"""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  _A(		lowerCAmelCase							):
							if isinstance(lowerCAmelCase			,  np.ndarray							):
														return list(tensor.shape							)

							A__       :     Dict								=			tf.shape(lowerCAmelCase							)

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

							A__       :     int								=			tensor.shape.as_list()

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

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

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase=1E-5			,  lowerCAmelCase=-1							):
							# 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(lowerCAmelCase			,  lowerCAmelCase							):
														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__       :     Optional[int]								=			tf.nn.moments(lowerCAmelCase			,  axes=[axis]			,  keepdims=lowerCAmelCase							)

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

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

def  _A(		lowerCAmelCase			,  lowerCAmelCase=0			,  lowerCAmelCase=-1							):
							# 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__       :     int								=			tf.shape(lowerCAmelCase							)
							A__       :     str								=			tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]							)
							A__       :     Any								=			tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]			,  axis=0							)
							return tf.reshape(lowerCAmelCase			,  lowerCAmelCase							)

def  _A(		lowerCAmelCase							):
							if not isinstance(lowerCAmelCase			,  tf.Tensor							):
														A__       :     Dict								=			tf.convert_to_tensor(lowerCAmelCase							)  # Catches stray NumPy inputs
							if encoder_attention_mask.shape.rank == 3:
														A__       :     Optional[Any]								=			encoder_attention_mask[:, None, :, :]
							if encoder_attention_mask.shape.rank == 2:
														A__       :     List[Any]								=			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__       :     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(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase = "input_ids"							):
							tf.debugging.assert_less(
							    lowerCAmelCase			,  tf.cast(lowerCAmelCase			,  dtype=tensor.dtype							)			,  message=(
							        F'''The maximum value of {tensor_name} ({tf.math.reduce_max(lowerCAmelCase							)}) must be smaller than the embedding '''
							        F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
							    )			,  )

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     List[str]								=			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__       :     Optional[Any]								=			[x for x in data if len(lowerCAmelCase							) > 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__       :     int								=			np.asarray(lowerCAmelCase							)

							A__       :     int								=			1
							A__       :     List[Any]								=			np.array_split(lowerCAmelCase			,  lowerCAmelCase							)

							# 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__       :     Dict								=			np.array_split(lowerCAmelCase			,  lowerCAmelCase							)

							if num_chunks > 1:
														for chunk_id, chunk_data in enumerate(lowerCAmelCase							):
																					A__       :     Optional[Any]								=			chunk_data
							else:
														A__       :     Dict								=			data

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



def  _A(		lowerCAmelCase							):

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

							return tf.nest.map_structure(_expand_single_ad_tensor			,  lowerCAmelCase							)






"""simple docstring"""



import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_UpperCamelCase								=		logging.get_logger(__name__)

_UpperCamelCase								=		{"vocab_file": "sentencepiece.model"}

_UpperCamelCase								=		{
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
}

_UpperCamelCase								=		{
    "google/rembert": 2_56,
}
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Dict					     =				VOCAB_FILES_NAMES
			_UpperCamelCase  :						List[str]					     =				PRETRAINED_VOCAB_FILES_MAP
			_UpperCamelCase  :						Any					     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES





			def __init__(			self   ,      snake_case_   ,      snake_case_=False   ,      snake_case_=True   ,      snake_case_=True   ,      snake_case_="[CLS]"   ,      snake_case_="[SEP]"   ,      snake_case_="[UNK]"   ,      snake_case_="[SEP]"   ,      snake_case_="[PAD]"   ,      snake_case_="[CLS]"   ,      snake_case_="[MASK]"   ,      **snake_case_   ,      ):







										'''simple docstring'''


										super().__init__(
										    do_lower_case=snake_case_   ,      remove_space=snake_case_   ,      keep_accents=snake_case_   ,      bos_token=snake_case_   ,      eos_token=snake_case_   ,      unk_token=snake_case_   ,      sep_token=snake_case_   ,      pad_token=snake_case_   ,      cls_token=snake_case_   ,      mask_token=snake_case_   ,      **snake_case_   ,      )

										A__       :     List[Any]								=			do_lower_case
										A__       :     Dict								=			remove_space
										A__       :     Optional[int]								=			keep_accents
										A__       :     Tuple								=			vocab_file

										A__       :     List[str]								=			spm.SentencePieceProcessor()
										self.sp_model.Load(snake_case_							)





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return len(self.sp_model							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			{self.convert_ids_to_tokens(snake_case_							): i for i in range(self.vocab_size							)}
										vocab.update(self.added_tokens_encoder							)
										return vocab





			def __getstate__(			self							):







										'''simple docstring'''


										A__       :     str								=			self.__dict__.copy()
										A__       :     Tuple								=			None
										return state





			def __setstate__(			self   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Dict								=			d
										A__       :     int								=			spm.SentencePieceProcessor()
										self.sp_model.Load(self.vocab_file							)





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_=False							):







										'''simple docstring'''


										A__       :     Tuple								=			self.sp_model.EncodeAsPieces(snake_case_							)
										return pieces





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										return self.sp_model.PieceToId(snake_case_							)





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										return self.sp_model.IdToPiece(snake_case_							)





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										A__       :     str								=			self.sp_model.decode_pieces(snake_case_							)
										return out_string





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_ = None							):







										'''simple docstring'''


										A__       :     List[Any]								=			[self.sep_token_id]
										A__       :     str								=			[self.cls_token_id]
										if token_ids_a is None:
																	return cls + token_ids_a + sep
										return cls + token_ids_a + sep + token_ids_a + sep





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_ = None   ,      snake_case_ = False							):







										'''simple docstring'''


										if already_has_special_tokens:
																	if token_ids_a is not None:
																								raise ValueError(
																								    """You should not supply a second sequence if the provided sequence of """
																								    """ids is already formatted with special tokens for the model."""							)
																	return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

										if token_ids_a is not None:
																	return [1] + ([0] * len(snake_case_							)) + [1] + ([0] * len(snake_case_							)) + [1]
										return [1] + ([0] * len(snake_case_							)) + [1]





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_ = None							):







										'''simple docstring'''


										A__       :     int								=			[self.sep_token_id]
										A__       :     Any								=			[self.cls_token_id]

										if token_ids_a is None:
																	return len(cls + token_ids_a + sep							) * [0]
										return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_ = None							):







										'''simple docstring'''


										if not os.path.isdir(snake_case_							):
																	logger.error("""Vocabulary path ({}) should be a directory""".format(snake_case_							)							)
																	return
										A__       :     Any								=			os.path.join(
										    snake_case_   ,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

										if os.path.abspath(self.vocab_file							) != os.path.abspath(snake_case_							):
																	copyfile(self.vocab_file   ,      snake_case_							)

										return (out_vocab_file,)





