




"""simple docstring"""



from typing import List, Optional, Union

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


SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE    =	{
    "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_  (						A_							):
    lowercase__						 =				'''informer'''
    lowercase__						 =				{
        '''hidden_size''': '''d_model''',
        '''num_attention_heads''': '''encoder_attention_heads''',
        '''num_hidden_layers''': '''encoder_layers''',
    }




    def __init__(   self						:	Any     ,			snake_case_						:	Optional[int] = None     ,			snake_case_						:	Optional[int] = None     ,			snake_case_						:	str = "student_t"     ,			snake_case_						:	str = "nll"     ,			snake_case_						:	int = 1     ,			snake_case_						:	List[int] = None     ,			snake_case_						:	Optional[Union[str, bool]] = "mean"     ,			snake_case_						:	int = 0     ,			snake_case_						:	int = 0     ,			snake_case_						:	int = 0     ,			snake_case_						:	int = 0     ,			snake_case_						:	Optional[List[int]] = None     ,			snake_case_						:	Optional[List[int]] = None     ,			snake_case_						:	int = 64     ,			snake_case_						:	int = 32     ,			snake_case_						:	int = 32     ,			snake_case_						:	int = 2     ,			snake_case_						:	int = 2     ,			snake_case_						:	int = 2     ,			snake_case_						:	int = 2     ,			snake_case_						:	bool = True     ,			snake_case_						:	str = "gelu"     ,			snake_case_						:	float = 0.05     ,			snake_case_						:	float = 0.1     ,			snake_case_						:	float = 0.1     ,			snake_case_						:	float = 0.1     ,			snake_case_						:	float = 0.1     ,			snake_case_						:	int = 100     ,			snake_case_						:	float = 0.02     ,			snake_case_						:	Optional[int]=True     ,			snake_case_						:	str = "prob"     ,			snake_case_						:	int = 5     ,			snake_case_						:	bool = True     ,			**snake_case_						:	Optional[Any]     ,			)				->							Any:




     '''simple docstring'''

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

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

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

     A__	=    num_parallel_samples

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

     A__	=    dropout
     A__	=    attention_dropout
     A__	=    activation_dropout
     A__	=    encoder_layerdrop
     A__	=    decoder_layerdrop

     A__	=    activation_function
     A__	=    init_std

     A__	=    use_cache

     # Informer
     A__	=    attention_type
     A__	=    sampling_factor
     A__	=    distil

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




    @property
    def 							__magic_name__							(   self						:	Tuple				)				->							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
     )











"""simple docstring"""



# Copyright 2022 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
import platform

import numpy as np
import psutil
import torch

from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file

from ..utils import is_npu_available, is_xpu_available







def   _SCREAMING_SNAKE_CASE       (							lowercase_=None						)						->		Any:
 if subparsers is not None:
  A__	=    subparsers.add_parser("env"						)
 else:
  A__	=    argparse.ArgumentParser("Accelerate env command"						)

 parser.add_argument(
     "--config_file"      ,			default=lowercase_      ,			help="The config file to use for the default values in the launching script."						)

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







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		Dict:
 A__	=    torch.__version__
 A__	=    torch.cuda.is_available()
 A__	=    is_xpu_available()
 A__	=    is_npu_available()

 A__	=    "Not found"
 # Get the default from the config file.
 if args.config_file is not None or os.path.isfile(lowercase_						):
  A__	=    load_config_from_file(args.config_file						).to_dict()

 A__	=    {
     "`Accelerate` version": version,
     "Platform": platform.platform(),
     "Python version": platform.python_version(),
     "Numpy version": np.__version__,
     "PyTorch version (GPU?)": f"""{pt_version} ({pt_cuda_available})""",
     "PyTorch XPU available": str(lowercase_						),
     "PyTorch NPU available": str(lowercase_						),
     "System RAM": f"""{psutil.virtual_memory().total / 10_24 ** 3:.2f} GB""",
 }
 if pt_cuda_available:
  A__	=    torch.cuda.get_device_name()

 print("\nCopy-and-paste the text below in your GitHub issue\n"						)
 print("\n".join([f"""- {prop}: {val}""" for prop, val in info.items()]						)						)

 print("- `Accelerate` default config:" if args.config_file is None else "- `Accelerate` config passed:"						)
 A__	=    (
     "\n".join([f"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()]						)
     if isinstance(lowercase_      ,			lowercase_						)
     else f"""\t{accelerate_config}"""
 )
 print(lowercase_						)

 A__	=    accelerate_config

 return info







def   _SCREAMING_SNAKE_CASE       (							)						->		int:
 A__	=    env_command_parser()
 A__	=    parser.parse_args()
 env_command(lowercase_						)
 return 0


if __name__ == "__main__":
      raise SystemExit(main())





