import math

import tensorflow as tf
from packaging import version


def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  tf.convert_to_tensor(_lowerCAmelCase				)
      SCREAMING_SNAKE_CASE_          =  0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0				)     ,      x.dtype				)				))

      return x * cdf


def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  tf.convert_to_tensor(_lowerCAmelCase				)
      SCREAMING_SNAKE_CASE_          =  tf.cast(math.pi     ,      x.dtype				)
      SCREAMING_SNAKE_CASE_          =  tf.cast(0.044715     ,      x.dtype				)
      SCREAMING_SNAKE_CASE_          =  0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi				) * (x + coeff * tf.pow(_lowerCAmelCase     ,      3				))				))

      return x * cdf


def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  tf.convert_to_tensor(_lowerCAmelCase				)

      return x * tf.tanh(tf.math.softplus(_lowerCAmelCase				)				)


def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  tf.convert_to_tensor(_lowerCAmelCase				)
      SCREAMING_SNAKE_CASE_          =  tf.cast(0.044715     ,      x.dtype				)
      SCREAMING_SNAKE_CASE_          =  tf.cast(0.7978845608     ,      x.dtype				)

      return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)				))


def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  tf.convert_to_tensor(_lowerCAmelCase				)
      SCREAMING_SNAKE_CASE_          =  tf.cast(1.702     ,      x.dtype				)
      return x * tf.math.sigmoid(coeff * x				)


def      a__ (		__UpperCamelCase				):
      return tf.clip_by_value(_gelu(_lowerCAmelCase				)     ,      -1_0     ,      1_0				)


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase=-1				):
      SCREAMING_SNAKE_CASE_          =  tf.split(_lowerCAmelCase     ,      2     ,      axis=_lowerCAmelCase				)
      return a * tf.math.sigmoid(_lowerCAmelCase				)


if version.parse(tf.version.VERSION) >= version.parse("2.4"):


      def      a__ (		__UpperCamelCase				):
            return tf.keras.activations.gelu(_lowerCAmelCase     ,      approximate=_lowerCAmelCase				)

      A     :  Optional[int]					     =       tf.keras.activations.gelu





      A     :  int					     =       approximate_gelu_wrap
else:
      A     :  List[Any]					     =       _gelu
      A     :  Optional[Any]					     =       _gelu_new


A     :  Any					     =       {
    """gelu""": gelu,
    """gelu_10""": gelu_aa,
    """gelu_fast""": gelu_fast,
    """gelu_new""": gelu_new,
    """glu""": glu,
    """mish""": mish,
    """quick_gelu""": quick_gelu,
    """relu""": tf.keras.activations.relu,
    """sigmoid""": tf.keras.activations.sigmoid,
    """silu""": tf.keras.activations.swish,
    """swish""": tf.keras.activations.swish,
    """tanh""": tf.keras.activations.tanh,
}





def      a__ (		__UpperCamelCase				):
      if activation_string in ACTaFN:
            return ACTaFN[activation_string]
      else:
            raise KeyError(F'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()				)}'''				)



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
      import torch

      from transformers import XLMRobertaModel


@require_sentencepiece
@require_tokenizers
@require_torch
class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  @slow
  def    __A     (							self :       Dict   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  XLMRobertaModel.from_pretrained("xlm-roberta-base"   )
        SCREAMING_SNAKE_CASE_          =  torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]   )
        # The dog is cute and lives in the garden house

        SCREAMING_SNAKE_CASE_          =  torch.Size((1, 12, 768)   )  # batch_size, sequence_length, embedding_vector_dim
        SCREAMING_SNAKE_CASE_          =  torch.tensor(
            [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]   )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )["last_hidden_state"].detach()
        self.assertEqual(output.shape  ,     __magic_name__   )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]  ,     __magic_name__  ,     atol=1e-3   )   )







  @slow
  def    __A     (							self :       Tuple   )  ->	List[str]:
        SCREAMING_SNAKE_CASE_          =  XLMRobertaModel.from_pretrained("xlm-roberta-large"   )
        SCREAMING_SNAKE_CASE_          =  torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]   )
        # The dog is cute and lives in the garden house

        SCREAMING_SNAKE_CASE_          =  torch.Size((1, 12, 1_024)   )  # batch_size, sequence_length, embedding_vector_dim
        SCREAMING_SNAKE_CASE_          =  torch.tensor(
            [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]   )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )["last_hidden_state"].detach()
        self.assertEqual(output.shape  ,     __magic_name__   )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]  ,     __magic_name__  ,     atol=1e-3   )   )


