




import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
  import PIL.Image

  from .features import FeatureType


_lowerCamelCase			:	Optional[List[str]]							 =     None
_lowerCamelCase			:	Tuple							 =     '<' if sys.byteorder == 'little' else '>'
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
_lowerCamelCase			:	int							 =     [
    np.dtype("""|b1"""),
    np.dtype("""|u1"""),
    np.dtype("""<u2"""),
    np.dtype(""">u2"""),
    np.dtype("""<i2"""),
    np.dtype(""">i2"""),
    np.dtype("""<u4"""),
    np.dtype(""">u4"""),
    np.dtype("""<i4"""),
    np.dtype(""">i4"""),
    np.dtype("""<f4"""),
    np.dtype(""">f4"""),
    np.dtype("""<f8"""),
    np.dtype(""">f8"""),
]


@dataclass
class        UpperCamelCase_					:

    '''simple docstring'''






    UpperCAmelCase__													=   True
    UpperCAmelCase__													=   None
    # Automatically constructed
    UpperCAmelCase__													=   "PIL.Image.Image"
    UpperCAmelCase__													=   pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()}  )
    UpperCAmelCase__													=   field(default='''Image'''	,      init=UpperCAmelCase__	,      repr=UpperCAmelCase__  )


    def __call__(    self   :			int)		->Union[str, Any]:





           '''simple docstring'''

           return self.pa_type


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any]      ,				UpperCAmelCase__   :			Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"])		->Any:





           '''simple docstring'''

           if config.PIL_AVAILABLE:
                  import PIL.Image
           else:
                  raise ImportError('''To support encoding images, please install \'Pillow\'.''')

           if isinstance(UpperCAmelCase__      ,				UpperCAmelCase__):
                  A__   				=					np.array(UpperCAmelCase__)

           if isinstance(UpperCAmelCase__      ,				UpperCAmelCase__):
                  return {"path": value, "bytes": None}
           elif isinstance(UpperCAmelCase__      ,				UpperCAmelCase__):
                  return {"path": None, "bytes": value}
           elif isinstance(UpperCAmelCase__      ,				np.ndarray):
                  # convert the image array to PNG/TIFF bytes
                  return encode_np_array(UpperCAmelCase__)
           elif isinstance(UpperCAmelCase__      ,				PIL.Image.Image):
                  # convert the PIL image to bytes (default format is PNG/TIFF)
                  return encode_pil_image(UpperCAmelCase__)
           elif value.get('''path''') is not None and os.path.isfile(value['''path''']):
                  # we set "bytes": None to not duplicate the data if they're already available locally
                  return {"bytes": None, "path": value.get('''path''')}
           elif value.get('''bytes''') is not None or value.get('''path''') is not None:
                  # store the image bytes, and path is used to infer the image format using the file extension
                  return {"bytes": value.get('''bytes'''), "path": value.get('''path''')}
           else:
                  raise ValueError(
                      f"""An image sample should have one of 'path' or 'bytes' but they are missing or None in {value}.""")


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any]      ,				UpperCAmelCase__   :			dict      ,				UpperCAmelCase__   :			Any=None)		->Union[str, Any]:





           '''simple docstring'''

           if not self.decode:
                  raise RuntimeError('''Decoding is disabled for this feature. Please use Image(decode=True) instead.''')

           if config.PIL_AVAILABLE:
                  import PIL.Image
           else:
                  raise ImportError('''To support decoding images, please install \'Pillow\'.''')

