
"""simple docstring"""




from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
    import numpy as np
    import tensorflow as tf

    from transformers import (
        TF_MODEL_FOR_CAUSAL_LM_MAPPING,
        TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_MASKED_LM_MAPPING,
        TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
        TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
        TF_MODEL_FOR_PRETRAINING_MAPPING,
        TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
        TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
        TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
        TFSharedEmbeddings,
    )

    if _tf_gpu_memory_limit is not None:
        A__   :						Optional[Any] 							=     tf.config.list_physical_devices("""GPU""")
        for gpu in gpus:
            # Restrict TensorFlow to only allocate x GB of memory on the GPUs
            try:
                tf.config.set_logical_device_configuration(
                    gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
                )
                A__   :						Optional[int] 							=     tf.config.list_logical_devices("""GPU""")
                print("""Logical GPUs""", logical_gpus)
            except RuntimeError as e:
                # Virtual devices must be set before GPUs have been initialized
                print(e)







@require_tf
class 	_lowercase    :


     '''simple docstring'''





     _A		    =				None
     _A		    =				()
     _A		    =				()
     _A		    =				True
     _A		    =				True
     _A		    =				True
     _A		    =				False



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase=False	)->       dict:
          UpperCAmelCase__				:			Union[str, Any]							=    copy.deepcopy(__UpperCamelCase	)

          if model_class in get_values(__UpperCamelCase	):
               UpperCAmelCase__				:			List[str]							=    {
                   k: tf.tile(tf.expand_dims(__UpperCamelCase			,      1	)			,      (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)	)
                   if isinstance(__UpperCamelCase			,      tf.Tensor	) and v.ndim > 0
                   else v
                   for k, v in inputs_dict.items()
               }

          if return_labels:
               if model_class in get_values(__UpperCamelCase	):
                    UpperCAmelCase__				:			Dict							=    tf.ones(self.model_tester.batch_size			,      dtype=tf.intaa	)
               elif model_class in get_values(__UpperCamelCase	):
                    UpperCAmelCase__				:			Optional[int]							=    tf.zeros(self.model_tester.batch_size			,      dtype=tf.intaa	)
                    UpperCAmelCase__				:			str							=    tf.zeros(self.model_tester.batch_size			,      dtype=tf.intaa	)
               elif model_class in [
                   *get_values(__UpperCamelCase	),
                   *get_values(__UpperCamelCase	),
               ]:
                    UpperCAmelCase__				:			Any							=    tf.zeros(self.model_tester.batch_size			,      dtype=tf.intaa	)
               elif model_class in get_values(__UpperCamelCase	):
                    UpperCAmelCase__				:			Tuple							=    tf.zeros(self.model_tester.batch_size			,      dtype=tf.intaa	)
               elif model_class in [
                   *get_values(__UpperCamelCase	),
                   *get_values(__UpperCamelCase	),
                   *get_values(__UpperCamelCase	),
                   *get_values(__UpperCamelCase	),
                   *get_values(__UpperCamelCase	),
               ]:
                    UpperCAmelCase__				:			int							=    tf.zeros(
                        (self.model_tester.batch_size, self.model_tester.seq_length)			,      dtype=tf.intaa	)
          return inputs_dict



     @slow
     def 			lowerCAmelCase__							(   self	)->       str:
          UpperCAmelCase__				,						UpperCAmelCase__				:			Optional[int]							=    self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes[:2]:
               UpperCAmelCase__				:			List[Any]							=    self._prepare_for_class(__UpperCamelCase			,      __UpperCamelCase	)
               UpperCAmelCase__				:			List[str]							=    model_class(__UpperCamelCase	)

               @tf.function
               def run_in_graph_mode():
                    return model(__UpperCamelCase	)

               UpperCAmelCase__				:			str							=    run_in_graph_mode()
               self.assertIsNotNone(__UpperCamelCase	)



