
"""simple docstring"""



import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest

class       lowerCAmelCase__				(						__A	):
     __a						=					(PNDMScheduler,)
     __a						=					(('num_inference_steps', 50),)







     def      lowercase	(							self			:			int    ,      **_lowerCamelCase			:			Dict			):
       _snake_case					     =       {
           '''num_train_timesteps''': 1000,
           '''beta_start''': 0.0_0_0_1,
           '''beta_end''': 0.0_2,
           '''beta_schedule''': '''linear''',
       }

       config.update(**__UpperCAmelCase			)
       return config







     def      lowercase	(							self			:			List[str]    ,      _lowerCamelCase			:			str=0    ,      **_lowerCamelCase			:			Optional[int]			):
       _snake_case					     =       dict(self.forward_default_kwargs			)
       _snake_case					     =       kwargs.pop('''num_inference_steps'''    ,      __UpperCAmelCase			)
       _snake_case					     =       self.dummy_sample
       _snake_case					     =       0.1 * sample
       _snake_case					     =       [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

       for scheduler_class in self.scheduler_classes:
         _snake_case					     =       self.get_scheduler_config(**__UpperCAmelCase			)
         _snake_case					     =       scheduler_class(**__UpperCAmelCase			)
         scheduler.set_timesteps(__UpperCAmelCase			)
         # copy over dummy past residuals
         _snake_case					     =       dummy_past_residuals[:]

         with tempfile.TemporaryDirectory() as tmpdirname:
           scheduler.save_config(__UpperCAmelCase			)
           _snake_case					     =       scheduler_class.from_pretrained(__UpperCAmelCase			)
           new_scheduler.set_timesteps(__UpperCAmelCase			)
           # copy over dummy past residuals
           _snake_case					     =       dummy_past_residuals[:]

         _snake_case					     =       scheduler.step_prk(__UpperCAmelCase    ,      __UpperCAmelCase    ,      __UpperCAmelCase    ,      **__UpperCAmelCase			).prev_sample
         _snake_case					     =       new_scheduler.step_prk(__UpperCAmelCase    ,      __UpperCAmelCase    ,      __UpperCAmelCase    ,      **__UpperCAmelCase			).prev_sample

         assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"

         _snake_case					     =       scheduler.step_plms(__UpperCAmelCase    ,      __UpperCAmelCase    ,      __UpperCAmelCase    ,      **__UpperCAmelCase			).prev_sample
         _snake_case					     =       new_scheduler.step_plms(__UpperCAmelCase    ,      __UpperCAmelCase    ,      __UpperCAmelCase    ,      **__UpperCAmelCase			).prev_sample

         assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"







     def      lowercase	(							self			:			Tuple			):
       pass







     def      lowercase	(							self			:			List[Any]    ,      _lowerCamelCase			:			List[str]=0    ,      **_lowerCamelCase			:			Optional[Any]			):
       _snake_case					     =       dict(self.forward_default_kwargs			)
       _snake_case					     =       kwargs.pop('''num_inference_steps'''    ,      __UpperCAmelCase			)
       _snake_case					     =       self.dummy_sample
       _snake_case					     =       0.1 * sample
       _snake_case					     =       [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

       for scheduler_class in self.scheduler_classes:
         _snake_case					     =       self.get_scheduler_config()
         _snake_case					     =       scheduler_class(**__UpperCAmelCase			)
         scheduler.set_timesteps(__UpperCAmelCase			)

         # copy over dummy past residuals (must be after setting timesteps)
         _snake_case					     =       dummy_past_residuals[:]

         with tempfile.TemporaryDirectory() as tmpdirname:
           scheduler.save_config(__UpperCAmelCase			)
           _snake_case					     =       scheduler_class.from_pretrained(__UpperCAmelCase			)
           # copy over dummy past residuals
           new_scheduler.set_timesteps(__UpperCAmelCase			)

           # copy over dummy past residual (must be after setting timesteps)
           _snake_case					     =       dummy_past_residuals[:]

         _snake_case					     =       scheduler.step_prk(__UpperCAmelCase    ,      __UpperCAmelCase    ,      __UpperCAmelCase    ,      **__UpperCAmelCase			).prev_sample
         _snake_case					     =       new_scheduler.step_prk(__UpperCAmelCase    ,      __UpperCAmelCase    ,      __UpperCAmelCase    ,      **__UpperCAmelCase			).prev_sample

         assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"

         _snake_case					     =       scheduler.step_plms(__UpperCAmelCase    ,      __UpperCAmelCase    ,      __UpperCAmelCase    ,      **__UpperCAmelCase			).prev_sample
         _snake_case					     =       new_scheduler.step_plms(__UpperCAmelCase    ,      __UpperCAmelCase    ,      __UpperCAmelCase    ,      **__UpperCAmelCase			).prev_sample

         assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"







     def      lowercase	(							self			:			List[str]    ,      **_lowerCamelCase			:			List[str]			):
       _snake_case					     =       self.scheduler_classes[0]
       _snake_case					     =       self.get_scheduler_config(**__UpperCAmelCase			)
       _snake_case					     =       scheduler_class(**__UpperCAmelCase			)

