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package com.example.arr

时间: 2025-08-08 11:12:57 浏览: 3
<think>我们正在处理用户的问题:如何在Android Studio中打开特定包com.example.arr 根据之前的对话,用户询问的是如何打开项目中的特定包(package)。在Android Studio中,包(package)是源代码组织的一种方式,通常对应于文件系统中的目录结构。 步骤: 1. 首先,用户需要确保已经正确打开了包含该包的项目(如之前的回答所述,通过打开项目根目录)。 2. 打开项目后,在Android Studio的Project工具窗口中,可以浏览项目结构,找到特定的包。 注意:在Android Studio中,包通常位于`app/src/main/java`或`app/src/main/kotlin`目录下(取决于项目使用的语言)。 具体操作: - 在左侧的Project工具窗口(如果未显示,可以通过View -> Tool Windows -> Project打开)中,选择“Project”视图(而不是“Android”视图),因为“Project”视图会显示完整的目录结构。 - 然后展开项目根目录 -> app -> src -> main -> java(或kotlin)-> 接着就会看到以包名命名的文件夹,例如com.example.arr。如果包名是多层级的,则继续展开直到找到目标包。 另外,用户也可以通过快速搜索来定位包: - 使用快捷键(在Windows/Linux上是Ctrl+N,在Mac上是Command+O)打开类搜索对话框,然后输入包名(例如com.example.arr)的一部分,然后选择相应的包(注意:在搜索框中,包名通常以斜杠分隔,如com/example/arr)。 如果用户想要打开包下的所有文件,可以右键点击该包,然后选择“Open in Explorer”(在Windows上)或“Reveal in Finder”(在Mac上)来在文件系统中打开该包对应的目录。 但是,用户的问题可能是在项目已经打开的情况下,如何快速导航到特定包。因此,我们主要介绍在Project工具窗口中的导航和搜索功能。 注意:如果用户还没有打开项目,请先按照之前的方法打开整个项目。 引用说明: 我们参考了之前对话中关于打开整个项目的步骤,但用户本次的问题并不直接涉及引用中的内容(引用[1]讲ANR,引用[2]讲数组和链表,引用[3]讲日历事件表,引用[4]是Flutter代码示例)。因此,我们主要基于Android Studio的使用常识来回答。 由于用户提到了“特定包com.example.arr”,我们假设用户已经打开了项目,现在需要导航到这个包。 因此,回答如下:</think>### 如何在 Android Studio 中打开特定包(com.example.arr) 在 Android Studio 中打开特定包需要先确保项目已正确加载,然后通过项目视图导航到目标包。以下是详细步骤: #### 操作步骤 1. **打开项目根目录**(确保项目已加载): - 通过 `File > Open` 选择项目根文件夹(包含 `build.gradle` 的目录)[^1] - 等待 Gradle 同步完成(右下角进度条消失) 2. **切换项目视图模式**: - 在左侧 **Project 工具栏** 顶部,将视图从 `Android` 改为 `Project` 模式 ![视图切换示意图](https://developer.android.com/static/studio/images/project-toolbar-views_2x.png) - 此时会显示完整的项目目录树 3. **导航到目标包**: - 展开路径:`app > src > main > java` - 按包名层级展开目录:`com > example > arr` - **右键点击目标包**(`arr`),选择 `Open in` > `Editor` 或直接双击打开 4. **快速定位技巧**: - **快捷键搜索**:`Shift 键连按两次`(Windows/Linux)或 `⇧⇧`(Mac) - 输入完整包名:`com.example.arr`,结果会显示包内所有文件 - 使用 **Navigate > Class**(`Ctrl+N`/`⌘O`)可搜索包中的具体类 #### 关键目录结构说明 ``` app └─ src └─ main └─ java └─ com └─ example // 父包 └─ arr // 目标包(包含Java/Kotlin源码) ├─ ClassA.java ├─ ClassB.kt └─ ... ``` > 包路径必须与文件系统中的实际目录完全匹配[^1] #### 常见问题处理 - **包未显示**: 1. 检查 `build.gradle` 的 `sourceSets` 配置是否包含该路径 2. 右键点击 `java` 目录 > `Mark Directory as > Sources Root` - **包名错误**: - 在类文件中确认包声明:`package com.example.arr;` - 文件物理位置必须与声明一致(如 `.../java/com/example/arr/ClassA.java`) #### 示例操作流程 ```mermaid graph TD A[打开项目根目录] --> B{Gradle同步成功?} B -->|是| C[切换Project视图] B -->|否| D[检查Gradle错误] C --> E[展开 app/src/main/java] E --> F[定位 com.example.arr] F --> G[右键打开包] ``` 通过以上步骤,你可以快速访问和编辑 `com.example.arr` 包中的代码文件。如果包内文件较多,建议使用搜索功能(`Ctrl+Shift+F`/`⌘⇧F`)精准定位。
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#下面程序运行时报错: C:\Users\Administrator\AppData\Local\Programs\Python\Python312\python.exe C:\Users\Administrator\AppData\Local\Programs\Python\Python312\Lib\site-packages\transformers\utils\generic.py Traceback (most recent call last): File "C:\Users\Administrator\AppData\Local\Programs\Python\Python312\Lib\site-packages\transformers\utils\generic.py", line 34, in <module> from ..utils import logging ImportError: attempted relative import with no known parent package 进程已结束,退出代码为 1 ------------------------------------------------------------------------------------------------ import inspect import json import os import tempfile import warnings from collections import OrderedDict, UserDict, defaultdict from collections.abc import Iterable, MutableMapping from contextlib import ExitStack, contextmanager from dataclasses import dataclass, fields, is_dataclass from enum import Enum from functools import partial, wraps from typing import Any, Callable, ContextManager, Optional, TypedDict import numpy as np from packaging import version from ..utils import logging from .import_utils import ( get_torch_version, is_flax_available, is_mlx_available, is_tf_available, is_torch_available, is_torch_fx_proxy, requires, ) _CAN_RECORD_REGISTRY = {} logger = logging.get_logger(__name__) if is_torch_available(): # required for @can_return_tuple decorator to work with torchdynamo import torch # noqa: F401 from ..model_debugging_utils import model_addition_debugger_context class cached_property(property): """ Descriptor that mimics @property but caches output in member variable. From tensorflow_datasets Built-in in functools from Python 3.8. """ def __get__(self, obj, objtype=None): # See docs.python.org/3/howto/descriptor.html#properties if obj is None: return self if self.fget is None: raise AttributeError("unreadable attribute") attr = "__cached_" + self.fget.__name__ cached = getattr(obj, attr, None) if cached is None: cached = self.fget(obj) setattr(obj, attr, cached) return cached # vendored from distutils.util def strtobool(val): """Convert a string representation of truth to true (1) or false (0). True values are 'y', 'yes', 't', 'true', 'on', and '1'; false values are 'n', 'no', 'f', 'false', 'off', and '0'. Raises ValueError if 'val' is anything else. """ val = val.lower() if val in {"y", "yes", "t", "true", "on", "1"}: return 1 if val in {"n", "no", "f", "false", "off", "0"}: return 0 raise ValueError(f"invalid truth value {val!r}") def infer_framework_from_repr(x): """ Tries to guess the framework of an object x from its repr (brittle but will help in is_tensor to try the frameworks in a smart order, without the need to import the frameworks). """ representation = str(type(x)) if representation.startswith("<class 'torch."): return "pt" elif representation.startswith("<class 'tensorflow."): return "tf" elif representation.startswith("<class 'jax"): return "jax" elif representation.startswith("<class 'numpy."): return "np" elif representation.startswith("<class 'mlx."): return "mlx" def _get_frameworks_and_test_func(x): """ Returns an (ordered since we are in Python 3.7+) dictionary framework to test function, which places the framework we can guess from the repr first, then Numpy, then the others. """ framework_to_test = { "pt": is_torch_tensor, "tf": is_tf_tensor, "jax": is_jax_tensor, "np": is_numpy_array, "mlx": is_mlx_array, } preferred_framework = infer_framework_from_repr(x) # We will test this one first, then numpy, then the others. frameworks = [] if preferred_framework is None else [preferred_framework] if preferred_framework != "np": frameworks.append("np") frameworks.extend([f for f in framework_to_test if f not in [preferred_framework, "np"]]) return {f: framework_to_test[f] for f in frameworks} def is_tensor(x): """ Tests if x is a torch.Tensor, tf.Tensor, jaxlib.xla_extension.DeviceArray, np.ndarray or mlx.array in the order defined by infer_framework_from_repr """ # This gives us a smart order to test the frameworks with the corresponding tests. framework_to_test_func = _get_frameworks_and_test_func(x) for test_func in framework_to_test_func.values(): if test_func(x): return True # Tracers if is_torch_fx_proxy(x): return True if is_flax_available(): from jax.core import Tracer if isinstance(x, Tracer): return True return False def _is_numpy(x): return isinstance(x, np.ndarray) def is_numpy_array(x): """ Tests if x is a numpy array or not. """ return _is_numpy(x) def _is_torch(x): import torch return isinstance(x, torch.Tensor) def is_torch_tensor(x): """ Tests if x is a torch tensor or not. Safe to call even if torch is not installed. """ return False if not is_torch_available() else _is_torch(x) def _is_torch_device(x): import torch return isinstance(x, torch.device) def is_torch_device(x): """ Tests if x is a torch device or not. Safe to call even if torch is not installed. """ return False if not is_torch_available() else _is_torch_device(x) def _is_torch_dtype(x): import torch if