

'''simple docstring'''




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   _SCREAMING_SNAKE_CASE   (   __a     ):



 def __get__(   self		:   Any			,				a__		:   int			,				a__		:   str=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'''      )
      __magic_name__						       =    '''__cached_''' + self.fget.__name__
      __magic_name__						       =    getattr(a__			,				a__			,				a__      )
      if cached is None:
           __magic_name__						       =    self.fget(a__      )
           setattr(a__			,				a__			,				a__      )
      return cached




def 						UpperCamelCase	(			a			)				->     Dict:






     '''simple docstring'''
     __magic_name__						       =    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 						UpperCamelCase	(			a			)				->     Optional[int]:






     '''simple docstring'''
     if is_torch_fx_proxy(a			):
          return True
     if is_torch_available():
          import torch

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

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

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

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

     return isinstance(a		,						np.ndarray			)




def 						UpperCamelCase	(			a			)				->     List[str]:






     '''simple docstring'''
     return isinstance(a		,						np.ndarray			)




def 						UpperCamelCase	(			a			)				->     Optional[int]:






     '''simple docstring'''
     return _is_numpy(a			)




def 						UpperCamelCase	(			a			)				->     Optional[int]:






     '''simple docstring'''
     import torch

     return isinstance(a		,						torch.Tensor			)




def 						UpperCamelCase	(			a			)				->     List[Any]:






     '''simple docstring'''
     return False if not is_torch_available() else _is_torch(a			)




def 						UpperCamelCase	(			a			)				->     List[str]:






     '''simple docstring'''
     import torch

     return isinstance(a		,						torch.device			)




def 						UpperCamelCase	(			a			)				->     Union[str, Any]:






     '''simple docstring'''
     return False if not is_torch_available() else _is_torch_device(a			)




def 						UpperCamelCase	(			a			)				->     int:






     '''simple docstring'''
     import torch

     if isinstance(a		,						a			):
          if hasattr(a		,						a			):
               __magic_name__						       =    getattr(a		,						a			)
          else:
               return False
     return isinstance(a		,						torch.dtype			)




def 						UpperCamelCase	(			a			)				->     Dict:






     '''simple docstring'''
     return False if not is_torch_available() else _is_torch_dtype(a			)




def 						UpperCamelCase	(			a			)				->     Tuple:






     '''simple docstring'''
     import tensorflow as tf

     return isinstance(a		,						tf.Tensor			)




def 						UpperCamelCase	(			a			)				->     Any:






     '''simple docstring'''
     return False if not is_tf_available() else _is_tensorflow(a			)




def 						UpperCamelCase	(			a			)				->     List[str]:






     '''simple docstring'''
     import tensorflow as tf

     # the `is_symbolic_tensor` predicate is only available starting with TF 2.14
     if hasattr(a		,						'''is_symbolic_tensor'''			):
          return tf.is_symbolic_tensor(a			)
     return type(a			) == tf.Tensor




def 						UpperCamelCase	(			a			)				->     int:






     '''simple docstring'''
     return False if not is_tf_available() else _is_tf_symbolic_tensor(a			)




def 						UpperCamelCase	(			a			)				->     Tuple:






     '''simple docstring'''
     import jax.numpy as jnp  # noqa: F811

     return isinstance(a		,						jnp.ndarray			)




def 						UpperCamelCase	(			a			)				->     str:






     '''simple docstring'''
     return False if not is_flax_available() else _is_jax(a			)




def 						UpperCamelCase	(			a			)				->     Any:






     '''simple docstring'''
     if isinstance(a		,						(dict, UserDict)			):
          return {k: to_py_obj(a			) for k, v in obj.items()}
     elif isinstance(a		,						(list, tuple)			):
          return [to_py_obj(a			) for o in obj]
     elif is_tf_tensor(a			):
          return obj.numpy().tolist()
     elif is_torch_tensor(a			):
          return obj.detach().cpu().tolist()
     elif is_jax_tensor(a			):
          return np.asarray(a			).tolist()
     elif isinstance(a		,						(np.ndarray, np.number)			):  # tolist also works on 0d np arrays
          return obj.tolist()
     else:
          return obj




def 						UpperCamelCase	(			a			)				->     str:






     '''simple docstring'''
     if isinstance(a		,						(dict, UserDict)			):
          return {k: to_numpy(a			) for k, v in obj.items()}
     elif isinstance(a		,						(list, tuple)			):
          return np.array(a			)
     elif is_tf_tensor(a			):
          return obj.numpy()
     elif is_torch_tensor(a			):
          return obj.detach().cpu().numpy()
     elif is_jax_tensor(a			):
          return np.asarray(a			)
     else:
          return obj






class   _SCREAMING_SNAKE_CASE   (   __a     ):



 def 			snake_case__  (   self		:   Dict      ):
      __magic_name__						       =    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.'''      )

