

from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
						from PIL import Image

						from ..image_utils import load_image

if is_tf_available():
						import tensorflow as tf

						from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
						from ..tf_utils import stable_softmax

if is_torch_available():
						from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

lowerCAmelCase							:  str            =						logging.get_logger(__name__)



@add_end_docstrings(UpperCAmelCase_     )
class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			def __init__(			self  :					Optional[Any]       ,						*lowerCAmelCase__  :					List[str]       ,						**lowerCAmelCase__  :					Optional[Any]):
									super().__init__(*lowerCAmelCase__       ,						**lowerCAmelCase__)
									requires_backends(self       ,						"vision")
									self.check_model_type(
									    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
									    if self.framework == "tf"
									    else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]       ,						lowerCAmelCase__  :					Dict=None):
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	{}
									if top_k is not None:
															SCREAMING_SNAKE_CASE_:			List[str]  		=	top_k
									return {}, {}, postprocess_params



			def __call__(			self  :					List[Any]       ,						lowerCAmelCase__  :					Union[str, List[str], "Image.Image", List["Image.Image"]]       ,						**lowerCAmelCase__  :					List[Any]):
									return super().__call__(lowerCAmelCase__       ,						**lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict       ,						lowerCAmelCase__  :					Any):
									SCREAMING_SNAKE_CASE_:			int  		=	load_image(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Any  		=	self.image_processor(images=lowerCAmelCase__       ,						return_tensors=self.framework)
									return model_inputs



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					Optional[int]):
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self.model(**lowerCAmelCase__)
									return model_outputs




			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]       ,						lowerCAmelCase__  :					Tuple       ,						lowerCAmelCase__  :					Dict=5):
									if top_k > self.model.config.num_labels:
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.model.config.num_labels

									if self.framework == "pt":
															SCREAMING_SNAKE_CASE_:			int  		=	model_outputs.logits.softmax(-1)[0]
															SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	probs.topk(lowerCAmelCase__)
									elif self.framework == "tf":
															SCREAMING_SNAKE_CASE_:			Optional[int]  		=	stable_softmax(model_outputs.logits       ,						axis=-1)[0]
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	tf.math.top_k(lowerCAmelCase__       ,						k=lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Dict  		=	topk.values.numpy(), topk.indices.numpy()
									else:
															raise ValueError(F"Unsupported framework: {self.framework}")

									SCREAMING_SNAKE_CASE_:			Tuple  		=	scores.tolist()
									SCREAMING_SNAKE_CASE_:			Tuple  		=	ids.tolist()
									return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCAmelCase__       ,						lowerCAmelCase__)]









from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass



@dataclass
class   __lowercase    :




			"""simple docstring"""







			_UpperCAmelCase       :							int
			_UpperCAmelCase       :							int







class   __lowercase    :




			"""simple docstring"""







			def __init__(			self  :					Any       ,						lowerCAmelCase__  :					int):
									SCREAMING_SNAKE_CASE_:			list[list[Edge]]  		=	[[] for _ in range(lowerCAmelCase__)]
									SCREAMING_SNAKE_CASE_:			Dict  		=	size



			def __getitem__(			self  :					Dict       ,						lowerCAmelCase__  :					int):
									return iter(self._graph[vertex])



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									return self._size



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict       ,						lowerCAmelCase__  :					int       ,						lowerCAmelCase__  :					int       ,						lowerCAmelCase__  :					int):
									if weight not in (0, 1):
															raise ValueError("Edge weight must be either 0 or 1.")

									if to_vertex < 0 or to_vertex >= self.size:
															raise ValueError("Vertex indexes must be in [0; size).")

									self._graph[from_vertex].append(Edge(lowerCAmelCase__       ,						lowerCAmelCase__))




			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]       ,						lowerCAmelCase__  :					int       ,						lowerCAmelCase__  :					int):
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	deque([start_vertex])
									SCREAMING_SNAKE_CASE_:			list[int | None]  		=	[None] * self.size
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	0

									while queue:
															SCREAMING_SNAKE_CASE_:			int  		=	queue.popleft()
															SCREAMING_SNAKE_CASE_:			str  		=	distances[current_vertex]
															if current_distance is None:
																					continue

															for edge in self[current_vertex]:
																					SCREAMING_SNAKE_CASE_:			Optional[int]  		=	current_distance + edge.weight
																					SCREAMING_SNAKE_CASE_:			str  		=	distances[edge.destination_vertex]
																					if (
																					    isinstance(lowerCAmelCase__       ,						lowerCAmelCase__)
																					    and new_distance >= dest_vertex_distance
																					):
																											continue
																					SCREAMING_SNAKE_CASE_:			Any  		=	new_distance
																					if edge.weight == 0:
																											queue.appendleft(edge.destination_vertex)
																					else:
																											queue.append(edge.destination_vertex)

									if distances[finish_vertex] is None:
															raise ValueError("No path from start_vertex to finish_vertex.")

									return distances[finish_vertex]


if __name__ == "__main__":
						import doctest

						doctest.testmod()






