"""simple docstring"""




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


__lowerCamelCase							 =     logging.get_logger(__name__)






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							b.T
							A__		   =							np.sum(np.square(UpperCamelCase__ )				,			axis=1 )
							A__		   =							np.sum(np.square(UpperCamelCase__ )				,			axis=0 )
							A__		   =							np.matmul(UpperCamelCase__				,			UpperCamelCase__ )
							A__		   =							aa[:, None] - 2 * ab + ba[None, :]
							return d






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							x.reshape(-1				,			3 )
							A__		   =							squared_euclidean_distance(UpperCamelCase__				,			UpperCamelCase__ )
							return np.argmin(UpperCamelCase__				,			axis=1 )


class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  Tuple  							=						['pixel_values']





						def __init__(				self						,__UpperCAmelCase = None						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = PILImageResampling.BILINEAR						,__UpperCAmelCase = True						,__UpperCAmelCase = True						,**__UpperCAmelCase						,)    -> None:
													super().__init__(**__UpperCAmelCase   )
													A__		   =							size if size is not None else {'height': 2_56, 'width': 2_56}
													A__		   =							get_size_dict(__UpperCAmelCase   )
													A__		   =							np.array(__UpperCAmelCase   ) if clusters is not None else None
													A__		   =							do_resize
													A__		   =							size
													A__		   =							resample
													A__		   =							do_normalize
													A__		   =							do_color_quantize





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = PILImageResampling.BILINEAR						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> np.ndarray:
													A__		   =							get_size_dict(__UpperCAmelCase   )
													if "height" not in size or "width" not in size:
																				raise ValueError(f'''Size dictionary must contain both height and width keys. Got {size.keys()}'''   )
													return resize(
													    __UpperCAmelCase						,size=(size['height'], size['width'])						,resample=__UpperCAmelCase						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,)    -> np.ndarray:
													A__		   =							rescale(image=__UpperCAmelCase						,scale=1 / 1_2_7.5						,data_format=__UpperCAmelCase   )
													A__		   =							image - 1
													return image





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = ChannelDimension.FIRST						,**__UpperCAmelCase						,)    -> PIL.Image.Image:
													A__		   =							do_resize if do_resize is not None else self.do_resize
													A__		   =							size if size is not None else self.size
													A__		   =							get_size_dict(__UpperCAmelCase   )
													A__		   =							resample if resample is not None else self.resample
													A__		   =							do_normalize if do_normalize is not None else self.do_normalize
													A__		   =							do_color_quantize if do_color_quantize is not None else self.do_color_quantize
													A__		   =							clusters if clusters is not None else self.clusters
													A__		   =							np.array(__UpperCAmelCase   )

													A__		   =							make_list_of_images(__UpperCAmelCase   )

													if not valid_images(__UpperCAmelCase   ):
																				raise ValueError(
																				    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
																				    'torch.Tensor, tf.Tensor or jax.ndarray.'   )

													if do_resize and size is None or resample is None:
																				raise ValueError('Size and resample must be specified if do_resize is True.'   )

													if do_color_quantize and clusters is None:
																				raise ValueError('Clusters must be specified if do_color_quantize is True.'   )

													# All transformations expect numpy arrays.
													A__		   =							[to_numpy_array(__UpperCAmelCase   ) for image in images]

													if do_resize:
																				A__		   =							[self.resize(image=__UpperCAmelCase						,size=__UpperCAmelCase						,resample=__UpperCAmelCase   ) for image in images]

													if do_normalize:
																				A__		   =							[self.normalize(image=__UpperCAmelCase   ) for image in images]

													if do_color_quantize:
																				A__		   =							[to_channel_dimension_format(__UpperCAmelCase						,ChannelDimension.LAST   ) for image in images]
																				# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
																				A__		   =							np.array(__UpperCAmelCase   )
																				A__		   =							color_quantize(__UpperCAmelCase						,__UpperCAmelCase   ).reshape(images.shape[:-1]   )

																				# flatten to (batch_size, height*width)
																				A__		   =							images.shape[0]
																				A__		   =							images.reshape(__UpperCAmelCase						,-1   )

																				# We need to convert back to a list of images to keep consistent behaviour across processors.
																				A__		   =							list(__UpperCAmelCase   )
													else:
																				A__		   =							[to_channel_dimension_format(__UpperCAmelCase						,__UpperCAmelCase   ) for image in images]

													A__		   =							{'input_ids': images}
													return BatchFeature(data=__UpperCAmelCase						,tensor_type=__UpperCAmelCase   )






"""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
													)





