


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						,    __A						,    __A						,    __A = None						,    __A = None						,    __A = None						,    __A = "longest"						,    __A = True						,    __A = 512						,    __A = None						,    __A = True						,    __A = True						,    __A = True						,    **__A						,    ):






     """simple docstring"""





     super().__init__()
     if use_fast_bert_tokenizer:
       lowerCamelCase			: Dict   =    FastBertTokenizer(
           __lowercase						,    token_out_type=tf.intaa						,    lower_case_nfd_strip_accents=__lowercase						,    **__lowercase			)
     else:
       lowerCamelCase			: List[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						,    )
       lowerCamelCase			: Any   =    BertTokenizerLayer(
           __lowercase						,    token_out_type=tf.intaa						,    lower_case=__lowercase						,    **__lowercase			)

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






   @classmethod
   def _snake_case   (		cls						,    __A						,    **__A			):  # noqa: F821






     """simple docstring"""





     lowerCamelCase			: int   =    kwargs.pop("do_lower_case"						,    __lowercase			)
     lowerCamelCase			: Union[str, Any]   =    tokenizer.do_lower_case if do_lower_case is None else do_lower_case
     lowerCamelCase			: int   =    kwargs.pop("cls_token_id"						,    __lowercase			)
     lowerCamelCase			: Optional[Any]   =    tokenizer.cls_token_id if cls_token_id is None else cls_token_id
     lowerCamelCase			: List[Any]   =    kwargs.pop("sep_token_id"						,    __lowercase			)
     lowerCamelCase			: Optional[int]   =    tokenizer.sep_token_id if sep_token_id is None else sep_token_id
     lowerCamelCase			: List[str]   =    kwargs.pop("pad_token_id"						,    __lowercase			)
     lowerCamelCase			: Tuple   =    tokenizer.pad_token_id if pad_token_id is None else pad_token_id

     lowerCamelCase			: Optional[Any]   =    tokenizer.get_vocab()
     lowerCamelCase			: Optional[Any]   =    sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]						,    key=lambda __A			: x[1]			)
     lowerCamelCase			: Tuple   =    [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 _snake_case   (		cls						,    __A						,    *__A						,    **__A			):






     """simple docstring"""





     try:
       lowerCamelCase			: Tuple   =    BertTokenizer.from_pretrained(__lowercase						,    *__lowercase						,    **__lowercase			)
     except:  # noqa: E722
       from .tokenization_bert_fast import BertTokenizerFast

       lowerCamelCase			: Union[str, Any]   =    BertTokenizerFast.from_pretrained(__lowercase						,    *__lowercase						,    **__lowercase			)
     return cls.from_tokenizer(__lowercase						,    **__lowercase			)






   def _snake_case   (		self						,    __A			):






     """simple docstring"""





     if self.do_lower_case:
       lowerCamelCase			: Dict   =    case_fold_utfa(__lowercase			)
     lowerCamelCase			: List[Any]   =    self.tf_tokenizer.tokenize(__lowercase			)
     return tokens.merge_dims(1						,    -1			)






   def _snake_case   (		self						,    __A						,    __A=None						,    __A=None						,    __A=None						,    __A=None						,    __A=None						,    __A=None						,    __A=None						,    ):






     """simple docstring"""





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

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






   def _snake_case   (		self			):






     """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,
     }







import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
   from PIL import Image
class      UpperCAmelCase_ ( unittest.TestCase		,			UpperCamelCase   ):

   '''simple docstring'''




   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: List[Any]   =    load_tool("image-segmentation"			)
     self.tool.setup()
     lowerCamelCase			: Optional[Any]   =    load_tool("image-segmentation"						,    remote=__A			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: List[str]   =    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"			)
     lowerCamelCase			: Dict   =    self.tool(__A						,    "cat"			)
     self.assertTrue(isinstance(__A						,    Image.Image			)			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: int   =    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"			)
     lowerCamelCase			: int   =    self.remote_tool(__A						,    "cat"			)
     self.assertTrue(isinstance(__A						,    Image.Image			)			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Optional[int]   =    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"			)
     lowerCamelCase			: Optional[Any]   =    self.tool(image=__A						,    label="cat"			)
     self.assertTrue(isinstance(__A						,    Image.Image			)			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: str   =    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"			)
     lowerCamelCase			: Dict   =    self.remote_tool(image=__A						,    label="cat"			)
     self.assertTrue(isinstance(__A						,    Image.Image			)			)



