



"""simple docstring"""







import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment







class       UpperCamelCase__   (	unittest.TestCase  ):




      """simple docstring"""




      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        lowerCAmelCase_   :				Union[str, Any]								= inspect.getfile(accelerate.test_utils       )
        lowerCAmelCase_   :				Dict								= os.path.sep.join(mod_file.split(os.path.sep       )[:-1] + ['scripts', 'test_script.py']       )
        lowerCAmelCase_   :				int								= os.path.sep.join(
            mod_file.split(os.path.sep       )[:-1] + ['scripts', 'test_distributed_data_loop.py']       )
        lowerCAmelCase_   :				Union[str, Any]								= os.path.sep.join(mod_file.split(os.path.sep       )[:-1] + ['scripts', 'test_ops.py']       )



      @require_multi_gpu
      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        print(F"Found {torch.cuda.device_count()} devices."       )
        lowerCAmelCase_   :				Optional[int]								= ['torchrun', F"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
        with patch_environment(omp_num_threads=1       ):
          execute_subprocess_async(SCREAMING_SNAKE_CASE_    ,			env=os.environ.copy()       )



      @require_multi_gpu
      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):
        print(F"Found {torch.cuda.device_count()} devices."       )
        lowerCAmelCase_   :				int								= ['torchrun', F"--nproc_per_node={torch.cuda.device_count()}", self.operation_file_path]
        print(F"Command: {cmd}"       )
        with patch_environment(omp_num_threads=1       ):
          execute_subprocess_async(SCREAMING_SNAKE_CASE_    ,			env=os.environ.copy()       )



      @require_multi_gpu
      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        lowerCAmelCase_   :				List[Any]								= ['torchrun', F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__       )]
        with patch_environment(omp_num_threads=1       ):
          execute_subprocess_async(SCREAMING_SNAKE_CASE_    ,			env=os.environ.copy()       )





      @require_multi_gpu
      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        print(F"Found {torch.cuda.device_count()} devices, using 2 devices only"       )
        lowerCAmelCase_   :				Any								= ['torchrun', F"--nproc_per_node={torch.cuda.device_count()}", self.data_loop_file_path]
        with patch_environment(omp_num_threads=1    ,			cuda_visible_devices='0,1'       ):
          execute_subprocess_async(SCREAMING_SNAKE_CASE_    ,			env=os.environ.copy()       )


if __name__ == "__main__":
     lowercase__		:				Dict          =	Accelerator()
     lowercase__		:				List[Any]          =	(accelerator.state.process_index + 2, 1_0)
     lowercase__		:				Any          =	torch.randint(0, 1_0, shape).to(accelerator.device)

     lowercase__		:				List[Any]          =	""""""

     lowercase__		:				Union[str, Any]          =	accelerator.pad_across_processes(tensor)
     if tensora.shape[0] != accelerator.state.num_processes + 1:
          error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
     if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
          error_msg += "Tensors have different values."
     if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
          error_msg += "Padding was not done with the right value (0)."

     lowercase__		:				Optional[Any]          =	accelerator.pad_across_processes(tensor, pad_first=True)
     if tensora.shape[0] != accelerator.state.num_processes + 1:
          error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
     lowercase__		:				Any          =	accelerator.state.num_processes - accelerator.state.process_index - 1
     if not torch.equal(tensora[index:], tensor):
          error_msg += "Tensors have different values."
     if not torch.all(tensora[:index] == 0):
          error_msg += "Padding was not done with the right value (0)."

     # Raise error at the end to make sure we don't stop at the first failure.
     if len(error_msg) > 0:
          raise ValueError(error_msg)










"""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
        )





