




'''simple docstring'''


from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


_snake_case				:				Dict       							=		logging.get_logger(__name__)
def 		snake_case_   (UpperCamelCase	:  Union[tf.Tensor, np.ndarray]				):





							'''simple docstring'''


							if isinstance(UpperCamelCase			,						np.ndarray				):
														return list(tensor.shape				)

							_a     					=    tf.shape(UpperCamelCase				)

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

							_a     					=    tensor.shape.as_list()

							return [dynamic[i] if s is None else s for i, s in enumerate(UpperCamelCase				)]
def 		snake_case_   (UpperCamelCase	:  tf.Tensor			,						UpperCamelCase	:  Optional[int] = None			,						UpperCamelCase	:  Optional[str] = None				):





							'''simple docstring'''


							return tf.nn.softmax(logits=logits + 1e-9			,						axis=UpperCamelCase			,						name=UpperCamelCase				)
def 		snake_case_   (UpperCamelCase	:  Dict			,						UpperCamelCase	:  List[Any]			,						UpperCamelCase	:  List[str]			,						UpperCamelCase	:  Tuple=1e-5			,						UpperCamelCase	:  Tuple=-1				):





							'''simple docstring'''


							if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(UpperCamelCase			,						UpperCamelCase				):
														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(UpperCamelCase			,						axes=[axis]			,						keepdims=UpperCamelCase				)

							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(UpperCamelCase				)[axis]
														_a     					=    tf.reshape(UpperCamelCase			,						UpperCamelCase				)
														_a     					=    tf.reshape(UpperCamelCase			,						UpperCamelCase				)

							# Compute layer normalization using the batch_normalization
							# function.
							_a     					=    tf.nn.batch_normalization(
							    UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						offset=UpperCamelCase			,						scale=UpperCamelCase			,						variance_epsilon=UpperCamelCase			,						)
							return outputs
def 		snake_case_   (UpperCamelCase	:  Dict			,						UpperCamelCase	:  List[str]=0			,						UpperCamelCase	:  str=-1				):





							'''simple docstring'''


							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(UpperCamelCase				)
							_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(UpperCamelCase			,						UpperCamelCase				)
def 		snake_case_   (UpperCamelCase	:  tf.Tensor				):





							'''simple docstring'''


							if not isinstance(UpperCamelCase			,						tf.Tensor				):
														_a     					=    tf.convert_to_tensor(UpperCamelCase				)  # 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 		snake_case_   (UpperCamelCase	:  tf.Tensor			,						UpperCamelCase	:  int			,						UpperCamelCase	:  str = "input_ids"				):





							'''simple docstring'''


							tf.debugging.assert_less(
							    UpperCamelCase			,						tf.cast(UpperCamelCase			,						dtype=tensor.dtype				)			,						message=(
							        f'The maximum value of {tensor_name} ({tf.math.reduce_max(UpperCamelCase				)}) must be smaller than the embedding '
							        f'layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'
							    )			,						)
def 		snake_case_   (UpperCamelCase	:  List[Any]			,						UpperCamelCase	:  List[Any]			,						UpperCamelCase	:  List[Any]				):





							'''simple docstring'''


							_a     					=    6_4512
							# 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(UpperCamelCase				) > 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(UpperCamelCase				)

							_a     					=    1
							_a     					=    np.array_split(UpperCamelCase			,						UpperCamelCase				)

							# 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(UpperCamelCase			,						UpperCamelCase				)

							if num_chunks > 1:
														for chunk_id, chunk_data in enumerate(UpperCamelCase				):
																					_a     					=    chunk_data
							else:
														_a     					=    data
def 		snake_case_   (UpperCamelCase	:  Any			,						UpperCamelCase	:  Any				):





							'''simple docstring'''


							if name in group.attrs:
														_a     					=    [n.decode('''utf8'''				) if hasattr(UpperCamelCase			,						'''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(UpperCamelCase			,						'''decode'''				) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]]				)
																					chunk_id += 1
							return data

def 		snake_case_   (UpperCamelCase	:  Tuple				):





							'''simple docstring'''


							def _expand_single_ad_tensor(UpperCamelCase	:  List[Any]				):
														if isinstance(UpperCamelCase			,						tf.Tensor				) and t.shape.rank == 1:
																					return tf.expand_dims(UpperCamelCase			,						axis=-1				)
														return t

							return tf.nest.map_structure(_expand_single_ad_tensor			,						UpperCamelCase				)







'''simple docstring'''


from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *

