






'''simple docstring'''







from typing import List, Optional, Union

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


_A			:    str					  =logging.get_logger(__name__)

_A			:    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 _lowercase			(       __lowercase	):
       a          = '''time_series_transformer'''
       a          = {
           '''hidden_size''': '''d_model''',
           '''num_attention_heads''': '''encoder_attention_heads''',
           '''num_hidden_layers''': '''encoder_layers''',
       }




       def __init__(							self:       List[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 = 100    ,							UpperCamelCase__:       float = 0.02    ,							UpperCamelCase__:       Tuple=True    ,							**UpperCamelCase__:       str    ,							):
              # time series specific configuration
              lowerCamelCase__      :			str 						= prediction_length
              lowerCamelCase__      :			Optional[Any] 						= context_length or prediction_length
              lowerCamelCase__      :			Tuple 						= distribution_output
              lowerCamelCase__      :			Any 						= loss
              lowerCamelCase__      :			List[Any] 						= input_size
              lowerCamelCase__      :			int 						= num_time_features
              lowerCamelCase__      :			Dict 						= lags_sequence
              lowerCamelCase__      :			Optional[int] 						= scaling
              lowerCamelCase__      :			int 						= num_dynamic_real_features
              lowerCamelCase__      :			Tuple 						= num_static_real_features
              lowerCamelCase__      :			Any 						= 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`"""  )
                     lowerCamelCase__      :			int 						= cardinality
              else:
                     lowerCamelCase__      :			Dict 						= [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`"""  )
                     lowerCamelCase__      :			str 						= embedding_dimension
              else:
                     lowerCamelCase__      :			str 						= [min(50    ,							(cat + 1) // 2  ) for cat in self.cardinality]
              lowerCamelCase__      :			Any 						= num_parallel_samples

              # Transformer architecture configuration
              lowerCamelCase__      :			Any 						= input_size * len(__a  ) + self._number_of_features
              lowerCamelCase__      :			List[str] 						= d_model
              lowerCamelCase__      :			Tuple 						= encoder_attention_heads
              lowerCamelCase__      :			Optional[int] 						= decoder_attention_heads
              lowerCamelCase__      :			Union[str, Any] 						= encoder_ffn_dim
              lowerCamelCase__      :			str 						= decoder_ffn_dim
              lowerCamelCase__      :			str 						= encoder_layers
              lowerCamelCase__      :			Any 						= decoder_layers

              lowerCamelCase__      :			Optional[int] 						= dropout
              lowerCamelCase__      :			List[str] 						= attention_dropout
              lowerCamelCase__      :			Tuple 						= activation_dropout
              lowerCamelCase__      :			Optional[int] 						= encoder_layerdrop
              lowerCamelCase__      :			int 						= decoder_layerdrop

              lowerCamelCase__      :			Optional[int] 						= activation_function
              lowerCamelCase__      :			Optional[Any] 						= init_std

              lowerCamelCase__      :			Optional[Any] 						= use_cache

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




       @property
       def 	lowerCamelCase_       (							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
              )









'''simple docstring'''







# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Optional[Any]:
							lowerCamelCase__      :			str 						= [False] * len(UpperCamelCase   )
							lowerCamelCase__      :			str 						= [-1] * len(UpperCamelCase   )

							def dfs(UpperCamelCase       ,    UpperCamelCase   ):
														lowerCamelCase__      :			Optional[int] 						= True
														lowerCamelCase__      :			Union[str, Any] 						= c
														for u in graph[v]:
																					if not visited[u]:
																												dfs(UpperCamelCase       ,    1 - c   )

							for i in range(len(UpperCamelCase   )   ):
														if not visited[i]:
																					dfs(UpperCamelCase       ,    0   )

							for i in range(len(UpperCamelCase   )   ):
														for j in graph[i]:
																					if color[i] == color[j]:
																												return False

							return True


# Adjacency list of graph
_A			:    int					  ={0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}



print(check_bipartite_dfs(graph))

