

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				):


		"""simple docstring"""




		def __init__(					self						,							__snake_case						,							__snake_case						,							__snake_case = None						,							__snake_case = None						,							__snake_case = None						,							__snake_case = "longest"						,							__snake_case = True						,							__snake_case = 5_12						,							__snake_case = None						,							__snake_case = True						,							__snake_case = True						,							__snake_case = True						,							**__snake_case						,							):
			super().__init__()
			if use_fast_bert_tokenizer:
				_UpperCamelCase :		Any								=    FastBertTokenizer(
				    __snake_case						,							token_out_type=tf.intaa						,							lower_case_nfd_strip_accents=__snake_case						,							**__snake_case)
			else:
				_UpperCamelCase :		Dict								=    tf.lookup.StaticVocabularyTable(
				    tf.lookup.KeyValueTensorInitializer(
				        keys=__snake_case						,							key_dtype=tf.string						,							values=tf.range(tf.size(__snake_case						,							out_type=tf.intaa)						,							dtype=tf.intaa)						,							value_dtype=tf.intaa						,							)						,							num_oov_buckets=1						,							)
				_UpperCamelCase :		Any								=    BertTokenizerLayer(
				    __snake_case						,							token_out_type=tf.intaa						,							lower_case=__snake_case						,							**__snake_case)

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


		@classmethod
		def 		A__       (					cls						,							__snake_case						,							**__snake_case):  # noqa: F821
			_UpperCamelCase :		int								=    kwargs.pop('do_lower_case'						,							__snake_case)
			_UpperCamelCase :		List[str]								=    tokenizer.do_lower_case if do_lower_case is None else do_lower_case
			_UpperCamelCase :		Any								=    kwargs.pop('cls_token_id'						,							__snake_case)
			_UpperCamelCase :		Tuple								=    tokenizer.cls_token_id if cls_token_id is None else cls_token_id
			_UpperCamelCase :		str								=    kwargs.pop('sep_token_id'						,							__snake_case)
			_UpperCamelCase :		Optional[Any]								=    tokenizer.sep_token_id if sep_token_id is None else sep_token_id
			_UpperCamelCase :		List[str]								=    kwargs.pop('pad_token_id'						,							__snake_case)
			_UpperCamelCase :		Any								=    tokenizer.pad_token_id if pad_token_id is None else pad_token_id

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


		@classmethod
		def 		A__       (					cls						,							__snake_case						,							*__snake_case						,							**__snake_case):
			try:
				_UpperCamelCase :		Tuple								=    BertTokenizer.from_pretrained(__snake_case						,							*__snake_case						,							**__snake_case)
			except:  # noqa: E722
				from .tokenization_bert_fast import BertTokenizerFast

				_UpperCamelCase :		Optional[Any]								=    BertTokenizerFast.from_pretrained(__snake_case						,							*__snake_case						,							**__snake_case)
			return cls.from_tokenizer(__snake_case						,							**__snake_case)


		def 		A__       (					self						,							__snake_case):
			if self.do_lower_case:
				_UpperCamelCase :		Dict								=    case_fold_utfa(__snake_case)
			_UpperCamelCase :		Optional[Any]								=    self.tf_tokenizer.tokenize(__snake_case)
			return tokens.merge_dims(1						,							-1)


