






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 					a					(				tf.keras.layers.Layer     ):



      """simple docstring"""


      def __init__(   self      :    Dict	,   __lowercase      :    List	,   __lowercase      :    bool	,   __lowercase      :    int = None	,   __lowercase      :    int = None	,   __lowercase      :    int = None	,   __lowercase      :    str = "longest"	,   __lowercase      :    bool = True	,   __lowercase      :    int = 512	,   __lowercase      :    int = None	,   __lowercase      :    bool = True	,   __lowercase      :    bool = True	,   __lowercase      :    bool = True	,   **__lowercase      :    List[str]	,   )							->   List[Any]:
        super().__init__()
        if use_fast_bert_tokenizer:
          __UpperCAmelCase							:			Any     =						FastBertTokenizer(
              __lowercase	,   token_out_type=tf.intaa	,   lower_case_nfd_strip_accents=__lowercase	,   **__lowercase      )
        else:
          __UpperCAmelCase							:			Optional[Any]     =						tf.lookup.StaticVocabularyTable(
              tf.lookup.KeyValueTensorInitializer(
                  keys=__lowercase	,   key_dtype=tf.string	,   values=tf.range(tf.size(__lowercase	,   out_type=tf.intaa      )	,   dtype=tf.intaa      )	,   value_dtype=tf.intaa	,   )	,   num_oov_buckets=1	,   )
          __UpperCAmelCase							:			Union[str, Any]     =						BertTokenizerLayer(
              __lowercase	,   token_out_type=tf.intaa	,   lower_case=__lowercase	,   **__lowercase      )

        __UpperCAmelCase							:			int     =						vocab_list
        __UpperCAmelCase							:			str     =						do_lower_case
        __UpperCAmelCase							:			Optional[Any]     =						cls_token_id or vocab_list.index("""[CLS]"""      )
        __UpperCAmelCase							:			int     =						sep_token_id or vocab_list.index("""[SEP]"""      )
        __UpperCAmelCase							:			List[Any]     =						pad_token_id or vocab_list.index("""[PAD]"""      )
        __UpperCAmelCase							:			int     =						ShrinkLongestTrimmer(max_length - 3	,   axis=1      )  # Allow room for special tokens
        __UpperCAmelCase							:			Optional[Any]     =						max_length
        __UpperCAmelCase							:			List[str]     =						padding
        __UpperCAmelCase							:			int     =						truncation
        __UpperCAmelCase							:			str     =						pad_to_multiple_of
        __UpperCAmelCase							:			str     =						return_token_type_ids
        __UpperCAmelCase							:			Optional[Any]     =						return_attention_mask
      @classmethod
      def 			UpperCAmelCase   (   cls      :    int	,   __lowercase      :    "PreTrainedTokenizerBase"	,   **__lowercase      :    Any      )							->   Union[str, Any]:  # noqa: F821
        __UpperCAmelCase							:			Union[str, Any]     =						kwargs.pop("""do_lower_case"""	,   __lowercase      )
        __UpperCAmelCase							:			Dict     =						tokenizer.do_lower_case if do_lower_case is None else do_lower_case
        __UpperCAmelCase							:			Optional[Any]     =						kwargs.pop("""cls_token_id"""	,   __lowercase      )
        __UpperCAmelCase							:			Dict     =						tokenizer.cls_token_id if cls_token_id is None else cls_token_id
        __UpperCAmelCase							:			Union[str, Any]     =						kwargs.pop("""sep_token_id"""	,   __lowercase      )
        __UpperCAmelCase							:			Union[str, Any]     =						tokenizer.sep_token_id if sep_token_id is None else sep_token_id
        __UpperCAmelCase							:			Tuple     =						kwargs.pop("""pad_token_id"""	,   __lowercase      )
        __UpperCAmelCase							:			Tuple     =						tokenizer.pad_token_id if pad_token_id is None else pad_token_id

        __UpperCAmelCase							:			Any     =						tokenizer.get_vocab()
        __UpperCAmelCase							:			Tuple     =						sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]	,   key=lambda __lowercase      : x[1]      )
        __UpperCAmelCase							:			List[str]     =						[entry[0] for entry in vocab]
        return cls(
            vocab_list=__lowercase	,   do_lower_case=__lowercase	,   cls_token_id=__lowercase	,   sep_token_id=__lowercase	,   pad_token_id=__lowercase	,   **__lowercase	,   )
      @classmethod
      def 			UpperCAmelCase   (   cls      :    Optional[int]	,   __lowercase      :    Union[str, os.PathLike]	,   *__lowercase      :    Dict	,   **__lowercase      :    Optional[int]      )							->   Optional[Any]:
        try:
          __UpperCAmelCase							:			Optional[Any]     =						BertTokenizer.from_pretrained(__lowercase	,   *__lowercase	,   **__lowercase      )
        except:  # noqa: E722
          from .tokenization_bert_fast import BertTokenizerFast

