



'''simple docstring'''





from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass

@dataclass
class   _A   :


						'''simple docstring'''





						_snake_case			:     Optional[int]      							=      42
						_snake_case			:     Tuple      							=      42

class   _A   :


						'''simple docstring'''





						def __init__(	self	:							Optional[int]							,      lowerCamelCase	:							Union[str, Any]	):






							'''simple docstring'''



							__lowercase		       =			[[] for _ in range(lowerCamelCase	)]
							__lowercase		       =			size



						def __getitem__(	self	:							Optional[Any]							,      lowerCamelCase	:							Union[str, Any]	):






							'''simple docstring'''



							return iter(self._graph[vertex]	)



						@property
						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							return self._size



						def     _snake_case	(	self	:							int							,      lowerCamelCase	:							List[str]							,      lowerCamelCase	:							int							,      lowerCamelCase	:							Any	):






							'''simple docstring'''



							if weight not in (0, 1):
								raise ValueError("Edge weight must be either 0 or 1."	)

							if to_vertex < 0 or to_vertex >= self.size:
								raise ValueError("Vertex indexes must be in [0; size)."	)

							self._graph[from_vertex].append(Edge(lowerCamelCase							,      lowerCamelCase	)	)



						def     _snake_case	(	self	:							Optional[int]							,      lowerCamelCase	:							int							,      lowerCamelCase	:							str	):






							'''simple docstring'''



							__lowercase		       =			deque([start_vertex]	)
							__lowercase		       =			[None] * self.size
							__lowercase		       =			0

							while queue:
								__lowercase		       =			queue.popleft()
								__lowercase		       =			distances[current_vertex]
								if current_distance is None:
									continue

								for edge in self[current_vertex]:
									__lowercase		       =			current_distance + edge.weight
									__lowercase		       =			distances[edge.destination_vertex]
									if (
									    isinstance(lowerCamelCase							,      lowerCamelCase	)
									    and new_distance >= dest_vertex_distance
									):
										continue
									__lowercase		       =			new_distance
									if edge.weight == 0:
										queue.appendleft(edge.destination_vertex	)
									else:
										queue.append(edge.destination_vertex	)

							if distances[finish_vertex] is None:
								raise ValueError("No path from start_vertex to finish_vertex."	)

							return distances[finish_vertex]


if __name__ == "__main__":
	import doctest

	doctest.testmod()








# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case__					:     List[str]    		=   {
    """configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
    """processing_mgp_str""": ["""MgpstrProcessor"""],
    """tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case__					:     Dict    		=   [
	    """MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """MgpstrModel""",
	    """MgpstrPreTrainedModel""",
	    """MgpstrForSceneTextRecognition""",
	]

if TYPE_CHECKING:
	from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
	from .processing_mgp_str import MgpstrProcessor
	from .tokenization_mgp_str import MgpstrTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mgp_str import (
		    MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MgpstrForSceneTextRecognition,
		    MgpstrModel,
		    MgpstrPreTrainedModel,
		)
else:
	import sys

	snake_case__					:     Union[str, Any]    		=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



