'''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 		__A       (__magic_name__   ):






       def __get__(      self				,     UpperCamelCase_				,     UpperCamelCase_=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"      )
              __UpperCAmelCase  :				Any       				=		"__cached_" + self.fget.__name__
              __UpperCAmelCase  :				int       				=		getattr(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      )
              if cached is None:
                     __UpperCAmelCase  :				Union[str, Any]       				=		self.fget(UpperCamelCase_      )
                     setattr(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      )
              return cached





def _lowercase	(      lowerCamelCase__						)					->       Any:





       """simple docstring"""


       __UpperCAmelCase  :				Optional[int]       				=		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 _lowercase	(      lowerCamelCase__						)					->       Optional[int]:





       """simple docstring"""


       if is_torch_fx_proxy(lowerCamelCase__						):
              return True
       if is_torch_available():
              import torch

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

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

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

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

       return isinstance(lowerCamelCase__							,    np.ndarray						)

def _lowercase	(      lowerCamelCase__						)					->       Optional[Any]:





       """simple docstring"""


       return isinstance(lowerCamelCase__							,    np.ndarray						)

def _lowercase	(      lowerCamelCase__						)					->       List[str]:





       """simple docstring"""


       return _is_numpy(lowerCamelCase__						)

def _lowercase	(      lowerCamelCase__						)					->       Union[str, Any]:





       """simple docstring"""


       import torch

       return isinstance(lowerCamelCase__							,    torch.Tensor						)

def _lowercase	(      lowerCamelCase__						)					->       Tuple:





       """simple docstring"""


       return False if not is_torch_available() else _is_torch(lowerCamelCase__						)

def _lowercase	(      lowerCamelCase__						)					->       int:





       """simple docstring"""


       import torch

       return isinstance(lowerCamelCase__							,    torch.device						)

def _lowercase	(      lowerCamelCase__						)					->       int:





       """simple docstring"""


       return False if not is_torch_available() else _is_torch_device(lowerCamelCase__						)

def _lowercase	(      lowerCamelCase__						)					->       Any:





       """simple docstring"""


       import torch

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

def _lowercase	(      lowerCamelCase__						)					->       Tuple:





       """simple docstring"""


       return False if not is_torch_available() else _is_torch_dtype(lowerCamelCase__						)

def _lowercase	(      lowerCamelCase__						)					->       Union[str, Any]:





       """simple docstring"""


       import tensorflow as tf

       return isinstance(lowerCamelCase__							,    tf.Tensor						)

def _lowercase	(      lowerCamelCase__						)					->       Tuple:





       """simple docstring"""


       return False if not is_tf_available() else _is_tensorflow(lowerCamelCase__						)

def _lowercase	(      lowerCamelCase__						)					->       Dict:





       """simple docstring"""


       import tensorflow as tf

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

def _lowercase	(      lowerCamelCase__						)					->       str:





       """simple docstring"""


       return False if not is_tf_available() else _is_tf_symbolic_tensor(lowerCamelCase__						)

def _lowercase	(      lowerCamelCase__						)					->       Tuple:





       """simple docstring"""


       import jax.numpy as jnp  # noqa: F811

       return isinstance(lowerCamelCase__							,    jnp.ndarray						)

def _lowercase	(      lowerCamelCase__						)					->       Optional[Any]:





       """simple docstring"""


       return False if not is_flax_available() else _is_jax(lowerCamelCase__						)

def _lowercase	(      lowerCamelCase__						)					->       str:





       """simple docstring"""


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

def _lowercase	(      lowerCamelCase__						)					->       List[str]:





       """simple docstring"""


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






class 		__A       (__magic_name__   ):






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[int]       				=		fields(self      )

              # Safety and consistency checks
              if not len(UpperCamelCase_      ):
                     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  :				int       				=		getattr(self				,     class_fields[0].name      )
              __UpperCAmelCase  :				Dict       				=		all(getattr(self				,     field.name      ) is None for field in class_fields[1:]      )

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






       def __delitem__(      self				,     *UpperCamelCase_				,     **UpperCamelCase_      ):
              raise Exception(f"""You cannot use ``__delitem__`` on a {self.__class__.__name__} instance."""      )






       def 			_snake_case						(      self				,     *UpperCamelCase_				,     **UpperCamelCase_      ):
              raise Exception(f"""You cannot use ``setdefault`` on a {self.__class__.__name__} instance."""      )






       def 			_snake_case						(      self				,     *UpperCamelCase_				,     **UpperCamelCase_      ):
              raise Exception(f"""You cannot use ``pop`` on a {self.__class__.__name__} instance."""      )






