






from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


UpperCAmelCase__				   =		logging.get_logger(__name__)
def 						_a    (      a :Union[tf.Tensor, np.ndarray] )							->			List[int]:
 if isinstance(a	,							np.ndarray ):
  return list(tensor.shape )

 a		  =      tf.shape(a )

 if tensor.shape == tf.TensorShape(a ):
  return dynamic

 a		  =      tensor.shape.as_list()

 return [dynamic[i] if s is None else s for i, s in enumerate(a )]
def 						_a    (      a :tf.Tensor	,							a :Optional[int] = None	,							a :Optional[str] = None )							->			tf.Tensor:
 return tf.nn.softmax(logits=logits + 1e-9	,							axis=a	,							name=a )
def 						_a    (      a :Tuple	,							a :str	,							a :List[str]	,							a :str=1e-5	,							a :List[str]=-1 )							->			Any:
 # This is a very simplified functional layernorm, designed to duplicate
 # the functionality of PyTorch nn.functional.layer_norm when this is needed to port
 # models in Transformers.

 if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(a	,							a ):
  raise NotImplementedError('''Only 1D weight and bias tensors are supported for now, with only a single axis.''' )

 # Get mean and variance on the axis to be normalized
 a  ,     a		  =      tf.nn.moments(a	,							axes=[axis]	,							keepdims=a )

 if axis != -1:
  # Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
  # on every dimension except axis
  a		  =      [1] * inputs.shape.rank
  a		  =      shape_list(a )[axis]
  a		  =      tf.reshape(a	,							a )
  a		  =      tf.reshape(a	,							a )

 # Compute layer normalization using the batch_normalization
 # function.
 a		  =      tf.nn.batch_normalization(
     a	,							a	,							a	,							offset=a	,							scale=a	,							variance_epsilon=a	,							)
 return outputs
def 						_a    (      a :Optional[Any]	,							a :Dict=0	,							a :Any=-1 )							->			List[Any]:
 # Replicates the behavior of torch.flatten in TF

 # If end_dim or start_dim is negative, count them from the end
 if end_dim < 0:
  end_dim += input.shape.rank
 if start_dim < 0:
  start_dim += input.shape.rank

 if start_dim == end_dim:
  return input

 a		  =      tf.shape(a )
 a		  =      tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
 a		  =      tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]	,							axis=0 )
 return tf.reshape(a	,							a )
def 						_a    (      a :tf.Tensor )							->			tf.Tensor:
 if not isinstance(a	,							tf.Tensor ):
  a		  =      tf.convert_to_tensor(a )  # Catches stray NumPy inputs
 if encoder_attention_mask.shape.rank == 3:
  a		  =      encoder_attention_mask[:, None, :, :]
 if encoder_attention_mask.shape.rank == 2:
  a		  =      encoder_attention_mask[:, None, None, :]
 # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
 # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
 # /transformer/transformer_layers.py#L270
 # encoder_extended_attention_mask = (encoder_extended_attention_mask ==
 # encoder_extended_attention_mask.transpose(-1, -2))
 a		  =      (
     tf.cast(1	,							encoder_attention_mask.dtype ) - encoder_extended_attention_mask
 ) * encoder_extended_attention_mask.dtype.min

 return encoder_extended_attention_mask
def 						_a    (      a :tf.Tensor	,							a :int	,							a :str = "input_ids" )							->			None:
 tf.debugging.assert_less(
     a	,							tf.cast(a	,							dtype=tensor.dtype )	,							message=(
         F"""The maximum value of {tensor_name} ({tf.math.reduce_max(a )}) must be smaller than the embedding """
         F"""layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."""
     )	,							)
def 						_a    (      a :Any	,							a :Optional[Any]	,							a :List[str] )							->			str:
 a		  =      64_512
 # Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
 # because in that case even chunking the array would not make the saving
 # possible.
 a		  =      [x for x in data if len(a ) > HDF5_OBJECT_HEADER_LIMIT]

 # Expecting this to never be true.
 if bad_attributes:
  raise RuntimeError(
      '''The following attributes cannot be saved to HDF5 file because '''
      F"""they are larger than {HDF5_OBJECT_HEADER_LIMIT} """
      F"""bytes: {bad_attributes}""" )

 a		  =      np.asarray(a )

 a		  =      1
 a		  =      np.array_split(a	,							a )

 # This will never loop forever thanks to the test above.
 while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
  num_chunks += 1
  a		  =      np.array_split(a	,							a )

 if num_chunks > 1:
  for chunk_id, chunk_data in enumerate(a ):
   a		  =      chunk_data
 else:
  a		  =      data
def 						_a    (      a :Optional[Any]	,							a :Dict )							->			Optional[int]:
 if name in group.attrs:
  a		  =      [n.decode('''utf8''' ) if hasattr(a	,							'''decode''' ) else n for n in group.attrs[name]]
 else:
  a		  =      []
  a		  =      0
  while "%s%d" % (name, chunk_id) in group.attrs:
   data.extend(
       [n.decode('''utf8''' ) if hasattr(a	,							'''decode''' ) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]] )
   chunk_id += 1
 return data

def 						_a    (      a :Any )							->			str:

 def _expand_single_ad_tensor(a :Optional[int] ):
  if isinstance(a	,							tf.Tensor ) and t.shape.rank == 1:
   return tf.expand_dims(a	,							axis=-1 )
  return t

 return tf.nest.map_structure(_expand_single_ad_tensor	,							a )










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 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      def       __lowerCAmelCase					(      self      :						str  )    ->int:




       """simple docstring"""


       a		  =      SMALL_MODEL_IDENTIFIER
       a		  =      '''pt'''
       a		  =      '''tf'''







      def       __lowerCAmelCase					(      self      :						Optional[Any] ,					__UpperCAmelCase      :						str  )    ->Union[str, Any]:




       """simple docstring"""


       a		  =      AutoModel.from_pretrained(self.test_model  )
       model_pt.save_pretrained(__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Any ,					__UpperCAmelCase      :						Union[str, Any]  )    ->List[str]:




       """simple docstring"""


       a		  =      TFAutoModel.from_pretrained(self.test_model ,					from_pt=__UpperCAmelCase  )
       model_tf.save_pretrained(__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Any  )    ->int:




       """simple docstring"""


       a		  =      '''mock_framework'''

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

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

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







      def       __lowerCAmelCase					(      self      :						str  )    ->int:




       """simple docstring"""


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

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

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







      def       __lowerCAmelCase					(      self      :						Optional[int]  )    ->List[str]:




       """simple docstring"""


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

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

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

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


