




"""simple docstring"""






from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


UpperCamelCase__						=			logging.get_logger(__name__)





def 							UpperCAmelCase		(		snake_case			:					Any    ):



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

					_lowerCAmelCase:Optional[int]									=  tf.shape(snake_case    )

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

					_lowerCAmelCase:List[Any]									=  tensor.shape.as_list()

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





def 							UpperCAmelCase		(		snake_case			:					int      ,       snake_case			:					Any = None      ,       snake_case			:					Union[str, Any] = None    ):
					return tf.nn.softmax(logits=logits + 1e-9      ,       axis=snake_case      ,       name=snake_case    )





def 							UpperCAmelCase		(		snake_case			:					Union[str, Any]      ,       snake_case			:					Union[str, Any]      ,       snake_case			:					Optional[int]      ,       snake_case			:					Tuple=1e-5      ,       snake_case			:					str=-1    ):



					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
					_lowerCAmelCase:str									=  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
										_lowerCAmelCase:Dict									=  [1] * inputs.shape.rank
										_lowerCAmelCase:List[Any]									=  shape_list(snake_case    )[axis]
										_lowerCAmelCase:Optional[Any]									=  tf.reshape(snake_case      ,       snake_case    )
										_lowerCAmelCase:Dict									=  tf.reshape(snake_case      ,       snake_case    )

					# Compute layer normalization using the batch_normalization
					# function.
					_lowerCAmelCase:Union[str, Any]									=  tf.nn.batch_normalization(
					    snake_case      ,       snake_case      ,       snake_case      ,       offset=snake_case      ,       scale=snake_case      ,       variance_epsilon=snake_case      ,       )
					return outputs





def 							UpperCAmelCase		(		snake_case			:					Union[str, Any]      ,       snake_case			:					List[Any]=0      ,       snake_case			:					List[Any]=-1    ):



					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

					_lowerCAmelCase:Dict									=  tf.shape(snake_case    )
					_lowerCAmelCase:List[Any]									=  tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]    )
					_lowerCAmelCase: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 							UpperCAmelCase		(		snake_case			:					Tuple    ):



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

					return encoder_extended_attention_mask





def 							UpperCAmelCase		(		snake_case			:					List[str]      ,       snake_case			:					Dict      ,       snake_case			:					List[Any] = "input_ids"    ):
					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 							UpperCAmelCase		(		snake_case			:					int      ,       snake_case			:					Dict      ,       snake_case			:					Optional[int]    ):
					_lowerCAmelCase:Union[str, Any]									=  64512
					# 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.
					_lowerCAmelCase:Optional[int]									=  [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}'    )

					_lowerCAmelCase:Dict									=  np.asarray(snake_case    )

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

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





def 							UpperCAmelCase		(		snake_case			:					Optional[int]      ,       snake_case			:					Dict    ):



					if name in group.attrs:
										_lowerCAmelCase:Tuple									=  [n.decode('''utf8'''    ) if hasattr(snake_case      ,       '''decode'''    ) else n for n in group.attrs[name]]
					else:
										_lowerCAmelCase:List[str]									=  []
										_lowerCAmelCase:Dict									=  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 							UpperCAmelCase		(		snake_case			:					int    ):



					def _expand_single_ad_tensor(snake_case			:					Optional[int]    ):
										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"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__						=			logging.get_logger(__name__)

UpperCamelCase__						=			{
    '''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}




class   a__			(       UpperCamelCase_  ):
						snake_case__             = '''roc_bert'''





						def __init__(							self      :	Union[str, Any]     ,a__      :	Union[str, Any]=3_0522     ,a__      :	List[Any]=768     ,a__      :	Tuple=12     ,a__      :	Optional[int]=12     ,a__      :	Any=3072     ,a__      :	Optional[int]="gelu"     ,a__      :	Union[str, Any]=0.1     ,a__      :	List[str]=0.1     ,a__      :	Dict=512     ,a__      :	int=2     ,a__      :	Dict=0.02     ,a__      :	Dict=1E-12     ,a__      :	int=True     ,a__      :	Optional[int]=0     ,a__      :	Union[str, Any]="absolute"     ,a__      :	List[Any]=None     ,a__      :	str=True     ,a__      :	str=True     ,a__      :	List[str]=768     ,a__      :	Optional[int]=910     ,a__      :	Any=512     ,a__      :	str=2_4858     ,a__      :	List[str]=True     ,**a__      :	str     ,)						->       List[str]:




											"""simple docstring"""







											_lowerCAmelCase:Tuple									=  vocab_size
											_lowerCAmelCase:Any									=  max_position_embeddings
											_lowerCAmelCase:Union[str, Any]									=  hidden_size
											_lowerCAmelCase:Optional[Any]									=  num_hidden_layers
											_lowerCAmelCase:int									=  num_attention_heads
											_lowerCAmelCase:int									=  intermediate_size
											_lowerCAmelCase:Union[str, Any]									=  hidden_act
											_lowerCAmelCase:Any									=  hidden_dropout_prob
											_lowerCAmelCase:List[Any]									=  attention_probs_dropout_prob
											_lowerCAmelCase:List[Any]									=  initializer_range
											_lowerCAmelCase:Dict									=  type_vocab_size
											_lowerCAmelCase:Dict									=  layer_norm_eps
											_lowerCAmelCase:str									=  use_cache
											_lowerCAmelCase:Any									=  enable_pronunciation
											_lowerCAmelCase:List[str]									=  enable_shape
											_lowerCAmelCase:Optional[int]									=  pronunciation_embed_dim
											_lowerCAmelCase:Union[str, Any]									=  pronunciation_vocab_size
											_lowerCAmelCase:str									=  shape_embed_dim
											_lowerCAmelCase:List[Any]									=  shape_vocab_size
											_lowerCAmelCase:str									=  concat_input
											_lowerCAmelCase:Optional[int]									=  position_embedding_type
											_lowerCAmelCase:Any									=  classifier_dropout
											super().__init__(pad_token_id=a__     ,**a__)






