

from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


lowerCAmelCase							:  Optional[int]            =						logging.get_logger(__name__)







def       A_				(   _UpperCAmelCase   ):
						if isinstance(_UpperCAmelCase    ,     np.ndarray   ):
												return list(tensor.shape   )

						SCREAMING_SNAKE_CASE_:			Any   		=	tf.shape(_UpperCAmelCase   )

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

						SCREAMING_SNAKE_CASE_:			Optional[int]   		=	tensor.shape.as_list()

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







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase = None    ,     _UpperCAmelCase = None   ):
						return tf.nn.softmax(logits=logits + 1e-9    ,     axis=_UpperCAmelCase    ,     name=_UpperCAmelCase   )







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase=1e-5    ,     _UpperCAmelCase=-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(_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
						SCREAMING_SNAKE_CASE_:			Optional[int]   		=	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
												SCREAMING_SNAKE_CASE_:			List[str]   		=	[1] * inputs.shape.rank
												SCREAMING_SNAKE_CASE_:			Optional[int]   		=	shape_list(_UpperCAmelCase   )[axis]
												SCREAMING_SNAKE_CASE_:			int   		=	tf.reshape(_UpperCAmelCase    ,     _UpperCAmelCase   )
												SCREAMING_SNAKE_CASE_:			int   		=	tf.reshape(_UpperCAmelCase    ,     _UpperCAmelCase   )

						# Compute layer normalization using the batch_normalization
						# function.
						SCREAMING_SNAKE_CASE_:			List[Any]   		=	tf.nn.batch_normalization(
						    _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     offset=_UpperCAmelCase    ,     scale=_UpperCAmelCase    ,     variance_epsilon=_UpperCAmelCase    ,     )
						return outputs







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase=0    ,     _UpperCAmelCase=-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

						SCREAMING_SNAKE_CASE_:			List[str]   		=	tf.shape(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			List[Any]   		=	tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]   )
						SCREAMING_SNAKE_CASE_:			Tuple   		=	tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]    ,     axis=0   )
						return tf.reshape(_UpperCAmelCase    ,     _UpperCAmelCase   )







def       A_				(   _UpperCAmelCase   ):
						if not isinstance(_UpperCAmelCase    ,     tf.Tensor   ):
												SCREAMING_SNAKE_CASE_:			Any   		=	tf.convert_to_tensor(_UpperCAmelCase   )  # Catches stray NumPy inputs
						if encoder_attention_mask.shape.rank == 3:
												SCREAMING_SNAKE_CASE_:			Tuple   		=	encoder_attention_mask[:, None, :, :]
						if encoder_attention_mask.shape.rank == 2:
												SCREAMING_SNAKE_CASE_:			str   		=	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))
						SCREAMING_SNAKE_CASE_:			Union[str, Any]   		=	(
						    tf.cast(1    ,     encoder_attention_mask.dtype   ) - encoder_extended_attention_mask
						) * encoder_extended_attention_mask.dtype.min

						return encoder_extended_attention_mask







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase = "input_ids"   ):
						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       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Union[str, Any]   		=	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.
						SCREAMING_SNAKE_CASE_:			Optional[Any]   		=	[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}"   )

						SCREAMING_SNAKE_CASE_:			Union[str, Any]   		=	np.asarray(_UpperCAmelCase   )

						SCREAMING_SNAKE_CASE_:			str   		=	1
						SCREAMING_SNAKE_CASE_:			str   		=	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
												SCREAMING_SNAKE_CASE_:			Any   		=	np.array_split(_UpperCAmelCase    ,     _UpperCAmelCase   )

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







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase   ):
						if name in group.attrs:
												SCREAMING_SNAKE_CASE_:			str   		=	[n.decode("utf8"   ) if hasattr(_UpperCAmelCase    ,     "decode"   ) else n for n in group.attrs[name]]
						else:
												SCREAMING_SNAKE_CASE_:			List[Any]   		=	[]
												SCREAMING_SNAKE_CASE_:			List[Any]   		=	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       A_				(   _UpperCAmelCase   ):

						def _expand_single_ad_tensor(_UpperCAmelCase   ):
												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   )









import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer


lowerCAmelCase							:  Any            =						logging.get_logger(__name__)

