

import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest

from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError

from .utils import require_pil

class     __snake_case    (				snake_case__  ):
       """simple docstring"""







       def 			a		(		self   )      ->							Optional[Any]:






              """simple docstring"""


              __snake_case       	=							pa.array(TypedSequence([1, 2, 3]   )   )
              self.assertEqual(arr.type      , pa.intaa()   )


       def 			a		(		self   )      ->							Any:






              """simple docstring"""


              with self.assertRaises(_UpperCamelCase   ):
                     __snake_case       	=							pa.array(TypedSequence([1, 2, 3]   )      , type=pa.intaa()   )


       def 			a		(		self   )      ->							int:






              """simple docstring"""


              with self.assertRaises(_UpperCamelCase   ):
                     __snake_case       	=							pa.array(TypedSequence([1, 2, 3]      , try_type=Value("""bool"""   )      , type=Value("""int64"""   )   )   )


       def 			a		(		self   )      ->							Optional[Any]:






              """simple docstring"""


              __snake_case       	=							pa.array(TypedSequence([1, 2, 3]      , type=Value("""int32"""   )   )   )
              self.assertEqual(arr.type      , pa.intaa()   )


       def 			a		(		self   )      ->							List[Any]:






              """simple docstring"""


              with self.assertRaises((TypeError, pa.lib.ArrowInvalid)   ):
                     __snake_case       	=							pa.array(TypedSequence(["""foo""", """bar"""]      , type=Value("""int64"""   )   )   )


       def 			a		(		self   )      ->							List[str]:






              """simple docstring"""


              __snake_case       	=							pa.array(TypedSequence([1, 2, 3]      , try_type=Value("""int32"""   )   )   )
              self.assertEqual(arr.type      , pa.intaa()   )


       def 			a		(		self   )      ->							int:






              """simple docstring"""


              __snake_case       	=							pa.array(TypedSequence(["""foo""", """bar"""]      , try_type=Value("""int64"""   )   )   )
              self.assertEqual(arr.type      , pa.string()   )


       def 			a		(		self   )      ->							Optional[Any]:






              """simple docstring"""


              __snake_case       	=							pa.array(TypedSequence([[[1, 2, 3]]]      , type=ArrayaD((1, 3)      , """int64"""   )   )   )
              self.assertEqual(arr.type      , ArrayaDExtensionType((1, 3)      , """int64"""   )   )


       def 			a		(		self   )      ->							Any:






              """simple docstring"""


              with self.assertRaises((TypeError, pa.lib.ArrowInvalid)   ):
                     __snake_case       	=							pa.array(TypedSequence(["""foo""", """bar"""]      , type=ArrayaD((1, 3)      , """int64"""   )   )   )


       def 			a		(		self   )      ->							Dict:






              """simple docstring"""


              __snake_case       	=							pa.array(TypedSequence([[[1, 2, 3]]]      , try_type=ArrayaD((1, 3)      , """int64"""   )   )   )
              self.assertEqual(arr.type      , ArrayaDExtensionType((1, 3)      , """int64"""   )   )


       def 			a		(		self   )      ->							List[Any]:






              """simple docstring"""


              __snake_case       	=							pa.array(TypedSequence(["""foo""", """bar"""]      , try_type=ArrayaD((1, 3)      , """int64"""   )   )   )
              self.assertEqual(arr.type      , pa.string()   )






       @require_pil
       def 			a		(		self   )      ->							Union[str, Any]:






              """simple docstring"""


              import PIL.Image

              __snake_case       	=							PIL.Image.fromarray(np.arange(10      , dtype=np.uinta   ).reshape(2      , 5   )   )
              with patch(
                  """datasets.arrow_writer.cast_to_python_objects"""      , side_effect=_UpperCamelCase   ) as mock_cast_to_python_objects:
                     __snake_case       	=							pa.array(TypedSequence([{"""path""": None, """bytes""": B"""image_bytes"""}, pil_image]      , type=Image()   )   )
                     __snake_case	,    __snake_case       	=							mock_cast_to_python_objects.call_args_list[-1]
                     self.assertIn("""optimize_list_casting"""      , _UpperCamelCase   )
                     self.assertFalse(kwargs["""optimize_list_casting"""]   )







def 						lowerCamelCase__      ( __A    :str	,__A    :int		):


       """simple docstring"""




       __snake_case       	=							pa.BufferReader(__A		) if isinstance(__A	,pa.Buffer		) else pa.memory_map(__A		)
       __snake_case       	=							pa.ipc.open_stream(__A		)
       __snake_case       	=							f.read_all()
       assert len(pa_table.to_batches()		) == expected_num_chunks
       assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
       del pa_table







