


import json
import os
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__UpperCamelCase    :						List[str]     =							logging.get_logger(__name__)


__UpperCamelCase    :						int     =							{
    'vocab_file': 'vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
    'merges_file': 'merges.txt',
}

__UpperCamelCase    :						Tuple     =							{
    'vocab_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'
        ),
    },
    'tokenizer_config_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'
        ),
    },
    'merges_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'
        ),
    },
}

__UpperCamelCase    :						Union[str, Any]     =							'</w>'
__UpperCamelCase    :						List[str]     =							'@@ '





def   _UpperCAmelCase       (     UpperCAmelCase      :			str      ):
      """simple docstring"""




      __lowerCamelCase     :      Optional[int]       						=						set()
      __lowerCamelCase     :      Any       						=						word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)      )
            __lowerCamelCase     :      List[str]       						=						char
      return pairs


# Speech2Text2 has no max input length
__UpperCamelCase    :						Any     =							{'facebook/s2t-wav2vec2-large-en-de': 1024}




class 							_UpperCamelCase (						A						):

   '''simple docstring'''







   a_				:							Optional[int]    = VOCAB_FILES_NAMES
   a_				:							str    = PRETRAINED_VOCAB_FILES_MAP
   a_				:							List[str]    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   a_				:							Optional[int]    = ["input_ids", "attention_mask"]

   def __init__(  self  : Optional[int]       ,  _lowerCamelCase  : Optional[Any]       ,  _lowerCamelCase  : List[str]="<s>"       ,  _lowerCamelCase  : int="<pad>"       ,  _lowerCamelCase  : int="</s>"       ,  _lowerCamelCase  : List[str]="<unk>"       ,  _lowerCamelCase  : Optional[int]=False       ,  _lowerCamelCase  : Dict=None       ,  **_lowerCamelCase  : List[Any]       ,  ):






         '''simple docstring'''



         super().__init__(
             unk_token=_lowerCamelCase       ,  bos_token=_lowerCamelCase       ,  eos_token=_lowerCamelCase       ,  pad_token=_lowerCamelCase       ,  do_lower_case=_lowerCamelCase       ,  **_lowerCamelCase       ,  )

         __lowerCamelCase     :      Union[str, Any]       						=						do_lower_case

         with open(_lowerCamelCase       ,  encoding="""utf-8"""							) as vocab_handle:
               __lowerCamelCase     :      Any       						=						json.load(_lowerCamelCase							)
         __lowerCamelCase     :      Union[str, Any]       						=						{v: k for k, v in self.encoder.items()}

         if merges_file is None:
               logger.info(F"""No merges files provided. {self.__class__.__name__} can only be used for decoding."""							)

               __lowerCamelCase     :      Dict       						=						None
               __lowerCamelCase     :      Dict       						=						None
         else:
               with open(_lowerCamelCase       ,  encoding="""utf-8"""							) as merges_handle:
                     __lowerCamelCase     :      Tuple       						=						merges_handle.read().split("""\n"""							)[:-1]

               __lowerCamelCase     :      Optional[int]       						=						[tuple(merge.split()[:2]							) for merge in merges]
               __lowerCamelCase     :      List[Any]       						=						dict(zip(_lowerCamelCase       ,  range(len(_lowerCamelCase							)							)							)							)
               __lowerCamelCase     :      List[Any]       						=						{}

   @property
   def   _snake_case    (  self  : Union[str, Any]							):






         '''simple docstring'''



         return len(self.decoder							)

   def   _snake_case    (  self  : Union[str, Any]							):






         '''simple docstring'''



         return dict(self.encoder       ,  **self.added_tokens_encoder							)

   def   _snake_case    (  self  : Union[str, Any]       ,  _lowerCamelCase  : Union[str, Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[Any]       						=						tuple(token[:-1]							) + (token[-1] + BPE_TOKEN_MERGES,)
         if token in self.cache:
               return self.cache[token]
         __lowerCamelCase     :      Union[str, Any]       						=						get_pairs(_lowerCamelCase							)

         if not pairs:
               return token

         while True:
               __lowerCamelCase     :      Optional[int]       						=						min(_lowerCamelCase       ,  key=lambda _lowerCamelCase							: self.bpe_ranks.get(_lowerCamelCase       ,  float("""inf"""							)							)							)
               if bigram not in self.bpe_ranks:
                     break
               __lowerCamelCase						, __lowerCamelCase     :      str       						=						bigram
               __lowerCamelCase     :      List[str]       						=						[]
               __lowerCamelCase     :      Any       						=						0
               while i < len(_lowerCamelCase							):
                     try:
                           __lowerCamelCase     :      Optional[Any]       						=						word.index(_lowerCamelCase       ,  _lowerCamelCase							)
                     except ValueError:
                           new_word.extend(word[i:]							)
                           break
                     else:
                           new_word.extend(word[i:j]							)
                           __lowerCamelCase     :      Dict       						=						j

