





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     str:
    lowerCamelCase__    :      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(_UpperCAmelCase						,	_UpperCAmelCase )


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     List[Any]:
    lowerCamelCase__ ,							lowerCamelCase__    :      List[str]					 =		emb.weight.shape
    lowerCamelCase__    :      Tuple					 =		nn.Linear(_UpperCAmelCase						,	_UpperCAmelCase						,	bias=_UpperCAmelCase )
    lowerCamelCase__    :      Dict					 =		emb.weight.data
    return lin_layer


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     str:
    lowerCamelCase__    :      Tuple					 =		torch.load(_UpperCAmelCase						,	map_location='cpu' )
    lowerCamelCase__    :      List[str]					 =		mam_aaa['args'] or mam_aaa['cfg']['model']
    lowerCamelCase__    :      Optional[int]					 =		mam_aaa['model']
    remove_ignore_keys_(_UpperCAmelCase )
    lowerCamelCase__    :      str					 =		state_dict['encoder.embed_tokens.weight'].shape[0]

    lowerCamelCase__    :      Union[str, Any]					 =		MaMaaaConfig(
        vocab_size=_UpperCAmelCase						,	max_position_embeddings=1024						,	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						,	encoder_layerdrop=args.encoder_layerdrop						,	decoder_layerdrop=args.decoder_layerdrop						,	dropout=args.dropout						,	attention_dropout=args.attention_dropout						,	activation_dropout=args.activation_dropout						,	activation_function='relu'						,	)

    lowerCamelCase__    :      Optional[Any]					 =		state_dict['decoder.embed_tokens.weight']
    lowerCamelCase__    :      Union[str, Any]					 =		MaMaaaForConditionalGeneration(_UpperCAmelCase )
    model.model.load_state_dict(_UpperCAmelCase						,	strict=_UpperCAmelCase )
    lowerCamelCase__    :      List[str]					 =		make_linear_from_emb(model.model.shared )

    return model


if __name__ == "__main__":
    _UpperCAmelCase				:       Tuple	   =				argparse.ArgumentParser()
    # Required parameters
    parser.add_argument("""fairseq_path""", type=str, help="""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.""")
    _UpperCAmelCase				:       str	   =				parser.parse_args()
    _UpperCAmelCase				:       Optional[Any]	   =				convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)










"""simple docstring"""







from typing import List, Optional, Union

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


lowercase__		:				Union[str, Any]          =	logging.get_logger(__name__)

lowercase__		:				List[str]          =	{
    """huggingface/informer-tourism-monthly""": (
        """https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"""
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}







class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     """informer"""
      _SCREAMING_SNAKE_CASE  				=     {
          """hidden_size""": """d_model""",
          """num_attention_heads""": """encoder_attention_heads""",
          """num_hidden_layers""": """encoder_layers""",
      }



