'''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       UpperCAmelCase__  (       _lowercase			):






   """simple docstring"""
   def __get__(self       ,				_a       ,				_a=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'				)
        lowercase_					:       Dict     				=					'__cached_' + self.fget.__name__
        lowercase_					:       Tuple     				=					getattr(A_       ,				A_       ,				A_				)
        if cached is None:
             lowercase_					:       List[Any]     				=					self.fget(A_				)
             setattr(A_       ,				A_       ,				A_				)
        return cached



def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[str]     				=					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   (       SCREAMING_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 _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     return isinstance(snake_case__  ,       np.ndarray							)

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     return _is_numpy(snake_case__							)

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     import torch

     return isinstance(snake_case__  ,       torch.Tensor							)

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     return False if not is_torch_available() else _is_torch(snake_case__							)

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     import torch

     return isinstance(snake_case__  ,       torch.device							)

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     return False if not is_torch_available() else _is_torch_device(snake_case__							)

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     import torch

     if isinstance(snake_case__  ,       snake_case__							):
          if hasattr(snake_case__  ,       snake_case__							):
               lowercase_					:       List[str]     				=					getattr(snake_case__  ,       snake_case__							)
          else:
               return False
     return isinstance(snake_case__  ,       torch.dtype							)

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     return False if not is_torch_available() else _is_torch_dtype(snake_case__							)

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     import tensorflow as tf

     return isinstance(snake_case__  ,       tf.Tensor							)

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     return False if not is_tf_available() else _is_tensorflow(snake_case__							)

def _UpperCamelCase   (       SCREAMING_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 _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     return False if not is_tf_available() else _is_tf_symbolic_tensor(snake_case__							)

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     import jax.numpy as jnp  # noqa: F811

     return isinstance(snake_case__  ,       jnp.ndarray							)

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     return False if not is_flax_available() else _is_jax(snake_case__							)

def _UpperCamelCase   (       SCREAMING_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 _UpperCamelCase   (       SCREAMING_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       UpperCAmelCase__  (       _lowercase			):






   """simple docstring"""
   def 		_lowerCamelCase (self				)    ->     str:
        lowercase_					:       Optional[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.'''				)

        lowercase_					:       List[str]     				=					getattr(self       ,				class_fields[0].name				)
        lowercase_					:       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_				):
                  lowercase_					:       Optional[int]     				=					first_field.items()
                  lowercase_					:       Optional[Any]     				=					True
             else:
                  try:
                       lowercase_					:       str     				=					iter(A_				)
                       lowercase_					:       List[Any]     				=					True
                  except TypeError:
                       lowercase_					:       str     				=					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
                                 lowercase_					:       Optional[Any]     				=					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:
                            lowercase_					:       Optional[Any]     				=					element[1]
             elif first_field is not None:
                  lowercase_					:       List[str]     				=					first_field
        else:
             for field in class_fields:
                  lowercase_					:       Any     				=					getattr(self       ,				field.name				)
                  if v is not None:
                       lowercase_					:       int     				=					v
   def __delitem__(self       ,				*_a       ,				**_a				)    ->     Tuple:
        raise Exception(f'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.'''				)
   def 		_lowerCamelCase (self       ,				*_a       ,				**_a				)    ->     Dict:
        raise Exception(f'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'''				)
   def 		_lowerCamelCase (self       ,				*_a       ,				**_a				)    ->     Dict:
        raise Exception(f'''You cannot use ``pop`` on a {self.__class__.__name__} instance.'''				)
   def 		_lowerCamelCase (self       ,				*_a       ,				**_a				)    ->     Dict:
        raise Exception(f'''You cannot use ``update`` on a {self.__class__.__name__} instance.'''				)
   def __getitem__(self       ,				_a				)    ->     str:
        if isinstance(A_       ,				A_				):
             lowercase_					:       Optional[int]     				=					dict(self.items()				)
             return inner_dict[k]
        else:
             return self.to_tuple()[k]
   def __setattr__(self       ,				_a       ,				_a				)    ->     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       ,				_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 		_lowerCamelCase (self				)    ->     Tuple[Any]:
        return tuple(self[k] for k in self.keys()				)



class       UpperCAmelCase__  (       _lowercase					,     _lowercase			):