@pytest.mark.parametrize("""writer_batch_size"""	,[None, 1, 1_0]		)
@pytest.mark.parametrize(
    """fields"""	,[None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]		)
def 						lowerCamelCase__      ( __A    :List[Any]	,__A    :Optional[Any]		):


       """simple docstring"""




       __snake_case       	=							pa.BufferOutputStream()
       __snake_case       	=							pa.schema(__A		) if fields else None
       with ArrowWriter(stream=__A	,schema=__A	,writer_batch_size=__A		) as writer:
              writer.write({"""col_1""": """foo""", """col_2""": 1}		)
              writer.write({"""col_1""": """bar""", """col_2""": 2}		)
              __snake_case	,    __snake_case       	=							writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       if not fields:
              __snake_case       	=							{"""col_1""": pa.string(), """col_2""": pa.intaa()}
       assert writer._schema == pa.schema(__A	,metadata=writer._schema.metadata		)
       _check_output(output.getvalue()	,expected_num_chunks=num_examples if writer_batch_size == 1 else 1		)







def 						lowerCamelCase__      ( ):


       """simple docstring"""




       __snake_case       	=							pa.BufferOutputStream()
       __snake_case       	=							Features({"""labels""": ClassLabel(names=["""neg""", """pos"""]		)}		)
       with ArrowWriter(stream=__A	,features=__A		) as writer:
              writer.write({"""labels""": 0}		)
              writer.write({"""labels""": 1}		)
              __snake_case	,    __snake_case       	=							writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       assert writer._schema == features.arrow_schema
       assert writer._schema.metadata == features.arrow_schema.metadata
       __snake_case       	=							pa.BufferReader(output.getvalue()		)
       __snake_case       	=							pa.ipc.open_stream(__A		)
       __snake_case       	=							f.read_all()
       __snake_case       	=							pa_table.schema
       assert pa_table.num_rows == 2
       assert schema == features.arrow_schema
       assert schema.metadata == features.arrow_schema.metadata
       assert features == Features.from_arrow_schema(__A		)







@pytest.mark.parametrize("""writer_batch_size"""	,[None, 1, 1_0]		)
def 						lowerCamelCase__      ( __A    :List[Any]		):


       """simple docstring"""




       __snake_case       	=							pa.BufferOutputStream()
       with ArrowWriter(
           stream=__A	,writer_batch_size=__A	,hash_salt="""split_name"""	,check_duplicates=__A	,) as writer:
              with pytest.raises(__A		):
                     writer.write({"""col_1""": """foo""", """col_2""": 1}	,key=[1, 2]		)
                     __snake_case	,    __snake_case       	=							writer.finalize()







@pytest.mark.parametrize("""writer_batch_size"""	,[None, 2, 1_0]		)
def 						lowerCamelCase__      ( __A    :List[Any]		):


       """simple docstring"""




       __snake_case       	=							pa.BufferOutputStream()
       with ArrowWriter(
           stream=__A	,writer_batch_size=__A	,hash_salt="""split_name"""	,check_duplicates=__A	,) as writer:
              with pytest.raises(__A		):
                     writer.write({"""col_1""": """foo""", """col_2""": 1}	,key=1_0		)
                     writer.write({"""col_1""": """bar""", """col_2""": 2}	,key=1_0		)
                     __snake_case	,    __snake_case       	=							writer.finalize()







@pytest.mark.parametrize("""writer_batch_size"""	,[None, 2, 1_0]		)
def 						lowerCamelCase__      ( __A    :Optional[Any]		):


       """simple docstring"""




       __snake_case       	=							pa.BufferOutputStream()
       with ArrowWriter(
           stream=__A	,writer_batch_size=__A	,hash_salt="""split_name"""	,check_duplicates=__A	,) as writer:
              writer.write({"""col_1""": """foo""", """col_2""": 1}	,key=1		)
              writer.write({"""col_1""": """bar""", """col_2""": 2}	,key=2		)
              __snake_case	,    __snake_case       	=							writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       _check_output(output.getvalue()	,expected_num_chunks=num_examples if writer_batch_size == 1 else 1		)







@pytest.mark.parametrize("""writer_batch_size"""	,[None, 1, 1_0]		)
@pytest.mark.parametrize(
    """fields"""	,[None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]		)
def 						lowerCamelCase__      ( __A    :Optional[Any]	,__A    :Dict		):


       """simple docstring"""




