


from typing import List, Optional, Union

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


A							:					List[Any]     						=     logging.get_logger(__name__)

A							:					Union[str, 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     UpperCamelCase(  _a   ):
					snake_case_		:				str										=	"""time_series_transformer"""
					snake_case_		:				int										=	{
					    """hidden_size""": """d_model""",
					    """num_attention_heads""": """encoder_attention_heads""",
					    """num_hidden_layers""": """encoder_layers""",
					}


					def __init__(						self	:		List[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] = [1, 2, 3, 4, 5, 6, 7]     ,    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 = 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	:		int = 6_4     ,    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	:		float = 0.1     ,    SCREAMING_SNAKE_CASE	:		int = 1_0_0     ,    SCREAMING_SNAKE_CASE	:		float = 0.02     ,    SCREAMING_SNAKE_CASE	:		Dict=True     ,    **SCREAMING_SNAKE_CASE	:		str     ,    )			->	str:




											'''simple docstring'''

											__snake_case				=		prediction_length
											__snake_case				=		context_length or prediction_length
											__snake_case				=		distribution_output
											__snake_case				=		loss
											__snake_case				=		input_size
											__snake_case				=		num_time_features
											__snake_case				=		lags_sequence
											__snake_case				=		scaling
											__snake_case				=		num_dynamic_real_features
											__snake_case				=		num_static_real_features
											__snake_case				=		num_static_categorical_features
											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`"						)
																	__snake_case				=		cardinality
											else:
																	__snake_case				=		[0]
											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`"						)
																	__snake_case				=		embedding_dimension
											else:
																	__snake_case				=		[min(5_0     ,    (cat + 1) // 2						) for cat in self.cardinality]
											__snake_case				=		num_parallel_samples

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

											__snake_case				=		dropout
											__snake_case				=		attention_dropout
											__snake_case				=		activation_dropout
											__snake_case				=		encoder_layerdrop
											__snake_case				=		decoder_layerdrop

											__snake_case				=		activation_function
											__snake_case				=		init_std

											__snake_case				=		use_cache

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






					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]						)			->	int:




											'''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
											)









from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A							:					Union[str, Any]     						=     logging.get_logger(__name__)

A							:					Tuple     						=     {
    'facebook/data2vec-vision-base-ft': (
        'https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'
    ),
}






class     UpperCamelCase(  _a   ):
					snake_case_		:				List[Any]										=	"""data2vec-vision"""


					def __init__(						self	:		Any     ,    SCREAMING_SNAKE_CASE	:		List[str]=7_6_8     ,    SCREAMING_SNAKE_CASE	:		Tuple=1_2     ,    SCREAMING_SNAKE_CASE	:		int=1_2     ,    SCREAMING_SNAKE_CASE	:		List[str]=3_0_7_2     ,    SCREAMING_SNAKE_CASE	:		Tuple="gelu"     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=0.0     ,    SCREAMING_SNAKE_CASE	:		List[Any]=0.0     ,    SCREAMING_SNAKE_CASE	:		Any=0.02     ,    SCREAMING_SNAKE_CASE	:		List[str]=1e-1_2     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=2_2_4     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=1_6     ,    SCREAMING_SNAKE_CASE	:		str=3     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=False     ,    SCREAMING_SNAKE_CASE	:		Any=False     ,    SCREAMING_SNAKE_CASE	:		Dict=False     ,    SCREAMING_SNAKE_CASE	:		int=False     ,    SCREAMING_SNAKE_CASE	:		Tuple=0.1     ,    SCREAMING_SNAKE_CASE	:		Any=0.1     ,    SCREAMING_SNAKE_CASE	:		Any=True     ,    SCREAMING_SNAKE_CASE	:		List[str]=[3, 5, 7, 1_1]     ,    SCREAMING_SNAKE_CASE	:		int=[1, 2, 3, 6]     ,    SCREAMING_SNAKE_CASE	:		List[str]=True     ,    SCREAMING_SNAKE_CASE	:		Tuple=0.4     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=2_5_6     ,    SCREAMING_SNAKE_CASE	:		Tuple=1     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=False     ,    SCREAMING_SNAKE_CASE	:		List[Any]=2_5_5     ,    **SCREAMING_SNAKE_CASE	:		Any     ,    )			->	List[str]:




											'''simple docstring'''

											super().__init__(**SCREAMING_SNAKE_CASE						)

											__snake_case				=		hidden_size
											__snake_case				=		num_hidden_layers
											__snake_case				=		num_attention_heads
											__snake_case				=		intermediate_size
											__snake_case				=		hidden_act
											__snake_case				=		hidden_dropout_prob
											__snake_case				=		attention_probs_dropout_prob
											__snake_case				=		initializer_range
											__snake_case				=		layer_norm_eps

											__snake_case				=		image_size
											__snake_case				=		patch_size
											__snake_case				=		num_channels
											__snake_case				=		use_mask_token
											__snake_case				=		use_absolute_position_embeddings
											__snake_case				=		use_relative_position_bias
											__snake_case				=		use_shared_relative_position_bias
											__snake_case				=		layer_scale_init_value
											__snake_case				=		drop_path_rate
											__snake_case				=		use_mean_pooling
											# decode head attributes (semantic segmentation)
											__snake_case				=		out_indices
											__snake_case				=		pool_scales
											# auxiliary head attributes (semantic segmentation)
											__snake_case				=		use_auxiliary_head
											__snake_case				=		auxiliary_loss_weight
											__snake_case				=		auxiliary_channels
											__snake_case				=		auxiliary_num_convs
											__snake_case				=		auxiliary_concat_input
											__snake_case				=		semantic_loss_ignore_index






class     UpperCamelCase(  _a   ):
					snake_case_		:				Any										=	version.parse("""1.11"""   )


					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	Mapping[str, Mapping[int, str]]:




											'''simple docstring'''

											return OrderedDict(
											    [
											        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
											    ]						)






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




											'''simple docstring'''

											return 1e-4





