






from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)










from queue import PriorityQueue
from typing import Any

import numpy as np







def 	_a      (						SCREAMING_SNAKE_CASE_							:				dict      ,      SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				set      ,      SCREAMING_SNAKE_CASE_							:				set      ,      SCREAMING_SNAKE_CASE_							:				dict      ,      SCREAMING_SNAKE_CASE_							:				dict      ,      SCREAMING_SNAKE_CASE_							:				PriorityQueue      ,      SCREAMING_SNAKE_CASE_							:				dict      ,      SCREAMING_SNAKE_CASE_							:				float | int      ,      ):
					for nxt, d in graph[v]:
										if nxt in visited_forward:
															continue
										__lowerCAmelCase				   =       cst_fwd.get(SCREAMING_SNAKE_CASE_      ,      np.inf     )
										__lowerCAmelCase				   =       cst_fwd[v] + d
										if new_cost_f < old_cost_f:
															queue.put((new_cost_f, nxt)     )
															__lowerCAmelCase				   =       new_cost_f
															__lowerCAmelCase				   =       v
										if nxt in visited_backward:
															if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
																				__lowerCAmelCase				   =       cst_fwd[v] + d + cst_bwd[nxt]
					return shortest_distance







def 	_a      (						SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				dict      ,      SCREAMING_SNAKE_CASE_							:				dict     ):
					__lowerCAmelCase				   =       -1

					__lowerCAmelCase				   =       set()
					__lowerCAmelCase				   =       set()
					__lowerCAmelCase				   =       {source: 0}
					__lowerCAmelCase				   =       {destination: 0}
					__lowerCAmelCase				   =       {source: None}
					__lowerCAmelCase				   =       {destination: None}
					__lowerCAmelCase				   =       PriorityQueue()
					__lowerCAmelCase				   =       PriorityQueue()

					__lowerCAmelCase				   =       np.inf

					queue_forward.put((0, source)     )
					queue_backward.put((0, destination)     )

					if source == destination:
										return 0

					while not queue_forward.empty() and not queue_backward.empty():
										__lowerCAmelCase   ,							__lowerCAmelCase				   =       queue_forward.get()
										visited_forward.add(SCREAMING_SNAKE_CASE_     )

										__lowerCAmelCase   ,							__lowerCAmelCase				   =       queue_backward.get()
										visited_backward.add(SCREAMING_SNAKE_CASE_     )

										__lowerCAmelCase				   =       pass_and_relaxation(
										    SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      )

										__lowerCAmelCase				   =       pass_and_relaxation(
										    SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      )

										if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
															break

					if shortest_distance != np.inf:
										__lowerCAmelCase				   =       shortest_distance
					return shortest_path_distance


UpperCamelCase__    			=			{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}
UpperCamelCase__    			=			{
    """B""": [["""E""", 1]],
    """C""": [["""B""", 1]],
    """D""": [["""C""", 1]],
    """F""": [["""D""", 1], ["""G""", 1]],
    """E""": [[None, np.inf]],
    """G""": [["""E""", 2]],
}

if __name__ == "__main__":
					import doctest

					doctest.testmod()