     @slow
     def 			lowerCAmelCase__							(   self	)->       Union[str, Any]:
          UpperCAmelCase__				,						UpperCAmelCase__				:			Union[str, Any]							=    self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes[:2]:
               UpperCAmelCase__				:			List[str]							=    self._prepare_for_class(__UpperCamelCase			,      __UpperCamelCase	)
               UpperCAmelCase__				:			Optional[int]							=    model_class(__UpperCamelCase	)

               @tf.function(experimental_compile=__UpperCamelCase	)
               def run_in_graph_mode():
                    return model(__UpperCamelCase	)

               UpperCAmelCase__				:			str							=    run_in_graph_mode()
               self.assertIsNotNone(__UpperCamelCase	)



     @slow
     def 			lowerCAmelCase__							(   self	)->       Dict:
          # This is a copy of the test_keras_fit method, but we use XLA compilation instead of eager
          UpperCAmelCase__				,						UpperCAmelCase__				:			int							=    self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes[:2]:
               UpperCAmelCase__				:			Optional[Any]							=    model_class(__UpperCamelCase	)
               if getattr(__UpperCamelCase			,      "hf_compute_loss"			,      __UpperCamelCase	):
                    # Test that model correctly compute the loss with kwargs
                    UpperCAmelCase__				:			List[Any]							=    self._prepare_for_class(inputs_dict.copy()			,      __UpperCamelCase			,      return_labels=__UpperCamelCase	)
                    # Is there a better way to remove these decoder inputs?
                    UpperCAmelCase__				:			int							=    {
                        key: val
                        for key, val in prepared_for_class.items()
                        if key not in ("head_mask", "decoder_head_mask", "cross_attn_head_mask", "decoder_input_ids")
                    }

                    UpperCAmelCase__				:			Any							=    {
                        "labels",
                        "label",
                        "label_ids",
                        "start_positions",
                        "start_position",
                        "end_positions",
                        "end_position",
                        "next_sentence_label",
                    }
                    UpperCAmelCase__				:			Union[str, Any]							=    possible_label_cols.intersection(set(__UpperCamelCase	)	)
                    self.assertGreater(len(__UpperCamelCase	)			,      0			,      msg="No matching label names found!"	)
                    UpperCAmelCase__				:			List[str]							=    {key: val for key, val in prepared_for_class.items() if key in label_names}
                    UpperCAmelCase__				:			Union[str, Any]							=    {key: val for key, val in prepared_for_class.items() if key not in label_names}
                    self.assertGreater(len(__UpperCamelCase	)			,      0	)

                    # Make sure it works with XLA!
                    model.compile(optimizer=tf.keras.optimizers.SGD(0.0	)			,      jit_compile=__UpperCamelCase	)
                    # Make sure the model fits without crashing regardless of where we pass the labels
                    UpperCAmelCase__				:			Optional[Any]							=    model.fit(
                        __UpperCamelCase			,      validation_data=__UpperCamelCase			,      steps_per_epoch=1			,      validation_steps=1			,      shuffle=__UpperCamelCase			,      verbose=0			,      )
                    UpperCAmelCase__				:			Optional[int]							=    history.history["loss"][0]
                    self.assertTrue(not isnan(__UpperCamelCase	)	)
                    UpperCAmelCase__				:			Union[str, Any]							=    history.history["val_loss"][0]
                    self.assertTrue(not isnan(__UpperCamelCase	)	)

                    # Now test it with separate labels, to make sure that path works in XLA too.
                    UpperCAmelCase__				:			Dict							=    model_class(__UpperCamelCase	)
                    model.compile(optimizer=tf.keras.optimizers.SGD(0.0	)			,      jit_compile=__UpperCamelCase	)
                    UpperCAmelCase__				:			List[Any]							=    model.fit(
                        __UpperCamelCase			,      __UpperCamelCase			,      validation_data=(inputs_minus_labels, labels)			,      steps_per_epoch=1			,      validation_steps=1			,      shuffle=__UpperCamelCase			,      verbose=0			,      )