                     if word[i] == first and i < len(_lowerCamelCase							) - 1 and word[i + 1] == second:
                           new_word.append(first + second							)
                           i += 2
                     else:
                           new_word.append(word[i]							)
                           i += 1
               __lowerCamelCase     :      List[Any]       						=						tuple(_lowerCamelCase							)
               __lowerCamelCase     :      Any       						=						new_word
               if len(_lowerCamelCase							) == 1:
                     break
               else:
                     __lowerCamelCase     :      Optional[Any]       						=						get_pairs(_lowerCamelCase							)
         __lowerCamelCase     :      List[str]       						=						""" """.join(_lowerCamelCase							)
         if word == "\n  " + BPE_TOKEN_MERGES:
               __lowerCamelCase     :      List[Any]       						=						"""\n""" + BPE_TOKEN_MERGES

         if word.endswith(_lowerCamelCase							):
               __lowerCamelCase     :      Any       						=						word.replace(_lowerCamelCase       ,  """"""							)

         __lowerCamelCase     :      List[str]       						=						word.replace(""" """       ,  _lowerCamelCase							)
         __lowerCamelCase     :      str       						=						word
         return word

   def   _snake_case    (  self  : Tuple       ,  _lowerCamelCase  : Any							):






         '''simple docstring'''



         if self.bpe_ranks is None:
               raise ValueError(
                   """This tokenizer was instantiated without a `merges.txt` file, so"""
                   """ that it can only be used for decoding, not for encoding."""
                   """Make sure to provide `merges.txt` file at instantiation to enable """
                   """encoding."""							)

         if self.do_lower_case:
               __lowerCamelCase     :      int       						=						text.lower()

         __lowerCamelCase     :      Optional[Any]       						=						text.split()

         __lowerCamelCase     :      Any       						=						[]
         for token in text:
               if token:
                     split_tokens.extend(list(self.bpe(_lowerCamelCase							).split(""" """							)							)							)

         return split_tokens

   def   _snake_case    (  self  : List[str]       ,  _lowerCamelCase  : str							):






         '''simple docstring'''



         return self.encoder.get(_lowerCamelCase       ,  self.encoder.get(self.unk_token							)							)

   def   _snake_case    (  self  : Optional[int]       ,  _lowerCamelCase  : int							):






         '''simple docstring'''



         __lowerCamelCase     :      Union[str, Any]       						=						self.decoder.get(_lowerCamelCase       ,  self.unk_token							)
         return result

   def   _snake_case    (  self  : int       ,  _lowerCamelCase  : List[str]							):






         '''simple docstring'''



         __lowerCamelCase     :      Union[str, Any]       						=						""" """.join(_lowerCamelCase							)

         # make sure @@ tokens are concatenated
         __lowerCamelCase     :      Any       						=						"""""".join(string.split(_lowerCamelCase							)							)

         return string


   def   _snake_case    (  self  : Union[str, Any]       ,  _lowerCamelCase  : str       ,  _lowerCamelCase  : Optional[str] = None							):






         '''simple docstring'''



         if not os.path.isdir(_lowerCamelCase							):
               logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""							)
               return
         __lowerCamelCase     :      int       						=						os.path.join(
             _lowerCamelCase       ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)
         __lowerCamelCase     :      Dict       						=						os.path.join(
             _lowerCamelCase       ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]							)

         with open(_lowerCamelCase       ,  """w"""       ,  encoding="""utf-8"""							) as f:
               f.write(json.dumps(self.encoder       ,  indent=2       ,  sort_keys=_lowerCamelCase       ,  ensure_ascii=_lowerCamelCase							) + """\n"""							)

         __lowerCamelCase     :      Any       						=						0
         if self.bpe_ranks is None:
               return (vocab_file,)

         with open(_lowerCamelCase       ,  """w"""       ,  encoding="""utf-8"""							) as writer:
               for bpe_tokens, token_index in sorted(self.bpe_ranks.items()       ,  key=lambda _lowerCamelCase							: kv[1]							):
                     if index != token_index:
                           logger.warning(
                               F"""Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."""
                               """ Please check that the tokenizer is not corrupted!"""							)
                           __lowerCamelCase     :      Optional[int]       						=						token_index
                     writer.write(""" """.join(_lowerCamelCase							) + """\n"""							)
                     index += 1

         return (vocab_file, merges_file)







import shutil
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_tf_cross_test,
    require_tf,
    require_torch,
    require_torchvision,
    require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import AutoProcessor, SamImageProcessor, SamProcessor

if is_torch_available():
    import torch

if is_tf_available():
    import tensorflow as tf



@require_vision
@require_torchvision
class 							_UpperCamelCase (						unittest.TestCase						):

