




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 						lowercase							(   UpperCamelCase__		):

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







def 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     Union[str, Any]			=						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_	(     snake_case_     ):
      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_	(     snake_case_     ):
      return isinstance(snake_case_,np.ndarray     )







def 				lowerCAmelCase_	(     snake_case_     ):
      return _is_numpy(snake_case_     )







def 				lowerCAmelCase_	(     snake_case_     ):
      import torch

      return isinstance(snake_case_,torch.Tensor     )







def 				lowerCAmelCase_	(     snake_case_     ):
      return False if not is_torch_available() else _is_torch(snake_case_     )







def 				lowerCAmelCase_	(     snake_case_     ):
      import torch

      return isinstance(snake_case_,torch.device     )







def 				lowerCAmelCase_	(     snake_case_     ):
      return False if not is_torch_available() else _is_torch_device(snake_case_     )







def 				lowerCAmelCase_	(     snake_case_     ):
      import torch

      if isinstance(snake_case_,snake_case_     ):
            if hasattr(snake_case_,snake_case_     ):
                  _A							:     Optional[int]			=						getattr(snake_case_,snake_case_     )
            else:
                  return False
      return isinstance(snake_case_,torch.dtype     )







def 				lowerCAmelCase_	(     snake_case_     ):
      return False if not is_torch_available() else _is_torch_dtype(snake_case_     )







def 				lowerCAmelCase_	(     snake_case_     ):
      import tensorflow as tf

      return isinstance(snake_case_,tf.Tensor     )







def 				lowerCAmelCase_	(     snake_case_     ):
      return False if not is_tf_available() else _is_tensorflow(snake_case_     )







def 				lowerCAmelCase_	(     snake_case_     ):
      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_	(     snake_case_     ):
      return False if not is_tf_available() else _is_tf_symbolic_tensor(snake_case_     )







def 				lowerCAmelCase_	(     snake_case_     ):
      import jax.numpy as jnp  # noqa: F811

      return isinstance(snake_case_,jnp.ndarray     )







def 				lowerCAmelCase_	(     snake_case_     ):
      return False if not is_flax_available() else _is_jax(snake_case_     )







def 				lowerCAmelCase_	(     snake_case_     ):
      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_	(     snake_case_     ):
      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 						lowercase							(   UpperCamelCase__		):

     def 					a__	(  self				)						->							str:
           _A							:     int			=						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.'''				)

           _A							:     str			=						getattr(self		,     class_fields[0].name				)
           _A							:     str			=						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				):
                       _A							:     int			=						first_field.items()
                       _A							:     Dict			=						True
                 else:
                       try:
                             _A							:     List[Any]			=						iter(_a				)
                             _A							:     List[Any]			=						True
                       except TypeError:
                             _A							:     Dict			=						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
                                         _A							:     Dict			=						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:
                                   _A							:     Any			=						element[1]
                 elif first_field is not None:
                       _A							:     List[str]			=						first_field
           else:
                 for field in class_fields:
                       _A							:     Any			=						getattr(self		,     field.name				)
                       if v is not None:
                             _A							:     int			=						v

     def __delitem__(  self		,     *_a		,     **_a				)						->							int:
           raise Exception(F'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.'''				)

     def 					a__	(  self		,     *_a		,     **_a				)						->							Dict:
           raise Exception(F'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'''				)

     def 					a__	(  self		,     *_a		,     **_a				)						->							Optional[Any]:
           raise Exception(F'''You cannot use ``pop`` on a {self.__class__.__name__} instance.'''				)

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

     def __getitem__(  self		,     _a				)						->							Optional[int]:
           if isinstance(_a		,     _a				):
                 _A							:     str			=						dict(self.items()				)
                 return inner_dict[k]
           else:
                 return self.to_tuple()[k]

     def __setattr__(  self		,     _a		,     _a				)						->							List[Any]:
           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		,     _a		,     _a				)						->							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 					a__	(  self				)						->							Tuple[Any]:
           return tuple(self[k] for k in self.keys()				)







class 						lowercase							(   UpperCamelCase__,UpperCamelCase__		):

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







class 						lowercase							(   UpperCamelCase__		):
     _a							 =						"longest"
     _a							 =						"max_length"
     _a							 =						"do_not_pad"







class 						lowercase							(   UpperCamelCase__		):
     _a							 =						"pt"
     _a							 =						"tf"
     _a							 =						"np"
     _a							 =						"jax"







class 						lowercase							:

     def __init__(  self		,     _a				)						->							Tuple:
           _A							:     List[Any]			=						context_managers
           _A							:     List[Any]			=						ExitStack()

     def __enter__(  self				)						->							List[str]:
           for context_manager in self.context_managers:
                 self.stack.enter_context(_a				)



     def __exit__(  self		,     *_a		,     **_a				)						->							Union[str, Any]:
           self.stack.__exit__(*_a		,     **_a				)







def 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     List[Any]			=						infer_framework(snake_case_     )
      if framework == "tf":
            _A							:     str			=						inspect.signature(model_class.call     )  # TensorFlow models
      elif framework == "pt":
            _A							:     Union[str, Any]			=						inspect.signature(model_class.forward     )  # PyTorch models
      else:
            _A							:     int			=						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_	(     snake_case_     ):
      _A							:     Dict			=						model_class.__name__
      _A							:     Optional[Any]			=						infer_framework(snake_case_     )
      if framework == "tf":
            _A							:     Any			=						inspect.signature(model_class.call     )  # TensorFlow models
      elif framework == "pt":
            _A							:     Optional[Any]			=						inspect.signature(model_class.forward     )  # PyTorch models
      else:
            _A							:     Optional[Any]			=						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_	(     snake_case_,snake_case_ = "",snake_case_ = "."     ):

      def _flatten_dict(snake_case_,snake_case_="",snake_case_="."     ):
            for k, v in d.items():
                  _A							:     Dict			=						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_	(     snake_case_,snake_case_ = False     ):
      if use_temp_dir:
            with tempfile.TemporaryDirectory() as tmp_dir:
                  yield tmp_dir
      else:
            yield working_dir







def 				lowerCAmelCase_	(     snake_case_,snake_case_=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_	(     snake_case_,snake_case_     ):
      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_	(     snake_case_,snake_case_=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_	(     snake_case_,snake_case_     ):
      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_	(     snake_case_     ):
      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_	(     snake_case_,snake_case_     ):
      for key, value in auto_map.items():
            if isinstance(snake_case_,(tuple, list)     ):
                  _A							:     int			=						[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:
                  _A							:     List[str]			=						f'''{repo_id}--{value}'''

      return auto_map







def 				lowerCAmelCase_	(     snake_case_     ):
      for base_class in inspect.getmro(snake_case_     ):
            _A							:     Optional[Any]			=						base_class.__module__
            _A							:     Tuple			=						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_	(     snake_case_     ):
      if number < 0:
            raise ValueError("""number must not be negative"""     )
      return number & (number - 1) == 0


if __name__ == "__main__":
  import doctest

  doctest.testmod()

