



from typing import List, Optional, Union

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


a__   :					str 						=      logging.get_logger(__name__)

a__   :					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__(				lowerCamelCase						):

   '''simple docstring'''




   A     :	Optional[int]												=     "informer"
   A     :	Tuple												=     {
       "hidden_size": "d_model",
       "num_attention_heads": "encoder_attention_heads",
       "num_hidden_layers": "encoder_layers",
   }


   def __init__(    self	:      int      ,					lowerCAmelCase	:      Optional[int] = None      ,					lowerCAmelCase	:      Optional[int] = None      ,					lowerCAmelCase	:      str = "student_t"      ,					lowerCAmelCase	:      str = "nll"      ,					lowerCAmelCase	:      int = 1      ,					lowerCAmelCase	:      List[int] = None      ,					lowerCAmelCase	:      Optional[Union[str, bool]] = "mean"      ,					lowerCAmelCase	:      int = 0      ,					lowerCAmelCase	:      int = 0      ,					lowerCAmelCase	:      int = 0      ,					lowerCAmelCase	:      int = 0      ,					lowerCAmelCase	:      Optional[List[int]] = None      ,					lowerCAmelCase	:      Optional[List[int]] = None      ,					lowerCAmelCase	:      int = 64      ,					lowerCAmelCase	:      int = 32      ,					lowerCAmelCase	:      int = 32      ,					lowerCAmelCase	:      int = 2      ,					lowerCAmelCase	:      int = 2      ,					lowerCAmelCase	:      int = 2      ,					lowerCAmelCase	:      int = 2      ,					lowerCAmelCase	:      bool = True      ,					lowerCAmelCase	:      str = "gelu"      ,					lowerCAmelCase	:      float = 0.05      ,					lowerCAmelCase	:      float = 0.1      ,					lowerCAmelCase	:      float = 0.1      ,					lowerCAmelCase	:      float = 0.1      ,					lowerCAmelCase	:      float = 0.1      ,					lowerCAmelCase	:      int = 1_00      ,					lowerCAmelCase	:      float = 0.02      ,					lowerCAmelCase	:      Dict=True      ,					lowerCAmelCase	:      str = "prob"      ,					lowerCAmelCase	:      int = 5      ,					lowerCAmelCase	:      bool = True      ,					**lowerCAmelCase	:      Optional[int]      ,					)						->					List[Any]:




         """simple docstring"""

         lowercase__  =	prediction_length
         lowercase__  =	context_length or prediction_length
         lowercase__  =	distribution_output
         lowercase__  =	loss
         lowercase__  =	input_size
         lowercase__  =	num_time_features
         lowercase__  =	lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
         lowercase__  =	scaling
         lowercase__  =	num_dynamic_real_features
         lowercase__  =	num_static_real_features
         lowercase__  =	num_static_categorical_features

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

         # set embedding_dimension
         if embedding_dimension and num_static_categorical_features > 0:
               if len(lowerCAmelCase) != num_static_categorical_features:
                     raise ValueError(
                         'The embedding dimension should be a list of the same length as `num_static_categorical_features`')
               lowercase__  =	embedding_dimension
         else:
               lowercase__  =	[min(50      ,					(cat + 1) // 2) for cat in self.cardinality]

         lowercase__  =	num_parallel_samples

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

         lowercase__  =	dropout
         lowercase__  =	attention_dropout
         lowercase__  =	activation_dropout
         lowercase__  =	encoder_layerdrop
         lowercase__  =	decoder_layerdrop

         lowercase__  =	activation_function
         lowercase__  =	init_std

         lowercase__  =	use_cache

         # Informer
         lowercase__  =	attention_type
         lowercase__  =	sampling_factor
         lowercase__  =	distil

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



   @property
   def       UpperCAmelCase		(    self	:      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
         )





# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async

def 	_lowerCAmelCase (    A__=None						):
      if subparsers is not None:
            lowercase__  =	subparsers.add_parser('test'						)
      else:
            lowercase__  =	argparse.ArgumentParser('Accelerate test command'						)

      parser.add_argument(
          '--config_file'    ,	default=A__    ,	help=(
              'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
              'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
              'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
              'with \'huggingface\'.'
          )    ,	)

      if subparsers is not None:
            parser.set_defaults(func=A__						)
      return parser

def 	_lowerCAmelCase (    A__						):
      lowercase__  =	os.path.sep.join(__file__.split(os.path.sep						)[:-2] + ['test_utils', 'scripts', 'test_script.py']						)

      if args.config_file is None:
            lowercase__  =	script_name
      else:
            lowercase__  =	F'''--config_file={args.config_file} {script_name}'''

      lowercase__  =	['accelerate-launch'] + test_args.split()
      lowercase__  =	execute_subprocess_async(A__    ,	env=os.environ.copy()						)
      if result.returncode == 0:
            print('Test is a success! You are ready for your distributed training!'						)

def 	_lowerCAmelCase (    ):
      lowercase__  =	test_command_parser()
      lowercase__  =	parser.parse_args()
      test_command(A__						)


if __name__ == "__main__":
       main()
