






import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
   from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


A_		:		List[str]          =			get_tests_dir('fixtures/test_sentencepiece.model')

A_		:		str          =			{'target_lang': 'fi', 'source_lang': 'en'}
A_		:		List[Any]          =			'>>zh<<'
A_		:		Optional[int]          =			'Helsinki-NLP/'

if is_torch_available():
   A_		:		Any          =			'pt'
elif is_tf_available():
   A_		:		Tuple          =			'tf'
else:
   A_		:		List[str]          =			'jax'




@require_sentencepiece
class     _lowerCAmelCase(	UpperCAmelCase_							,     unittest.TestCase ):





       """simple docstring"""







       a    :		Optional[int]									=MarianTokenizer
       a    :		Union[str, Any]									=False
       a    :		Optional[int]									=True





       def       _a   ( self							):
         super().setUp()
         UpperCamelCase_: Dict   				=    ['</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est', '\u0120', '<pad>']
         UpperCamelCase_: int   				=    dict(zip(_lowerCamelCase  ,      range(len(_lowerCamelCase							)							)							)							)
         UpperCamelCase_: Dict   				=    Path(self.tmpdirname							)
         save_json(_lowerCamelCase  ,      save_dir / VOCAB_FILES_NAMES['vocab']							)
         save_json(_lowerCamelCase  ,      save_dir / VOCAB_FILES_NAMES['tokenizer_config_file']							)
         if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
           copyfile(_lowerCamelCase  ,      save_dir / VOCAB_FILES_NAMES['source_spm']							)
           copyfile(_lowerCamelCase  ,      save_dir / VOCAB_FILES_NAMES['target_spm']							)

         UpperCamelCase_: Any   				=    MarianTokenizer.from_pretrained(self.tmpdirname							)
         tokenizer.save_pretrained(self.tmpdirname							)





       def       _a   ( self  ,      **_lowerCamelCase							):
         return MarianTokenizer.from_pretrained(self.tmpdirname  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase							):
         return (
             "This is a test",
             "This is a test",
         )





       def       _a   ( self							):
         UpperCamelCase_: str   				=    '</s>'
         UpperCamelCase_: Optional[Any]   				=    0

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCamelCase							)  ,      _lowerCamelCase							)
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCamelCase							)  ,      _lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: List[str]   				=    list(self.get_tokenizer().get_vocab().keys()							)

         self.assertEqual(vocab_keys[0]  ,      '</s>'							)
         self.assertEqual(vocab_keys[1]  ,      '<unk>'							)
         self.assertEqual(vocab_keys[-1]  ,      '<pad>'							)
         self.assertEqual(len(_lowerCamelCase							)  ,      9							)





       def       _a   ( self							):
         self.assertEqual(self.get_tokenizer().vocab_size  ,      9							)





       def       _a   ( self							):
         UpperCamelCase_: Optional[Any]   				=    MarianTokenizer.from_pretrained(f'''{ORG_NAME}opus-mt-en-de'''							)
         UpperCamelCase_: List[str]   				=    en_de_tokenizer(['I am a small frog']  ,      return_tensors=_lowerCamelCase							)
         self.assertIsInstance(_lowerCamelCase  ,      _lowerCamelCase							)
         UpperCamelCase_: List[str]   				=    [3_8, 1_2_1, 1_4, 6_9_7, 3_8_8_4_8, 0]
         self.assertListEqual(_lowerCamelCase  ,      batch.input_ids[0]							)

         UpperCamelCase_: Union[str, Any]   				=    tempfile.mkdtemp()
         en_de_tokenizer.save_pretrained(_lowerCamelCase							)
         UpperCamelCase_: List[Any]   				=    [x.name for x in Path(_lowerCamelCase							).glob('*'							)]
         self.assertIn('source.spm'  ,      _lowerCamelCase							)
         MarianTokenizer.from_pretrained(_lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    self.get_tokenizer()

         UpperCamelCase_: Any   				=    tok(
             ['I am a small frog' * 1_0_0_0, 'I am a small frog']  ,      padding=_lowerCamelCase  ,      truncation=_lowerCamelCase  ,      return_tensors=_lowerCamelCase							)
         self.assertIsInstance(_lowerCamelCase  ,      _lowerCamelCase							)
         self.assertEqual(batch.input_ids.shape  ,      (2, 5_1_2)							)





       def       _a   ( self							):
         UpperCamelCase_: Union[str, Any]   				=    self.get_tokenizer()
         UpperCamelCase_: Any   				=    tok(['I am a tiny frog', 'I am a small frog']  ,      padding=_lowerCamelCase  ,      return_tensors=_lowerCamelCase							)
         self.assertIsInstance(_lowerCamelCase  ,      _lowerCamelCase							)
         self.assertEqual(batch_smaller.input_ids.shape  ,      (2, 1_0)							)