           if token_per_repo_id is None:
                  A__   				=					{}

           A__   				=					value['''path'''], value['''bytes''']
           if bytes_ is None:
                  if path is None:
                         raise ValueError(f"""An image should have one of 'path' or 'bytes' but both are None in {value}.""")
                  else:
                         if is_local_path(UpperCAmelCase__):
                                A__   				=					PIL.Image.open(UpperCAmelCase__)
                         else:
                                A__   				=					path.split('''::''')[-1]
                                try:
                                       A__   				=					string_to_dict(UpperCAmelCase__      ,				config.HUB_DATASETS_URL)['''repo_id''']
                                       A__   				=					token_per_repo_id.get(UpperCAmelCase__)
                                except ValueError:
                                       A__   				=					None
                                with xopen(UpperCAmelCase__      ,				'''rb'''      ,				use_auth_token=UpperCAmelCase__) as f:
                                       A__   				=					BytesIO(f.read())
                                A__   				=					PIL.Image.open(bytes_)
           else:
                  A__   				=					PIL.Image.open(BytesIO(bytes_))
           image.load()  # to avoid "Too many open files" errors
           return image


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->List[str]:





           '''simple docstring'''

           from .features import Value

           return (
               self
               if self.decode
               else {
                   "bytes": Value('''binary'''),
                   "path": Value('''string'''),
               }
           )


    def  SCREAMING_SNAKE_CASE							(    self   :			List[str]      ,				UpperCAmelCase__   :			Union[pa.StringArray, pa.StructArray, pa.ListArray])		->Dict:





           '''simple docstring'''

           if pa.types.is_string(storage.type):
                  A__   				=					pa.array([None] * len(UpperCAmelCase__)      ,				type=pa.binary())
                  A__   				=					pa.StructArray.from_arrays([bytes_array, storage]      ,				['''bytes''', '''path''']      ,				mask=storage.is_null())
           elif pa.types.is_binary(storage.type):
                  A__   				=					pa.array([None] * len(UpperCAmelCase__)      ,				type=pa.string())
                  A__   				=					pa.StructArray.from_arrays([storage, path_array]      ,				['''bytes''', '''path''']      ,				mask=storage.is_null())
           elif pa.types.is_struct(storage.type):
                  if storage.type.get_field_index('''bytes''') >= 0:
                         A__   				=					storage.field('''bytes''')
                  else:
                         A__   				=					pa.array([None] * len(UpperCAmelCase__)      ,				type=pa.binary())
                  if storage.type.get_field_index('''path''') >= 0:
                         A__   				=					storage.field('''path''')
                  else:
                         A__   				=					pa.array([None] * len(UpperCAmelCase__)      ,				type=pa.string())
                  A__   				=					pa.StructArray.from_arrays([bytes_array, path_array]      ,				['''bytes''', '''path''']      ,				mask=storage.is_null())
           elif pa.types.is_list(storage.type):
                  A__   				=					pa.array(
                      [encode_np_array(np.array(UpperCAmelCase__))['''bytes'''] if arr is not None else None for arr in storage.to_pylist()]      ,				type=pa.binary()      ,				)
                  A__   				=					pa.array([None] * len(UpperCAmelCase__)      ,				type=pa.string())
                  A__   				=					pa.StructArray.from_arrays(
                      [bytes_array, path_array]      ,				['''bytes''', '''path''']      ,				mask=bytes_array.is_null())
           return array_cast(UpperCAmelCase__      ,				self.pa_type)





    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any]      ,				UpperCAmelCase__   :			pa.StructArray)		->Tuple:





           '''simple docstring'''

           @no_op_if_value_is_null
           def path_to_bytes(UpperCAmelCase__   :			List[Any]):
                  with xopen(UpperCAmelCase__      ,				'''rb''') as f:
                         A__   				=					f.read()
                  return bytes_

           A__   				=					pa.array(
               [
                   (path_to_bytes(x['''path''']) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
                   for x in storage.to_pylist()
               ]      ,				type=pa.binary()      ,				)
           A__   				=					pa.array(
               [os.path.basename(UpperCAmelCase__) if path is not None else None for path in storage.field('''path''').to_pylist()]      ,				type=pa.string()      ,				)
           A__   				=					pa.StructArray.from_arrays([bytes_array, path_array]      ,				['''bytes''', '''path''']      ,				mask=bytes_array.is_null())
           return array_cast(UpperCAmelCase__      ,				self.pa_type)







def SCREAMING_SNAKE_CASE       ( )     -> List[str]:



       """simple docstring"""

       if config.PIL_AVAILABLE:
              import PIL.Image
       else:
              raise ImportError('''To support encoding images, please install \'Pillow\'.'''		)

       global _IMAGE_COMPRESSION_FORMATS
       if _IMAGE_COMPRESSION_FORMATS is None:
              PIL.Image.init()
              A__   				=					list(set(PIL.Image.OPEN.keys()		) & set(PIL.Image.SAVE.keys()		)		)
       return _IMAGE_COMPRESSION_FORMATS







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> bytes:



       """simple docstring"""

       A__   				=					BytesIO()
       if image.format in list_image_compression_formats():
              A__   				=					image.format
       else:
              A__   				=					'''PNG''' if image.mode in ['''1''', '''L''', '''LA''', '''RGB''', '''RGBA'''] else '''TIFF'''
       image.save(lowercase_				,       format=lowercase_		)
       return buffer.getvalue()







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> dict:



       """simple docstring"""

       if hasattr(lowercase_				,       '''filename'''		) and image.filename != "":
              return {"path": image.filename, "bytes": None}
       else:
              return {"path": None, "bytes": image_to_bytes(lowercase_		)}







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> dict:



       """simple docstring"""

       if config.PIL_AVAILABLE:
              import PIL.Image
       else:
              raise ImportError('''To support encoding images, please install \'Pillow\'.'''		)

       A__   				=					array.dtype
       A__   				=					dtype.byteorder if dtype.byteorder != '''=''' else _NATIVE_BYTEORDER
       A__   				=					dtype.kind
       A__   				=					dtype.itemsize

       A__   				=					None

       # Multi-channel array case (only np.dtype("|u1") is allowed)
       if array.shape[2:]:
              A__   				=					np.dtype('''|u1'''		)
              if dtype_kind not in ["u", "i"]:
                     raise TypeError(
                         f"""Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays."""		)
              if dtype is not dest_dtype:
                     warnings.warn(f"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'"""		)
    # Exact match
       elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
              A__   				=					dtype
       else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
              while dtype_itemsize >= 1:
                     A__   				=					dtype_byteorder + dtype_kind + str(lowercase_		)
                     A__   				=					np.dtype(lowercase_		)
                     if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
                            warnings.warn(f"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'"""		)
                            break
                     else:
                            dtype_itemsize //= 2

       if dest_dtype is None:
              raise TypeError(
                  f"""Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}"""		)
       A__   				=					PIL.Image.fromarray(array.astype(lowercase_		)		)
       return {"path": None, "bytes": image_to_bytes(lowercase_		)}







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> List[dict]:



       """simple docstring"""

       if config.PIL_AVAILABLE:
              import PIL.Image
       else:
              raise ImportError('''To support encoding images, please install \'Pillow\'.'''		)

       if objs:
              A__   				=					first_non_null_value(lowercase_		)
              if isinstance(lowercase_				,       lowercase_		):
                     return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
              if isinstance(lowercase_				,       np.ndarray		):
                     A__   				=					no_op_if_value_is_null(lowercase_		)
                     return [obj_to_image_dict_func(lowercase_		) for obj in objs]
              elif isinstance(lowercase_				,       PIL.Image.Image		):
                     A__   				=					no_op_if_value_is_null(lowercase_		)
                     return [obj_to_image_dict_func(lowercase_		) for obj in objs]
              else:
                     return objs
       else:
              return objs








import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
  _lowerCamelCase			:	List[Any]							 =     """pt"""
elif is_tf_available():
  _lowerCamelCase			:	int							 =     """tf"""
else:
  _lowerCamelCase			:	Dict							 =     """jax"""


class        UpperCamelCase_					(				UpperCAmelCase__	,      unittest.TestCase  ):

    '''simple docstring'''






    UpperCAmelCase__													=   PerceiverTokenizer
    UpperCAmelCase__													=   False


    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->int:





           '''simple docstring'''

           super().setUp()
           A__   				=					PerceiverTokenizer()
           tokenizer.save_pretrained(self.tmpdirname)


    @cached_property
    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->Any:





           '''simple docstring'''

           return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver''')


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any]      ,				**UpperCAmelCase__   :			List[str])		->PerceiverTokenizer:





           '''simple docstring'''

           return self.tokenizer_class.from_pretrained(self.tmpdirname      ,				**UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			List[Any]      ,				UpperCAmelCase__   :			Optional[int]=False      ,				UpperCAmelCase__   :			Union[str, Any]=20      ,				UpperCAmelCase__   :			Dict=5)		->Tuple[str, list]:





           '''simple docstring'''

           A__   				=					[]
           for i in range(len(UpperCAmelCase__)):
                  try:
                         A__   				=					tokenizer.decode([i]      ,				clean_up_tokenization_spaces=UpperCAmelCase__)
                  except UnicodeDecodeError:
                         pass
                  toks.append((i, tok))

           A__   				=					list(filter(lambda UpperCAmelCase__: re.match(R'''^[ a-zA-Z]+$'''      ,				t[1])      ,				UpperCAmelCase__))
           A__   				=					list(filter(lambda UpperCAmelCase__: [t[0]] == tokenizer.encode(t[1]      ,				add_special_tokens=UpperCAmelCase__)      ,				UpperCAmelCase__))
           if max_length is not None and len(UpperCAmelCase__) > max_length:
                  A__   				=					toks[:max_length]
           if min_length is not None and len(UpperCAmelCase__) < min_length and len(UpperCAmelCase__) > 0:
                  while len(UpperCAmelCase__) < min_length:
                         A__   				=					toks + toks
        # toks_str = [t[1] for t in toks]
           A__   				=					[t[0] for t in toks]

           # Ensure consistency
           A__   				=					tokenizer.decode(UpperCAmelCase__      ,				clean_up_tokenization_spaces=UpperCAmelCase__)
           if " " not in output_txt and len(UpperCAmelCase__) > 1:
                  A__   				=					(
                      tokenizer.decode([toks_ids[0]]      ,				clean_up_tokenization_spaces=UpperCAmelCase__)
                      + ''' '''
                      + tokenizer.decode(toks_ids[1:]      ,				clean_up_tokenization_spaces=UpperCAmelCase__)
                  )
           if with_prefix_space:
                  A__   				=					''' ''' + output_txt
           A__   				=					tokenizer.encode(UpperCAmelCase__      ,				add_special_tokens=UpperCAmelCase__)
           return output_txt, output_ids


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int])		->str:





           '''simple docstring'''

           A__   				=					self.perceiver_tokenizer
           A__   				=					'''Unicode €.'''
           A__   				=					tokenizer(UpperCAmelCase__)
           A__   				=					[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
           self.assertEqual(encoded['''input_ids''']      ,				UpperCAmelCase__)

           # decoding
           A__   				=					tokenizer.decode(UpperCAmelCase__)
           self.assertEqual(UpperCAmelCase__      ,				'''[CLS]Unicode €.[SEP]''')

           A__   				=					tokenizer('''e è é ê ë''')
           A__   				=					[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
           self.assertEqual(encoded['''input_ids''']      ,				UpperCAmelCase__)
           # decoding
           A__   				=					tokenizer.decode(UpperCAmelCase__)
           self.assertEqual(UpperCAmelCase__      ,				'''[CLS]e è é ê ë[SEP]''')

           # encode/decode, but with `encode` instead of `__call__`
           self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë'''))      ,				'''[CLS]e è é ê ë[SEP]''')


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->Dict:





           '''simple docstring'''

           A__   				=					self.perceiver_tokenizer
           A__   				=					['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
           # fmt: off
           A__   				=					[4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
           # fmt: on
           A__   				=					tokenizer(UpperCAmelCase__      ,				padding=UpperCAmelCase__      ,				return_tensors=UpperCAmelCase__)
           self.assertIsInstance(UpperCAmelCase__      ,				UpperCAmelCase__)

           if FRAMEWORK != "jax":
                  A__   				=					list(batch.input_ids.numpy()[0])
           else:
                  A__   				=					list(batch.input_ids.tolist()[0])

           self.assertListEqual(UpperCAmelCase__      ,				UpperCAmelCase__)

           self.assertEqual((2, 38)      ,				batch.input_ids.shape)
           self.assertEqual((2, 38)      ,				batch.attention_mask.shape)


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->Any:





           '''simple docstring'''

           A__   				=					self.perceiver_tokenizer
           A__   				=					['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
           A__   				=					tokenizer(UpperCAmelCase__      ,				padding=UpperCAmelCase__      ,				return_tensors=UpperCAmelCase__)
           # check if input_ids are returned and no decoder_input_ids
           self.assertIn('''input_ids'''      ,				UpperCAmelCase__)
           self.assertIn('''attention_mask'''      ,				UpperCAmelCase__)
           self.assertNotIn('''decoder_input_ids'''      ,				UpperCAmelCase__)
           self.assertNotIn('''decoder_attention_mask'''      ,				UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->Tuple:





           '''simple docstring'''

           A__   				=					self.perceiver_tokenizer
           A__   				=					[
               '''Summary of the text.''',
               '''Another summary.''',
           ]
           A__   				=					tokenizer(
               text_target=UpperCAmelCase__      ,				max_length=32      ,				padding='''max_length'''      ,				truncation=UpperCAmelCase__      ,				return_tensors=UpperCAmelCase__)
           self.assertEqual(32      ,				targets['''input_ids'''].shape[1])


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->Tuple:





           '''simple docstring'''

           A__   				=					self.get_tokenizers()
           for tokenizer in tokenizers:
                  with self.subTest(f"""{tokenizer.__class__.__name__}"""):
                         self.assertNotEqual(tokenizer.model_max_length      ,				42)

        # Now let's start the test
           A__   				=					self.get_tokenizers()
           for tokenizer in tokenizers:
                  with self.subTest(f"""{tokenizer.__class__.__name__}"""):
                         # Isolate this from the other tests because we save additional tokens/etc
                         A__   				=					tempfile.mkdtemp()

                         A__   				=					''' He is very happy, UNwant\u00E9d,running'''
                         A__   				=					tokenizer.encode(UpperCAmelCase__      ,				add_special_tokens=UpperCAmelCase__)
                         tokenizer.save_pretrained(UpperCAmelCase__)

                         A__   				=					tokenizer.__class__.from_pretrained(UpperCAmelCase__)
                         A__   				=					after_tokenizer.encode(UpperCAmelCase__      ,				add_special_tokens=UpperCAmelCase__)
                         self.assertListEqual(UpperCAmelCase__      ,				UpperCAmelCase__)

                         shutil.rmtree(UpperCAmelCase__)

           A__   				=					self.get_tokenizers(model_max_length=42)
           for tokenizer in tokenizers:
                  with self.subTest(f"""{tokenizer.__class__.__name__}"""):
                         # Isolate this from the other tests because we save additional tokens/etc
                         A__   				=					tempfile.mkdtemp()

                         A__   				=					''' He is very happy, UNwant\u00E9d,running'''
                         tokenizer.add_tokens(['''bim''', '''bambam'''])
                         A__   				=					tokenizer.additional_special_tokens
                         additional_special_tokens.append('''new_additional_special_token''')
                         tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens})
                         A__   				=					tokenizer.encode(UpperCAmelCase__      ,				add_special_tokens=UpperCAmelCase__)
                         tokenizer.save_pretrained(UpperCAmelCase__)

                         A__   				=					tokenizer.__class__.from_pretrained(UpperCAmelCase__)
                         A__   				=					after_tokenizer.encode(UpperCAmelCase__      ,				add_special_tokens=UpperCAmelCase__)
                         self.assertListEqual(UpperCAmelCase__      ,				UpperCAmelCase__)
                         self.assertIn('''new_additional_special_token'''      ,				after_tokenizer.additional_special_tokens)
                         self.assertEqual(after_tokenizer.model_max_length      ,				42)

