






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					:Union[str, Any]      ,				a					:Optional[Any]      ,				a					:Tuple      ,				a					:Tuple=1e-5      ,				a					:Tuple=-1					)				->     Optional[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					:Dict      ,				a					:Union[str, Any]=0      ,				a					:Any=-1					)				->     List[str]:
 # 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					:Optional[Any]      ,				a					:int      ,				a					:List[str]					)				->     List[Any]:
 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					:Tuple      ,				a					:Tuple					)				->     List[Any]:
 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					:List[Any]					)				->     Tuple:

 def _expand_single_ad_tensor(a					: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 diffusers.utils.testing_utils import require_onnxruntime






@require_onnxruntime
class 						lowercase_   :






      '''simple docstring'''




      pass


