





import math

import tensorflow as tf
from packaging import version


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       _UpperCamelCase              =						tf.convert_to_tensor(lowerCAmelCase					)
       _UpperCamelCase              =						0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0					)    ,       x.dtype					)					))

       return x * cdf


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       _UpperCamelCase              =						tf.convert_to_tensor(lowerCAmelCase					)
       _UpperCamelCase              =						tf.cast(math.pi    ,       x.dtype					)
       _UpperCamelCase              =						tf.cast(0.0_4_4_7_1_5    ,       x.dtype					)
       _UpperCamelCase              =						0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi					) * (x + coeff * tf.pow(lowerCAmelCase    ,       3					))					))

       return x * cdf


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       _UpperCamelCase              =						tf.convert_to_tensor(lowerCAmelCase					)

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


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       _UpperCamelCase              =						tf.convert_to_tensor(lowerCAmelCase					)
       _UpperCamelCase              =						tf.cast(0.0_4_4_7_1_5    ,       x.dtype					)
       _UpperCamelCase              =						tf.cast(0.7_9_7_8_8_4_5_6_0_8    ,       x.dtype					)

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


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       _UpperCamelCase              =						tf.convert_to_tensor(lowerCAmelCase					)
       _UpperCamelCase              =						tf.cast(1.7_0_2    ,       x.dtype					)
       return x * tf.math.sigmoid(coeff * x					)


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       return tf.clip_by_value(_gelu(lowerCAmelCase					)    ,       -10    ,       10					)


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase=-1					):
       _UpperCamelCase       ,		_UpperCamelCase              =						tf.split(lowerCAmelCase    ,       2    ,       axis=lowerCAmelCase					)
       return a * tf.math.sigmoid(lowerCAmelCase					)


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


       def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
              return tf.keras.activations.gelu(lowerCAmelCase    ,       approximate=lowerCAmelCase					)

       lowercase    : Tuple    	=  tf.keras.activations.gelu

       lowercase    : Dict    	=  approximate_gelu_wrap
else:
       lowercase    : Optional[int]    	=  _gelu
       lowercase    : Optional[int]    	=  _gelu_new


lowercase    : Union[str, 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     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       if activation_string in ACTaFN:
              return ACTaFN[activation_string]
       else:
              raise KeyError(f'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()					)}'''					)













from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch

@require_torch
@require_tf
class  __A(						__UpperCAmelCase ):

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						SMALL_MODEL_IDENTIFIER
            _UpperCamelCase              =						'''pt'''
            _UpperCamelCase              =						'''tf'''

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						AutoModel.from_pretrained(self.test_model      )
            model_pt.save_pretrained(A      )

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						TFAutoModel.from_pretrained(self.test_model,       from_pt=A      )
            model_tf.save_pretrained(A      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						'''mock_framework'''

            # Framework provided - return whatever the user provides
            _UpperCamelCase              =						FeaturesManager.determine_framework(self.test_model,       A      )
            self.assertEqual(A,       A      )

            # Local checkpoint and framework provided - return provided framework
            # PyTorch checkpoint
            with TemporaryDirectory() as local_pt_ckpt:
                   self._setup_pt_ckpt(A      )
                   _UpperCamelCase              =						FeaturesManager.determine_framework(A,       A      )
                   self.assertEqual(A,       A      )

            # TensorFlow checkpoint
            with TemporaryDirectory() as local_tf_ckpt:
                   self._setup_tf_ckpt(A      )
                   _UpperCamelCase              =						FeaturesManager.determine_framework(A,       A      )
                   self.assertEqual(A,       A      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            with TemporaryDirectory() as local_pt_ckpt:
                   self._setup_pt_ckpt(A      )
                   _UpperCamelCase              =						FeaturesManager.determine_framework(A      )
                   self.assertEqual(A,       self.framework_pt      )

            # TensorFlow checkpoint
            with TemporaryDirectory() as local_tf_ckpt:
                   self._setup_tf_ckpt(A      )
                   _UpperCamelCase              =						FeaturesManager.determine_framework(A      )
                   self.assertEqual(A,       self.framework_tf      )

            # Invalid local checkpoint
            with TemporaryDirectory() as local_invalid_ckpt:
                   with self.assertRaises(A      ):
                          _UpperCamelCase              =						FeaturesManager.determine_framework(A      )



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						MagicMock(return_value=A      )
            with patch('''transformers.onnx.features.is_tf_available''',       A      ):
                   _UpperCamelCase              =						FeaturesManager.determine_framework(self.test_model      )
                   self.assertEqual(A,       self.framework_pt      )

            # PyTorch not in environment -> use TensorFlow
            _UpperCamelCase              =						MagicMock(return_value=A      )
            with patch('''transformers.onnx.features.is_torch_available''',       A      ):
                   _UpperCamelCase              =						FeaturesManager.determine_framework(self.test_model      )
                   self.assertEqual(A,       self.framework_tf      )

            # Both in environment -> use PyTorch
            _UpperCamelCase              =						MagicMock(return_value=A      )
            _UpperCamelCase              =						MagicMock(return_value=A      )
            with patch('''transformers.onnx.features.is_tf_available''',       A      ), patch(
                '''transformers.onnx.features.is_torch_available''',       A      ):
                   _UpperCamelCase              =						FeaturesManager.determine_framework(self.test_model      )
                   self.assertEqual(A,       self.framework_pt      )

            # Both not in environment -> raise error
            _UpperCamelCase              =						MagicMock(return_value=A      )
            _UpperCamelCase              =						MagicMock(return_value=A      )
            with patch('''transformers.onnx.features.is_tf_available''',       A      ), patch(
                '''transformers.onnx.features.is_torch_available''',       A      ):
                   with self.assertRaises(A      ):
                          _UpperCamelCase              =						FeaturesManager.determine_framework(self.test_model      )