                         A__   				=					tokenizer.__class__.from_pretrained(UpperCAmelCase__      ,				model_max_length=43)
                         self.assertEqual(tokenizer.model_max_length      ,				43)

                         shutil.rmtree(UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int])		->Optional[int]:





           '''simple docstring'''

           A__   				=					[]
           if self.test_slow_tokenizer:
                  tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))

           if self.test_rust_tokenizer:
                  tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))

           for tokenizer_class, tokenizer_utils in tokenizer_list:
                  with tempfile.TemporaryDirectory() as tmp_dir:
                         tokenizer_utils.save_pretrained(UpperCAmelCase__)

                         with open(os.path.join(UpperCAmelCase__      ,				'''special_tokens_map.json''')      ,				encoding='''utf-8''') as json_file:
                                A__   				=					json.load(UpperCAmelCase__)

                         with open(os.path.join(UpperCAmelCase__      ,				'''tokenizer_config.json''')      ,				encoding='''utf-8''') as json_file:
                                A__   				=					json.load(UpperCAmelCase__)

                         A__   				=					[f"""<extra_id_{i}>""" for i in range(125)]

                         A__   				=					added_tokens_extra_ids + [
                             '''an_additional_special_token'''
                         ]
                         A__   				=					added_tokens_extra_ids + [
                             '''an_additional_special_token'''
                         ]

                         with open(os.path.join(UpperCAmelCase__      ,				'''special_tokens_map.json''')      ,				'''w'''      ,				encoding='''utf-8''') as outfile:
                                json.dump(UpperCAmelCase__      ,				UpperCAmelCase__)
                         with open(os.path.join(UpperCAmelCase__      ,				'''tokenizer_config.json''')      ,				'''w'''      ,				encoding='''utf-8''') as outfile:
                                json.dump(UpperCAmelCase__      ,				UpperCAmelCase__)

                         # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                         # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                         # "special_tokens_map.json" files
                         A__   				=					tokenizer_class.from_pretrained(
                             UpperCAmelCase__      ,				)
                         self.assertIn(
                             '''an_additional_special_token'''      ,				tokenizer_without_change_in_init.additional_special_tokens)
                         self.assertEqual(
                             ['''an_additional_special_token''']      ,				tokenizer_without_change_in_init.convert_ids_to_tokens(
                                 tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token''']))      ,				)

                         # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                         A__   				=					added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''      ,				lstrip=UpperCAmelCase__)]
                         A__   				=					tokenizer_class.from_pretrained(
                             UpperCAmelCase__      ,				additional_special_tokens=UpperCAmelCase__      ,				)

                         self.assertIn('''a_new_additional_special_token'''      ,				tokenizer.additional_special_tokens)
                         self.assertEqual(
                             ['''a_new_additional_special_token''']      ,				tokenizer.convert_ids_to_tokens(
                                 tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token''']))      ,				)


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->Union[str, Any]:





           '''simple docstring'''

           A__   				=					self.perceiver_tokenizer
           self.assertEqual(tokenizer.decode([178])      ,				'''�''')


    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Tuple:





           '''simple docstring'''

           pass


    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->Optional[int]:





           '''simple docstring'''

           pass


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->List[Any]:





           '''simple docstring'''

           pass


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->str:





           '''simple docstring'''

           pass





    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->Tuple:





           '''simple docstring'''

           A__   				=					self.get_tokenizers(fast=UpperCAmelCase__      ,				do_lower_case=UpperCAmelCase__)
           for tokenizer in tokenizers:
                  with self.subTest(f"""{tokenizer.__class__.__name__}"""):
                         A__   				=					['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
                         A__   				=					tokenizer.convert_tokens_to_string(UpperCAmelCase__)

                         self.assertIsInstance(UpperCAmelCase__      ,				UpperCAmelCase__)


