





'''simple docstring'''


# Copyright 2021 The HuggingFace Inc. 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 packaging import version

from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    copy_func,
    replace_return_docstrings,
)
from .generic import (
    ContextManagers,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    TensorType,
    add_model_info_to_auto_map,
    cached_property,
    can_return_loss,
    expand_dims,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CLOUDFRONT_DISTRIB_PREFIX,
    DISABLE_TELEMETRY,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    move_cache,
    send_example_telemetry,
    try_to_load_from_cache,
)
from .import_utils import (
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    TORCH_FX_REQUIRED_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_jieba_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_librosa_available,
    is_natten_available,
    is_ninja_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sacremoses_available,
    is_safetensors_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_sudachi_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_bfaa_cpu_available,
    is_torch_bfaa_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_neuroncore_available,
    is_torch_tensorrt_fx_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchvision_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    requires_backends,
    torch_only_method,
)


lowerCAmelCase_ : Dict	  =       """pytorch_model.bin"""
lowerCAmelCase_ : Any	  =       """pytorch_model.bin.index.json"""
lowerCAmelCase_ : str	  =       """adapter_config.json"""
lowerCAmelCase_ : List[str]	  =       """adapter_model.bin"""
lowerCAmelCase_ : Dict	  =       """adapter_model.safetensors"""
lowerCAmelCase_ : List[Any]	  =       """tf_model.h5"""
lowerCAmelCase_ : Dict	  =       """tf_model.h5.index.json"""
lowerCAmelCase_ : Any	  =       """model.ckpt"""
lowerCAmelCase_ : Optional[int]	  =       """flax_model.msgpack"""
lowerCAmelCase_ : Any	  =       """flax_model.msgpack.index.json"""
lowerCAmelCase_ : int	  =       """model.safetensors"""
lowerCAmelCase_ : Optional[Any]	  =       """model.safetensors.index.json"""
lowerCAmelCase_ : Any	  =       """config.json"""
lowerCAmelCase_ : List[str]	  =       """preprocessor_config.json"""
lowerCAmelCase_ : Union[str, Any]	  =       FEATURE_EXTRACTOR_NAME
lowerCAmelCase_ : Dict	  =       """generation_config.json"""
lowerCAmelCase_ : Dict	  =       """modelcard.json"""

lowerCAmelCase_ : List[str]	  =       """▁"""
lowerCAmelCase_ : Optional[Any]	  =       SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

lowerCAmelCase_ : Tuple	  =       [
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
lowerCAmelCase_ : str	  =       [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
lowerCAmelCase_ : Optional[int]	  =       [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]







def  _lowerCamelCase	(__lowerCamelCase	:  Optional[int]  ) ->     Dict:
			if version.parse(__lowerCamelCase  ) < version.parse(__lowerCamelCase  ):
						if "dev" in min_version:
									a__	 =			(
									    "This example requires a source install from HuggingFace Transformers (see "
									    "`https://huggingface.co/docs/transformers/installation#install-from-source`),"
									)
						else:
									a__	 =			f'''This example requires a minimum version of {min_version},'''
						error_message += f''' but the version found is {__version__}.\n'''
						raise ImportError(
						    error_message
						    + "Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other "
						    "versions of HuggingFace Transformers."  )






from __future__ import annotations

from typing import Any

class __UpperCamelCase     :






					'''simple docstring'''



					def __init__(				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	Optional[int]									= num_of_nodes
						UpperCAmelCase__:	list[list[int]]									= []
						UpperCAmelCase__:	dict[int, int]									= {}





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						self.m_edges.append([u_node, v_node, weight]						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):

						if self.m_component[u_node] == u_node:
							return u_node
						return self.find_component(self.m_component[u_node]						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):

						if self.m_component[u_node] != u_node:
							for k in self.m_component:
								UpperCAmelCase__:	Optional[Any]									= self.find_component(lowerCamelCase__						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):

						if component_size[u_node] <= component_size[v_node]:
							UpperCAmelCase__:	Tuple									= v_node
							component_size[v_node] += component_size[u_node]
							self.set_component(lowerCamelCase__						)

						elif component_size[u_node] >= component_size[v_node]:
							UpperCAmelCase__:	Dict									= self.find_component(lowerCamelCase__						)
							component_size[u_node] += component_size[v_node]
							self.set_component(lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Dict									= []
						UpperCAmelCase__:	int									= 0

						UpperCAmelCase__:	list[Any]									= [-1] * self.m_num_of_nodes

						# A list of components (initialized to all of the nodes)
						for node in range(self.m_num_of_nodes						):
							self.m_component.update({node: node}						)
							component_size.append(1						)

						UpperCAmelCase__:	Union[str, Any]									= self.m_num_of_nodes

						while num_of_components > 1:
							for edge in self.m_edges:
								UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	str									= edge

								UpperCAmelCase__:	str									= self.m_component[u]
								UpperCAmelCase__:	Optional[Any]									= self.m_component[v]

								if u_component != v_component:

									for component in (u_component, v_component):
										if (
										    minimum_weight_edge[component] == -1
										    or minimum_weight_edge[component][2] > w
										):
											UpperCAmelCase__:	List[Any]									= [u, v, w]

							for edge in minimum_weight_edge:
								if isinstance(lowerCamelCase__				,      lowerCamelCase__						):
									UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	str									= edge

									UpperCAmelCase__:	str									= self.m_component[u]
									UpperCAmelCase__:	int									= self.m_component[v]

									if u_component != v_component:
										mst_weight += w
										self.union(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						)
										print(F"Added edge [{u} - {v}]\nAdded weight: {w}\n"						)
										num_of_components -= 1

							UpperCAmelCase__:	Any									= [-1] * self.m_num_of_nodes
						print(F"The total weight of the minimal spanning tree is: {mst_weight}"						)



def 			_A	(      ):
	pass


if __name__ == "__main__":
					import doctest

					doctest.testmod()