		def 		A__       (					self						,							__snake_case						,							__snake_case=None						,							__snake_case=None						,							__snake_case=None						,							__snake_case=None						,							__snake_case=None						,							__snake_case=None						,							__snake_case=None						,							):
			if padding is None:
				_UpperCamelCase :		Dict								=    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:
				_UpperCamelCase :		Tuple								=    self.max_length
			if truncation is None:
				_UpperCamelCase :		Dict								=    self.truncation
			if pad_to_multiple_of is None:
				_UpperCamelCase :		Dict								=    self.pad_to_multiple_of
			if return_token_type_ids is None:
				_UpperCamelCase :		Optional[Any]								=    self.return_token_type_ids
			if return_attention_mask is None:
				_UpperCamelCase :		Dict								=    self.return_attention_mask
			if not isinstance(__snake_case						,							tf.Tensor):
				_UpperCamelCase :		List[str]								=    tf.convert_to_tensor(__snake_case)
			if text_pair is not None and not isinstance(__snake_case						,							tf.Tensor):
				_UpperCamelCase :		int								=    tf.convert_to_tensor(__snake_case)
			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:
				_UpperCamelCase						,		_UpperCamelCase :		Optional[Any]								=    text[:, 0], text[:, 1]
			_UpperCamelCase :		List[Any]								=    self.unpaired_tokenize(__snake_case)
			if text_pair is None:  # Unpaired text
				if truncation:
					_UpperCamelCase :		Any								=    text[:, : max_length - 2]  # Allow room for special tokens
				_UpperCamelCase						,		_UpperCamelCase :		int								=    combine_segments(
				    (text,)						,							start_of_sequence_id=self.cls_token_id						,							end_of_segment_id=self.sep_token_id)
			else:  # Paired text
				_UpperCamelCase :		Tuple								=    self.unpaired_tokenize(__snake_case)
				if truncation:
					_UpperCamelCase						,		_UpperCamelCase :		Dict								=    self.paired_trimmer.trim([text, text_pair])
				_UpperCamelCase						,		_UpperCamelCase :		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":
				_UpperCamelCase :		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
					_UpperCamelCase :		Optional[int]								=    pad_to_multiple_of * (-tf.math.floordiv(-pad_length						,							__snake_case))
			else:
				_UpperCamelCase :		List[Any]								=    max_length

			_UpperCamelCase						,		_UpperCamelCase :		List[Any]								=    pad_model_inputs(__snake_case						,							max_seq_length=__snake_case						,							pad_value=self.pad_token_id)
			_UpperCamelCase :		Dict								=    {'input_ids': input_ids}
			if return_attention_mask:
				_UpperCamelCase :		str								=    attention_mask
			if return_token_type_ids:
				_UpperCamelCase						,		_UpperCamelCase :		Any								=    pad_model_inputs(
				    __snake_case						,							max_seq_length=__snake_case						,							pad_value=self.pad_token_id)
				_UpperCamelCase :		str								=    token_type_ids
			return output






		def 		A__       (					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"""
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

from .config_args import default_config_file, load_config_from_file
from .config_utils import SubcommandHelpFormatter


__a           =							"Update an existing config file with the latest defaults while maintaining the old configuration."
def   A_    (     _lowercase      ):







						'''simple docstring'''


						snake_case_					:int		=     args.config_file
						if config_file is None and Path(_lowercase      ).exists():
												snake_case_					:str		=     default_config_file
						elif not Path(_lowercase      ).exists():
												raise ValueError(f"""The passed config file located at {config_file} doesn't exist."""      )
						snake_case_					:str		=     load_config_from_file(_lowercase      )

						if config_file.endswith(""".json"""      ):
												config.to_json_file(_lowercase      )
						else:
												config.to_yaml_file(_lowercase      )
						return config_file
def   A_    (     _lowercase,   _lowercase      ):







						'''simple docstring'''


						snake_case_					:Any		=     parser.add_parser("""update""",   parents=_lowercase,   help=_lowercase,   formatter_class=_lowercase      )
						parser.add_argument(
						    """--config_file""",   default=_lowercase,   help=(
						        """The path to the config file to update. Will default to a file named default_config.yaml in the cache """
						        """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
						        """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
						        """with 'huggingface'."""
						    ),   )

						parser.set_defaults(func=_lowercase      )
						return parser

def   A_    (     _lowercase      ):







						'''simple docstring'''


						snake_case_					:str		=     update_config(_lowercase      )
						print(f"""Sucessfully updated the configuration file at {config_file}."""      )