   '''simple docstring'''







   def   _snake_case    (  self  : List[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      List[Any]       						=						tempfile.mkdtemp()
         __lowerCamelCase     :      Union[str, Any]       						=						SamImageProcessor()
         __lowerCamelCase     :      int       						=						SamProcessor(_lowerCamelCase							)
         processor.save_pretrained(self.tmpdirname							)

   def   _snake_case    (  self  : Tuple       ,  **_lowerCamelCase  : Any							):






         '''simple docstring'''



         return AutoProcessor.from_pretrained(self.tmpdirname       ,  **_lowerCamelCase							).image_processor

   def   _snake_case    (  self  : List[Any]							):






         '''simple docstring'''



         shutil.rmtree(self.tmpdirname							)

   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         __lowerCamelCase     :      Any       						=						[np.random.randint(2_5_5       ,  size=(3, 3_0, 4_0_0)       ,  dtype=np.uinta							)]

         __lowerCamelCase     :      Optional[int]       						=						[Image.fromarray(np.moveaxis(_lowerCamelCase       ,  0       ,  -1							)							) for x in image_inputs]

         return image_inputs

   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Tuple       						=						SamProcessor(image_processor=self.get_image_processor()							)
         processor.save_pretrained(self.tmpdirname							)

         __lowerCamelCase     :      Optional[int]       						=						self.get_image_processor(do_normalize=_lowerCamelCase       ,  padding_value=1.0							)

         __lowerCamelCase     :      Optional[Any]       						=						SamProcessor.from_pretrained(self.tmpdirname       ,  do_normalize=_lowerCamelCase       ,  padding_value=1.0							)

         self.assertEqual(processor.image_processor.to_json_string()       ,  image_processor_add_kwargs.to_json_string()							)
         self.assertIsInstance(processor.image_processor       ,  _lowerCamelCase							)

   def   _snake_case    (  self  : Union[str, Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      int       						=						self.get_image_processor()

         __lowerCamelCase     :      Tuple       						=						SamProcessor(image_processor=_lowerCamelCase							)

         __lowerCamelCase     :      Optional[Any]       						=						self.prepare_image_inputs()

         __lowerCamelCase     :      List[str]       						=						image_processor(_lowerCamelCase       ,  return_tensors="""np"""							)
         __lowerCamelCase     :      List[Any]       						=						processor(images=_lowerCamelCase       ,  return_tensors="""np"""							)

         input_feat_extract.pop("""original_sizes"""							)  # pop original_sizes as it is popped in the processor
         input_feat_extract.pop("""reshaped_input_sizes"""							)  # pop original_sizes as it is popped in the processor

         for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum()       ,  input_processor[key].sum()       ,  delta=1E-2							)


   @require_torch
   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Dict       						=						self.get_image_processor()

         __lowerCamelCase     :      Union[str, Any]       						=						SamProcessor(image_processor=_lowerCamelCase							)
         __lowerCamelCase     :      Union[str, Any]       						=						[torch.ones((1, 3, 5, 5)							)]

         __lowerCamelCase     :      Any       						=						[[1_7_6_4, 2_6_4_6]]

         __lowerCamelCase     :      Any       						=						[[6_8_3, 1_0_2_4]]
         __lowerCamelCase     :      Tuple       						=						processor.post_process_masks(_lowerCamelCase       ,  _lowerCamelCase       ,  _lowerCamelCase							)
         self.assertEqual(masks[0].shape       ,  (1, 3, 1_7_6_4, 2_6_4_6)							)

         __lowerCamelCase     :      Union[str, Any]       						=						processor.post_process_masks(
             _lowerCamelCase       ,  torch.tensor(_lowerCamelCase							)       ,  torch.tensor(_lowerCamelCase							)							)
         self.assertEqual(masks[0].shape       ,  (1, 3, 1_7_6_4, 2_6_4_6)							)

         # should also work with np
         __lowerCamelCase     :      Any       						=						[np.ones((1, 3, 5, 5)							)]
         __lowerCamelCase     :      int       						=						processor.post_process_masks(_lowerCamelCase       ,  np.array(_lowerCamelCase							)       ,  np.array(_lowerCamelCase							)							)

         self.assertEqual(masks[0].shape       ,  (1, 3, 1_7_6_4, 2_6_4_6)							)