                    UpperCAmelCase__				:			int							=    history.history["loss"][0]
                    self.assertTrue(not isnan(__UpperCamelCase	)	)
                    UpperCAmelCase__				:			str							=    history.history["val_loss"][0]
                    self.assertTrue(not isnan(__UpperCamelCase	)	)



     @slow
     def 			lowerCAmelCase__							(   self	)->       str:
          UpperCAmelCase__				,						UpperCAmelCase__				:			Any							=    self.model_tester.prepare_config_and_inputs_for_common()
          UpperCAmelCase__				:			int							=    True
          UpperCAmelCase__				:			int							=    True

          if hasattr(__UpperCamelCase			,      "use_cache"	):
               UpperCAmelCase__				:			Optional[int]							=    True

          UpperCAmelCase__				:			Optional[Any]							=    getattr(self.model_tester			,      "encoder_seq_length"			,      self.model_tester.seq_length	)
          UpperCAmelCase__				:			Dict							=    getattr(self.model_tester			,      "key_length"			,      __UpperCamelCase	)

          for model_class in self.all_model_classes[:2]:
               UpperCAmelCase__				:			Any							=    self._prepare_for_class(__UpperCamelCase			,      __UpperCamelCase	)
               UpperCAmelCase__				:			str							=    model_class(__UpperCamelCase	)
               model.build()
               UpperCAmelCase__				:			Optional[Any]							=    len(model(__UpperCamelCase	)	)

               for key in list(class_inputs_dict.keys()	):
                    # Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
                    if key not in model.input_signature:
                         del class_inputs_dict[key]
                    # Check it's a tensor, in case the inputs dict has some bools in it too
                    elif isinstance(class_inputs_dict[key]			,      tf.Tensor	) and class_inputs_dict[key].dtype.is_integer:
                         UpperCAmelCase__				:			List[Any]							=    tf.cast(class_inputs_dict[key]			,      tf.intaa	)

               if set(class_inputs_dict.keys()	) != set(model.input_signature.keys()	):
                    continue  # Some models have inputs that the preparation functions don't create, we skip those

               with tempfile.TemporaryDirectory() as tmpdirname:
                    model.save_pretrained(__UpperCamelCase			,      saved_model=__UpperCamelCase	)
                    UpperCAmelCase__				:			Dict							=    os.path.join(__UpperCamelCase			,      "saved_model"			,      "1"	)
                    UpperCAmelCase__				:			Optional[Any]							=    tf.keras.models.load_model(__UpperCamelCase	)
                    UpperCAmelCase__				:			Optional[int]							=    model(__UpperCamelCase	)

                    if self.is_encoder_decoder:
                         UpperCAmelCase__				:			Optional[int]							=    outputs["encoder_hidden_states"]
                         UpperCAmelCase__				:			Any							=    outputs["encoder_attentions"]
                    else:
                         UpperCAmelCase__				:			str							=    outputs["hidden_states"]
                         UpperCAmelCase__				:			Optional[int]							=    outputs["attentions"]

                    self.assertEqual(len(__UpperCamelCase	)			,      __UpperCamelCase	)

                    UpperCAmelCase__				:			Optional[int]							=    getattr(
                        self.model_tester			,      "expected_num_hidden_layers"			,      self.model_tester.num_hidden_layers + 1	)

                    self.assertEqual(len(__UpperCamelCase	)			,      __UpperCamelCase	)
                    self.assertListEqual(
                        list(output_hidden_states[0].shape[-2:]	)			,      [self.model_tester.seq_length, self.model_tester.hidden_size]			,      )

                    self.assertEqual(len(__UpperCamelCase	)			,      self.model_tester.num_hidden_layers	)
                    self.assertListEqual(
                        list(output_attentions[0].shape[-3:]	)			,      [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]			,      )



     @slow
     def 			lowerCAmelCase__							(   self	)->       Union[str, Any]:
          tf.keras.mixed_precision.set_global_policy("mixed_float16"	)