   """simple docstring"""
   @classmethod
   def 		_lowerCamelCase (cls       ,				_a				)    ->     Tuple:
        raise ValueError(
            f'''{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys()				)}'''				)



class       UpperCAmelCase__  (       _lowercase			):






   """simple docstring"""
   A							:       str     =    '''longest'''
   A							:       Optional[int]     =    '''max_length'''
   A							:       Any     =    '''do_not_pad'''



class       UpperCAmelCase__  (       _lowercase			):






   """simple docstring"""
   A							:       List[Any]     =    '''pt'''
   A							:       Union[str, Any]     =    '''tf'''
   A							:       Tuple     =    '''np'''
   A							:       Dict     =    '''jax'''



class       UpperCAmelCase__  :






   """simple docstring"""
   def __init__(self       ,				_a				)    ->     Any:
        lowercase_					:       List[Any]     				=					context_managers
        lowercase_					:       List[Any]     				=					ExitStack()
   def __enter__(self				)    ->     List[Any]:
        for context_manager in self.context_managers:
             self.stack.enter_context(A_				)





   def __exit__(self       ,				*_a       ,				**_a				)    ->     Any:
        self.stack.__exit__(*A_       ,				**A_				)



def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       Tuple     				=					infer_framework(snake_case__							)
     if framework == "tf":
          lowercase_					:       Dict     				=					inspect.signature(model_class.call							)  # TensorFlow models
     elif framework == "pt":
          lowercase_					:       List[Any]     				=					inspect.signature(model_class.forward							)  # PyTorch models
     else:
          lowercase_					:       Union[str, Any]     				=					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   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       Optional[int]     				=					model_class.__name__
     lowercase_					:       Dict     				=					infer_framework(snake_case__							)
     if framework == "tf":
          lowercase_					:       Tuple     				=					inspect.signature(model_class.call							)  # TensorFlow models
     elif framework == "pt":
          lowercase_					:       int     				=					inspect.signature(model_class.forward							)  # PyTorch models
     else:
          lowercase_					:       Union[str, 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 _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_ = ""  ,       SCREAMING_SNAKE_CASE_ = "."							):

     def _flatten_dict(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_=""  ,       SCREAMING_SNAKE_CASE_="."							):
          for k, v in d.items():
               lowercase_					:       Any     				=					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 _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_ = False							):
     if use_temp_dir:
          with tempfile.TemporaryDirectory() as tmp_dir:
               yield tmp_dir
     else:
          yield working_dir

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_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 _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_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 _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_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 _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_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 _UpperCamelCase   (       SCREAMING_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 _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     for key, value in auto_map.items():
          if isinstance(snake_case__  ,       (tuple, list)							):
               lowercase_					:       Dict     				=					[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:
               lowercase_					:       List[Any]     				=					f'''{repo_id}--{value}'''

     return auto_map






def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     for base_class in inspect.getmro(snake_case__							):
          lowercase_					:       Any     				=					base_class.__module__
          lowercase_					:       List[Any]     				=					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


_A								=   {
    'configuration_bigbird_pegasus': [
        'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BigBirdPegasusConfig',
        'BigBirdPegasusOnnxConfig',
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A								=   [
     'BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
     'BigBirdPegasusForCausalLM',
     'BigBirdPegasusForConditionalGeneration',
     'BigBirdPegasusForQuestionAnswering',
     'BigBirdPegasusForSequenceClassification',
     'BigBirdPegasusModel',
     'BigBirdPegasusPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_bigbird_pegasus import (
     BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BigBirdPegasusConfig,
     BigBirdPegasusOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bigbird_pegasus import (
      BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
      BigBirdPegasusForCausalLM,
      BigBirdPegasusForConditionalGeneration,
      BigBirdPegasusForQuestionAnswering,
      BigBirdPegasusForSequenceClassification,
      BigBirdPegasusModel,
      BigBirdPegasusPreTrainedModel,
  )


else:
 import sys

 _A								=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