       @slow
       def       _a   ( self							):
         # fmt: off
         UpperCamelCase_: Tuple   				=    {'input_ids': [[4_3_4_9_5, 4_6_2, 2_0, 4_2_1_6_4, 1_3_6_9, 5_2, 4_6_4, 1_3_2, 1_7_0_3, 4_9_2, 1_3, 7_4_9_1, 3_8_9_9_9, 6, 8, 4_6_4, 1_3_2, 1_7_0_3, 4_9_2, 1_3, 4_6_6_9, 3_7_8_6_7, 1_3, 7_5_2_5, 2_7, 1_5_9_3, 9_8_8, 1_3, 3_3_9_7_2, 7_0_2_9, 6, 2_0, 8_2_5_1, 3_8_3, 2, 2_7_0, 5_8_6_6, 3_7_8_8, 2, 2_3_5_3, 8_2_5_1, 1_2_3_3_8, 2, 1_3_9_5_8, 3_8_7, 2, 3_6_2_9, 6_9_5_3, 1_8_8, 2_9_0_0, 2, 1_3_9_5_8, 8_0_1_1, 1_1_5_0_1, 2_3, 8_4_6_0, 4_0_7_3, 3_4_0_0_9, 2_0, 4_3_5, 1_1_4_3_9, 2_7, 8, 8_4_6_0, 4_0_7_3, 6_0_0_4, 2_0, 9_9_8_8, 3_7_5, 2_7, 3_3, 2_6_6, 1_9_4_5, 1_0_7_6, 1_3_5_0, 3_7_8_6_7, 3_2_8_8, 5, 5_7_7, 1_0_7_6, 4_3_7_4, 8, 5_0_8_2, 5, 2_6_4_5_3, 2_5_7, 5_5_6, 4_0_3, 2, 2_4_2, 1_3_2, 3_8_3, 3_1_6, 4_9_2, 8, 1_0_7_6_7, 6, 3_1_6, 3_0_4, 4_2_3_9, 3, 0], [1_4_8, 1_5_7_2_2, 1_9, 1_8_3_9, 1_2, 1_3_5_0, 1_3, 2_2_3_2_7, 5_0_8_2, 5_4_1_8, 4_7_5_6_7, 3_5_9_3_8, 5_9, 3_1_8, 1_9_5_5_2, 1_0_8, 2_1_8_3, 5_4, 1_4_9_7_6, 4_8_3_5, 3_2, 5_4_7, 1_1_1_4, 8, 3_1_5, 2_4_1_7, 5, 9_2, 1_9_0_8_8, 3, 0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0], [3_6, 6_3_9_5, 1_2_5_7_0, 3_9_1_4_7, 1_1_5_9_7, 6, 2_6_6, 4, 4_5_4_0_5, 7_2_9_6, 3, 0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=_lowerCamelCase  ,      model_name='Helsinki-NLP/opus-mt-en-de'  ,      revision='1a8c2263da11e68e50938f97e10cd57820bd504c'  ,      decode_kwargs={'use_source_tokenizer': True}  ,      )





       def       _a   ( self							):
         UpperCamelCase_: Union[str, Any]   				=    MarianTokenizer.from_pretrained('hf-internal-testing/test-marian-two-vocabs'							)

         UpperCamelCase_: List[Any]   				=    'Tämä on testi'
         UpperCamelCase_: Dict   				=    'This is a test'

         UpperCamelCase_: Dict   				=    [7_6, 7, 2_0_4_7, 2]
         UpperCamelCase_: List[str]   				=    [6_9, 1_2, 1_1, 9_4_0, 2]

         UpperCamelCase_: Tuple   				=    tokenizer(_lowerCamelCase							).input_ids
         self.assertListEqual(_lowerCamelCase  ,      _lowerCamelCase							)

         UpperCamelCase_: List[str]   				=    tokenizer(text_target=_lowerCamelCase							).input_ids
         self.assertListEqual(_lowerCamelCase  ,      _lowerCamelCase							)

         UpperCamelCase_: str   				=    tokenizer.decode(_lowerCamelCase  ,      skip_special_tokens=_lowerCamelCase							)
         self.assertEqual(_lowerCamelCase  ,      _lowerCamelCase							)







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A_		:		int		     =			logging.get_logger(__name__)




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		str									=['''pixel_values''']





       def __init__( self  ,      _lowerCamelCase = True  ,      _lowerCamelCase = None  ,      _lowerCamelCase = PILImageResampling.BICUBIC  ,      _lowerCamelCase = True  ,      _lowerCamelCase = True  ,      _lowerCamelCase = 1 / 2_5_5  ,      _lowerCamelCase = None  ,      _lowerCamelCase = True  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         super().__init__(**_lowerCamelCase							)
         UpperCamelCase_: Any   				=    size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
         UpperCamelCase_: Any   				=    get_size_dict(_lowerCamelCase							)
         UpperCamelCase_: Any   				=    crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
         UpperCamelCase_: List[Any]   				=    get_size_dict(_lowerCamelCase  ,      default_to_square=_lowerCamelCase  ,      param_name='crop_size'							)

