

import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple

import numpy as np

from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy


if is_flax_available():
      import jax.numpy as jnp






class 						__magic_name__    (       _lowercase    ):


 def __get__(						self		: Optional[int]			,						lowerCAmelCase__		: int			,						lowerCAmelCase__		: Tuple=None    )    ->      Dict:
   # See docs.python.org/3/howto/descriptor.html#properties
   if obj is None:
     return self
   if self.fget is None:
     raise AttributeError("unreadable attribute"    )
   UpperCAmelCase					      =  "__cached_" + self.fget.__name__
   UpperCAmelCase					      =  getattr(A_			,						A_			,						A_    )
   if cached is None:
     UpperCAmelCase					      =  self.fget(A_    )
     setattr(A_			,						A_			,						A_    )
   return cached

def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  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      _lowerCAmelCase(	__A			):
  if is_torch_fx_proxy(snake_case__			):
    return True
  if is_torch_available():
    import torch

    if isinstance(snake_case__   ,       torch.Tensor			):
      return True
  if is_tf_available():
    import tensorflow as tf

    if isinstance(snake_case__   ,       tf.Tensor			):
      return True

  if is_flax_available():
    import jax.numpy as jnp
    from jax.core import Tracer

    if isinstance(snake_case__   ,       (jnp.ndarray, Tracer)			):
      return True

  return isinstance(snake_case__   ,       np.ndarray			)
def      _lowerCAmelCase(	__A			):
  return isinstance(snake_case__   ,       np.ndarray			)
def      _lowerCAmelCase(	__A			):
  return _is_numpy(snake_case__			)
def      _lowerCAmelCase(	__A			):
  import torch

  return isinstance(snake_case__   ,       torch.Tensor			)
def      _lowerCAmelCase(	__A			):
  return False if not is_torch_available() else _is_torch(snake_case__			)
def      _lowerCAmelCase(	__A			):
  import torch

  return isinstance(snake_case__   ,       torch.device			)
def      _lowerCAmelCase(	__A			):
  return False if not is_torch_available() else _is_torch_device(snake_case__			)
def      _lowerCAmelCase(	__A			):
  import torch

  if isinstance(snake_case__   ,       snake_case__			):
    if hasattr(snake_case__   ,       snake_case__			):
      UpperCAmelCase					      =  getattr(snake_case__   ,       snake_case__			)
    else:
      return False
  return isinstance(snake_case__   ,       torch.dtype			)
def      _lowerCAmelCase(	__A			):
  return False if not is_torch_available() else _is_torch_dtype(snake_case__			)
def      _lowerCAmelCase(	__A			):
  import tensorflow as tf

  return isinstance(snake_case__   ,       tf.Tensor			)
def      _lowerCAmelCase(	__A			):
  return False if not is_tf_available() else _is_tensorflow(snake_case__			)
def      _lowerCAmelCase(	__A			):
  import tensorflow as tf

  # the `is_symbolic_tensor` predicate is only available starting with TF 2.14
  if hasattr(snake_case__   ,       "is_symbolic_tensor"			):
    return tf.is_symbolic_tensor(snake_case__			)
  return type(snake_case__			) == tf.Tensor
def      _lowerCAmelCase(	__A			):
  return False if not is_tf_available() else _is_tf_symbolic_tensor(snake_case__			)
def      _lowerCAmelCase(	__A			):
  import jax.numpy as jnp  # noqa: F811

  return isinstance(snake_case__   ,       jnp.ndarray			)
def      _lowerCAmelCase(	__A			):
  return False if not is_flax_available() else _is_jax(snake_case__			)
def      _lowerCAmelCase(	__A			):
  if isinstance(snake_case__   ,       (dict, UserDict)			):
    return {k: to_py_obj(snake_case__			) for k, v in obj.items()}
  elif isinstance(snake_case__   ,       (list, tuple)			):
    return [to_py_obj(snake_case__			) for o in obj]
  elif is_tf_tensor(snake_case__			):
    return obj.numpy().tolist()
  elif is_torch_tensor(snake_case__			):
    return obj.detach().cpu().tolist()
  elif is_jax_tensor(snake_case__			):
    return np.asarray(snake_case__			).tolist()
  elif isinstance(snake_case__   ,       (np.ndarray, np.number)			):  # tolist also works on 0d np arrays
    return obj.tolist()
  else:
    return obj
def      _lowerCAmelCase(	__A			):
  if isinstance(snake_case__   ,       (dict, UserDict)			):
    return {k: to_numpy(snake_case__			) for k, v in obj.items()}
  elif isinstance(snake_case__   ,       (list, tuple)			):
    return np.array(snake_case__			)
  elif is_tf_tensor(snake_case__			):
    return obj.numpy()
  elif is_torch_tensor(snake_case__			):
    return obj.detach().cpu().numpy()
  elif is_jax_tensor(snake_case__			):
    return np.asarray(snake_case__			)
  else:
    return obj






class 						__magic_name__    (       _lowercase    ):


 def _UpperCamelCase	(						self		: str    )    ->      str:
   UpperCAmelCase					      =  fields(self    )