          __UpperCAmelCase							:			str     =						BertTokenizerFast.from_pretrained(__lowercase	,   *__lowercase	,   **__lowercase      )
        return cls.from_tokenizer(__lowercase	,   **__lowercase      )
      def 			UpperCAmelCase   (   self      :    str	,   __lowercase      :    Any      )							->   List[str]:
        if self.do_lower_case:
          __UpperCAmelCase							:			Union[str, Any]     =						case_fold_utfa(__lowercase      )
        __UpperCAmelCase							:			Optional[int]     =						self.tf_tokenizer.tokenize(__lowercase      )
        return tokens.merge_dims(1	,   -1      )
      def 			UpperCAmelCase   (   self      :    str	,   __lowercase      :    str	,   __lowercase      :    Dict=None	,   __lowercase      :    Any=None	,   __lowercase      :    List[str]=None	,   __lowercase      :    Optional[int]=None	,   __lowercase      :    Optional[Any]=None	,   __lowercase      :    Optional[int]=None	,   __lowercase      :    Dict=None	,   )							->   Union[str, Any]:
        if padding is None:
          __UpperCAmelCase							:			List[Any]     =						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							:			str     =						self.max_length
        if truncation is None:
          __UpperCAmelCase							:			Any     =						self.truncation
        if pad_to_multiple_of is None:
          __UpperCAmelCase							:			int     =						self.pad_to_multiple_of
        if return_token_type_ids is None:
          __UpperCAmelCase							:			List[str]     =						self.return_token_type_ids
        if return_attention_mask is None:
          __UpperCAmelCase							:			Tuple     =						self.return_attention_mask
        if not isinstance(__lowercase	,   tf.Tensor      ):
          __UpperCAmelCase							:			str     =						tf.convert_to_tensor(__lowercase      )
        if text_pair is not None and not isinstance(__lowercase	,   tf.Tensor      ):
          __UpperCAmelCase							:			List[str]     =						tf.convert_to_tensor(__lowercase      )
        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							:			Dict     =						text[:, 0], text[:, 1]
        __UpperCAmelCase							:			List[Any]     =						self.unpaired_tokenize(__lowercase      )
        if text_pair is None:  # Unpaired text
          if truncation:
            __UpperCAmelCase							:			int     =						text[:, : max_length - 2]  # Allow room for special tokens
          __UpperCAmelCase   ,				__UpperCAmelCase							:			Union[str, Any]     =						combine_segments(
              (text,)	,   start_of_sequence_id=self.cls_token_id	,   end_of_segment_id=self.sep_token_id      )
        else:  # Paired text
          __UpperCAmelCase							:			str     =						self.unpaired_tokenize(__lowercase      )
          if truncation:
            __UpperCAmelCase   ,				__UpperCAmelCase							:			str     =						self.paired_trimmer.trim([text, text_pair]      )
          __UpperCAmelCase   ,				__UpperCAmelCase							:			str     =						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							:			Optional[Any]     =						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							:			str     =						pad_to_multiple_of * (-tf.math.floordiv(-pad_length	,   __lowercase      ))
        else:
          __UpperCAmelCase							:			int     =						max_length

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




      def 			UpperCAmelCase   (   self      :    Dict      )							->   List[str]:
        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,
        }










# 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	: Union[str, Any]      =				"Update an existing config file with the latest defaults while maintaining the old configuration."






def        lowerCamelCase__   (					__lowerCamelCase    :  str	):
  __UpperCAmelCase							:			Optional[Any]     =						args.config_file
  if config_file is None and Path(__lowerCamelCase	).exists():
    __UpperCAmelCase							:			int     =						default_config_file
  elif not Path(__lowerCamelCase	).exists():
    raise ValueError(f"""The passed config file located at {config_file} doesn't exist."""	)
  __UpperCAmelCase							:			Tuple     =						load_config_from_file(__lowerCamelCase	)

  if config_file.endswith(""".json"""	):
    config.to_json_file(__lowerCamelCase	)
  else:
    config.to_yaml_file(__lowerCamelCase	)
  return config_file






def        lowerCamelCase__   (					__lowerCamelCase    :  Dict						,	__lowerCamelCase    :  List[str]	):
  __UpperCAmelCase							:			List[str]     =						parser.add_parser("""update"""						,	parents=__lowerCamelCase						,	help=__lowerCamelCase						,	formatter_class=__lowerCamelCase	)
  parser.add_argument(
      """--config_file"""						,	default=__lowerCamelCase						,	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=__lowerCamelCase	)
  return parser







def        lowerCamelCase__   (					__lowerCamelCase    :  Optional[int]	):
  __UpperCAmelCase							:			Optional[Any]     =						update_config(__lowerCamelCase	)
  print(f"""Sucessfully updated the configuration file at {config_file}."""	)