          # try/finally block to ensure subsequent tests run in float32
          try:
               UpperCAmelCase__				,						UpperCAmelCase__				:			str							=    self.model_tester.prepare_config_and_inputs_for_common()
               for model_class in self.all_model_classes[:2]:
                    UpperCAmelCase__				:			int							=    self._prepare_for_class(__UpperCamelCase			,      __UpperCamelCase	)
                    UpperCAmelCase__				:			Any							=    model_class(__UpperCamelCase	)
                    UpperCAmelCase__				:			Optional[Any]							=    model(__UpperCamelCase	)

                    self.assertIsNotNone(__UpperCamelCase	)
          finally:
               tf.keras.mixed_precision.set_global_policy("float32"	)



     @slow
     def 			lowerCAmelCase__							(   self	)->       int:
          UpperCAmelCase__				,						UpperCAmelCase__				:			List[Any]							=    self.model_tester.prepare_config_and_inputs_for_common()
          # head_mask and decoder_head_mask has different shapes than other input args
          if "head_mask" in inputs_dict:
               del inputs_dict["head_mask"]
          if "decoder_head_mask" in inputs_dict:
               del inputs_dict["decoder_head_mask"]
          if "cross_attn_head_mask" in inputs_dict:
               del inputs_dict["cross_attn_head_mask"]
          UpperCAmelCase__				:			str							=    {
              module_member
              for model_class in self.all_model_classes
              for module in (import_module(model_class.__module__	),)
              for module_member_name in dir(__UpperCamelCase	)
              if module_member_name.endswith("MainLayer"	)
              for module_member in (getattr(__UpperCamelCase			,      __UpperCamelCase	),)
              if isinstance(__UpperCamelCase			,      __UpperCamelCase	)
              and tf.keras.layers.Layer in module_member.__bases__
              and getattr(__UpperCamelCase			,      "_keras_serializable"			,      __UpperCamelCase	)
          }

          for main_layer_class in tf_main_layer_classes:
               # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
               if "T5" in main_layer_class.__name__:
                    # Take the same values than in TFT5ModelTester for this shared layer
                    UpperCAmelCase__				:			Optional[Any]							=    TFSharedEmbeddings(self.model_tester.vocab_size			,      self.model_tester.hidden_size			,      name="shared"	)
                    UpperCAmelCase__				:			Union[str, Any]							=    False
                    UpperCAmelCase__				:			Optional[Any]							=    main_layer_class(__UpperCamelCase			,      embed_tokens=__UpperCamelCase	)
               else:
                    UpperCAmelCase__				:			List[Any]							=    main_layer_class(__UpperCamelCase	)

               UpperCAmelCase__				:			Optional[int]							=    {
                   name: tf.keras.Input(tensor.shape[1:]			,      dtype=tensor.dtype	) for name, tensor in inputs_dict.items()
               }

               if hasattr(self.model_tester			,      "num_labels"	):
                    UpperCAmelCase__				:			List[str]							=    self.model_tester.num_labels
               else:
                    UpperCAmelCase__				:			Dict							=    2

               UpperCAmelCase__				:			Any							=    tf.data.Dataset.from_tensor_slices(
                   (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)	))	).batch(1	)

               UpperCAmelCase__				:			str							=    main_layer(__UpperCamelCase	)[0]
               UpperCAmelCase__				:			List[str]							=    tf.keras.layers.Dense(__UpperCamelCase			,      activation="softmax"			,      name="outputs"	)(__UpperCamelCase	)
               UpperCAmelCase__				:			Optional[Any]							=    tf.keras.models.Model(inputs=__UpperCamelCase			,      outputs=[outputs]	)

               model.compile(loss="binary_crossentropy"			,      optimizer="adam"			,      metrics=["binary_accuracy"]	)
               model.fit(__UpperCamelCase			,      epochs=1	)

               with tempfile.TemporaryDirectory() as tmpdirname:
                    UpperCAmelCase__				:			Optional[int]							=    os.path.join(__UpperCamelCase			,      "keras_model.h5"	)
                    model.save(__UpperCamelCase	)
                    if "T5" in main_layer_class.__name__:
                         UpperCAmelCase__				:			Any							=    tf.keras.models.load_model(
                             __UpperCamelCase			,      custom_objects={
                                 main_layer_class.__name__: main_layer_class,
                                 "TFSharedEmbeddings": TFSharedEmbeddings,
                             }			,      )
                    else:
                         UpperCAmelCase__				:			Tuple							=    tf.keras.models.load_model(
                             __UpperCamelCase			,      custom_objects={main_layer_class.__name__: main_layer_class}	)
                    assert isinstance(__UpperCamelCase			,      tf.keras.Model	)
                    model(__UpperCamelCase	)