       __snake_case       	=							pa.BufferOutputStream()
       __snake_case       	=							pa.schema(__A		) if fields else None
       with ArrowWriter(stream=__A	,schema=__A	,writer_batch_size=__A		) as writer:
              writer.write_batch({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}		)
              writer.write_batch({"""col_1""": [], """col_2""": []}		)
              __snake_case	,    __snake_case       	=							writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       if not fields:
              __snake_case       	=							{"""col_1""": pa.string(), """col_2""": pa.intaa()}
       assert writer._schema == pa.schema(__A	,metadata=writer._schema.metadata		)
       _check_output(output.getvalue()	,expected_num_chunks=num_examples if writer_batch_size == 1 else 1		)







@pytest.mark.parametrize("""writer_batch_size"""	,[None, 1, 1_0]		)
@pytest.mark.parametrize(
    """fields"""	,[None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]		)
def 						lowerCamelCase__      ( __A    :str	,__A    :Dict		):


       """simple docstring"""




       __snake_case       	=							pa.BufferOutputStream()
       __snake_case       	=							pa.schema(__A		) if fields else None
       with ArrowWriter(stream=__A	,schema=__A	,writer_batch_size=__A		) as writer:
              writer.write_table(pa.Table.from_pydict({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}		)		)
              __snake_case	,    __snake_case       	=							writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       if not fields:
              __snake_case       	=							{"""col_1""": pa.string(), """col_2""": pa.intaa()}
       assert writer._schema == pa.schema(__A	,metadata=writer._schema.metadata		)
       _check_output(output.getvalue()	,expected_num_chunks=num_examples if writer_batch_size == 1 else 1		)







@pytest.mark.parametrize("""writer_batch_size"""	,[None, 1, 1_0]		)
@pytest.mark.parametrize(
    """fields"""	,[None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]		)
def 						lowerCamelCase__      ( __A    :Dict	,__A    :Optional[Any]		):


       """simple docstring"""




       __snake_case       	=							pa.BufferOutputStream()
       __snake_case       	=							pa.schema(__A		) if fields else None
       with ArrowWriter(stream=__A	,schema=__A	,writer_batch_size=__A		) as writer:
              writer.write_row(pa.Table.from_pydict({"""col_1""": ["""foo"""], """col_2""": [1]}		)		)
              writer.write_row(pa.Table.from_pydict({"""col_1""": ["""bar"""], """col_2""": [2]}		)		)
              __snake_case	,    __snake_case       	=							writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       if not fields:
              __snake_case       	=							{"""col_1""": pa.string(), """col_2""": pa.intaa()}
       assert writer._schema == pa.schema(__A	,metadata=writer._schema.metadata		)
       _check_output(output.getvalue()	,expected_num_chunks=num_examples if writer_batch_size == 1 else 1		)







def 						lowerCamelCase__      ( ):


       """simple docstring"""




       with tempfile.TemporaryDirectory() as tmp_dir:
              __snake_case       	=							{"""col_1""": pa.string(), """col_2""": pa.intaa()}
              __snake_case       	=							os.path.join(__A	,"""test.arrow"""		)
              with ArrowWriter(path=__A	,schema=pa.schema(__A		)		) as writer:
                     writer.write_batch({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}		)
                     __snake_case	,    __snake_case       	=							writer.finalize()
              assert num_examples == 2
              assert num_bytes > 0
              assert writer._schema == pa.schema(__A	,metadata=writer._schema.metadata		)
              _check_output(__A	,1		)







def 						lowerCamelCase__      ( __A    :Tuple		):


       """simple docstring"""




       if pa.types.is_list(__A		):
              return get_base_dtype(arr_type.value_type		)
       else:
              return arr_type







def 						lowerCamelCase__      ( __A    :str	,__A    :Optional[int]		):


       """simple docstring"""




       if isinstance(lst[0]	,__A		):
              change_first_primitive_element_in_list(lst[0]	,__A		)
       else:
              __snake_case       	=							value







@pytest.mark.parametrize("""optimized_int_type, expected_dtype"""	,[(None, pa.intaa()), (Value("""int32"""		), pa.intaa())]		)
@pytest.mark.parametrize("""sequence"""	,[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]		)
def 						lowerCamelCase__      ( __A    :Optional[Any]	,__A    :str	,__A    :List[str]		):


       """simple docstring"""




       __snake_case       	=							pa.array(TypedSequence(__A	,optimized_int_type=__A		)		)
       assert get_base_dtype(arr.type		) == expected_dtype







@pytest.mark.parametrize(
    """col, expected_dtype"""	,[
        ("""attention_mask""", pa.inta()),
        ("""special_tokens_mask""", pa.inta()),
        ("""token_type_ids""", pa.inta()),
        ("""input_ids""", pa.intaa()),
        ("""other""", pa.intaa()),
    ]	,)
@pytest.mark.parametrize("""sequence"""	,[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]		)
def 						lowerCamelCase__      ( __A    :List[str]	,__A    :Dict	,__A    :Dict		):