   # Safety and consistency checks
   if not len(A_    ):
     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."    )

   UpperCAmelCase					      =  getattr(self			,						class_fields[0].name    )
   UpperCAmelCase					      =  all(getattr(self			,						field.name    ) is None for field in class_fields[1:]    )

   if other_fields_are_none and not is_tensor(A_    ):
     if isinstance(A_			,						A_    ):
       UpperCAmelCase					      =  first_field.items()
       UpperCAmelCase					      =  True
     else:
       try:
         UpperCAmelCase					      =  iter(A_    )
         UpperCAmelCase					      =  True
       except TypeError:
         UpperCAmelCase					      =  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(A_    ):
         if (
             not isinstance(A_			,						(list, tuple)    )
             or not len(A_    ) == 2
             or not isinstance(element[0]			,						A_    )
         ):
           if idx == 0:
             # If we do not have an iterator of key/values, set it as attribute
             UpperCAmelCase					      =  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:
           UpperCAmelCase					      =  element[1]
     elif first_field is not None:
       UpperCAmelCase					      =  first_field
   else:
     for field in class_fields:
       UpperCAmelCase					      =  getattr(self			,						field.name    )
       if v is not None:
         UpperCAmelCase					      =  v


 def __delitem__(						self		: Optional[int]			,						*lowerCAmelCase__		: Optional[int]			,						**lowerCAmelCase__		: Dict    )    ->      Tuple:
   raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance."    )


 def _UpperCamelCase	(						self		: int			,						*lowerCAmelCase__		: Optional[int]			,						**lowerCAmelCase__		: List[Any]    )    ->      Dict:
   raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance."    )


 def _UpperCamelCase	(						self		: List[Any]			,						*lowerCAmelCase__		: Optional[Any]			,						**lowerCAmelCase__		: Dict    )    ->      Dict:
   raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance."    )


 def _UpperCamelCase	(						self		: Tuple			,						*lowerCAmelCase__		: Union[str, Any]			,						**lowerCAmelCase__		: Dict    )    ->      Dict:
   raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance."    )


 def __getitem__(						self		: int			,						lowerCAmelCase__		: Any    )    ->      str:
   if isinstance(A_			,						A_    ):
     UpperCAmelCase					      =  dict(self.items()    )
     return inner_dict[k]
   else:
     return self.to_tuple()[k]


 def __setattr__(						self		: Optional[Any]			,						lowerCAmelCase__		: int			,						lowerCAmelCase__		: Dict    )    ->      Dict:
   if name in self.keys() and value is not None:
     # Don't call self.__setitem__ to avoid recursion errors
     super().__setitem__(A_			,						A_    )
   super().__setattr__(A_			,						A_    )


 def __setitem__(						self		: int			,						lowerCAmelCase__		: Dict			,						lowerCAmelCase__		: Tuple    )    ->      int:
   # Will raise a KeyException if needed
   super().__setitem__(A_			,						A_    )
   # Don't call self.__setattr__ to avoid recursion errors
   super().__setattr__(A_			,						A_    )


 def _UpperCamelCase	(						self		: Optional[Any]    )    ->      Tuple[Any]:
   return tuple(self[k] for k in self.keys()    )






class 						__magic_name__    (       _lowercase	,						_lowercase    ):