      def __init__(      self				:      Any    ,			SCREAMING_SNAKE_CASE_				:      Optional[int] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[int] = None    ,			SCREAMING_SNAKE_CASE_				:      str = "student_t"    ,			SCREAMING_SNAKE_CASE_				:      str = "nll"    ,			SCREAMING_SNAKE_CASE_				:      int = 1    ,			SCREAMING_SNAKE_CASE_				:      List[int] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[Union[str, bool]] = "mean"    ,			SCREAMING_SNAKE_CASE_				:      int = 0    ,			SCREAMING_SNAKE_CASE_				:      int = 0    ,			SCREAMING_SNAKE_CASE_				:      int = 0    ,			SCREAMING_SNAKE_CASE_				:      int = 0    ,			SCREAMING_SNAKE_CASE_				:      Optional[List[int]] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[List[int]] = None    ,			SCREAMING_SNAKE_CASE_				:      int = 6_4    ,			SCREAMING_SNAKE_CASE_				:      int = 3_2    ,			SCREAMING_SNAKE_CASE_				:      int = 3_2    ,			SCREAMING_SNAKE_CASE_				:      int = 2    ,			SCREAMING_SNAKE_CASE_				:      int = 2    ,			SCREAMING_SNAKE_CASE_				:      int = 2    ,			SCREAMING_SNAKE_CASE_				:      int = 2    ,			SCREAMING_SNAKE_CASE_				:      bool = True    ,			SCREAMING_SNAKE_CASE_				:      str = "gelu"    ,			SCREAMING_SNAKE_CASE_				:      float = 0.05    ,			SCREAMING_SNAKE_CASE_				:      float = 0.1    ,			SCREAMING_SNAKE_CASE_				:      float = 0.1    ,			SCREAMING_SNAKE_CASE_				:      float = 0.1    ,			SCREAMING_SNAKE_CASE_				:      float = 0.1    ,			SCREAMING_SNAKE_CASE_				:      int = 1_0_0    ,			SCREAMING_SNAKE_CASE_				:      float = 0.02    ,			SCREAMING_SNAKE_CASE_				:      str=True    ,			SCREAMING_SNAKE_CASE_				:      str = "prob"    ,			SCREAMING_SNAKE_CASE_				:      int = 5    ,			SCREAMING_SNAKE_CASE_				:      bool = True    ,			**SCREAMING_SNAKE_CASE_				:      int    ,			):
        # time series specific configuration
        lowerCAmelCase_   :				Dict								= prediction_length
        lowerCAmelCase_   :				List[str]								= context_length or prediction_length
        lowerCAmelCase_   :				List[Any]								= distribution_output
        lowerCAmelCase_   :				int								= loss
        lowerCAmelCase_   :				Optional[int]								= input_size
        lowerCAmelCase_   :				Tuple								= num_time_features
        lowerCAmelCase_   :				List[str]								= lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
        lowerCAmelCase_   :				int								= scaling
        lowerCAmelCase_   :				List[Any]								= num_dynamic_real_features
        lowerCAmelCase_   :				Union[str, Any]								= num_static_real_features
        lowerCAmelCase_   :				Optional[int]								= num_static_categorical_features

        # set cardinality
        if cardinality and num_static_categorical_features > 0:
          if len(SCREAMING_SNAKE_CASE_       ) != num_static_categorical_features:
            raise ValueError(
                'The cardinality should be a list of the same length as `num_static_categorical_features`'       )
          lowerCAmelCase_   :				str								= cardinality
        else:
          lowerCAmelCase_   :				Any								= [0]

        # set embedding_dimension
        if embedding_dimension and num_static_categorical_features > 0:
          if len(SCREAMING_SNAKE_CASE_       ) != num_static_categorical_features:
            raise ValueError(
                'The embedding dimension should be a list of the same length as `num_static_categorical_features`'       )
          lowerCAmelCase_   :				Optional[int]								= embedding_dimension
        else:
          lowerCAmelCase_   :				Union[str, Any]								= [min(5_0    ,			(cat + 1) // 2       ) for cat in self.cardinality]

        lowerCAmelCase_   :				Optional[int]								= num_parallel_samples

        # Transformer architecture configuration
        lowerCAmelCase_   :				Any								= input_size * len(self.lags_sequence       ) + self._number_of_features
        lowerCAmelCase_   :				Any								= d_model
        lowerCAmelCase_   :				Union[str, Any]								= encoder_attention_heads
        lowerCAmelCase_   :				Optional[Any]								= decoder_attention_heads
        lowerCAmelCase_   :				Any								= encoder_ffn_dim
        lowerCAmelCase_   :				List[str]								= decoder_ffn_dim
        lowerCAmelCase_   :				Optional[Any]								= encoder_layers
        lowerCAmelCase_   :				Tuple								= decoder_layers

        lowerCAmelCase_   :				Optional[int]								= dropout
        lowerCAmelCase_   :				Dict								= attention_dropout
        lowerCAmelCase_   :				int								= activation_dropout
        lowerCAmelCase_   :				Dict								= encoder_layerdrop
        lowerCAmelCase_   :				str								= decoder_layerdrop

        lowerCAmelCase_   :				Union[str, Any]								= activation_function
        lowerCAmelCase_   :				Union[str, Any]								= init_std

        lowerCAmelCase_   :				Union[str, Any]								= use_cache

        # Informer
        lowerCAmelCase_   :				Optional[int]								= attention_type
        lowerCAmelCase_   :				Any								= sampling_factor
        lowerCAmelCase_   :				int								= distil

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





      @property
      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        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
        )





