
'''simple docstring'''


import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer

class 		__lowercase			(					tf.keras.layers.Layer      ):

			def __init__(self     ,				A     ,				A     ,				A = None     ,				A = None     ,				A = None     ,				A = "longest"     ,				A = True     ,				A = 5_1_2     ,				A = None     ,				A = True     ,				A = True     ,				A = True     ,				**A     ,				):
								super().__init__()
								if use_fast_bert_tokenizer:
													lowerCamelCase_   : int       =						FastBertTokenizer(
													    A     ,				token_out_type=tf.intaa     ,				lower_case_nfd_strip_accents=A     ,				**A )
								else:
													lowerCamelCase_   : Optional[int]       =						tf.lookup.StaticVocabularyTable(
													    tf.lookup.KeyValueTensorInitializer(
													        keys=A     ,				key_dtype=tf.string     ,				values=tf.range(tf.size(A     ,				out_type=tf.intaa )     ,				dtype=tf.intaa )     ,				value_dtype=tf.intaa     ,				)     ,				num_oov_buckets=1     ,				)
													lowerCamelCase_   : Optional[Any]       =						BertTokenizerLayer(
													    A     ,				token_out_type=tf.intaa     ,				lower_case=A     ,				**A )

								lowerCamelCase_   : Tuple       =						vocab_list
								lowerCamelCase_   : Any       =						do_lower_case
								lowerCamelCase_   : int       =						cls_token_id or vocab_list.index('''[CLS]''' )
								lowerCamelCase_   : Any       =						sep_token_id or vocab_list.index('''[SEP]''' )
								lowerCamelCase_   : List[Any]       =						pad_token_id or vocab_list.index('''[PAD]''' )
								lowerCamelCase_   : List[Any]       =						ShrinkLongestTrimmer(max_length - 3     ,				axis=1 )  # Allow room for special tokens
								lowerCamelCase_   : List[Any]       =						max_length
								lowerCamelCase_   : Union[str, Any]       =						padding
								lowerCamelCase_   : Dict       =						truncation
								lowerCamelCase_   : List[Any]       =						pad_to_multiple_of
								lowerCamelCase_   : Optional[Any]       =						return_token_type_ids
								lowerCamelCase_   : Optional[Any]       =						return_attention_mask

			@classmethod
			def     UpperCAmelCase__	(cls     ,				A     ,				**A ):  # noqa: F821
								lowerCamelCase_   : Union[str, Any]       =						kwargs.pop('''do_lower_case'''     ,				A )
								lowerCamelCase_   : Optional[int]       =						tokenizer.do_lower_case if do_lower_case is None else do_lower_case
								lowerCamelCase_   : Any       =						kwargs.pop('''cls_token_id'''     ,				A )
								lowerCamelCase_   : int       =						tokenizer.cls_token_id if cls_token_id is None else cls_token_id
								lowerCamelCase_   : Optional[int]       =						kwargs.pop('''sep_token_id'''     ,				A )
								lowerCamelCase_   : Union[str, Any]       =						tokenizer.sep_token_id if sep_token_id is None else sep_token_id
								lowerCamelCase_   : Dict       =						kwargs.pop('''pad_token_id'''     ,				A )
								lowerCamelCase_   : List[Any]       =						tokenizer.pad_token_id if pad_token_id is None else pad_token_id

								lowerCamelCase_   : List[str]       =						tokenizer.get_vocab()
								lowerCamelCase_   : Any       =						sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]     ,				key=lambda A : x[1] )
								lowerCamelCase_   : List[str]       =						[entry[0] for entry in vocab]
								return cls(
								    vocab_list=A     ,				do_lower_case=A     ,				cls_token_id=A     ,				sep_token_id=A     ,				pad_token_id=A     ,				**A     ,				)

			@classmethod
			def     UpperCAmelCase__	(cls     ,				A     ,				*A     ,				**A ):
								try:
													lowerCamelCase_   : Optional[Any]       =						BertTokenizer.from_pretrained(A     ,				*A     ,				**A )
								except:  # noqa: E722
													from .tokenization_bert_fast import BertTokenizerFast

													lowerCamelCase_   : Optional[int]       =						BertTokenizerFast.from_pretrained(A     ,				*A     ,				**A )
								return cls.from_tokenizer(A     ,				**A )

			def     UpperCAmelCase__	(self     ,				A ):
								if self.do_lower_case:
													lowerCamelCase_   : List[Any]       =						case_fold_utfa(A )
								lowerCamelCase_   : List[Any]       =						self.tf_tokenizer.tokenize(A )
								return tokens.merge_dims(1     ,				-1 )