 @classmethod
 def _UpperCamelCase	(						cls		: Union[str, Any]			,						lowerCAmelCase__		: int    )    ->      Tuple:
   raise ValueError(
       f"{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys()    )}"    )






class 						__magic_name__    (       _lowercase    ):
 UpperCAmelCase							    =						'''longest'''
 UpperCAmelCase							    =						'''max_length'''
 UpperCAmelCase							    =						'''do_not_pad'''






class 						__magic_name__    (       _lowercase    ):
 UpperCAmelCase							    =						'''pt'''
 UpperCAmelCase							    =						'''tf'''
 UpperCAmelCase							    =						'''np'''
 UpperCAmelCase							    =						'''jax'''






class 						__magic_name__    :


 def __init__(						self		: int			,						lowerCAmelCase__		: List[ContextManager]    )    ->      Any:
   UpperCAmelCase					      =  context_managers
   UpperCAmelCase					      =  ExitStack()


 def __enter__(						self		: Tuple    )    ->      List[Any]:
   for context_manager in self.context_managers:
     self.stack.enter_context(A_    )


 def __exit__(						self		: int			,						*lowerCAmelCase__		: str			,						**lowerCAmelCase__		: str    )    ->      Any:
   self.stack.__exit__(*A_			,						**A_    )

def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  infer_framework(snake_case__			)
  if framework == "tf":
    UpperCAmelCase					      =  inspect.signature(model_class.call			)  # TensorFlow models
  elif framework == "pt":
    UpperCAmelCase					      =  inspect.signature(model_class.forward			)  # PyTorch models
  else:
    UpperCAmelCase					      =  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      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  model_class.__name__
  UpperCAmelCase					      =  infer_framework(snake_case__			)
  if framework == "tf":
    UpperCAmelCase					      =  inspect.signature(model_class.call			)  # TensorFlow models
  elif framework == "pt":
    UpperCAmelCase					      =  inspect.signature(model_class.forward			)  # PyTorch models
  else:
    UpperCAmelCase					      =  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      _lowerCAmelCase(	__A   ,       __A = ""   ,       __A = "."			):

  def _flatten_dict(__A   ,       __A=""   ,       __A="."			):
    for k, v in d.items():
      UpperCAmelCase					      =  str(snake_case__			) + delimiter + str(snake_case__			) if parent_key else k
      if v and isinstance(snake_case__   ,       snake_case__			):
        yield from flatten_dict(snake_case__   ,       snake_case__   ,       delimiter=snake_case__			).items()
      else:
        yield key, v

  return dict(_flatten_dict(snake_case__   ,       snake_case__   ,       snake_case__			)			)
@contextmanager
def      _lowerCAmelCase(	__A   ,       __A = False			):
  if use_temp_dir:
    with tempfile.TemporaryDirectory() as tmp_dir:
      yield tmp_dir
  else:
    yield working_dir
def      _lowerCAmelCase(	__A   ,       __A=None			):
  if is_numpy_array(snake_case__			):
    return np.transpose(snake_case__   ,       axes=snake_case__			)
  elif is_torch_tensor(snake_case__			):
    return array.T if axes is None else array.permute(*snake_case__			)
  elif is_tf_tensor(snake_case__			):
    import tensorflow as tf

    return tf.transpose(snake_case__   ,       perm=snake_case__			)
  elif is_jax_tensor(snake_case__			):
    return jnp.transpose(snake_case__   ,       axes=snake_case__			)
  else:
    raise ValueError(F"Type not supported for transpose: {type(snake_case__			)}."			)
def      _lowerCAmelCase(	__A   ,       __A			):
  if is_numpy_array(snake_case__			):
    return np.reshape(snake_case__   ,       snake_case__			)
  elif is_torch_tensor(snake_case__			):
    return array.reshape(*snake_case__			)
  elif is_tf_tensor(snake_case__			):
    import tensorflow as tf

    return tf.reshape(snake_case__   ,       snake_case__			)
  elif is_jax_tensor(snake_case__			):
    return jnp.reshape(snake_case__   ,       snake_case__			)
  else:
    raise ValueError(F"Type not supported for reshape: {type(snake_case__			)}."			)
def      _lowerCAmelCase(	__A   ,       __A=None			):
  if is_numpy_array(snake_case__			):
    return np.squeeze(snake_case__   ,       axis=snake_case__			)
  elif is_torch_tensor(snake_case__			):
    return array.squeeze() if axis is None else array.squeeze(dim=snake_case__			)
  elif is_tf_tensor(snake_case__			):
    import tensorflow as tf

    return tf.squeeze(snake_case__   ,       axis=snake_case__			)
  elif is_jax_tensor(snake_case__			):
    return jnp.squeeze(snake_case__   ,       axis=snake_case__			)
  else:
    raise ValueError(F"Type not supported for squeeze: {type(snake_case__			)}."			)
def      _lowerCAmelCase(	__A   ,       __A			):
  if is_numpy_array(snake_case__			):
    return np.expand_dims(snake_case__   ,       snake_case__			)
  elif is_torch_tensor(snake_case__			):
    return array.unsqueeze(dim=snake_case__			)
  elif is_tf_tensor(snake_case__			):
    import tensorflow as tf