     @slow
     def 			lowerCAmelCase__							(   self	)->       Optional[Any]:
          UpperCAmelCase__				,						UpperCAmelCase__				:			Tuple							=    self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes[:2]:
               UpperCAmelCase__				:			Optional[Any]							=    model_class(__UpperCamelCase	)

               UpperCAmelCase__				:			str							=    copy.deepcopy(__UpperCamelCase	)

               if not self.is_encoder_decoder:
                    UpperCAmelCase__				:			Optional[int]							=    inputs["input_ids"]
                    del inputs["input_ids"]
               else:
                    UpperCAmelCase__				:			Dict							=    inputs["input_ids"]
                    UpperCAmelCase__				:			str							=    inputs.get("decoder_input_ids"			,      __UpperCamelCase	)
                    del inputs["input_ids"]
                    inputs.pop("decoder_input_ids"			,      __UpperCamelCase	)

               if not self.is_encoder_decoder:
                    UpperCAmelCase__				:			Optional[Any]							=    model.get_input_embeddings()(__UpperCamelCase	)
               else:
                    UpperCAmelCase__				:			Optional[Any]							=    model.get_input_embeddings()(__UpperCamelCase	)
                    UpperCAmelCase__				:			Any							=    model.get_input_embeddings()(__UpperCamelCase	)

               UpperCAmelCase__				:			List[Any]							=    self._prepare_for_class(__UpperCamelCase			,      __UpperCamelCase	)

               @tf.function
               def run_in_graph_mode():
                    return model(__UpperCamelCase	)

               UpperCAmelCase__				:			int							=    run_in_graph_mode()
               self.assertIsNotNone(__UpperCamelCase	)



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase			,      __UpperCamelCase	)->       Optional[Any]:
          # special tokens cannot be bad tokens
          UpperCAmelCase__				:			Dict							=    []
          if model.config.bos_token_id is not None:
               special_tokens.append(model.config.bos_token_id	)
          if model.config.pad_token_id is not None:
               special_tokens.append(model.config.pad_token_id	)
          if model.config.eos_token_id is not None:
               special_tokens.append(model.config.eos_token_id	)

          # create random bad tokens that are not special tokens
          UpperCAmelCase__				:			Dict							=    []
          while len(__UpperCamelCase	) < num_bad_tokens:
               UpperCAmelCase__				:			List[Any]							=    tf.squeeze(ids_tensor((1, 1)			,      self.model_tester.vocab_size	)			,      0	).numpy()[0]
               if token not in special_tokens:
                    bad_tokens.append(__UpperCamelCase	)
          return bad_tokens



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase	)->       Optional[Any]:
          for token_id in output_ids[0].numpy().tolist():
               self.assertGreaterEqual(__UpperCamelCase			,      0	)
               self.assertLess(__UpperCamelCase			,      self.model_tester.vocab_size	)



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase			,      __UpperCamelCase	)->       int:
          # for all bad word tokens
          for bad_word_ids in bad_words_ids:
               # for all slices in batch
               for generated_ids_slice in generated_ids:
                    # for all word idx
                    for i in range(len(__UpperCamelCase	)			,      len(__UpperCamelCase	)	):
                         # if tokens match
                         if generated_ids_slice[i - len(__UpperCamelCase	) : i] == bad_word_ids:
                              return True
          return False





"""simple docstring"""




import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


A__   :						Optional[Any] 							=     set()
A__   :						List[str] 							=     get_logger(__name__)



def 				a__			(					lowerCAmelCase						:							Optional[str] = None		):