         __lowerCamelCase     :      List[str]       						=						[[1, 0], [0, 1]]
         with self.assertRaises(_lowerCamelCase							):
               __lowerCamelCase     :      Union[str, Any]       						=						processor.post_process_masks(_lowerCamelCase       ,  np.array(_lowerCamelCase							)       ,  np.array(_lowerCamelCase							)							)



@require_vision
@require_tf
class 							_UpperCamelCase (						unittest.TestCase						):

   '''simple docstring'''







   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[Any]       						=						tempfile.mkdtemp()
         __lowerCamelCase     :      int       						=						SamImageProcessor()
         __lowerCamelCase     :      Dict       						=						SamProcessor(_lowerCamelCase							)
         processor.save_pretrained(self.tmpdirname							)

   def   _snake_case    (  self  : List[str]       ,  **_lowerCamelCase  : List[Any]							):






         '''simple docstring'''



         return AutoProcessor.from_pretrained(self.tmpdirname       ,  **_lowerCamelCase							).image_processor

   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         shutil.rmtree(self.tmpdirname							)

   def   _snake_case    (  self  : List[str]							):






         '''simple docstring'''



         __lowerCamelCase     :      Any       						=						[np.random.randint(2_5_5       ,  size=(3, 3_0, 4_0_0)       ,  dtype=np.uinta							)]

         __lowerCamelCase     :      str       						=						[Image.fromarray(np.moveaxis(_lowerCamelCase       ,  0       ,  -1							)							) for x in image_inputs]

         return image_inputs

   def   _snake_case    (  self  : Union[str, Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      int       						=						SamProcessor(image_processor=self.get_image_processor()							)
         processor.save_pretrained(self.tmpdirname							)

         __lowerCamelCase     :      Dict       						=						self.get_image_processor(do_normalize=_lowerCamelCase       ,  padding_value=1.0							)

         __lowerCamelCase     :      Tuple       						=						SamProcessor.from_pretrained(self.tmpdirname       ,  do_normalize=_lowerCamelCase       ,  padding_value=1.0							)

         self.assertEqual(processor.image_processor.to_json_string()       ,  image_processor_add_kwargs.to_json_string()							)
         self.assertIsInstance(processor.image_processor       ,  _lowerCamelCase							)

   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         __lowerCamelCase     :      Union[str, Any]       						=						self.get_image_processor()

         __lowerCamelCase     :      Tuple       						=						SamProcessor(image_processor=_lowerCamelCase							)

         __lowerCamelCase     :      str       						=						self.prepare_image_inputs()

         __lowerCamelCase     :      List[Any]       						=						image_processor(_lowerCamelCase       ,  return_tensors="""np"""							)
         __lowerCamelCase     :      Union[str, Any]       						=						processor(images=_lowerCamelCase       ,  return_tensors="""np"""							)

         input_feat_extract.pop("""original_sizes"""							)  # pop original_sizes as it is popped in the processor
         input_feat_extract.pop("""reshaped_input_sizes"""							)  # pop reshaped_input_sizes as it is popped in the processor

         for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum()       ,  input_processor[key].sum()       ,  delta=1E-2							)


   @require_tf
   def   _snake_case    (  self  : List[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      List[Any]       						=						self.get_image_processor()

         __lowerCamelCase     :      str       						=						SamProcessor(image_processor=_lowerCamelCase							)
         __lowerCamelCase     :      Any       						=						[tf.ones((1, 3, 5, 5)							)]

         __lowerCamelCase     :      Optional[Any]       						=						[[1_7_6_4, 2_6_4_6]]

         __lowerCamelCase     :      List[str]       						=						[[6_8_3, 1_0_2_4]]
         __lowerCamelCase     :      Optional[int]       						=						processor.post_process_masks(_lowerCamelCase       ,  _lowerCamelCase       ,  _lowerCamelCase       ,  return_tensors="""tf"""							)
         self.assertEqual(masks[0].shape       ,  (1, 3, 1_7_6_4, 2_6_4_6)							)

         __lowerCamelCase     :      Optional[Any]       						=						processor.post_process_masks(
             _lowerCamelCase       ,  tf.convert_to_tensor(_lowerCamelCase							)       ,  tf.convert_to_tensor(_lowerCamelCase							)       ,  return_tensors="""tf"""       ,  )
         self.assertEqual(masks[0].shape       ,  (1, 3, 1_7_6_4, 2_6_4_6)							)

