



'''simple docstring'''






import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


UpperCamelCase__:     Optional[int]										=    logging.get_logger(__name__)

class        SCREAMING_SNAKE_CASE(							A__    ):







 """simple docstring"""






 def __init__(		self    :  Optional[Any]	,    __snake_case    :  Union[List[ControlNetModel], Tuple[ControlNetModel]]	)    ->	Dict:
        super().__init__()
        UpperCAmelCase				: List[str]											=	nn.ModuleList(__snake_case	)







 def   A			(		self    :  List[Any]	,    __snake_case    :  torch.FloatTensor	,    __snake_case    :  Union[torch.Tensor, float, int]	,    __snake_case    :  torch.Tensor	,    __snake_case    :  List[torch.tensor]	,    __snake_case    :  List[float]	,    __snake_case    :  Optional[torch.Tensor] = None	,    __snake_case    :  Optional[torch.Tensor] = None	,    __snake_case    :  Optional[torch.Tensor] = None	,    __snake_case    :  Optional[Dict[str, Any]] = None	,    __snake_case    :  bool = False	,    __snake_case    :  bool = True	,    )    ->	Union[ControlNetOutput, Tuple]:
        for i, (image, scale, controlnet) in enumerate(zip(__snake_case	,    __snake_case	,    self.nets	)	):
               UpperCAmelCase				: Union[str, Any]											=	controlnet(
                   __snake_case	,    __snake_case	,    __snake_case	,    __snake_case	,    __snake_case	,    __snake_case	,    __snake_case	,    __snake_case	,    __snake_case	,    __snake_case	,    __snake_case	,    )

               # merge samples
               if i == 0:
                      UpperCAmelCase				: int											=	down_samples, mid_sample
               else:
                      UpperCAmelCase				: Optional[Any]											=	[
                          samples_prev + samples_curr
                          for samples_prev, samples_curr in zip(__snake_case	,    __snake_case	)
                      ]
                      mid_block_res_sample += mid_sample

        return down_block_res_samples, mid_block_res_sample







 def   A			(		self    :  Optional[Any]	,    __snake_case    :  Union[str, os.PathLike]	,    __snake_case    :  bool = True	,    __snake_case    :  Callable = None	,    __snake_case    :  bool = False	,    __snake_case    :  Optional[str] = None	,    )    ->	Any:
        UpperCAmelCase				: List[str]											=	0
        UpperCAmelCase				: Dict											=	save_directory
        for controlnet in self.nets:
               controlnet.save_pretrained(
                   __snake_case	,    is_main_process=__snake_case	,    save_function=__snake_case	,    safe_serialization=__snake_case	,    variant=__snake_case	,    )

               idx += 1
               UpperCAmelCase				: List[str]											=	model_path_to_save + F"""_{idx}"""







 @classmethod
 def   A			(		cls    :  List[str]	,    __snake_case    :  Optional[Union[str, os.PathLike]]	,    **__snake_case    :  int	)    ->	Optional[Any]:
        UpperCAmelCase				: int											=	0
        UpperCAmelCase				: Union[str, Any]											=	[]

        # load controlnet and append to list until no controlnet directory exists anymore
        # first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
        # second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
        UpperCAmelCase				: int											=	pretrained_model_path
        while os.path.isdir(__snake_case	):
               UpperCAmelCase				: int											=	ControlNetModel.from_pretrained(__snake_case	,    **__snake_case	)
               controlnets.append(__snake_case	)

               idx += 1
               UpperCAmelCase				: Any											=	pretrained_model_path + F"""_{idx}"""

        logger.info(F"""{len(__snake_case	)} controlnets loaded from {pretrained_model_path}."""	)

        if len(__snake_case	) == 0:
               raise ValueError(
                   F"""No ControlNets found under {os.path.dirname(__snake_case	)}. Expected at least {pretrained_model_path + "_0"}."""	)

        return cls(__snake_case	)










'''simple docstring'''






from typing import List, Optional, Union

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


UpperCamelCase__:     Optional[Any]										=    logging.get_logger(__name__)

UpperCamelCase__:     Tuple										=    {
    "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        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






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







	def __init__(		self    :  str	,    __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] = [1, 2, 3, 4, 5, 6, 7]	,    __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 = 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    :  int = 64	,    __snake_case    :  float = 0.1	,    __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[Any]=True	,    **__snake_case    :  List[Any]	,    )    ->	List[str]:
								# time series specific configuration
								UpperCAmelCase				: List[Any]											=	prediction_length
								UpperCAmelCase				: List[Any]											=	context_length or prediction_length
								UpperCAmelCase				: Tuple											=	distribution_output
								UpperCAmelCase				: Optional[Any]											=	loss
								UpperCAmelCase				: Tuple											=	input_size
								UpperCAmelCase				: Optional[int]											=	num_time_features
								UpperCAmelCase				: Dict											=	lags_sequence
								UpperCAmelCase				: Any											=	scaling
								UpperCAmelCase				: Tuple											=	num_dynamic_real_features
								UpperCAmelCase				: Any											=	num_static_real_features
								UpperCAmelCase				: Optional[int]											=	num_static_categorical_features
								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`'''	)
															UpperCAmelCase				: Any											=	cardinality
								else:
															UpperCAmelCase				: Optional[Any]											=	[0]
								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`'''	)
															UpperCAmelCase				: Optional[int]											=	embedding_dimension
								else:
															UpperCAmelCase				: Optional[Any]											=	[min(50	,    (cat + 1) // 2	) for cat in self.cardinality]
								UpperCAmelCase				: List[Any]											=	num_parallel_samples

								# Transformer architecture configuration
								UpperCAmelCase				: int											=	input_size * len(__snake_case	) + self._number_of_features
								UpperCAmelCase				: int											=	d_model
								UpperCAmelCase				: str											=	encoder_attention_heads
								UpperCAmelCase				: str											=	decoder_attention_heads
								UpperCAmelCase				: List[str]											=	encoder_ffn_dim
								UpperCAmelCase				: Any											=	decoder_ffn_dim
								UpperCAmelCase				: Any											=	encoder_layers
								UpperCAmelCase				: str											=	decoder_layers

								UpperCAmelCase				: Optional[int]											=	dropout
								UpperCAmelCase				: Union[str, Any]											=	attention_dropout
								UpperCAmelCase				: List[Any]											=	activation_dropout
								UpperCAmelCase				: Optional[int]											=	encoder_layerdrop
								UpperCAmelCase				: Dict											=	decoder_layerdrop

								UpperCAmelCase				: Tuple											=	activation_function
								UpperCAmelCase				: Dict											=	init_std

								UpperCAmelCase				: Optional[int]											=	use_cache

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







	@property
	def   A			(		self    :  Union[str, Any]	)    ->	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
								)





