




"""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 :       Tuple   ,  _lowerCamelCase :       List   ,  _lowerCamelCase :       bool   ,  _lowerCamelCase :       int = None   ,  _lowerCamelCase :       int = None   ,  _lowerCamelCase :       int = None   ,  _lowerCamelCase :       str = "longest"   ,  _lowerCamelCase :       bool = True   ,  _lowerCamelCase :       int = 5_12   ,  _lowerCamelCase :       int = None   ,  _lowerCamelCase :       bool = True   ,  _lowerCamelCase :       bool = True   ,  _lowerCamelCase :       bool = True   ,  **_lowerCamelCase :       Optional[Any]   ,  ):



					"""simple docstring"""

					super().__init__()
					if use_fast_bert_tokenizer:
						A_	:					Optional[Any]						 =		FastBertTokenizer(
						    _lowerCamelCase   ,  token_out_type=tf.intaa   ,  lower_case_nfd_strip_accents=_lowerCamelCase   ,  **_lowerCamelCase							)
					else:
						A_	:					List[Any]						 =		tf.lookup.StaticVocabularyTable(
						    tf.lookup.KeyValueTensorInitializer(
						        keys=_lowerCamelCase   ,  key_dtype=tf.string   ,  values=tf.range(tf.size(_lowerCamelCase   ,  out_type=tf.intaa							)   ,  dtype=tf.intaa							)   ,  value_dtype=tf.intaa   ,  )   ,  num_oov_buckets=1   ,  )
						A_	:					Any						 =		BertTokenizerLayer(
						    _lowerCamelCase   ,  token_out_type=tf.intaa   ,  lower_case=_lowerCamelCase   ,  **_lowerCamelCase							)

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







				@classmethod
				def        a_	(				cls :       Optional[Any]   ,  _lowerCamelCase :       "PreTrainedTokenizerBase"   ,  **_lowerCamelCase :       str							):  # noqa: F821



					"""simple docstring"""

					A_	:					Tuple						 =		kwargs.pop('''do_lower_case'''   ,  _lowerCamelCase							)
					A_	:					Union[str, Any]						 =		tokenizer.do_lower_case if do_lower_case is None else do_lower_case
					A_	:					int						 =		kwargs.pop('''cls_token_id'''   ,  _lowerCamelCase							)
					A_	:					List[Any]						 =		tokenizer.cls_token_id if cls_token_id is None else cls_token_id
					A_	:					Union[str, Any]						 =		kwargs.pop('''sep_token_id'''   ,  _lowerCamelCase							)
					A_	:					List[Any]						 =		tokenizer.sep_token_id if sep_token_id is None else sep_token_id
					A_	:					int						 =		kwargs.pop('''pad_token_id'''   ,  _lowerCamelCase							)
					A_	:					List[Any]						 =		tokenizer.pad_token_id if pad_token_id is None else pad_token_id

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







				@classmethod
				def        a_	(				cls :       Optional[Any]   ,  _lowerCamelCase :       Union[str, os.PathLike]   ,  *_lowerCamelCase :       Dict   ,  **_lowerCamelCase :       List[Any]							):



					"""simple docstring"""

					try:
						A_	:					List[Any]						 =		BertTokenizer.from_pretrained(_lowerCamelCase   ,  *_lowerCamelCase   ,  **_lowerCamelCase							)
					except:  # noqa: E722
						from .tokenization_bert_fast import BertTokenizerFast

						A_	:					int						 =		BertTokenizerFast.from_pretrained(_lowerCamelCase   ,  *_lowerCamelCase   ,  **_lowerCamelCase							)
					return cls.from_tokenizer(_lowerCamelCase   ,  **_lowerCamelCase							)







				def        a_	(				self :       Union[str, Any]   ,  _lowerCamelCase :       int							):



					"""simple docstring"""

					if self.do_lower_case:
						A_	:					int						 =		case_fold_utfa(_lowerCamelCase							)
					A_	:					List[Any]						 =		self.tf_tokenizer.tokenize(_lowerCamelCase							)
					return tokens.merge_dims(1   ,  -1							)







				def        a_	(				self :       Union[str, Any]   ,  _lowerCamelCase :       Optional[Any]   ,  _lowerCamelCase :       List[str]=None   ,  _lowerCamelCase :       List[Any]=None   ,  _lowerCamelCase :       Optional[int]=None   ,  _lowerCamelCase :       Union[str, Any]=None   ,  _lowerCamelCase :       int=None   ,  _lowerCamelCase :       Optional[int]=None   ,  _lowerCamelCase :       Optional[Any]=None   ,  ):



