
from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
SCREAMING_SNAKE_CASE							:Dict							 =				0

SCREAMING_SNAKE_CASE							:Optional[int]							 =				[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				[[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

SCREAMING_SNAKE_CASE							:Optional[int]							 =				tuple[int, int]





class 							UpperCAmelCase      :




	'''simple docstring'''





	def __init__(       self					: Optional[int]							,A					: int							,A					: int							,A					: int							,A					: int							,A					: int							,A					: Node | None							,):
					__A   			= pos_x
					__A   			= pos_y
					__A   			= (pos_y, pos_x)
					__A   			= goal_x
					__A   			= goal_y
					__A   			= g_cost
					__A   			= parent
					__A   			= self.calculate_heuristic()
					__A   			= self.g_cost + self.h_cost






	def 				UpperCamelCase_  (       self					: Optional[int] ):
					__A   			= self.pos_x - self.goal_x
					__A   			= self.pos_y - self.goal_y
					if HEURISTIC == 1:
									return abs(A ) + abs(A )
					else:
									return sqrt(dy**2 + dx**2 )







	def __lt__(       self					: Union[str, Any]							,A					: Node ):
					return self.f_cost < other.f_cost





class 							UpperCAmelCase      :




	'''simple docstring'''





	def __init__(       self					: Optional[Any]							,A					: TPosition							,A					: TPosition ):
					__A   			= Node(start[1]							,start[0]							,goal[1]							,goal[0]							,0							,A )
					__A   			= Node(goal[1]							,goal[0]							,goal[1]							,goal[0]							,9_99_99							,A )

					__A   			= [self.start]
					__A   			= []

					__A   			= False






	def 				UpperCamelCase_  (       self					: Dict ):
					while self.open_nodes:
									# Open Nodes are sorted using __lt__
									self.open_nodes.sort()
									__A   			= self.open_nodes.pop(0 )

									if current_node.pos == self.target.pos:
													return self.retrace_path(A )

									self.closed_nodes.append(A )
									__A   			= self.get_successors(A )

									for child_node in successors:
													if child_node in self.closed_nodes:
																	continue

													if child_node not in self.open_nodes:
																	self.open_nodes.append(A )
													else:
																	# retrieve the best current path
																	__A   			= self.open_nodes.pop(self.open_nodes.index(A ) )

																	if child_node.g_cost < better_node.g_cost:
																					self.open_nodes.append(A )
																	else:
																					self.open_nodes.append(A )

					return [self.start.pos]






	def 				UpperCamelCase_  (       self					: List[str]							,A					: Node ):
					__A   			= []
					for action in delta:
									__A   			= parent.pos_x + action[1]
									__A   			= parent.pos_y + action[0]
									if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(A ) - 1):
													continue

									if grid[pos_y][pos_x] != 0:
													continue

									successors.append(
									    Node(
									        A							,A							,self.target.pos_y							,self.target.pos_x							,parent.g_cost + 1							,A							,) )
					return successors







	def 				UpperCamelCase_  (       self					: int							,A					: Node | None ):
					__A   			= node
					__A   			= []
					while current_node is not None:
									path.append((current_node.pos_y, current_node.pos_x) )
									__A   			= current_node.parent
					path.reverse()
					return path





class 							UpperCAmelCase      :




	'''simple docstring'''





	def __init__(       self					: List[str]							,A					: TPosition							,A					: TPosition ):
					__A   			= AStar(A							,A )
					__A   			= AStar(A							,A )
					__A   			= False






	def 				UpperCamelCase_  (       self					: List[str] ):
					while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
									self.fwd_astar.open_nodes.sort()
									self.bwd_astar.open_nodes.sort()
									__A   			= self.fwd_astar.open_nodes.pop(0 )
									__A   			= self.bwd_astar.open_nodes.pop(0 )

									if current_bwd_node.pos == current_fwd_node.pos:
													return self.retrace_bidirectional_path(
													    A							,A )

									self.fwd_astar.closed_nodes.append(A )
									self.bwd_astar.closed_nodes.append(A )

									__A   			= current_bwd_node
									__A   			= current_fwd_node

									__A   			= {
									    self.fwd_astar: self.fwd_astar.get_successors(A ),
									    self.bwd_astar: self.bwd_astar.get_successors(A ),
									}

									for astar in [self.fwd_astar, self.bwd_astar]:
													for child_node in successors[astar]:
																	if child_node in astar.closed_nodes:
																					continue

																	if child_node not in astar.open_nodes:
																					astar.open_nodes.append(A )
																	else:
																					# retrieve the best current path
																					__A   			= astar.open_nodes.pop(
																					    astar.open_nodes.index(A ) )

																					if child_node.g_cost < better_node.g_cost:
																									astar.open_nodes.append(A )
																					else:
																									astar.open_nodes.append(A )

					return [self.fwd_astar.start.pos]







	def 				UpperCamelCase_  (       self					: Dict							,A					: Node							,A					: Node ):
					__A   			= self.fwd_astar.retrace_path(A )
					__A   			= self.bwd_astar.retrace_path(A )
					bwd_path.pop()
					bwd_path.reverse()
					__A   			= fwd_path + bwd_path
					return path


if __name__ == "__main__":
					# all coordinates are given in format [y,x]
					SCREAMING_SNAKE_CASE							:List[Any]							 =				(0, 0)
					SCREAMING_SNAKE_CASE							:int							 =				(len(grid) - 1, len(grid[0]) - 1)
					for elem in grid:
										print(elem)

					SCREAMING_SNAKE_CASE							:Dict							 =				time.time()
					SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				AStar(init, goal)
					SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				a_star.search()
					SCREAMING_SNAKE_CASE							:Optional[int]							 =				time.time() - start_time
					print(f'''AStar execution time = {end_time:f} seconds''')

					SCREAMING_SNAKE_CASE							:Tuple							 =				time.time()
					SCREAMING_SNAKE_CASE							:List[Any]							 =				BidirectionalAStar(init, goal)
					SCREAMING_SNAKE_CASE							:str							 =				time.time() - bd_start_time
					print(f'''BidirectionalAStar execution time = {bd_end_time:f} seconds''')


# 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,
)


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

SCREAMING_SNAKE_CASE							:Optional[int]							 =				'▁'
SCREAMING_SNAKE_CASE							:Optional[Any]							 =				SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

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


def 						UpperCAmelCase			(	a_						)		->	Dict:







				"""simple docstring"""



				if version.parse(a_						) < version.parse(a_						):
								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."						)
