






from __future__ import annotations

from queue import Queue





class        a__      :






		def __init__(      self						)				->				None:
				__A    =							{}






		def 					_lowerCamelCase					(      self						)				->				None:
				for i in self.vertices:
						print(lowercase__							, " : "							, " -> ".join([str(lowercase__						) for j in self.vertices[i]]						)						)






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__						)				->				None:
				if from_vertex in self.vertices:
						self.vertices[from_vertex].append(lowercase__						)
				else:
						__A    =							[to_vertex]






		def 					_lowerCamelCase					(      self							, lowercase__						)				->				set[int]:
				__A    =							set()

				# create a first in first out queue to store all the vertices for BFS
				__A    =							Queue()

				# mark the source node as visited and enqueue it
				visited.add(lowercase__						)
				queue.put(lowercase__						)

				while not queue.empty():
						__A    =							queue.get()

						# loop through all adjacent vertex and enqueue it if not yet visited
						for adjacent_vertex in self.vertices[vertex]:
								if adjacent_vertex not in visited:
										queue.put(lowercase__						)
										visited.add(lowercase__						)
				return visited


if __name__ == "__main__":
					from doctest import testmod

					testmod(verbose=True)

					snake_case_   :	List[str]							      =Graph()
					g.add_edge(0, 1)
					g.add_edge(0, 2)
					g.add_edge(1, 2)
					g.add_edge(2, 0)
					g.add_edge(2, 3)
					g.add_edge(3, 3)

					g.print_graph()
					# 0  :  1 -> 2
					# 1  :  2
					# 2  :  0 -> 3
					# 3  :  3

					assert sorted(g.bfs(2)) == [0, 1, 2, 3]







"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE						 =     {"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     ["""T5Tokenizer"""]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     ["""T5TokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     [
        """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """T5EncoderModel""",
        """T5ForConditionalGeneration""",
        """T5Model""",
        """T5PreTrainedModel""",
        """load_tf_weights_in_t5""",
        """T5ForQuestionAnswering""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     [
        """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFT5EncoderModel""",
        """TFT5ForConditionalGeneration""",
        """TFT5Model""",
        """TFT5PreTrainedModel""",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     [
        """FlaxT5EncoderModel""",
        """FlaxT5ForConditionalGeneration""",
        """FlaxT5Model""",
        """FlaxT5PreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_ta import TaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_ta_fast import TaTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_ta import (
            T5_PRETRAINED_MODEL_ARCHIVE_LIST,
            TaEncoderModel,
            TaForConditionalGeneration,
            TaForQuestionAnswering,
            TaModel,
            TaPreTrainedModel,
            load_tf_weights_in_ta,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_ta import (
            TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFTaEncoderModel,
            TFTaForConditionalGeneration,
            TFTaModel,
            TFTaPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_ta import (
            FlaxTaEncoderModel,
            FlaxTaForConditionalGeneration,
            FlaxTaModel,
            FlaxTaPreTrainedModel,
        )


else:
    import sys

    _SCREAMING_SNAKE_CASE						 =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



