
from typing import List, Optional, Union

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


lowercase__		:Tuple   =    logging.get_logger(__name__)

lowercase__		:int   =    {
    "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 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       Any       					='''time_series_transformer'''
							lowercase_       :       str       					={
							    '''hidden_size''': '''d_model''',
							    '''num_attention_heads''': '''encoder_attention_heads''',
							    '''num_hidden_layers''': '''encoder_layers''',
							}




							def __init__(				self   ,A__ = None   ,A__ = None   ,A__ = "student_t"   ,A__ = "nll"   ,A__ = 1   ,A__ = [1, 2, 3, 4, 5, 6, 7]   ,A__ = "mean"   ,A__ = 0   ,A__ = 0   ,A__ = 0   ,A__ = 0   ,A__ = None   ,A__ = None   ,A__ = 3_2   ,A__ = 3_2   ,A__ = 2   ,A__ = 2   ,A__ = 2   ,A__ = 2   ,A__ = True   ,A__ = "gelu"   ,A__ = 6_4   ,A__ = 0.1   ,A__ = 0.1   ,A__ = 0.1   ,A__ = 0.1   ,A__ = 0.1   ,A__ = 1_0_0   ,A__ = 0.02   ,A__=True   ,**A__   ,):
												# time series specific configuration
												lowercase							=						prediction_length
												lowercase							=						context_length or prediction_length
												lowercase							=						distribution_output
												lowercase							=						loss
												lowercase							=						input_size
												lowercase							=						num_time_features
												lowercase							=						lags_sequence
												lowercase							=						scaling
												lowercase							=						num_dynamic_real_features
												lowercase							=						num_static_real_features
												lowercase							=						num_static_categorical_features
												if cardinality and num_static_categorical_features > 0:
																	if len(A__) != num_static_categorical_features:
																						raise ValueError(
																						    '''The cardinality should be a list of the same length as `num_static_categorical_features`''')
																	lowercase							=						cardinality
												else:
																	lowercase							=						[0]
												if embedding_dimension and num_static_categorical_features > 0:
																	if len(A__) != num_static_categorical_features:
																						raise ValueError(
																						    '''The embedding dimension should be a list of the same length as `num_static_categorical_features`''')
																	lowercase							=						embedding_dimension
												else:
																	lowercase							=						[min(5_0   ,(cat + 1) // 2) for cat in self.cardinality]
												lowercase							=						num_parallel_samples

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

												lowercase							=						dropout
												lowercase							=						attention_dropout
												lowercase							=						activation_dropout
												lowercase							=						encoder_layerdrop
												lowercase							=						decoder_layerdrop

												lowercase							=						activation_function
												lowercase							=						init_std

												lowercase							=						use_cache

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





							@property
							def  A__      (				self):
												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
												)






'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowercase :  Union[str, Any]    							=       {
    "configuration_pix2struct": [
        "PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Pix2StructConfig",
        "Pix2StructTextConfig",
        "Pix2StructVisionConfig",
    ],
    "processing_pix2struct": ["Pix2StructProcessor"],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase :  Dict    							=       ["Pix2StructImageProcessor"]


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase :  List[str]    							=       [
							    "PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "Pix2StructPreTrainedModel",
							    "Pix2StructForConditionalGeneration",
							    "Pix2StructVisionModel",
							    "Pix2StructTextModel",
							]

if TYPE_CHECKING:
							from .configuration_pixastruct import (
							    PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    PixaStructConfig,
							    PixaStructTextConfig,
							    PixaStructVisionConfig,
							)
							from .processing_pixastruct import PixaStructProcessor

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_pixastruct import PixaStructImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_pixastruct import (
														    PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    PixaStructForConditionalGeneration,
														    PixaStructPreTrainedModel,
														    PixaStructTextModel,
														    PixaStructVisionModel,
														)

else:
							import sys

							_lowercase :  Optional[int]    							=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