       _snake_case					     =       10
       _snake_case					     =       self.dummy_model()
       _snake_case					     =       self.dummy_sample_deter
       scheduler.set_timesteps(__UpperCAmelCase			)

       for i, t in enumerate(scheduler.prk_timesteps			):
         _snake_case					     =       model(__UpperCAmelCase    ,      __UpperCAmelCase			)
         _snake_case					     =       scheduler.step_prk(__UpperCAmelCase    ,      __UpperCAmelCase    ,      __UpperCAmelCase			).prev_sample

       for i, t in enumerate(scheduler.plms_timesteps			):
         _snake_case					     =       model(__UpperCAmelCase    ,      __UpperCAmelCase			)
         _snake_case					     =       scheduler.step_plms(__UpperCAmelCase    ,      __UpperCAmelCase    ,      __UpperCAmelCase			).prev_sample

       return sample







     def      lowercase	(							self			:			Tuple			):
       _snake_case					     =       dict(self.forward_default_kwargs			)

       _snake_case					     =       kwargs.pop('''num_inference_steps'''    ,      __UpperCAmelCase			)

       for scheduler_class in self.scheduler_classes:
         _snake_case					     =       self.get_scheduler_config()
         _snake_case					     =       scheduler_class(**__UpperCAmelCase			)

         _snake_case					     =       self.dummy_sample
         _snake_case					     =       0.1 * sample

         if num_inference_steps is not None and hasattr(__UpperCAmelCase    ,      '''set_timesteps'''			):
           scheduler.set_timesteps(__UpperCAmelCase			)
         elif num_inference_steps is not None and not hasattr(__UpperCAmelCase    ,      '''set_timesteps'''			):
           _snake_case					     =       num_inference_steps

         # copy over dummy past residuals (must be done after set_timesteps)
         _snake_case					     =       [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
         _snake_case					     =       dummy_past_residuals[:]

         _snake_case					     =       scheduler.step_prk(__UpperCAmelCase    ,      0    ,      __UpperCAmelCase    ,      **__UpperCAmelCase			).prev_sample
         _snake_case					     =       scheduler.step_prk(__UpperCAmelCase    ,      1    ,      __UpperCAmelCase    ,      **__UpperCAmelCase			).prev_sample

         self.assertEqual(output_a.shape    ,      sample.shape			)
         self.assertEqual(output_a.shape    ,      output_a.shape			)

         _snake_case					     =       scheduler.step_plms(__UpperCAmelCase    ,      0    ,      __UpperCAmelCase    ,      **__UpperCAmelCase			).prev_sample
         _snake_case					     =       scheduler.step_plms(__UpperCAmelCase    ,      1    ,      __UpperCAmelCase    ,      **__UpperCAmelCase			).prev_sample

         self.assertEqual(output_a.shape    ,      sample.shape			)
         self.assertEqual(output_a.shape    ,      output_a.shape			)







     def      lowercase	(							self			:			Union[str, Any]			):
       for timesteps in [100, 1000]:
         self.check_over_configs(num_train_timesteps=__UpperCAmelCase			)







     def      lowercase	(							self			:			Any			):
       for steps_offset in [0, 1]:
         self.check_over_configs(steps_offset=__UpperCAmelCase			)

       _snake_case					     =       self.scheduler_classes[0]
       _snake_case					     =       self.get_scheduler_config(steps_offset=1			)
       _snake_case					     =       scheduler_class(**__UpperCAmelCase			)
       scheduler.set_timesteps(10			)
       assert torch.equal(
           scheduler.timesteps    ,      torch.LongTensor(
               [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]			)    ,      )







     def      lowercase	(							self			:			List[Any]			):
       for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]    ,      [0.0_0_2, 0.0_2]			):
         self.check_over_configs(beta_start=__UpperCAmelCase    ,      beta_end=__UpperCAmelCase			)







     def      lowercase	(							self			:			Optional[int]			):
       for schedule in ["linear", "squaredcos_cap_v2"]:
         self.check_over_configs(beta_schedule=__UpperCAmelCase			)







     def      lowercase	(							self			:			List[Any]			):
       for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=__UpperCAmelCase			)







     def      lowercase	(							self			:			List[str]			):
       for t in [1, 5, 10]:
         self.check_over_forward(time_step=__UpperCAmelCase			)







     def      lowercase	(							self			:			Any			):
       for t, num_inference_steps in zip([1, 5, 10]    ,      [10, 50, 100]			):
         self.check_over_forward(num_inference_steps=__UpperCAmelCase			)







     def      lowercase	(							self			:			List[str]			):
       # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
       _snake_case					     =       27

       for scheduler_class in self.scheduler_classes:
         _snake_case					     =       self.dummy_sample
         _snake_case					     =       0.1 * sample

