

'''simple docstring'''
from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


_snake_case         =  logging.get_logger(__name__)







def  _A		(     snake_case					)      ->     List[int]:
   if isinstance(snake_case		,							np.ndarray					):
      return list(tensor.shape					)

   _lowercase      :				Tuple      					=       tf.shape(snake_case					)

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

   _lowercase      :				Any      					=       tensor.shape.as_list()

   return [dynamic[i] if s is None else s for i, s in enumerate(snake_case					)]







def  _A		(     snake_case		,							snake_case = None		,							snake_case = None					)      ->     tf.Tensor:
   return tf.nn.softmax(logits=logits + 1E-9		,							axis=snake_case		,							name=snake_case					)







def  _A		(     snake_case		,							snake_case		,							snake_case		,							snake_case=1E-5		,							snake_case=-1					)      ->     str:
   # 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(snake_case		,							snake_case					):
      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
   _lowercase      :				Any      					=       tf.nn.moments(snake_case		,							axes=[axis]		,							keepdims=snake_case					)

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

   # Compute layer normalization using the batch_normalization
   # function.
   _lowercase      :				Any      					=       tf.nn.batch_normalization(
       snake_case		,							snake_case		,							snake_case		,							offset=snake_case		,							scale=snake_case		,							variance_epsilon=snake_case		,							)
   return outputs







def  _A		(     snake_case		,							snake_case=0		,							snake_case=-1					)      ->     Union[str, 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

   _lowercase      :				Union[str, Any]      					=       tf.shape(snake_case					)
   _lowercase      :				Any      					=       tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]					)
   _lowercase      :				Optional[int]      					=       tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]		,							axis=0					)
   return tf.reshape(snake_case		,							snake_case					)







def  _A		(     snake_case					)      ->     tf.Tensor:
   if not isinstance(snake_case		,							tf.Tensor					):
      _lowercase      :				Optional[Any]      					=       tf.convert_to_tensor(snake_case					)  # Catches stray NumPy inputs
   if encoder_attention_mask.shape.rank == 3:
      _lowercase      :				Dict      					=       encoder_attention_mask[:, None, :, :]
   if encoder_attention_mask.shape.rank == 2:
      _lowercase      :				Tuple      					=       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))
   _lowercase      :				Dict      					=       (
       tf.cast(1		,							encoder_attention_mask.dtype					) - encoder_extended_attention_mask
   ) * encoder_extended_attention_mask.dtype.min

   return encoder_extended_attention_mask







def  _A		(     snake_case		,							snake_case		,							snake_case = "input_ids"					)      ->     None:
   tf.debugging.assert_less(
       snake_case		,							tf.cast(snake_case		,							dtype=tensor.dtype					)		,							message=(
           F'''The maximum value of {tensor_name} ({tf.math.reduce_max(snake_case					)}) must be smaller than the embedding '''
           F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
       )		,							)







def  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     Optional[Any]:
   _lowercase      :				Optional[int]      					=       6_45_12
   # 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.
   _lowercase      :				Union[str, Any]      					=       [x for x in data if len(snake_case					) > 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}'''					)

   _lowercase      :				Dict      					=       np.asarray(snake_case					)

   _lowercase      :				str      					=       1
   _lowercase      :				Union[str, Any]      					=       np.array_split(snake_case		,							snake_case					)

   # 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
      _lowercase      :				int      					=       np.array_split(snake_case		,							snake_case					)

   if num_chunks > 1:
      for chunk_id, chunk_data in enumerate(snake_case					):
         _lowercase      :				Optional[int]      					=       chunk_data
   else:
      _lowercase      :				List[str]      					=       data







def  _A		(     snake_case		,							snake_case					)      ->     int:
   if name in group.attrs:
      _lowercase      :				Any      					=       [n.decode("utf8"					) if hasattr(snake_case		,							"decode"					) else n for n in group.attrs[name]]
   else:
      _lowercase      :				Union[str, Any]      					=       []
      _lowercase      :				List[Any]      					=       0
      while "%s%d" % (name, chunk_id) in group.attrs:
         data.extend(
             [n.decode("utf8"					) if hasattr(snake_case		,							"decode"					) else n for n in group.attrs["%s%d" % (name, chunk_id)]]					)
         chunk_id += 1
   return data







def  _A		(     snake_case					)      ->     Tuple:

   def _expand_single_ad_tensor(snake_case					):
      if isinstance(snake_case		,							tf.Tensor					) and t.shape.rank == 1:
         return tf.expand_dims(snake_case		,							axis=-1					)
      return t

   return tf.nest.map_structure(_expand_single_ad_tensor		,							snake_case					)









'''simple docstring'''
import logging

from transformers import PretrainedConfig


_snake_case         =  logging.getLogger(__name__)


_snake_case         =  {
    'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							'bertabs'


   def __init__(			self      ,    _UpperCamelCase=30522      ,    _UpperCamelCase=512      ,    _UpperCamelCase=6      ,    _UpperCamelCase=512      ,    _UpperCamelCase=8      ,    _UpperCamelCase=512      ,    _UpperCamelCase=0.2      ,    _UpperCamelCase=6      ,    _UpperCamelCase=768      ,    _UpperCamelCase=8      ,    _UpperCamelCase=2048      ,    _UpperCamelCase=0.2      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      super().__init__(**_UpperCamelCase  )

      _lowercase      :				List[str]      					=       vocab_size
      _lowercase      :				Tuple      					=       max_pos

      _lowercase      :				int      					=       enc_layers
      _lowercase      :				str      					=       enc_hidden_size
      _lowercase      :				Optional[Any]      					=       enc_heads
      _lowercase      :				Union[str, Any]      					=       enc_ff_size
      _lowercase      :				Tuple      					=       enc_dropout

      _lowercase      :				Dict      					=       dec_layers
      _lowercase      :				List[str]      					=       dec_hidden_size
      _lowercase      :				List[str]      					=       dec_heads
      _lowercase      :				str      					=       dec_ff_size
      _lowercase      :				str      					=       dec_dropout