isinstance(x, str): if hasattr(torch, x): x = getattr(torch, x) else: return False return isinstance(x, torch.dtype) def is_torch_dtype(x): """ Tests if x is a torch dtype or not. Safe to call even if torch is not installed. """ return False if not is_torch_available() else _is_torch_dtype(x) def _is_tensorflow(x): import tensorflow as tf return isinstance(x, tf.Tensor) def is_tf_tensor(x): """ Tests if x is a tensorflow tensor or not. Safe to call even if tensorflow is not installed. """ return False if not is_tf_available() else _is_tensorflow(x) def _is_tf_symbolic_tensor(x): import tensorflow as tf # the is_symbolic_tensor predicate is only available starting with TF 2.14 if hasattr(tf, "is_symbolic_tensor"): return tf.is_symbolic_tensor(x) return isinstance(x, tf.Tensor) def is_tf_symbolic_tensor(x): """ Tests if x is a tensorflow symbolic tensor or not (ie. not eager). Safe to call even if tensorflow is not installed. """ return False if not is_tf_available() else _is_tf_symbolic_tensor(x) def _is_jax(x): import jax.numpy as jnp # noqa: F811 return isinstance(x, jnp.ndarray) def is_jax_tensor(x): """ Tests if x is a Jax tensor or not. Safe to call even if jax is not installed. """ return False if not is_flax_available() else _is_jax(x) def _is_mlx(x): import mlx.core as mx return isinstance(x, mx.array) def is_mlx_array(x): """ Tests if x is a mlx array or not. Safe to call even when mlx is not installed. """ return False if not is_mlx_available() else _is_mlx(x) def to_py_obj(obj): """ Convert a TensorFlow tensor, PyTorch tensor, Numpy array or python list to a python list. """ if isinstance(obj, (int, float)): return obj elif isinstance(obj, (dict, UserDict)): return {k: to_py_obj(v) for k, v in obj.items()} elif isinstance(obj, (list, tuple)): try: arr = np.array(obj) if np.issubdtype(arr.dtype, np.integer) or np.issubdtype(arr.dtype, np.floating): return arr.tolist() except Exception: pass return [to_py_obj(o) for o in obj] framework_to_py_obj = { "pt": lambda obj: obj.tolist(), "tf": lambda obj: obj.numpy().tolist(), "jax": lambda obj: np.asarray(obj).tolist(), "np": lambda obj: obj.tolist(), } # This gives us a smart order to test the frameworks with the corresponding tests. framework_to_test_func = _get_frameworks_and_test_func(obj) for framework, test_func in framework_to_test_func.items(): if test_func(obj): return framework_to_py_obj[framework](obj) # tolist also works on 0d np arrays if isinstance(obj, np.number): return obj.tolist() else: return obj def to_numpy(obj): """ Convert a TensorFlow tensor, PyTorch tensor, Numpy array or python list to a Numpy array. """ framework_to_numpy = { "pt": lambda obj: obj.detach().cpu().numpy(), "tf": lambda obj: obj.numpy(), "jax": lambda obj: np.asarray(obj), "np": lambda obj: obj, } if isinstance(obj, (dict, UserDict)): return {k: to_numpy(v) for k, v in obj.items()} elif isinstance(obj, (list, tuple)): return np.array(obj) # This gives us a smart order to test the frameworks with the corresponding tests. framework_to_test_func = _get_frameworks_and_test_func(obj) for framework, test_func in framework_to_test_func.items(): if test_func(obj): return framework_to_numpy[framework](obj) return obj class ModelOutput(OrderedDict): """ Base class for all model outputs as dataclass. Has a __getitem__ that allows indexing by integer or slice (like a tuple) or strings (like a dictionary) that will ignore the None attributes. Otherwise behaves like a regular python dictionary. <Tip warning={true}> You can't unpack a ModelOutput directly. Use the [~utils.ModelOutput.to_tuple] method to convert it to a tuple before. </Tip> """ def __init_subclass__(cls) -> None: """Register subclasses as pytree nodes. This is necessary to synchronize gradients when using torch.nn.parallel.DistributedDataParallel with static_graph=True with modules that output ModelOutput subclasses. """ if is_torch_available(): if version.parse(get_torch_version()) >= version.parse("2.2"): from torch.utils._pytree import register_pytree_node register_pytree_node( cls, _model_output_flatten, partial(_model_output_unflatten, output_type=cls), serialized_type_name=f"{cls.