     '''simple docstring'''

     def decorator(lowerCAmelCase						:							Callable		):
          global _emitted_deprecation_warnings

          if inspect.isclass(lowerCAmelCase		):
               UpperCAmelCase__				:			Any							=    deprecated_class_or_function.__init__
               UpperCAmelCase__				:			Optional[int]							=    deprecated_class_or_function.__name__
          else:
               UpperCAmelCase__				:			str							=    deprecated_class_or_function
               UpperCAmelCase__				:			List[Any]							=    deprecated_function.__name__
               # Support deprecating __init__ class method: class name instead
               UpperCAmelCase__				:			List[str]							=    name if name != "__init__" else deprecated_function.__qualname__.split("."		)[-2]

          UpperCAmelCase__				:			Optional[Any]							=    (
              F"{name} is deprecated and will be removed in the next major version of datasets." + F" {help_message}"
              if help_message
              else ""
          )

          @wraps(lowerCAmelCase		)
          def wrapper(*lowerCAmelCase						:							int							,     **lowerCAmelCase						:							str		):
               UpperCAmelCase__				:			List[str]							=    hash(lowerCAmelCase		)
               if func_hash not in _emitted_deprecation_warnings:
                    warnings.warn(lowerCAmelCase							,     category=lowerCAmelCase							,     stacklevel=2		)
                    _emitted_deprecation_warnings.add(lowerCAmelCase		)
               return deprecated_function(*lowerCAmelCase							,     **lowerCAmelCase		)

          UpperCAmelCase__				:			List[str]							=    "deprecated"

          if inspect.isclass(lowerCAmelCase		):
               UpperCAmelCase__				:			List[str]							=    wrapper
               return deprecated_class_or_function
          else:
               return wrapper

     return decorator







class 	_lowercase    (							enum.EnumMeta					):


     '''simple docstring'''





     def __getattribute__(   cls			,      __UpperCamelCase	)->       Optional[Any]:
          UpperCAmelCase__				:			Optional[int]							=    super().__getattribute__(__UpperCamelCase	)
          if isinstance(__UpperCamelCase			,      enum.Enum	) and obj._on_access:
               obj._on_access()
          return obj



     def __getitem__(   cls			,      __UpperCamelCase	)->       Optional[int]:
          UpperCAmelCase__				:			Union[str, Any]							=    super().__getitem__(__UpperCamelCase	)
          if member._on_access:
               member._on_access()
          return member



     def __call__(   cls			,      __UpperCamelCase			,      __UpperCamelCase=None			,      *, __UpperCamelCase=None			,      __UpperCamelCase=None			,      __UpperCamelCase=None			,      __UpperCamelCase=1	)->       Dict:
          UpperCAmelCase__				:			Optional[int]							=    super().__call__(__UpperCamelCase			,      __UpperCamelCase			,      module=__UpperCamelCase			,      qualname=__UpperCamelCase			,      type=__UpperCamelCase			,      start=__UpperCamelCase	)
          if isinstance(__UpperCamelCase			,      enum.Enum	) and obj._on_access:
               obj._on_access()
          return obj







class 	_lowercase    (							enum.Enum						, metaclass=lowerCAmelCase_					):


     '''simple docstring'''





     def __new__(   cls			,      __UpperCamelCase	)->       Union[str, Any]:
          UpperCAmelCase__				:			int							=    object.__new__(cls	)
          UpperCAmelCase__				:			int							=    value
          UpperCAmelCase__				:			Optional[int]							=    member.deprecate
          return member



     @property
     def 			lowerCAmelCase__							(   self	)->       Union[str, Any]:
          return ""



     def 			lowerCAmelCase__							(   self	)->       int:
          UpperCAmelCase__				:			Tuple							=    F" {self.help_message}" if self.help_message else ""
          warnings.warn(
              F"'{self.__objclass__.__name__}' is deprecated and will be removed in the next major version of datasets."
              + help_message			,      __UpperCamelCase			,      stacklevel=3			,      )