       def 			_snake_case						(      self				,     *UpperCamelCase_				,     **UpperCamelCase_      ):
              raise Exception(f"""You cannot use ``update`` on a {self.__class__.__name__} instance."""      )






       def __getitem__(      self				,     UpperCamelCase_      ):
              if isinstance(UpperCamelCase_				,     UpperCamelCase_      ):
                     __UpperCAmelCase  :				List[str]       				=		dict(self.items()      )
                     return inner_dict[k]
              else:
                     return self.to_tuple()[k]






       def __setattr__(      self				,     UpperCamelCase_				,     UpperCamelCase_      ):
              if name in self.keys() and value is not None:
                     # Don't call self.__setitem__ to avoid recursion errors
                     super().__setitem__(UpperCamelCase_				,     UpperCamelCase_      )
              super().__setattr__(UpperCamelCase_				,     UpperCamelCase_      )






       def __setitem__(      self				,     UpperCamelCase_				,     UpperCamelCase_      ):
              # Will raise a KeyException if needed
              super().__setitem__(UpperCamelCase_				,     UpperCamelCase_      )
              # Don't call self.__setattr__ to avoid recursion errors
              super().__setattr__(UpperCamelCase_				,     UpperCamelCase_      )







       def 			_snake_case						(      self      ):
              return tuple(self[k] for k in self.keys()      )






class 		__A       (__magic_name__ ,					__magic_name__   ):






       @classmethod
       def 			_snake_case						(      cls				,     UpperCamelCase_      ):
              raise ValueError(
                  f"""{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys()      )}"""      )






class 		__A       (__magic_name__   ):
       snake_case       :Optional[Any]				    =					"longest"
       snake_case       :str				    =					"max_length"
       snake_case       :Optional[Any]				    =					"do_not_pad"






class 		__A       (__magic_name__   ):
       snake_case       :int				    =					"pt"
       snake_case       :Union[str, Any]				    =					"tf"
       snake_case       :int				    =					"np"
       snake_case       :str				    =					"jax"






class 		__A       :






       def __init__(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Tuple       				=		context_managers
              __UpperCAmelCase  :				Optional[int]       				=		ExitStack()






       def __enter__(      self      ):
              for context_manager in self.context_managers:
                     self.stack.enter_context(UpperCamelCase_      )







       def __exit__(      self				,     *UpperCamelCase_				,     **UpperCamelCase_      ):
              self.stack.__exit__(*UpperCamelCase_				,     **UpperCamelCase_      )





def _lowercase	(      lowerCamelCase__						)					->       str:





       """simple docstring"""


       __UpperCAmelCase  :				List[str]       				=		infer_framework(lowerCamelCase__						)
       if framework == "tf":
              __UpperCAmelCase  :				List[Any]       				=		inspect.signature(model_class.call						)  # TensorFlow models
       elif framework == "pt":
              __UpperCAmelCase  :				Dict       				=		inspect.signature(model_class.forward						)  # PyTorch models
       else:
              __UpperCAmelCase  :				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 _lowercase	(      lowerCamelCase__						)					->       Optional[Any]:





       """simple docstring"""


       __UpperCAmelCase  :				List[str]       				=		model_class.__name__
       __UpperCAmelCase  :				Optional[int]       				=		infer_framework(lowerCamelCase__						)
       if framework == "tf":
              __UpperCAmelCase  :				int       				=		inspect.signature(model_class.call						)  # TensorFlow models
       elif framework == "pt":
              __UpperCAmelCase  :				List[Any]       				=		inspect.signature(model_class.forward						)  # PyTorch models
       else:
              __UpperCAmelCase  :				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 _lowercase	(      lowerCamelCase__							,    lowerCamelCase__ = ""							,    lowerCamelCase__ = "."						)					->       Optional[Any]:





       """simple docstring"""


       def _flatten_dict(lowerCamelCase__							,    lowerCamelCase__=""							,    lowerCamelCase__="."						):
              for k, v in d.items():
                     __UpperCAmelCase  :				Union[str, Any]       				=		str(lowerCamelCase__						) + delimiter + str(lowerCamelCase__						) if parent_key else k
                     if v and isinstance(lowerCamelCase__							,    lowerCamelCase__						):
                            yield from flatten_dict(lowerCamelCase__							,    lowerCamelCase__							,    delimiter=lowerCamelCase__						).items()
                     else:
                            yield key, v

       return dict(_flatten_dict(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)						)

@contextmanager
def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__ = False						)					->       Dict:





       """simple docstring"""


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

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__=None						)					->       Optional[int]:





