




"""simple docstring"""

from __future__ import annotations

from collections import deque
from queue import Queue
from timeit import timeit

__A							=     {
    "A": ["B", "C"],
    "B": ["A", "D", "E"],
    "C": ["A", "F"],
    "D": ["B"],
    "E": ["B", "F"],
    "F": ["C", "E"],
}



def 					UpperCamelCase__       (						lowercase__					:       dict    ,       lowercase__					:       str				):
		snake_case						:  Dict        =			{start}
		snake_case						:  int        =			[start]
		snake_case						:  Queue        =			Queue()
		queue.put(lowercase__				)
		while not queue.empty():
				snake_case						:  Union[str, Any]        =			queue.get()
				for w in graph[v]:
						if w not in explored:
								explored.add(lowercase__				)
								result.append(lowercase__				)
								queue.put(lowercase__				)
		return result



def 					UpperCamelCase__       (						lowercase__					:       dict    ,       lowercase__					:       str				):
		snake_case						:  Optional[Any]        =			{start}
		snake_case						:  List[str]        =			[start]
		snake_case						:  int        =			deque([start]				)
		while queue:
				snake_case						:  Optional[int]        =			queue.popleft()
				for child in graph[v]:
						if child not in visited:
								visited.add(lowercase__				)
								result.append(lowercase__				)
								queue.append(lowercase__				)
		return result



def 					UpperCamelCase__       (						lowercase__					:       str				):
		snake_case						:  Dict        =			F'''from __main__ import G, {name}'''
		snake_case						:  Union[str, Any]        =			1_0000
		snake_case						:  List[Any]        =			timeit(F'''{name}(G, \'A\')'''    ,       setup=lowercase__    ,       number=lowercase__				)
		print(F'''{name:<35} finished {number} runs in {res:.5f} seconds'''				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							benchmark_function("breadth_first_search")
							benchmark_function("breadth_first_search_with_deque")
							# breadth_first_search                finished 10000 runs in 0.20999 seconds
							# breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds







"""simple docstring"""

from typing import TYPE_CHECKING

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


__A							=     {"configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     [
							    "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "GPTJForCausalLM",
							    "GPTJForQuestionAnswering",
							    "GPTJForSequenceClassification",
							    "GPTJModel",
							    "GPTJPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     [
							    "TFGPTJForCausalLM",
							    "TFGPTJForQuestionAnswering",
							    "TFGPTJForSequenceClassification",
							    "TFGPTJModel",
							    "TFGPTJPreTrainedModel",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     [
							    "FlaxGPTJForCausalLM",
							    "FlaxGPTJModel",
							    "FlaxGPTJPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gptj import (
														    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTJForCausalLM,
														    GPTJForQuestionAnswering,
														    GPTJForSequenceClassification,
														    GPTJModel,
														    GPTJPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_gptj import (
														    TFGPTJForCausalLM,
														    TFGPTJForQuestionAnswering,
														    TFGPTJForSequenceClassification,
														    TFGPTJModel,
														    TFGPTJPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
							import sys

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

