


"""simple docstring"""
def 		lowercase							(     _snake_case						:      dict     )     ->set:
	"""simple docstring"""





	__snake_case	:						int						=				set()
	# edges = list of graph's edges
	__snake_case	:						int						=				get_edges(_snake_case     )

	# While there are still elements in edges list, take an arbitrary edge
	# (from_node, to_node) and add his extremity to chosen_vertices and then
	# remove all arcs adjacent to the from_node and to_node
	while edges:
		__snake_case	,							__snake_case	:						str						=				edges.pop()
		chosen_vertices.add(_snake_case     )
		chosen_vertices.add(_snake_case     )
		for edge in edges.copy():
			if from_node in edge or to_node in edge:
				edges.discard(_snake_case     )
	return chosen_vertices






def 		lowercase							(     _snake_case						:      dict     )     ->set:
	"""simple docstring"""





	__snake_case	:						Optional[Any]						=				set()
	for from_node, to_nodes in graph.items():
		for to_node in to_nodes:
			edges.add((from_node, to_node)     )
	return edges


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
			# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")










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


_lowercase: str      			=		logging.get_logger(__name__)

_lowercase: List[str]      			=		{
    "microsoft/trocr-base-handwritten": (
        "https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}



class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""
	__A							=							"trocr"
	__A							=							["past_key_values"]
	__A							=							{
	    "num_attention_heads": "decoder_attention_heads",
	    "hidden_size": "d_model",
	    "num_hidden_layers": "decoder_layers",
	}




	def __init__(self		,       lowerCamelCase_=50265		,       lowerCamelCase_=1024		,       lowerCamelCase_=12		,       lowerCamelCase_=16		,       lowerCamelCase_=4096		,       lowerCamelCase_="gelu"		,       lowerCamelCase_=512		,       lowerCamelCase_=0.1		,       lowerCamelCase_=0.0		,       lowerCamelCase_=0.0		,       lowerCamelCase_=2		,       lowerCamelCase_=0.02		,       lowerCamelCase_=0.0		,       lowerCamelCase_=True		,       lowerCamelCase_=False		,       lowerCamelCase_=True		,       lowerCamelCase_=True		,       lowerCamelCase_=1		,       lowerCamelCase_=0		,       lowerCamelCase_=2		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

						a					     =   vocab_size
						a					     =   d_model
						a					     =   decoder_layers
						a					     =   decoder_attention_heads
						a					     =   decoder_ffn_dim
						a					     =   activation_function
						a					     =   max_position_embeddings
						a					     =   dropout
						a					     =   attention_dropout
						a					     =   activation_dropout
						a					     =   init_std
						a					     =   decoder_layerdrop
						a					     =   use_cache
						a					     =   scale_embedding
						a					     =   use_learned_position_embeddings
						a					     =   layernorm_embedding

						super().__init__(
						    pad_token_id=lowerCamelCase_		,       bos_token_id=lowerCamelCase_		,       eos_token_id=lowerCamelCase_		,       decoder_start_token_id=lowerCamelCase_		,       **lowerCamelCase_		,       )