lowerCAmelCase							:  Dict            =						{"""vocab_file""": """vocab.txt"""}

lowerCAmelCase							:  List[str]            =						{
    """vocab_file""": {
        """YituTech/conv-bert-base""": """https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt""",
        """YituTech/conv-bert-medium-small""": (
            """https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt"""
        ),
        """YituTech/conv-bert-small""": """https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt""",
    }
}

lowerCAmelCase							:  List[Any]            =						{
    """YituTech/conv-bert-base""": 512,
    """YituTech/conv-bert-medium-small""": 512,
    """YituTech/conv-bert-small""": 512,
}


lowerCAmelCase							:  Tuple            =						{
    """YituTech/conv-bert-base""": {"""do_lower_case""": True},
    """YituTech/conv-bert-medium-small""": {"""do_lower_case""": True},
    """YituTech/conv-bert-small""": {"""do_lower_case""": True},
}



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							str      							=					VOCAB_FILES_NAMES
			_UpperCAmelCase       :							List[str]      							=					PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase       :							List[Any]      							=					PRETRAINED_INIT_CONFIGURATION
			_UpperCAmelCase       :							Optional[Any]      							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase       :							Dict      							=					ConvBertTokenizer



			def __init__(			self  :					Optional[Any]       ,						lowerCAmelCase__  :					Optional[int]=None       ,						lowerCAmelCase__  :					List[str]=None       ,						lowerCAmelCase__  :					str=True       ,						lowerCAmelCase__  :					Any="[UNK]"       ,						lowerCAmelCase__  :					Optional[Any]="[SEP]"       ,						lowerCAmelCase__  :					Any="[PAD]"       ,						lowerCAmelCase__  :					Dict="[CLS]"       ,						lowerCAmelCase__  :					Dict="[MASK]"       ,						lowerCAmelCase__  :					Tuple=True       ,						lowerCAmelCase__  :					str=None       ,						**lowerCAmelCase__  :					Dict       ,						):
									super().__init__(
									    lowerCAmelCase__       ,						tokenizer_file=lowerCAmelCase__       ,						do_lower_case=lowerCAmelCase__       ,						unk_token=lowerCAmelCase__       ,						sep_token=lowerCAmelCase__       ,						pad_token=lowerCAmelCase__       ,						cls_token=lowerCAmelCase__       ,						mask_token=lowerCAmelCase__       ,						tokenize_chinese_chars=lowerCAmelCase__       ,						strip_accents=lowerCAmelCase__       ,						**lowerCAmelCase__       ,						)

									SCREAMING_SNAKE_CASE_:			List[str]  		=	json.loads(self.backend_tokenizer.normalizer.__getstate__())
									if (
									    normalizer_state.get("lowercase"       ,						lowerCAmelCase__) != do_lower_case
									    or normalizer_state.get("strip_accents"       ,						lowerCAmelCase__) != strip_accents
									    or normalizer_state.get("handle_chinese_chars"       ,						lowerCAmelCase__) != tokenize_chinese_chars
									):
															SCREAMING_SNAKE_CASE_:			Optional[int]  		=	getattr(lowerCAmelCase__       ,						normalizer_state.pop("type"))
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	do_lower_case
															SCREAMING_SNAKE_CASE_:			List[str]  		=	strip_accents
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	tokenize_chinese_chars
															SCREAMING_SNAKE_CASE_:			Optional[int]  		=	normalizer_class(**lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	do_lower_case



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					Optional[int]       ,						lowerCAmelCase__  :					Union[str, Any]=None):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	[self.cls_token_id] + token_ids_a + [self.sep_token_id]

									if token_ids_a:
															output += token_ids_a + [self.sep_token_id]

									return output



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]       ,						lowerCAmelCase__  :					List[int]       ,						lowerCAmelCase__  :					Optional[List[int]] = None):
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	[self.sep_token_id]
									SCREAMING_SNAKE_CASE_:			int  		=	[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 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]       ,						lowerCAmelCase__  :					str       ,						lowerCAmelCase__  :					Optional[str] = None):
									SCREAMING_SNAKE_CASE_:			Any  		=	self._tokenizer.model.save(lowerCAmelCase__       ,						name=lowerCAmelCase__)
									return tuple(lowerCAmelCase__)