					"""simple docstring"""

					if padding is None:
						A_	:					Optional[int]						 =		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:
						A_	:					Any						 =		self.max_length
					if truncation is None:
						A_	:					Dict						 =		self.truncation
					if pad_to_multiple_of is None:
						A_	:					Optional[int]						 =		self.pad_to_multiple_of
					if return_token_type_ids is None:
						A_	:					Optional[int]						 =		self.return_token_type_ids
					if return_attention_mask is None:
						A_	:					List[str]						 =		self.return_attention_mask
					if not isinstance(_lowerCamelCase   ,  tf.Tensor							):
						A_	:					Tuple						 =		tf.convert_to_tensor(_lowerCamelCase							)
					if text_pair is not None and not isinstance(_lowerCamelCase   ,  tf.Tensor							):
						A_	:					Optional[Any]						 =		tf.convert_to_tensor(_lowerCamelCase							)
					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:
						A_	:					List[str]						 =		text[:, 0], text[:, 1]
					A_	:					Union[str, Any]						 =		self.unpaired_tokenize(_lowerCamelCase							)
					if text_pair is None:  # Unpaired text
						if truncation:
							A_	:					List[str]						 =		text[:, : max_length - 2]  # Allow room for special tokens
						A_	:					Optional[Any]						 =		combine_segments(
						    (text,)   ,  start_of_sequence_id=self.cls_token_id   ,  end_of_segment_id=self.sep_token_id							)
					else:  # Paired text
						A_	:					Any						 =		self.unpaired_tokenize(_lowerCamelCase							)
						if truncation:
							A_	:					Union[str, Any]						 =		self.paired_trimmer.trim([text, text_pair]							)
						A_	:					Tuple						 =		combine_segments(
						    (text, text_pair)   ,  start_of_sequence_id=self.cls_token_id   ,  end_of_segment_id=self.sep_token_id							)
					if padding == "longest":
						A_	:					Dict						 =		input_ids.bounding_shape(axis=1							)
						if pad_to_multiple_of is not None:
							# No ceiling division in tensorflow, so we negate floordiv instead
							A_	:					Dict						 =		pad_to_multiple_of * (-tf.math.floordiv(-pad_length   ,  _lowerCamelCase							))
					else:
						A_	:					Optional[Any]						 =		max_length

					A_	:					Dict						 =		pad_model_inputs(_lowerCamelCase   ,  max_seq_length=_lowerCamelCase   ,  pad_value=self.pad_token_id							)
					A_	:					int						 =		{'''input_ids''': input_ids}
					if return_attention_mask:
						A_	:					Dict						 =		attention_mask
					if return_token_type_ids:
						A_	:					Tuple						 =		pad_model_inputs(
						    _lowerCamelCase   ,  max_seq_length=_lowerCamelCase   ,  pad_value=self.pad_token_id							)
						A_	:					Optional[int]						 =		token_type_ids
					return output







				def        a_	(				self :       Union[str, Any]							):



					"""simple docstring"""

					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'''







import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
					snake_case_  :							Union[str, Any]            =	[]

					snake_case_  :							List[Any]            =	os.path.join("tests", "models", "**", "test_modeling_*.py")
					snake_case_  :							int            =	glob.glob(pattern)
					# TODO: deal with TF/Flax too
					snake_case_  :							Optional[int]            =	[
					    x for x in test_files if not (x.startswith("test_modeling_tf_") or x.startswith("test_modeling_flax_"))
					]

					for test_file in test_files:
										snake_case_  :							Any            =	get_tester_classes(test_file)
										for tester_class in tester_classes:
															# A few tester classes don't have `parent` parameter in `__init__`.
															# TODO: deal this better
															try:
																				snake_case_  :							Tuple            =	tester_class(parent=None)
															except Exception:
																				continue
															if hasattr(tester, "get_config"):
																				snake_case_  :							Any            =	tester.get_config()
																				for k, v in config.to_dict().items():
																									if isinstance(v, int):
																														snake_case_  :							int            =	None
																														if k in ["vocab_size"]:
																																			snake_case_  :							Optional[Any]            =	1_00
																														elif k in ["max_position_embeddings"]:
																																			snake_case_  :							Any            =	1_28
																														elif k in ["hidden_size", "d_model"]:
																																			snake_case_  :							str            =	40
																														elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
																																			snake_case_  :							str            =	5
																														if target is not None and v > target:
																																			failures.append(
																																			    f'''{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`'''
																																			    f''' with config["{k}"] = {v} which is too large for testing! Set its value to be smaller'''
																																			    f''' than {target}.'''
																																			)

					if len(failures) > 0:
										raise Exception(f'''There were {len(failures)} failures:\n''' + "\n".join(failures))





