




"""simple docstring"""




from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

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


if TYPE_CHECKING:
					from ... import FeatureExtractionMixin, TensorType

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

lowerCAmelCase							:    List[Any]  							=							{
    """openai/imagegpt-small""": """""",
    """openai/imagegpt-medium""": """""",
    """openai/imagegpt-large""": """""",
}






class 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''
				__UpperCamelCase    						=		"imagegpt"
				__UpperCamelCase    						=		["past_key_values"]
				__UpperCamelCase    						=		{
				    "hidden_size": "n_embd",
				    "max_position_embeddings": "n_positions",
				    "num_attention_heads": "n_head",
				    "num_hidden_layers": "n_layer",
				}





				def __init__( self	,				_a=512 + 1	,				_a=32 * 32	,				_a=512	,				_a=24	,				_a=8	,				_a=None	,				_a="quick_gelu"	,				_a=0.1	,				_a=0.1	,				_a=0.1	,				_a=1e-5	,				_a=0.02	,				_a=True	,				_a=True	,				_a=False	,				_a=False	,				_a=False	,				**_a	,				):





							"""simple docstring"""
							lowerCamelCase       =      vocab_size
							lowerCamelCase       =      n_positions
							lowerCamelCase       =      n_embd
							lowerCamelCase       =      n_layer
							lowerCamelCase       =      n_head
							lowerCamelCase       =      n_inner
							lowerCamelCase       =      activation_function
							lowerCamelCase       =      resid_pdrop
							lowerCamelCase       =      embd_pdrop
							lowerCamelCase       =      attn_pdrop
							lowerCamelCase       =      layer_norm_epsilon
							lowerCamelCase       =      initializer_range
							lowerCamelCase       =      scale_attn_weights
							lowerCamelCase       =      use_cache
							lowerCamelCase       =      scale_attn_by_inverse_layer_idx
							lowerCamelCase       =      reorder_and_upcast_attn
							lowerCamelCase       =      tie_word_embeddings

							super().__init__(tie_word_embeddings=_a	,				**_a				)






class 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''





				@property
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							return OrderedDict(
							    [
							        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
							    ]				)





				def      _lowerCAmelCase       ( self	,				_a	,				_a = 1	,				_a = -1	,				_a = False	,				_a = None	,				_a = 3	,				_a = 32	,				_a = 32	,				):





							"""simple docstring"""
							lowerCamelCase       =      self._generate_dummy_images(_a	,				_a	,				_a	,				_a				)
							lowerCamelCase       =      dict(preprocessor(images=_a	,				return_tensors=_a				)				)

							return inputs









"""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 					a__				(       snake_case__       )		->      Dict:
			lowerCamelCase       =      [False] * len(snake_case__       )
			lowerCamelCase       =      [-1] * len(snake_case__       )

			def dfs(snake_case__ ,				snake_case__       ):
						lowerCamelCase       =      True
						lowerCamelCase       =      c
						for u in graph[v]:
									if not visited[u]:
												dfs(snake_case__ ,				1 - c       )

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

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

			return True


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


print(check_bipartite_dfs(graph))



