

from typing import List, Optional, Union

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


_lowerCamelCase      :					Tuple					    =      logging.get_logger(__name__)

_lowerCamelCase      :					Optional[Any]					    =      {
    '''huggingface/time-series-transformer-tourism-monthly''': (
        '''https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'''
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    """time_series_transformer"""
       lowercase_						=    {
           """hidden_size""": """d_model""",
           """num_attention_heads""": """encoder_attention_heads""",
           """num_hidden_layers""": """encoder_layers""",
       }

       def __init__(							self ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = "student_t" ,   lowercase__ = "nll" ,   lowercase__ = 1 ,   lowercase__ = [1, 2, 3, 4, 5, 6, 7] ,   lowercase__ = "mean" ,   lowercase__ = 0 ,   lowercase__ = 0 ,   lowercase__ = 0 ,   lowercase__ = 0 ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = 3_2 ,   lowercase__ = 3_2 ,   lowercase__ = 2 ,   lowercase__ = 2 ,   lowercase__ = 2 ,   lowercase__ = 2 ,   lowercase__ = True ,   lowercase__ = "gelu" ,   lowercase__ = 6_4 ,   lowercase__ = 0.1 ,   lowercase__ = 0.1 ,   lowercase__ = 0.1 ,   lowercase__ = 0.1 ,   lowercase__ = 0.1 ,   lowercase__ = 1_0_0 ,   lowercase__ = 0.02 ,   lowercase__=True ,   **lowercase__ ,   ):







             '''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
             __A    =scaling
             __A    =num_dynamic_real_features
             __A    =num_static_real_features
             __A    =num_static_categorical_features
             if cardinality and num_static_categorical_features > 0:
                   if len(lowercase__     ) != 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]
             if embedding_dimension and num_static_categorical_features > 0:
                   if len(lowercase__     ) != 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(5_0 ,   (cat + 1) // 2     ) for cat in self.cardinality]
             __A    =num_parallel_samples

             # Transformer architecture configuration
             __A    =input_size * len(lowercase__     ) + 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

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


       @property
       def     __UpperCamelCase     (							self     ):







             '''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 os
import re
import shutil
import sys
import tempfile
import unittest

import black


_lowerCamelCase      :					Union[str, Any]					    =      os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_lowerCamelCase      :					str					    =      '''    \"""
    Output class for the scheduler\'s step function output.

    Args:
        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
            denoising loop.
        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
            The predicted denoised sample (x_{0}) based on the model output from the current timestep.
            `pred_original_sample` can be used to preview progress or for guidance.
    \"""

    prev_sample: torch.FloatTensor
    pred_original_sample: Optional[torch.FloatTensor] = None
'''





class        lowerCAmelCase__  (					unittest.TestCase			):



       '''simple docstring'''







       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =tempfile.mkdtemp()
             os.makedirs(os.path.join(self.diffusers_dir ,   '''schedulers/'''     )     )
             __A    =self.diffusers_dir
             shutil.copy(
                 os.path.join(lowercase__ ,   '''src/diffusers/schedulers/scheduling_ddpm.py'''     ) ,   os.path.join(self.diffusers_dir ,   '''schedulers/scheduling_ddpm.py'''     ) ,   )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    ='''src/diffusers'''
             shutil.rmtree(self.diffusers_dir     )

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__=None     ):







             '''simple docstring'''



             __A    =comment + f'''\nclass {class_name}(nn.Module):\n''' + class_code
             if overwrite_result is not None:
                   __A    =comment + f'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
             __A    =black.Mode(target_versions={black.TargetVersion.PYaa} ,   line_length=1_1_9     )
             __A    =black.format_str(lowercase__ ,   mode=lowercase__     )
             __A    =os.path.join(self.diffusers_dir ,   '''new_code.py'''     )
             with open(lowercase__ ,   '''w''' ,   newline='''\n'''     ) as f:
                   f.write(lowercase__     )
             if overwrite_result is None:
                   self.assertTrue(len(check_copies.is_copy_consistent(lowercase__     )     ) == 0     )
             else:
                   check_copies.is_copy_consistent(f.name ,   overwrite=lowercase__     )
                   with open(lowercase__ ,   '''r'''     ) as f:
                         self.assertTrue(f.read() ,   lowercase__     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput'''     )
             self.assertEqual(lowercase__ ,   lowercase__     )


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             self.check_copy_consistency(
                 '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' ,   '''DDPMSchedulerOutput''' ,   REFERENCE_CODE + '''\n''' ,   )

             # With no empty line at the end
             self.check_copy_consistency(
                 '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' ,   '''DDPMSchedulerOutput''' ,   lowercase__ ,   )

             # Copy consistency with rename
             self.check_copy_consistency(
                 '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' ,   '''TestSchedulerOutput''' ,   re.sub('''DDPM''' ,   '''Test''' ,   lowercase__     ) ,   )

             # Copy consistency with a really long name
             __A    ='''TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
             self.check_copy_consistency(
                 f'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}''' ,   f'''{long_class_name}SchedulerOutput''' ,   re.sub('''Bert''' ,   lowercase__ ,   lowercase__     ) ,   )

             # Copy consistency with overwrite
             self.check_copy_consistency(
                 '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' ,   '''TestSchedulerOutput''' ,   lowercase__ ,   overwrite_result=re.sub('''DDPM''' ,   '''Test''' ,   lowercase__     ) ,   )