         _snake_case					     =       self.get_scheduler_config()
         _snake_case					     =       scheduler_class(**__UpperCAmelCase			)

         scheduler.set_timesteps(__UpperCAmelCase			)

         # before power of 3 fix, would error on first step, so we only need to do two
         for i, t in enumerate(scheduler.prk_timesteps[:2]			):
           _snake_case					     =       scheduler.step_prk(__UpperCAmelCase    ,      __UpperCAmelCase    ,      __UpperCAmelCase			).prev_sample







     def      lowercase	(							self			:			Optional[int]			):
       with self.assertRaises(__UpperCAmelCase			):
         _snake_case					     =       self.scheduler_classes[0]
         _snake_case					     =       self.get_scheduler_config()
         _snake_case					     =       scheduler_class(**__UpperCAmelCase			)

         scheduler.step_plms(self.dummy_sample    ,      1    ,      self.dummy_sample			).prev_sample







     def      lowercase	(							self			:			Union[str, Any]			):
       _snake_case					     =       self.full_loop()
       _snake_case					     =       torch.sum(torch.abs(__UpperCAmelCase			)			)
       _snake_case					     =       torch.mean(torch.abs(__UpperCAmelCase			)			)

       assert abs(result_sum.item() - 198.1318			) < 1e-2
       assert abs(result_mean.item() - 0.2_5_8_0			) < 1e-3







     def      lowercase	(							self			:			Optional[Any]			):
       _snake_case					     =       self.full_loop(prediction_type='''v_prediction'''			)
       _snake_case					     =       torch.sum(torch.abs(__UpperCAmelCase			)			)
       _snake_case					     =       torch.mean(torch.abs(__UpperCAmelCase			)			)

       assert abs(result_sum.item() - 67.3986			) < 1e-2
       assert abs(result_mean.item() - 0.0_8_7_8			) < 1e-3







     def      lowercase	(							self			:			Dict			):
       # We specify different beta, so that the first alpha is 0.99
       _snake_case					     =       self.full_loop(set_alpha_to_one=__UpperCAmelCase    ,      beta_start=0.0_1			)
       _snake_case					     =       torch.sum(torch.abs(__UpperCAmelCase			)			)
       _snake_case					     =       torch.mean(torch.abs(__UpperCAmelCase			)			)

       assert abs(result_sum.item() - 230.0399			) < 1e-2
       assert abs(result_mean.item() - 0.2_9_9_5			) < 1e-3







     def      lowercase	(							self			:			Tuple			):
       # We specify different beta, so that the first alpha is 0.99
       _snake_case					     =       self.full_loop(set_alpha_to_one=__UpperCAmelCase    ,      beta_start=0.0_1			)
       _snake_case					     =       torch.sum(torch.abs(__UpperCAmelCase			)			)
       _snake_case					     =       torch.mean(torch.abs(__UpperCAmelCase			)			)

       assert abs(result_sum.item() - 186.9482			) < 1e-2
       assert abs(result_mean.item() - 0.2_4_3_4			) < 1e-3




"""simple docstring"""




from typing import List, Optional, Union

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


__lowerCamelCase							 =     logging.get_logger(__name__)

__lowerCamelCase							 =     {
    "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 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  List[Any]  							=						'time_series_transformer'
						lowerCAmelCase__     :  Dict  							=						{
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'encoder_attention_heads',
						    'num_hidden_layers': 'encoder_layers',
						}





						def __init__(				self						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = "student_t"						,__UpperCAmelCase = "nll"						,__UpperCAmelCase = 1						,__UpperCAmelCase = [1, 2, 3, 4, 5, 6, 7]						,__UpperCAmelCase = "mean"						,__UpperCAmelCase = 0						,__UpperCAmelCase = 0						,__UpperCAmelCase = 0						,__UpperCAmelCase = 0						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = 32						,__UpperCAmelCase = 32						,__UpperCAmelCase = 2						,__UpperCAmelCase = 2						,__UpperCAmelCase = 2						,__UpperCAmelCase = 2						,__UpperCAmelCase = True						,__UpperCAmelCase = "gelu"						,__UpperCAmelCase = 64						,__UpperCAmelCase = 0.1						,__UpperCAmelCase = 0.1						,__UpperCAmelCase = 0.1						,__UpperCAmelCase = 0.1						,__UpperCAmelCase = 0.1						,__UpperCAmelCase = 1_00						,__UpperCAmelCase = 0.0_2						,__UpperCAmelCase=True						,**__UpperCAmelCase						,)    -> List[str]:
													# time series specific configuration
													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(__UpperCAmelCase   ) != 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(__UpperCAmelCase   ) != 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(__UpperCAmelCase   ) + 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=__UpperCAmelCase						,**__UpperCAmelCase   )





						@property
						def  snake_case__		(				self   )    -> int:
													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
													)