__module__}.{cls.__name__}", ) else: from torch.utils._pytree import _register_pytree_node _register_pytree_node( cls, _model_output_flatten, partial(_model_output_unflatten, output_type=cls), ) def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # Subclasses of ModelOutput must use the @dataclass decorator # This check is done in __init__ because the @dataclass decorator operates after __init_subclass__ # issubclass() would return True for issubclass(ModelOutput, ModelOutput) when False is needed # Just need to check that the current class is not ModelOutput is_modeloutput_subclass = self.__class__ != ModelOutput if is_modeloutput_subclass and not is_dataclass(self): raise TypeError( f"{self.__module__}.{self.__class__.__name__} is not a dataclass." " This is a subclass of ModelOutput and so must use the @dataclass decorator." ) def __post_init__(self): """Check the ModelOutput dataclass. Only occurs if @dataclass decorator has been used. """ class_fields = fields(self) # Safety and consistency checks if not len(class_fields): raise ValueError(f"{self.__class__.__name__} has no fields.") if not all(field.default is None for field in class_fields[1:]): raise ValueError(f"{self.__class__.__name__} should not have more than one required field.") first_field = getattr(self, class_fields[0].name) other_fields_are_none = all(getattr(self, field.name) is None for field in class_fields[1:]) if other_fields_are_none and not is_tensor(first_field): if isinstance(first_field, dict): iterator = first_field.items() first_field_iterator = True else: try: iterator = iter(first_field) first_field_iterator = True except TypeError: first_field_iterator = False # if we provided an iterator as first field and the iterator is a (key, value) iterator # set the associated fields if first_field_iterator: for idx, element in enumerate(iterator): if not isinstance(element, (list, tuple)) or len(element) != 2 or not isinstance(element[0], str): if idx == 0: # If we do not have an iterator of key/values, set it as attribute self[class_fields[0].name] = first_field else: # If we have a mixed iterator, raise an error raise ValueError( f"Cannot set key/value for {element}. It needs to be a tuple (key, value)." ) break setattr(self, element[0], element[1]) if element[1] is not None: self[element[0]] = element[1] elif first_field is not None: self[class_fields[0].name] = first_field else: for field in class_fields: v = getattr(self, field.name) if v is not None: self[field.name] = v def __delitem__(self, *args, **kwargs): raise Exception(f"You cannot use __delitem__ on a {self.__class__.__name__} instance.") def setdefault(self, *args, **kwargs): raise Exception(f"You cannot use setdefault on a {self.__class__.__name__} instance.") def pop(self, *args, **kwargs): raise Exception(f"You cannot use pop on a {self.__class__.__name__} instance.") def update(self, *args, **kwargs): raise Exception(f"You cannot use update on a {self.__class__.__name__} instance.") def __getitem__(self, k): if isinstance(k, str): inner_dict = dict(self.items()) return inner_dict[k] else: return self.to_tuple()[k] def __setattr__(self, name, value): if name in self.keys() and value is not None: # Don't call self.__setitem__ to avoid recursion errors super().__setitem__(name, value) super().__setattr__(name, value) def __setitem__(self, key, value): # Will raise a KeyException if needed super().__setitem__(key, value) # Don't call self.__setattr__ to avoid recursion errors super().