      __magic_name__						       =    getattr(self			,				class_fields[0].name      )
      __magic_name__						       =    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__      ):
                __magic_name__						       =    first_field.items()
                __magic_name__						       =    True
           else:
                try:
                     __magic_name__						       =    iter(a__      )
                     __magic_name__						       =    True
                except TypeError:
                     __magic_name__						       =    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
                               __magic_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:
                          __magic_name__						       =    element[1]
           elif first_field is not None:
                __magic_name__						       =    first_field
      else:
           for field in class_fields:
                __magic_name__						       =    getattr(self			,				field.name      )
                if v is not None:
                     __magic_name__						       =    v



 def __delitem__(   self		:   List[Any]			,				*a__		:   Union[str, Any]			,				**a__		:   Tuple      ):
      raise Exception(F'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.'''      )



 def 			snake_case__  (   self		:   List[str]			,				*a__		:   List[Any]			,				**a__		:   Optional[int]      ):
      raise Exception(F'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'''      )



 def 			snake_case__  (   self		:   str			,				*a__		:   List[str]			,				**a__		:   str      ):
      raise Exception(F'''You cannot use ``pop`` on a {self.__class__.__name__} instance.'''      )



 def 			snake_case__  (   self		:   List[Any]			,				*a__		:   Optional[int]			,				**a__		:   List[str]      ):
      raise Exception(F'''You cannot use ``update`` on a {self.__class__.__name__} instance.'''      )



 def __getitem__(   self		:   str			,				a__		:   Tuple      ):
      if isinstance(a__			,				a__      ):
           __magic_name__						       =    dict(self.items()      )
           return inner_dict[k]
      else:
           return self.to_tuple()[k]



 def __setattr__(   self		:   Optional[int]			,				a__		:   Tuple			,				a__		:   str      ):
      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		:   Optional[Any]			,				a__		:   Any			,				a__		:   List[str]      ):
      # Will raise a KeyException if needed
      super().__setitem__(a__			,				a__      )
      # Don't call self.__setattr__ to avoid recursion errors
      super().__setattr__(a__			,				a__      )



 def 			snake_case__  (   self		:   Optional[int]      ):
      return tuple(self[k] for k in self.keys()      )






class   _SCREAMING_SNAKE_CASE   (   __a		,__a     ):



 @classmethod
 def 			snake_case__  (   cls		:   int			,				a__		:   Dict      ):
      raise ValueError(
          F'''{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys()      )}'''      )






class   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:Union[str, Any]      	=					"""longest"""
 __SCREAMING_SNAKE_CASE					:Union[str, Any]      	=					"""max_length"""
 __SCREAMING_SNAKE_CASE					:Tuple      	=					"""do_not_pad"""






class   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:Optional[int]      	=					"""pt"""
 __SCREAMING_SNAKE_CASE					:Any      	=					"""tf"""
 __SCREAMING_SNAKE_CASE					:str      	=					"""np"""
 __SCREAMING_SNAKE_CASE					:List[Any]      	=					"""jax"""






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   str			,				a__		:   List[ContextManager]      ):
      __magic_name__						       =    context_managers
      __magic_name__						       =    ExitStack()



 def __enter__(   self		:   Union[str, Any]      ):
      for context_manager in self.context_managers:
           self.stack.enter_context(a__      )



 def __exit__(   self		:   Optional[int]			,				*a__		:   List[str]			,				**a__		:   Dict      ):
      self.stack.__exit__(*a__			,				**a__      )




def 						UpperCamelCase	(			a			)				->     Optional[int]:






     '''simple docstring'''
     __magic_name__						       =    infer_framework(a			)
     if framework == "tf":
          __magic_name__						       =    inspect.signature(model_class.call			)  # TensorFlow models
     elif framework == "pt":
          __magic_name__						       =    inspect.signature(model_class.forward			)  # PyTorch models
     else:
          __magic_name__						       =    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 						UpperCamelCase	(			a			)				->     Optional[Any]:






     '''simple docstring'''
     __magic_name__						       =    model_class.__name__
     __magic_name__						       =    infer_framework(a			)
     if framework == "tf":
          __magic_name__						       =    inspect.signature(model_class.call			)  # TensorFlow models
     elif framework == "pt":
          __magic_name__						       =    inspect.signature(model_class.forward			)  # PyTorch models
     else:
          __magic_name__						       =    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 						UpperCamelCase	(			a		,						a = ""		,						a = "."			)				->     Optional[int]:






