UpperCAmelCase     :						bool = True       ,  __UpperCAmelCase     :						int=True       ,  __UpperCAmelCase     :						int = 1_0       ,  __UpperCAmelCase     :						int = 2_5       ,  __UpperCAmelCase     :						int = 3       ,  **__UpperCAmelCase     :						str       ,  )						->				int:
         """simple docstring"""



         UpperCAmelCase__								=  prediction_length
         UpperCAmelCase__								=  context_length if context_length is not None else prediction_length
         UpperCAmelCase__								=  distribution_output
         UpperCAmelCase__								=  loss
         UpperCAmelCase__								=  input_size
         UpperCAmelCase__								=  num_time_features
         UpperCAmelCase__								=  lags_sequence
         UpperCAmelCase__								=  scaling
         UpperCAmelCase__								=  num_dynamic_real_features
         UpperCAmelCase__								=  num_static_real_features
         UpperCAmelCase__								=  num_static_categorical_features
         if cardinality is not None and num_static_categorical_features > 0:
               if len(__UpperCAmelCase		) != num_static_categorical_features:
                     raise ValueError(
                         "The cardinality should be a list of the same length as `num_static_categorical_features`"		)
               UpperCAmelCase__								=  cardinality
         else:
               UpperCAmelCase__								=  [0]
         if embedding_dimension is not None and num_static_categorical_features > 0:
               if len(__UpperCAmelCase		) != num_static_categorical_features:
                     raise ValueError(
                         "The embedding dimension should be a list of the same length as `num_static_categorical_features`"		)
               UpperCAmelCase__								=  embedding_dimension
         else:
               UpperCAmelCase__								=  [min(5_0       ,  (cat + 1) // 2		) for cat in self.cardinality]
         UpperCAmelCase__								=  num_parallel_samples

         # Transformer architecture configuration
         UpperCAmelCase__								=  input_size * len(self.lags_sequence		) + self._number_of_features
         UpperCAmelCase__								=  d_model
         UpperCAmelCase__								=  encoder_attention_heads
         UpperCAmelCase__								=  decoder_attention_heads
         UpperCAmelCase__								=  encoder_ffn_dim
         UpperCAmelCase__								=  decoder_ffn_dim
         UpperCAmelCase__								=  encoder_layers
         UpperCAmelCase__								=  decoder_layers

         UpperCAmelCase__								=  dropout
         UpperCAmelCase__								=  attention_dropout
         UpperCAmelCase__								=  activation_dropout
         UpperCAmelCase__								=  encoder_layerdrop
         UpperCAmelCase__								=  decoder_layerdrop

         UpperCAmelCase__								=  activation_function
         UpperCAmelCase__								=  init_std

         UpperCAmelCase__								=  use_cache

         # Autoformer
         UpperCAmelCase__								=  label_length
         UpperCAmelCase__								=  moving_average
         UpperCAmelCase__								=  autocorrelation_factor

         super().__init__(is_encoder_decoder=__UpperCAmelCase       ,  **__UpperCAmelCase		)





   @property
   def 			lowercase_      (self     :						List[str]		)						->				int:
         """simple docstring"""



         return (
             sum(self.embedding_dimension		)
             + self.num_dynamic_real_features
             + self.num_time_features
             + self.num_static_real_features
             + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
         )




import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration




def    lowerCAmelCase_ (     __A							)     ->				Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  [
          "encoder.version",
          "decoder.version",
          "model.encoder.version",
          "model.decoder.version",
          "_float_tensor",
          "decoder.output_projection.weight",
      ]
      for k in ignore_keys:
            state_dict.pop(__A,  __A							)




def    lowerCAmelCase_ (     __A							)     ->				Optional[int]:







      '''simple docstring'''


      UpperCAmelCase__		,      UpperCAmelCase__								=  emb.weight.shape
      UpperCAmelCase__								=  nn.Linear(__A,  __A,  bias=__A							)
      UpperCAmelCase__								=  emb.weight.data
      return lin_layer




def    lowerCAmelCase_ (     __A,  __A="facebook/mbart-large-en-ro",  __A=False,  __A=False							)     ->				Tuple:







      '''simple docstring'''


      UpperCAmelCase__								=  torch.load(__A,  map_location="cpu"							)["model"]
      remove_ignore_keys_(__A							)
      UpperCAmelCase__								=  state_dict["encoder.embed_tokens.weight"].shape[0]

      UpperCAmelCase__								=  MBartConfig.from_pretrained(__A,  vocab_size=__A							)
      if mbart_aa and finetuned:
            UpperCAmelCase__								=  "relu"

      UpperCAmelCase__								=  state_dict["decoder.embed_tokens.weight"]
      UpperCAmelCase__								=  MBartForConditionalGeneration(__A							)
      model.model.load_state_dict(__A							)

      if finetuned:
            UpperCAmelCase__								=  make_linear_from_emb(model.model.shared							)

      return model


if __name__ == "__main__":
      UpperCamelCase__											=					argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          'fairseq_path', type=str, help='bart.large, bart.large.cnn or a 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.')
      parser.add_argument(
          '--hf_config',
          default='facebook/mbart-large-cc25',
          type=str,
          help='Which huggingface architecture to use: mbart-large',
      )
      parser.add_argument('--mbart_50', action='store_true', help='whether the model is mMART-50 checkpoint')
      parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
      UpperCamelCase__											=					parser.parse_args()
      UpperCamelCase__											=					convert_fairseq_mbart_checkpoint_from_disk(
          args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
      )
      model.save_pretrained(args.pytorch_dump_folder_path)