__setattr__(key, value) def __reduce__(self): if not is_dataclass(self): return super().__reduce__() callable, _args, *remaining = super().__reduce__() args = tuple(getattr(self, field.name) for field in fields(self)) return callable, args, *remaining def to_tuple(self) -> tuple[Any]: """ Convert self to a tuple containing all the attributes/keys that are not None. """ return tuple(self[k] for k in self.keys()) if is_torch_available(): import torch.utils._pytree as _torch_pytree def _model_output_flatten(output: ModelOutput) -> tuple[list[Any], "_torch_pytree.Context"]: return list(output.values()), list(output.keys()) def _model_output_unflatten( values: Iterable[Any], context: "_torch_pytree.Context", output_type=None, ) -> ModelOutput: return output_type(**dict(zip(context, values))) if version.parse(get_torch_version()) >= version.parse("2.2"): _torch_pytree.register_pytree_node( ModelOutput, _model_output_flatten, partial(_model_output_unflatten, output_type=ModelOutput), serialized_type_name=f"{ModelOutput.__module__}.{ModelOutput.__name__}", ) else: _torch_pytree._register_pytree_node( ModelOutput, _model_output_flatten, partial(_model_output_unflatten, output_type=ModelOutput), ) class ExplicitEnum(str, Enum): """ Enum with more explicit error message for missing values. """ @classmethod def _missing_(cls, value): raise ValueError( f"{value} is not a valid {cls.__name__}, please select one of {list(cls._value2member_map_.keys())}" ) class PaddingStrategy(ExplicitEnum): """ Possible values for the padding argument in [PreTrainedTokenizerBase.__call__]. Useful for tab-completion in an IDE. """ LONGEST = "longest" MAX_LENGTH = "max_length" DO_NOT_PAD = "do_not_pad" class TensorType(ExplicitEnum): """ Possible values for the return_tensors argument in [PreTrainedTokenizerBase.__call__]. Useful for tab-completion in an IDE. """ PYTORCH = "pt" TENSORFLOW = "tf" NUMPY = "np" JAX = "jax" MLX = "mlx" class ContextManagers: """ Wrapper for contextlib.ExitStack which enters a collection of context managers. Adaptation of ContextManagers in the fastcore library. """ def __init__(self, context_managers: list[ContextManager]): self.context_managers = context_managers self.stack = ExitStack() def __enter__(self): for context_manager in self.context_managers: self.stack.enter_context(context_manager) def __exit__(self, *args, **kwargs): self.stack.__exit__(*args, **kwargs) def can_return_loss(model_class): """ Check if a given model can return loss. Args: model_class (type): The class of the model. """ framework = infer_framework(model_class) if framework == "tf": signature = inspect.signature(model_class.call) # TensorFlow models elif framework == "pt": signature = inspect.signature(model_class.forward) # PyTorch models else: signature = inspect.signature(model_class.__call__) # Flax models for p in signature.parameters: if p == "return_loss" and signature.parameters[p].default is True: return True return False def find_labels(model_class): """ Find the labels used by a given model. Args: model_class (type): The class of the model. """ model_name = model_class.__name__ framework = infer_framework(model_class) if framework == "tf": signature = inspect.signature(model_class.call) # TensorFlow models elif framework == "pt": signature = inspect.signature(model_class.forward) # PyTorch models else: signature = inspect.signature(model_class.