



import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
   from .testing_utils import (
       floats_tensor,
       load_hf_numpy,
       load_image,
       load_numpy,
       load_pt,
       nightly,
       parse_flag_from_env,
       print_tensor_test,
       require_torch_a,
       require_torch_gpu,
       skip_mps,
       slow,
       torch_all_close,
       torch_device,
   )
   from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


__lowerCamelCase								=      get_logger(__name__)







def  _snake_case			(							__snake_case     )  ->  Any:






      '''simple docstring'''







      if version.parse(__snake_case     ) < version.parse(__snake_case     ):
            if "dev" in min_version:
                  UpperCAmelCase_	:						List[str]					   =		(
                      "This example requires a source install from HuggingFace diffusers (see "
                      "`https://huggingface.co/docs/diffusers/installation#install-from-source`),"
                  )
            else:
                  UpperCAmelCase_	:						Optional[Any]					   =		F"""This example requires a minimum version of {min_version},"""
            error_message += F""" but the version found is {__version__}.\n"""
            raise ImportError(__snake_case     )





import heapq







def  _snake_case			(							__snake_case       ,	__snake_case       ,	__snake_case     )  ->  str:






      '''simple docstring'''







      UpperCAmelCase_	:						List[str]					   =		[(0, start)]  # cost from start node,end node
      UpperCAmelCase_	:						List[Any]					   =		set()
      while heap:
            ((UpperCAmelCase_)   ,   (UpperCAmelCase_))	:						str					   =		heapq.heappop(__snake_case     )
            if u in visited:
                  continue
            visited.add(__snake_case     )
            if u == end:
                  return cost
            for v, c in graph[u]:
                  if v in visited:
                        continue
                  UpperCAmelCase_	:						Tuple					   =		cost + c
                  heapq.heappush(__snake_case       ,	(next_item, v)     )
      return -1


__lowerCamelCase								=      {
    '''A''': [['''B''', 2], ['''C''', 5]],
    '''B''': [['''A''', 2], ['''D''', 3], ['''E''', 1], ['''F''', 1]],
    '''C''': [['''A''', 5], ['''F''', 3]],
    '''D''': [['''B''', 3]],
    '''E''': [['''B''', 4], ['''F''', 3]],
    '''F''': [['''C''', 3], ['''E''', 3]],
}
__lowerCamelCase								=      {
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''F''', 3]],
    '''F''': [],
}
__lowerCamelCase								=      {
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''G''', 2]],
    '''F''': [],
    '''G''': [['''F''', 1]],
}

__lowerCamelCase								=      dijkstra(G, '''E''', '''C''')
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

__lowerCamelCase								=      dijkstra(Ga, '''E''', '''F''')
print(short_distance)  # E -- 3 --> F == 3

__lowerCamelCase								=      dijkstra(Ga, '''E''', '''F''')
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
   import doctest

   doctest.testmod()
