




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 	UpperCamelCase__			(tf.keras.layers.Layer  ):

    '''simple docstring'''



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

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







    @classmethod
    def 					_lowercase  (					cls  ,						UpperCamelCase__  ,						**UpperCamelCase__  )  ->   Any:  # noqa: F821
           lowerCamelCase     :   Dict    				=      kwargs.pop("do_lower_case"  ,						UpperCamelCase__  )
           lowerCamelCase     :   Tuple    				=      tokenizer.do_lower_case if do_lower_case is None else do_lower_case
           lowerCamelCase     :   str    				=      kwargs.pop("cls_token_id"  ,						UpperCamelCase__  )
           lowerCamelCase     :   Any    				=      tokenizer.cls_token_id if cls_token_id is None else cls_token_id
           lowerCamelCase     :   Union[str, Any]    				=      kwargs.pop("sep_token_id"  ,						UpperCamelCase__  )
           lowerCamelCase     :   Dict    				=      tokenizer.sep_token_id if sep_token_id is None else sep_token_id
           lowerCamelCase     :   Optional[int]    				=      kwargs.pop("pad_token_id"  ,						UpperCamelCase__  )
           lowerCamelCase     :   Optional[int]    				=      tokenizer.pad_token_id if pad_token_id is None else pad_token_id

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







    @classmethod
    def 					_lowercase  (					cls  ,						UpperCamelCase__  ,						*UpperCamelCase__  ,						**UpperCamelCase__  )  ->   List[str]:
           try:
                  lowerCamelCase     :   Optional[int]    				=      BertTokenizer.from_pretrained(UpperCamelCase__  ,						*UpperCamelCase__  ,						**UpperCamelCase__  )
           except:  # noqa: E722
                  from .tokenization_bert_fast import BertTokenizerFast

                  lowerCamelCase     :   List[str]    				=      BertTokenizerFast.from_pretrained(UpperCamelCase__  ,						*UpperCamelCase__  ,						**UpperCamelCase__  )
           return cls.from_tokenizer(UpperCamelCase__  ,						**UpperCamelCase__  )







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   Any:
           if self.do_lower_case:
                  lowerCamelCase     :   List[Any]    				=      case_fold_utfa(UpperCamelCase__  )
           lowerCamelCase     :   Any    				=      self.tf_tokenizer.tokenize(UpperCamelCase__  )
           return tokens.merge_dims(1  ,						-1  )







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

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







    def 					_lowercase  (					self  )  ->   Any:
           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,
           }












import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
       import torch

       from transformers import GPTaLMHeadModel




@require_torch
@require_sentencepiece
@require_tokenizers
class 	UpperCamelCase__			(unittest.TestCase  ):

    '''simple docstring'''



    @slow
    @unittest.skip("Model is not available."  )
    def 					_lowercase  (					self  )  ->   List[Any]:
           lowerCamelCase     :   List[Any]    				=      "nvidia/megatron-gpt2-345m/"
           if "MYDIR" in os.environ:
                  lowerCamelCase     :   Union[str, Any]    				=      os.path.join(os.environ["MYDIR"]  ,						UpperCamelCase__  )
           lowerCamelCase     :   Any    				=      GPTaLMHeadModel.from_pretrained(UpperCamelCase__  )
           model.to(UpperCamelCase__  )
           model.half()

           lowerCamelCase     :   Dict    				=      torch.tensor(
               [[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]]  ,						device=UpperCamelCase__  ,						dtype=torch.long  ,						)

           with torch.no_grad():
                  lowerCamelCase     :   Any    				=      model(UpperCamelCase__  ).logits

           lowerCamelCase     :   Union[str, Any]    				=      torch.Size((1, 9, 5_0257)  )
           self.assertEqual(output.shape  ,						UpperCamelCase__  )

           lowerCamelCase     :   List[str]    				=      torch.tensor(
               [
                   4.9414,
                   -0.2920,
                   -1.2148,
                   -4.0273,
                   -0.5161,
                   -5.2109,
                   -1.2412,
                   -1.8301,
                   -1.7734,
                   -4.7148,
                   -0.2317,
                   -1.0811,
                   -2.1777,
                   0.4141,
                   -3.7969,
                   -4.0586,
                   -2.5332,
                   -3.3809,
                   4.3867,
               ]  ,						device=UpperCamelCase__  ,						dtype=torch.half  ,						)

           for i in range(19  ):
                  lowerCamelCase				,   lowerCamelCase     :   Optional[int]    				=      8 * i // 17, 2792 * i  # along the diagonal
                  lowerCamelCase				,   lowerCamelCase     :   Tuple    				=      output[0, r, c], expected_diag[i]
                  lowerCamelCase     :   List[str]    				=      F'''row={r} col={c} computed={computed} expected={expected}'''
                  self.assertAlmostEqual(UpperCamelCase__  ,						UpperCamelCase__  ,						delta=1e-4  ,						msg=UpperCamelCase__  )






