

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

import black


A__						=							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.
A__						=							"    \"\"\"\n    Output class for the scheduler's step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"\"\"\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n"

class 				__UpperCamelCase				(					unittest.TestCase			):







	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]      ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]  , __UpperCamelCase: Union[str, Any]  , __UpperCamelCase: Optional[int]  , __UpperCamelCase: str  , __UpperCamelCase: Optional[int]=None      ):

						'''simple docstring'''





						__magic_name__							=    comment + F'\nclass {class_name}(nn.Module):\n' + class_code
						if overwrite_result is not None:
											__magic_name__							=    comment + F'\nclass {class_name}(nn.Module):\n' + overwrite_result
						__magic_name__							=    black.Mode(target_versions={black.TargetVersion.PYaa}  , line_length=1_19      )
						__magic_name__							=    black.format_str(__UpperCamelCase  , mode=__UpperCamelCase      )
						__magic_name__							=    os.path.join(self.diffusers_dir  , 'new_code.py'      )
						with open(__UpperCamelCase  , 'w'  , newline='\n'      ) as f:
											f.write(__UpperCamelCase      )
						if overwrite_result is None:
											self.assertTrue(len(check_copies.is_copy_consistent(__UpperCamelCase      )      ) == 0      )
						else:
											check_copies.is_copy_consistent(f.name  , overwrite=__UpperCamelCase      )
											with open(__UpperCamelCase  , 'r'      ) as f:
																self.assertTrue(f.read()  , __UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						__magic_name__							=    check_copies.find_code_in_diffusers('schedulers.scheduling_ddpm.DDPMSchedulerOutput'      )
						self.assertEqual(__UpperCamelCase  , __UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]      ):

						'''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'  , __UpperCamelCase  , )

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

						# Copy consistency with a really long name
						__magic_name__							=    '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'  , __UpperCamelCase  , __UpperCamelCase      )  , )

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







from typing import List, Optional, Union

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


A__						=							logging.get_logger(__name__)

A__						=							{
    "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				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   Optional[int]       				=  "time_series_transformer"
	_lowercase  :   Dict       				=  {
	    "hidden_size": "d_model",
	    "num_attention_heads": "encoder_attention_heads",
	    "num_hidden_layers": "encoder_layers",
	}







	def __init__(   self: str  , __UpperCamelCase: Optional[int] = None  , __UpperCamelCase: Optional[int] = None  , __UpperCamelCase: str = "student_t"  , __UpperCamelCase: str = "nll"  , __UpperCamelCase: int = 1  , __UpperCamelCase: List[int] = [1, 2, 3, 4, 5, 6, 7]  , __UpperCamelCase: Optional[Union[str, bool]] = "mean"  , __UpperCamelCase: int = 0  , __UpperCamelCase: int = 0  , __UpperCamelCase: int = 0  , __UpperCamelCase: int = 0  , __UpperCamelCase: Optional[List[int]] = None  , __UpperCamelCase: Optional[List[int]] = None  , __UpperCamelCase: int = 32  , __UpperCamelCase: int = 32  , __UpperCamelCase: int = 2  , __UpperCamelCase: int = 2  , __UpperCamelCase: int = 2  , __UpperCamelCase: int = 2  , __UpperCamelCase: bool = True  , __UpperCamelCase: str = "gelu"  , __UpperCamelCase: int = 64  , __UpperCamelCase: float = 0.1  , __UpperCamelCase: float = 0.1  , __UpperCamelCase: float = 0.1  , __UpperCamelCase: float = 0.1  , __UpperCamelCase: float = 0.1  , __UpperCamelCase: int = 1_00  , __UpperCamelCase: float = 0.02  , __UpperCamelCase: Any=True  , **__UpperCamelCase: Optional[Any]  , ):

						'''simple docstring'''





						__magic_name__							=    prediction_length
						__magic_name__							=    context_length or prediction_length
						__magic_name__							=    distribution_output
						__magic_name__							=    loss
						__magic_name__							=    input_size
						__magic_name__							=    num_time_features
						__magic_name__							=    lags_sequence
						__magic_name__							=    scaling
						__magic_name__							=    num_dynamic_real_features
						__magic_name__							=    num_static_real_features
						__magic_name__							=    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`'      )
											__magic_name__							=    cardinality
						else:
											__magic_name__							=    [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`'      )
											__magic_name__							=    embedding_dimension
						else:
											__magic_name__							=    [min(50  , (cat + 1) // 2      ) for cat in self.cardinality]
						__magic_name__							=    num_parallel_samples

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

						__magic_name__							=    dropout
						__magic_name__							=    attention_dropout
						__magic_name__							=    activation_dropout
						__magic_name__							=    encoder_layerdrop
						__magic_name__							=    decoder_layerdrop

						__magic_name__							=    activation_function
						__magic_name__							=    init_std

						__magic_name__							=    use_cache

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







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''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
						)