       """simple docstring"""


       if is_numpy_array(lowerCamelCase__						):
              return np.transpose(lowerCamelCase__							,    axes=lowerCamelCase__						)
       elif is_torch_tensor(lowerCamelCase__						):
              return array.T if axes is None else array.permute(*lowerCamelCase__						)
       elif is_tf_tensor(lowerCamelCase__						):
              import tensorflow as tf

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

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       List[Any]:





       """simple docstring"""


       if is_numpy_array(lowerCamelCase__						):
              return np.reshape(lowerCamelCase__							,    lowerCamelCase__						)
       elif is_torch_tensor(lowerCamelCase__						):
              return array.reshape(*lowerCamelCase__						)
       elif is_tf_tensor(lowerCamelCase__						):
              import tensorflow as tf

              return tf.reshape(lowerCamelCase__							,    lowerCamelCase__						)
       elif is_jax_tensor(lowerCamelCase__						):
              return jnp.reshape(lowerCamelCase__							,    lowerCamelCase__						)
       else:
              raise ValueError(f"""Type not supported for reshape: {type(lowerCamelCase__						)}."""						)

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__=None						)					->       Tuple:





       """simple docstring"""


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

              return tf.squeeze(lowerCamelCase__							,    axis=lowerCamelCase__						)
       elif is_jax_tensor(lowerCamelCase__						):
              return jnp.squeeze(lowerCamelCase__							,    axis=lowerCamelCase__						)
       else:
              raise ValueError(f"""Type not supported for squeeze: {type(lowerCamelCase__						)}."""						)

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       int:





       """simple docstring"""


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

              return tf.expand_dims(lowerCamelCase__							,    axis=lowerCamelCase__						)
       elif is_jax_tensor(lowerCamelCase__						):
              return jnp.expand_dims(lowerCamelCase__							,    axis=lowerCamelCase__						)
       else:
              raise ValueError(f"""Type not supported for expand_dims: {type(lowerCamelCase__						)}."""						)

def _lowercase	(      lowerCamelCase__						)					->       Any:





       """simple docstring"""


       if is_numpy_array(lowerCamelCase__						):
              return np.size(lowerCamelCase__						)
       elif is_torch_tensor(lowerCamelCase__						):
              return array.numel()
       elif is_tf_tensor(lowerCamelCase__						):
              import tensorflow as tf

              return tf.size(lowerCamelCase__						)
       elif is_jax_tensor(lowerCamelCase__						):
              return array.size
       else:
              raise ValueError(f"""Type not supported for expand_dims: {type(lowerCamelCase__						)}."""						)

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       List[Any]:





       """simple docstring"""


       for key, value in auto_map.items():
              if isinstance(lowerCamelCase__							,    (tuple, list)						):
                     __UpperCAmelCase  :				List[Any]       				=		[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  :				List[str]       				=		f"""{repo_id}--{value}"""

       return auto_map


def _lowercase	(      lowerCamelCase__						)					->       int:





       """simple docstring"""


       for base_class in inspect.getmro(lowerCamelCase__						):
              __UpperCAmelCase  :				Dict       				=		base_class.__module__
              __UpperCAmelCase  :				Dict       				=		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 transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments

def _lowercase	(      )					->       Dict:





       """simple docstring"""


       __UpperCAmelCase  :				str       				=		HfArgumentParser(lowerCamelCase__						)
       __UpperCAmelCase  :				Optional[Any]       				=		parser.parse_args_into_dataclasses()[0]
       __UpperCAmelCase  :				Any       				=		TensorFlowBenchmark(args=lowerCamelCase__						)
       try:
              __UpperCAmelCase  :				List[Any]       				=		parser.parse_args_into_dataclasses()[0]
       except ValueError as e:
              __UpperCAmelCase  :				str       				=		"Arg --no_{0} is no longer used, please use --no-{0} instead."
              __UpperCAmelCase  :				Tuple       				=		" ".join(str(lowerCamelCase__						).split(" "						)[:-1]						)
              __UpperCAmelCase  :				Any       				=		""
              __UpperCAmelCase  :				List[Any]       				=		eval(str(lowerCamelCase__						).split(" "						)[-1]						)
              __UpperCAmelCase  :				Optional[int]       				=		[]
              for arg in depreciated_args:
                     # arg[2:] removes '--'
                     if arg[2:] in TensorFlowBenchmark.deprecated_args:
                            # arg[5:] removes '--no_'
                            full_error_msg += arg_error_msg.format(arg[5:]						)
                     else:
                            wrong_args.append(lowerCamelCase__						)
              if len(lowerCamelCase__						) > 0:
                     __UpperCAmelCase  :				Union[str, Any]       				=		full_error_msg + begin_error_msg + str(lowerCamelCase__						)
              raise ValueError(lowerCamelCase__						)
       benchmark.run()


if __name__ == "__main__":
   main()