         UpperCamelCase_: Optional[int]   				=    do_resize
         UpperCamelCase_: Tuple   				=    do_rescale
         UpperCamelCase_: Dict   				=    do_normalize
         UpperCamelCase_: Optional[int]   				=    do_center_crop
         UpperCamelCase_: Tuple   				=    crop_size
         UpperCamelCase_: Optional[int]   				=    size
         UpperCamelCase_: Dict   				=    resample
         UpperCamelCase_: Tuple   				=    rescale_factor
         UpperCamelCase_: Tuple   				=    image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         UpperCamelCase_: Dict   				=    image_std if image_std is not None else IMAGENET_DEFAULT_STD





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = PILImageResampling.BILINEAR  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: Optional[Any]   				=    get_size_dict(_lowerCamelCase							)

         if "shortest_edge" in size:
           UpperCamelCase_: str   				=    get_resize_output_image_size(_lowerCamelCase  ,      size=size['shortest_edge']  ,      default_to_square=_lowerCamelCase							)
           # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
         elif "height" in size and "width" in size:
           UpperCamelCase_: Any   				=    (size['height'], size['width'])
         else:
           raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''							)
         return resize(_lowerCamelCase  ,      size=_lowerCamelCase  ,      resample=_lowerCamelCase  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: List[str]   				=    get_size_dict(_lowerCamelCase							)
         if "height" not in size or "width" not in size:
           raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''							)
         return center_crop(_lowerCamelCase  ,      size=(size['height'], size['width'])  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      **_lowerCamelCase							):
         return rescale(_lowerCamelCase  ,      scale=_lowerCamelCase  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         return normalize(_lowerCamelCase  ,      mean=_lowerCamelCase  ,      std=_lowerCamelCase  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = ChannelDimension.FIRST  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: List[str]   				=    do_resize if do_resize is not None else self.do_resize
         UpperCamelCase_: str   				=    do_rescale if do_rescale is not None else self.do_rescale
         UpperCamelCase_: Dict   				=    do_normalize if do_normalize is not None else self.do_normalize
         UpperCamelCase_: List[str]   				=    do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCamelCase_: Optional[Any]   				=    crop_size if crop_size is not None else self.crop_size
         UpperCamelCase_: List[str]   				=    get_size_dict(_lowerCamelCase  ,      param_name='crop_size'  ,      default_to_square=_lowerCamelCase							)
         UpperCamelCase_: Tuple   				=    resample if resample is not None else self.resample
         UpperCamelCase_: str   				=    rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCamelCase_: Dict   				=    image_mean if image_mean is not None else self.image_mean
         UpperCamelCase_: Dict   				=    image_std if image_std is not None else self.image_std

         UpperCamelCase_: Any   				=    size if size is not None else self.size
         UpperCamelCase_: Optional[Any]   				=    get_size_dict(_lowerCamelCase							)

         if not is_batched(_lowerCamelCase							):
           UpperCamelCase_: Dict   				=    [images]

         if not valid_images(_lowerCamelCase							):
           raise ValueError(
               'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
               'torch.Tensor, tf.Tensor or jax.ndarray.'							)

         if do_resize and size is None:
           raise ValueError('Size must be specified if do_resize is True.'							)

         if do_center_crop and crop_size is None:
           raise ValueError('Crop size must be specified if do_center_crop is True.'							)

         if do_rescale and rescale_factor is None:
           raise ValueError('Rescale factor must be specified if do_rescale is True.'							)

         # All transformations expect numpy arrays.
         UpperCamelCase_: List[str]   				=    [to_numpy_array(_lowerCamelCase							) for image in images]

         if do_resize:
           UpperCamelCase_: List[str]   				=    [self.resize(image=_lowerCamelCase  ,      size=_lowerCamelCase  ,      resample=_lowerCamelCase							) for image in images]

         if do_center_crop:
           UpperCamelCase_: List[str]   				=    [self.center_crop(image=_lowerCamelCase  ,      size=_lowerCamelCase							) for image in images]

         if do_rescale:
           UpperCamelCase_: int   				=    [self.rescale(image=_lowerCamelCase  ,      scale=_lowerCamelCase							) for image in images]

         if do_normalize:
           UpperCamelCase_: Optional[Any]   				=    [self.normalize(image=_lowerCamelCase  ,      mean=_lowerCamelCase  ,      std=_lowerCamelCase							) for image in images]

         UpperCamelCase_: List[str]   				=    [to_channel_dimension_format(_lowerCamelCase  ,      _lowerCamelCase							) for image in images]

         UpperCamelCase_: Optional[int]   				=    {'pixel_values': images}
         return BatchFeature(data=_lowerCamelCase  ,      tensor_type=_lowerCamelCase							)