






'''simple docstring'''







import heapq





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> set[int]:
							lowerCamelCase__      :			list[list] 						= []

							# for each node and his adjacency list add them and the rank of the node to queue
							# using heapq module the queue will be filled like a Priority Queue
							# heapq works with a min priority queue, so I used -1*len(v) to build it
							for key, value in graph.items():
														# O(log(n))
														heapq.heappush(UpperCamelCase       ,    [-1 * len(UpperCamelCase   ), (key, value)]   )

							# chosen_vertices = set of chosen vertices
							lowerCamelCase__      :			Tuple 						= set()

							# while queue isn't empty and there are still edges
							#   (queue[0][0] is the rank of the node with max rank)
							while queue and queue[0][0] != 0:
														# extract vertex with max rank from queue and add it to chosen_vertices
														lowerCamelCase__      :			Dict 						= heapq.heappop(UpperCamelCase   )[1][0]
														chosen_vertices.add(UpperCamelCase   )

														# Remove all arcs adjacent to argmax
														for elem in queue:
																					# if v haven't adjacent node, skip
																					if elem[0] == 0:
																												continue
																					# if argmax is reachable from elem
																					# remove argmax from elem's adjacent list and update his rank
																					if argmax in elem[1][1]:
																												lowerCamelCase__      :			Dict 						= elem[1][1].index(UpperCamelCase   )
																												del elem[1][1][index]
																												elem[0] += 1
        # re-order the queue
														heapq.heapify(UpperCamelCase   )
							return chosen_vertices


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			_A			:    str					  ={0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
			print(F'Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}')









'''simple docstring'''







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


_A			:    Any					  =logging.get_logger(__name__)

_A			:    Dict					  ={
    '''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			(       _lowercase	):
							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:       Optional[Any]    ,							UpperCamelCase__:       int=50_265    ,							UpperCamelCase__:       int=1_024    ,							UpperCamelCase__:       Optional[Any]=12    ,							UpperCamelCase__:       Dict=16    ,							UpperCamelCase__:       int=4_096    ,							UpperCamelCase__:       Tuple="gelu"    ,							UpperCamelCase__:       int=512    ,							UpperCamelCase__:       Dict=0.1    ,							UpperCamelCase__:       Tuple=0.0    ,							UpperCamelCase__:       str=0.0    ,							UpperCamelCase__:       Any=2    ,							UpperCamelCase__:       Dict=0.02    ,							UpperCamelCase__:       Optional[Any]=0.0    ,							UpperCamelCase__:       str=True    ,							UpperCamelCase__:       Tuple=False    ,							UpperCamelCase__:       Union[str, Any]=True    ,							UpperCamelCase__:       Tuple=True    ,							UpperCamelCase__:       Dict=1    ,							UpperCamelCase__:       List[str]=0    ,							UpperCamelCase__:       Union[str, Any]=2    ,							**UpperCamelCase__:       str    ,							):
														lowerCamelCase__      :			Any 						= vocab_size
														lowerCamelCase__      :			Tuple 						= d_model
														lowerCamelCase__      :			Any 						= decoder_layers
														lowerCamelCase__      :			Dict 						= decoder_attention_heads
														lowerCamelCase__      :			str 						= decoder_ffn_dim
														lowerCamelCase__      :			Tuple 						= activation_function
														lowerCamelCase__      :			Dict 						= max_position_embeddings
														lowerCamelCase__      :			int 						= dropout
														lowerCamelCase__      :			int 						= attention_dropout
														lowerCamelCase__      :			List[Any] 						= activation_dropout
														lowerCamelCase__      :			Union[str, Any] 						= init_std
														lowerCamelCase__      :			Optional[int] 						= decoder_layerdrop
														lowerCamelCase__      :			Dict 						= use_cache
														lowerCamelCase__      :			Any 						= scale_embedding
														lowerCamelCase__      :			Optional[int] 						= use_learned_position_embeddings
														lowerCamelCase__      :			List[str] 						= layernorm_embedding

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