    return tf.expand_dims(snake_case__   ,       axis=snake_case__			)
  elif is_jax_tensor(snake_case__			):
    return jnp.expand_dims(snake_case__   ,       axis=snake_case__			)
  else:
    raise ValueError(F"Type not supported for expand_dims: {type(snake_case__			)}."			)
def      _lowerCAmelCase(	__A			):
  if is_numpy_array(snake_case__			):
    return np.size(snake_case__			)
  elif is_torch_tensor(snake_case__			):
    return array.numel()
  elif is_tf_tensor(snake_case__			):
    import tensorflow as tf

    return tf.size(snake_case__			)
  elif is_jax_tensor(snake_case__			):
    return array.size
  else:
    raise ValueError(F"Type not supported for expand_dims: {type(snake_case__			)}."			)
def      _lowerCAmelCase(	__A   ,       __A			):
  for key, value in auto_map.items():
    if isinstance(snake_case__   ,       (tuple, list)			):
      UpperCAmelCase					      =  [F"{repo_id}--{v}" if (v is not None and "--" not in v) else v for v in value]
    elif value is not None and "--" not in value:
      UpperCAmelCase					      =  F"{repo_id}--{value}"

  return auto_map





def      _lowerCAmelCase(	__A			):
  for base_class in inspect.getmro(snake_case__			):
    UpperCAmelCase					      =  base_class.__module__
    UpperCAmelCase					      =  base_class.__name__
    if module.startswith("tensorflow"			) or module.startswith("keras"			) or name == "TFPreTrainedModel":
      return "tf"
    elif module.startswith("torch"			) or name == "PreTrainedModel":
      return "pt"
    elif module.startswith("flax"			) or module.startswith("jax"			) or name == "FlaxPreTrainedModel":
      return "flax"
  else:
    raise TypeError(F"Could not infer framework from class {model_class}."			)







def      _lowerCAmelCase(	__A   ,       __A			):
  return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def      _lowerCAmelCase(	__A   ,       __A=0			):
  return sorted(__A   ,       key=lambda __A			: x[column]			)
def      _lowerCAmelCase(	__A   ,       __A   ,       __A=float("inf"			)			):

  for i in range(points_counts - 1			):
    for j in range(i + 1   ,       __A			):
      UpperCAmelCase					      =  euclidean_distance_sqr(points[i]   ,       points[j]			)
      if current_dis < min_dis:
        UpperCAmelCase					      =  current_dis
  return min_dis
def      _lowerCAmelCase(	__A   ,       __A   ,       __A=float("inf"			)			):

  for i in range(min(6   ,       points_counts - 1			)   ,       __A			):
    for j in range(max(0   ,       i - 6			)   ,       __A			):
      UpperCAmelCase					      =  euclidean_distance_sqr(points[i]   ,       points[j]			)
      if current_dis < min_dis:
        UpperCAmelCase					      =  current_dis
  return min_dis
def      _lowerCAmelCase(	__A   ,       __A   ,       __A			):

  # base case
  if points_counts <= 3:
    return dis_between_closest_pair(__A   ,       __A			)

  # recursion
  UpperCAmelCase					      =  points_counts // 2
  UpperCAmelCase					      =  closest_pair_of_points_sqr(
      __A   ,       points_sorted_on_y[:mid]   ,       __A			)
  UpperCAmelCase					      =  closest_pair_of_points_sqr(
      __A   ,       points_sorted_on_y[mid:]   ,       points_counts - mid			)
  UpperCAmelCase					      =  min(__A   ,       __A			)
  UpperCAmelCase					      =  []
  for point in points_sorted_on_x:
    if abs(point[0] - points_sorted_on_x[mid][0]			) < closest_pair_dis:
      cross_strip.append(__A			)

  UpperCAmelCase					      =  dis_between_closest_in_strip(
      __A   ,       len(__A			)   ,       __A			)
  return min(__A   ,       __A			)
def      _lowerCAmelCase(	__A   ,       __A			):
  UpperCAmelCase					      =  column_based_sort(__A   ,       column=0			)
  UpperCAmelCase					      =  column_based_sort(__A   ,       column=1			)
  return (
      closest_pair_of_points_sqr(
          __A   ,       __A   ,       __A			)
  ) ** 0.5


if __name__ == "__main__":
      lowerCAmelCase__       = [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
      print("Distance:", closest_pair_of_points(points, len(points)))