         # should also work with np
         __lowerCamelCase     :      Tuple       						=						[np.ones((1, 3, 5, 5)							)]
         __lowerCamelCase     :      str       						=						processor.post_process_masks(
             _lowerCamelCase       ,  np.array(_lowerCamelCase							)       ,  np.array(_lowerCamelCase							)       ,  return_tensors="""tf"""							)

         self.assertEqual(masks[0].shape       ,  (1, 3, 1_7_6_4, 2_6_4_6)							)

         __lowerCamelCase     :      Union[str, Any]       						=						[[1, 0], [0, 1]]
         with self.assertRaises(tf.errors.InvalidArgumentError							):
               __lowerCamelCase     :      Optional[Any]       						=						processor.post_process_masks(
                   _lowerCamelCase       ,  np.array(_lowerCamelCase							)       ,  np.array(_lowerCamelCase							)       ,  return_tensors="""tf"""							)



@require_vision
@require_torchvision
class 							_UpperCamelCase (						unittest.TestCase						):

   '''simple docstring'''







   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[int]       						=						tempfile.mkdtemp()
         __lowerCamelCase     :      Union[str, Any]       						=						SamImageProcessor()
         __lowerCamelCase     :      Dict       						=						SamProcessor(_lowerCamelCase							)
         processor.save_pretrained(self.tmpdirname							)

   def   _snake_case    (  self  : Optional[int]       ,  **_lowerCamelCase  : Tuple							):






         '''simple docstring'''



         return AutoProcessor.from_pretrained(self.tmpdirname       ,  **_lowerCamelCase							).image_processor

   def   _snake_case    (  self  : List[str]							):






         '''simple docstring'''



         shutil.rmtree(self.tmpdirname							)

   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         __lowerCamelCase     :      List[str]       						=						[np.random.randint(2_5_5       ,  size=(3, 3_0, 4_0_0)       ,  dtype=np.uinta							)]

         __lowerCamelCase     :      str       						=						[Image.fromarray(np.moveaxis(_lowerCamelCase       ,  0       ,  -1							)							) for x in image_inputs]

         return image_inputs

   @is_pt_tf_cross_test
   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         __lowerCamelCase     :      Union[str, Any]       						=						self.get_image_processor()

         __lowerCamelCase     :      Tuple       						=						SamProcessor(image_processor=_lowerCamelCase							)
         __lowerCamelCase     :      str       						=						np.random.randint(0       ,  2       ,  size=(1, 3, 5, 5)							).astype(np.floataa							)
         __lowerCamelCase     :      Tuple       						=						[tf.convert_to_tensor(_lowerCamelCase							)]
         __lowerCamelCase     :      List[str]       						=						[torch.tensor(_lowerCamelCase							)]

         __lowerCamelCase     :      Optional[int]       						=						[[1_7_6_4, 2_6_4_6]]

         __lowerCamelCase     :      Tuple       						=						[[6_8_3, 1_0_2_4]]
         __lowerCamelCase     :      int       						=						processor.post_process_masks(
             _lowerCamelCase       ,  _lowerCamelCase       ,  _lowerCamelCase       ,  return_tensors="""tf"""							)
         __lowerCamelCase     :      Any       						=						processor.post_process_masks(
             _lowerCamelCase       ,  _lowerCamelCase       ,  _lowerCamelCase       ,  return_tensors="""pt"""							)

         self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy()							)							)


   @is_pt_tf_cross_test
   def   _snake_case    (  self  : Dict							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[int]       						=						self.get_image_processor()

         __lowerCamelCase     :      int       						=						SamProcessor(image_processor=_lowerCamelCase							)

         __lowerCamelCase     :      Any       						=						self.prepare_image_inputs()

         __lowerCamelCase     :      Any       						=						image_processor(_lowerCamelCase       ,  return_tensors="""pt"""							)["""pixel_values"""].numpy()
         __lowerCamelCase     :      Any       						=						processor(images=_lowerCamelCase       ,  return_tensors="""pt"""							)["""pixel_values"""].numpy()

         __lowerCamelCase     :      Dict       						=						image_processor(_lowerCamelCase       ,  return_tensors="""tf"""							)["""pixel_values"""].numpy()
         __lowerCamelCase     :      int       						=						processor(images=_lowerCamelCase       ,  return_tensors="""tf"""							)["""pixel_values"""].numpy()

         self.assertTrue(np.allclose(_lowerCamelCase       ,  _lowerCamelCase							)							)
         self.assertTrue(np.allclose(_lowerCamelCase       ,  _lowerCamelCase							)							)
         self.assertTrue(np.allclose(_lowerCamelCase       ,  _lowerCamelCase							)							)