__call__) # Flax models if "QuestionAnswering" in model_name: return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")] else: return [p for p in signature.parameters if "label" in p] def flatten_dict(d: MutableMapping, parent_key: str = "", delimiter: str = "."): """Flatten a nested dict into a single level dict.""" def _flatten_dict(d, parent_key="", delimiter="."): for k, v in d.items(): key = str(parent_key) + delimiter + str(k) if parent_key else k if v and isinstance(v, MutableMapping): yield from flatten_dict(v, key, delimiter=delimiter).items() else: yield key, v return dict(_flatten_dict(d, parent_key, delimiter)) @contextmanager def working_or_temp_dir(working_dir, use_temp_dir: bool = False): if use_temp_dir: with tempfile.TemporaryDirectory() as tmp_dir: yield tmp_dir else: yield working_dir def transpose(array, axes=None): """ Framework-agnostic version of numpy.transpose that will work on torch/TensorFlow/Jax tensors as well as NumPy arrays. """ if is_numpy_array(array): return np.transpose(array, axes=axes) elif is_torch_tensor(array): return array.T if axes is None else array.permute(*axes) elif is_tf_tensor(array): import tensorflow as tf return tf.transpose(array, perm=axes) elif is_jax_tensor(array): import jax.numpy as jnp return jnp.transpose(array, axes=axes) else: raise ValueError(f"Type not supported for transpose: {type(array)}.") def reshape(array, newshape): """ Framework-agnostic version of numpy.reshape that will work on torch/TensorFlow/Jax tensors as well as NumPy arrays. """ if is_numpy_array(array): return np.reshape(array, newshape) elif is_torch_tensor(array): return array.reshape(*newshape) elif is_tf_tensor(array): import tensorflow as tf return tf.reshape(array, newshape) elif is_jax_tensor(array): import jax.numpy as jnp return jnp.reshape(array, newshape) else: raise ValueError(f"Type not supported for reshape: {type(array)}.") def squeeze(array, axis=None): """ Framework-agnostic version of numpy.squeeze that will work on torch/TensorFlow/Jax tensors as well as NumPy arrays. """ if is_numpy_array(array): return np.squeeze(array, axis=axis) elif is_torch_tensor(array): return array.squeeze() if axis is None else array.squeeze(dim=axis) elif is_tf_tensor(array): import tensorflow as tf return tf.squeeze(array, axis=axis) elif is_jax_tensor(array): import jax.numpy as jnp return jnp.squeeze(array, axis=axis) else: raise ValueError(f"Type not supported for squeeze: {type(array)}.") def expand_dims(array, axis): """ Framework-agnostic version of numpy.expand_dims that will work on torch/TensorFlow/Jax tensors as well as NumPy arrays. """ if is_numpy_array(array): return np.expand_dims(array, axis) elif is_torch_tensor(array): return array.unsqueeze(dim=axis) elif is_tf_tensor(array): import tensorflow as tf return tf.expand_dims(array, axis=axis) elif is_jax_tensor(array): import jax.numpy as jnp return jnp.expand_dims(array, axis=axis) else: raise ValueError(f"Type not supported for expand_dims: {type(array)}.") def tensor_size(array): """ Framework-agnostic version of numpy.size that will work on torch/TensorFlow/Jax tensors as well as NumPy arrays. 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""" # Class instance object, so that a call to register can be reflected into all other files correctly, even if # a new instance is created (in order to locally override a given function) _global_mapping = {} def __init__(self): self._local_mapping = {} def __getitem__(self, key): # First check if instance has a local override if key in self._local_mapping: return self._local_mapping[key] return self._global_mapping[key] def __setitem__(self, key, value): # Allow local update of the default functions without impacting other instances self._local_mapping.update({key: value}) def __delitem__(self, key): del self._local_mapping[key] def __iter__(self): # Ensure we use all keys, with the overwritten ones on top return iter({**self._global_mapping, **self._local_mapping}) def __len__(self): return len(self._global_mapping.keys() | self._local_mapping.keys()) @classmethod def register(cls, key: str, value: Callable): cls._global_mapping.update({key: value}) def valid_keys(self) -> list[str]: return list(self.keys())

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