     '''simple docstring'''

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

     return dict(_flatten_dict(a		,						a		,						a			)			)




@contextmanager
def 						UpperCamelCase	(			a		,						a = False			)				->     Any:






     '''simple docstring'''
     if use_temp_dir:
          with tempfile.TemporaryDirectory() as tmp_dir:
               yield tmp_dir
     else:
          yield working_dir




def 						UpperCamelCase	(			a		,						a=None			)				->     List[Any]:






     '''simple docstring'''
     if is_numpy_array(a			):
          return np.transpose(a		,						axes=a			)
     elif is_torch_tensor(a			):
          return array.T if axes is None else array.permute(*a			)
     elif is_tf_tensor(a			):
          import tensorflow as tf

          return tf.transpose(a		,						perm=a			)
     elif is_jax_tensor(a			):
          return jnp.transpose(a		,						axes=a			)
     else:
          raise ValueError(F'''Type not supported for transpose: {type(a			)}.'''			)




def 						UpperCamelCase	(			a		,						a			)				->     List[Any]:






     '''simple docstring'''
     if is_numpy_array(a			):
          return np.reshape(a		,						a			)
     elif is_torch_tensor(a			):
          return array.reshape(*a			)
     elif is_tf_tensor(a			):
          import tensorflow as tf

          return tf.reshape(a		,						a			)
     elif is_jax_tensor(a			):
          return jnp.reshape(a		,						a			)
     else:
          raise ValueError(F'''Type not supported for reshape: {type(a			)}.'''			)




def 						UpperCamelCase	(			a		,						a=None			)				->     int:






     '''simple docstring'''
     if is_numpy_array(a			):
          return np.squeeze(a		,						axis=a			)
     elif is_torch_tensor(a			):
          return array.squeeze() if axis is None else array.squeeze(dim=a			)
     elif is_tf_tensor(a			):
          import tensorflow as tf

          return tf.squeeze(a		,						axis=a			)
     elif is_jax_tensor(a			):
          return jnp.squeeze(a		,						axis=a			)
     else:
          raise ValueError(F'''Type not supported for squeeze: {type(a			)}.'''			)




def 						UpperCamelCase	(			a		,						a			)				->     Union[str, Any]:






     '''simple docstring'''
     if is_numpy_array(a			):
          return np.expand_dims(a		,						a			)
     elif is_torch_tensor(a			):
          return array.unsqueeze(dim=a			)
     elif is_tf_tensor(a			):
          import tensorflow as tf

          return tf.expand_dims(a		,						axis=a			)
     elif is_jax_tensor(a			):
          return jnp.expand_dims(a		,						axis=a			)
     else:
          raise ValueError(F'''Type not supported for expand_dims: {type(a			)}.'''			)




def 						UpperCamelCase	(			a			)				->     List[str]:






     '''simple docstring'''
     if is_numpy_array(a			):
          return np.size(a			)
     elif is_torch_tensor(a			):
          return array.numel()
     elif is_tf_tensor(a			):
          import tensorflow as tf

          return tf.size(a			)
     elif is_jax_tensor(a			):
          return array.size
     else:
          raise ValueError(F'''Type not supported for expand_dims: {type(a			)}.'''			)




def 						UpperCamelCase	(			a		,						a			)				->     List[Any]:






     '''simple docstring'''
     for key, value in auto_map.items():
          if isinstance(a		,						(tuple, list)			):
               __magic_name__						       =    [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:
               __magic_name__						       =    F'''{repo_id}--{value}'''

     return auto_map




def 						UpperCamelCase	(			a			)				->     Optional[int]:






     '''simple docstring'''
     for base_class in inspect.getmro(a			):
          __magic_name__						       =    base_class.__module__
          __magic_name__						       =    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}.'''			)









'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCAmelCase         =						{"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase         =						["GLPNFeatureExtractor"]
      _lowerCAmelCase         =						["GLPNImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase         =						[
          "GLPN_PRETRAINED_MODEL_ARCHIVE_LIST",
          "GLPNForDepthEstimation",
          "GLPNLayer",
          "GLPNModel",
          "GLPNPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_glpn import GLPNFeatureExtractor
            from .image_processing_glpn import GLPNImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_glpn import (
                GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
                GLPNForDepthEstimation,
                GLPNLayer,
                GLPNModel,
                GLPNPreTrainedModel,
            )


else:
      import sys

      _lowerCAmelCase         =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