			def     UpperCAmelCase__	(self     ,				A     ,				A=None     ,				A=None     ,				A=None     ,				A=None     ,				A=None     ,				A=None     ,				A=None     ,				):
								if padding is None:
													lowerCamelCase_   : str       =						self.padding
								if padding not in ("longest", "max_length"):
													raise ValueError('''Padding must be either \'longest\' or \'max_length\'!''' )
								if max_length is not None and text_pair is not None:
													# Because we have to instantiate a Trimmer to do it properly
													raise ValueError('''max_length cannot be overridden at call time when truncating paired texts!''' )
								if max_length is None:
													lowerCamelCase_   : Optional[int]       =						self.max_length
								if truncation is None:
													lowerCamelCase_   : Any       =						self.truncation
								if pad_to_multiple_of is None:
													lowerCamelCase_   : Optional[int]       =						self.pad_to_multiple_of
								if return_token_type_ids is None:
													lowerCamelCase_   : Union[str, Any]       =						self.return_token_type_ids
								if return_attention_mask is None:
													lowerCamelCase_   : Optional[int]       =						self.return_attention_mask
								if not isinstance(A     ,				tf.Tensor ):
													lowerCamelCase_   : Dict       =						tf.convert_to_tensor(A )
								if text_pair is not None and not isinstance(A     ,				tf.Tensor ):
													lowerCamelCase_   : Dict       =						tf.convert_to_tensor(A )
								if text_pair is not None:
													if text.shape.rank > 1:
																		raise ValueError('''text argument should not be multidimensional when a text pair is supplied!''' )
													if text_pair.shape.rank > 1:
																		raise ValueError('''text_pair should not be multidimensional!''' )
								if text.shape.rank == 2:
													lowerCamelCase_   : Union[str, Any]       =						text[:, 0], text[:, 1]
								lowerCamelCase_   : Union[str, Any]       =						self.unpaired_tokenize(A )
								if text_pair is None:  # Unpaired text
													if truncation:
																		lowerCamelCase_   : Union[str, Any]       =						text[:, : max_length - 2]  # Allow room for special tokens
													lowerCamelCase_   : Optional[int]       =						combine_segments(
													    (text,)     ,				start_of_sequence_id=self.cls_token_id     ,				end_of_segment_id=self.sep_token_id )
								else:  # Paired text
													lowerCamelCase_   : Optional[int]       =						self.unpaired_tokenize(A )
													if truncation:
																		lowerCamelCase_   : List[str]       =						self.paired_trimmer.trim([text, text_pair] )
													lowerCamelCase_   : Optional[Any]       =						combine_segments(
													    (text, text_pair)     ,				start_of_sequence_id=self.cls_token_id     ,				end_of_segment_id=self.sep_token_id )
								if padding == "longest":
													lowerCamelCase_   : str       =						input_ids.bounding_shape(axis=1 )
													if pad_to_multiple_of is not None:
																		# No ceiling division in tensorflow, so we negate floordiv instead
																		lowerCamelCase_   : Optional[Any]       =						pad_to_multiple_of * (-tf.math.floordiv(-pad_length     ,				A ))
								else:
													lowerCamelCase_   : Any       =						max_length

								lowerCamelCase_   : str       =						pad_model_inputs(A     ,				max_seq_length=A     ,				pad_value=self.pad_token_id )
								lowerCamelCase_   : List[str]       =						{"input_ids": input_ids}
								if return_attention_mask:
													lowerCamelCase_   : Union[str, Any]       =						attention_mask
								if return_token_type_ids:
													lowerCamelCase_   : Optional[int]       =						pad_model_inputs(
													    A     ,				max_seq_length=A     ,				pad_value=self.pad_token_id )
													lowerCamelCase_   : int       =						token_type_ids
								return output






			def     UpperCAmelCase__	(self ):
								return {
								    "vocab_list": self.vocab_list,
								    "do_lower_case": self.do_lower_case,
								    "cls_token_id": self.cls_token_id,
								    "sep_token_id": self.sep_token_id,
								    "pad_token_id": self.pad_token_id,
								}




'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowercase							:				Any					=       {
    '''configuration_camembert''': ['''CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CamembertConfig''', '''CamembertOnnxConfig'''],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				Any					=       ['''CamembertTokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				str					=       ['''CamembertTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				List[Any]					=       [
					    '''CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''CamembertForCausalLM''',
					    '''CamembertForMaskedLM''',
					    '''CamembertForMultipleChoice''',
					    '''CamembertForQuestionAnswering''',
					    '''CamembertForSequenceClassification''',
					    '''CamembertForTokenClassification''',
					    '''CamembertModel''',
					    '''CamembertPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				Any					=       [
					    '''TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFCamembertForCausalLM''',
					    '''TFCamembertForMaskedLM''',
					    '''TFCamembertForMultipleChoice''',
					    '''TFCamembertForQuestionAnswering''',
					    '''TFCamembertForSequenceClassification''',
					    '''TFCamembertForTokenClassification''',
					    '''TFCamembertModel''',
					    '''TFCamembertPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_camembert import CamembertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_camembert_fast import CamembertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_camembert import (
										    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CamembertForCausalLM,
										    CamembertForMaskedLM,
										    CamembertForMultipleChoice,
										    CamembertForQuestionAnswering,
										    CamembertForSequenceClassification,
										    CamembertForTokenClassification,
										    CamembertModel,
										    CamembertPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_camembert import (
										    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFCamembertForCausalLM,
										    TFCamembertForMaskedLM,
										    TFCamembertForMultipleChoice,
										    TFCamembertForQuestionAnswering,
										    TFCamembertForSequenceClassification,
										    TFCamembertForTokenClassification,
										    TFCamembertModel,
										    TFCamembertPreTrainedModel,
										)

else:
					import sys

					__lowercase							:				Union[str, Any]					=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