       """simple docstring"""




       __snake_case       	=							pa.array(OptimizedTypedSequence(__A	,col=__A		)		)
       assert get_base_dtype(arr.type		) == expected_dtype

       # not in range
       if col != "other":
              # avoids errors due to in-place modifications
              __snake_case       	=							copy.deepcopy(__A		)
              __snake_case       	=							np.iinfo(expected_dtype.to_pandas_dtype()		).max + 1
              change_first_primitive_element_in_list(__A	,__A		)
              __snake_case       	=							pa.array(OptimizedTypedSequence(__A	,col=__A		)		)
              assert get_base_dtype(arr.type		) == pa.intaa()







@pytest.mark.parametrize("""raise_exception"""	,[False, True]		)
def 						lowerCamelCase__      ( __A    :List[Any]	,__A    :Union[str, Any]		):


       """simple docstring"""




       __snake_case       	=							str(tmp_path / """dataset-train.arrow"""		)
       try:
              with ArrowWriter(path=__A		) as writer:
                     if raise_exception:
                            raise pa.lib.ArrowInvalid()
                     else:
                            writer.stream.close()
       except pa.lib.ArrowInvalid:
              pass
       finally:
              assert writer.stream.closed







def 						lowerCamelCase__      ( __A    :Tuple		):


       """simple docstring"""




       __snake_case       	=							"""mock://dataset-train.arrow"""
       with ArrowWriter(path=__A	,storage_options=mockfs.storage_options		) as writer:
              assert isinstance(writer._fs	,type(__A		)		)
              assert writer._fs.storage_options == mockfs.storage_options
              writer.write({"""col_1""": """foo""", """col_2""": 1}		)
              writer.write({"""col_1""": """bar""", """col_2""": 2}		)
              __snake_case	,    __snake_case       	=							writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       assert mockfs.exists(__A		)







def 						lowerCamelCase__      ( ):


       """simple docstring"""




       __snake_case       	=							pa.BufferOutputStream()
       with ParquetWriter(stream=__A		) as writer:
              writer.write({"""col_1""": """foo""", """col_2""": 1}		)
              writer.write({"""col_1""": """bar""", """col_2""": 2}		)
              __snake_case	,    __snake_case       	=							writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       __snake_case       	=							pa.BufferReader(output.getvalue()		)
       __snake_case       	=							pq.read_table(__A		)
       assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}







@require_pil
@pytest.mark.parametrize("""embed_local_files"""	,[False, True]		)
def 						lowerCamelCase__      ( __A    :Dict	,__A    :Optional[int]		):


       """simple docstring"""




       import PIL.Image

       __snake_case       	=							str(tmp_path / """test_image_rgb.jpg"""		)
       PIL.Image.fromarray(np.zeros((5, 5)	,dtype=np.uinta		)		).save(__A	,format="""png"""		)
       __snake_case       	=							pa.BufferOutputStream()
       with ParquetWriter(
           stream=__A	,features=Features({"""image""": Image()}		)	,embed_local_files=__A		) as writer:
              writer.write({"""image""": image_path}		)
              writer.finalize()
       __snake_case       	=							pa.BufferReader(output.getvalue()		)
       __snake_case       	=							pq.read_table(__A		)
       __snake_case       	=							pa_table.to_pydict()
       if embed_local_files:
              assert isinstance(out["""image"""][0]["""path"""]	,__A		)
              with open(__A	,"""rb"""		) as f:
                     assert out["image"][0]["bytes"] == f.read()
       else:
              assert out["image"][0]["path"] == image_path
              assert out["image"][0]["bytes"] is None







def 						lowerCamelCase__      ( ):


       """simple docstring"""




       __snake_case       	=							pa.schema([pa.field("""col_1"""	,pa.string()	,nullable=__A		)]		)
       __snake_case       	=							pa.BufferOutputStream()
       with ArrowWriter(stream=__A		) as writer:
              writer._build_writer(inferred_schema=__A		)
              assert writer._schema == pa.schema([pa.field("""col_1"""	,pa.string()		)]		)




import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


UpperCamelCase__		 =     logging.get_logger(__name__)


UpperCamelCase__		 =     {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all LED models at https://huggingface.co/models?filter=LED
UpperCamelCase__		 =     {
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

UpperCamelCase__		 =     {
    '''allenai/led-base-16384''': 16384,
}







@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 						lowerCamelCase__      ( ):


       """simple docstring"""




       __snake_case       	=							(
           list(range(ord("""!"""		)	,ord("""~"""		) + 1		)		) + list(range(ord("""¡"""		)	,ord("""¬"""		) + 1		)		) + list(range(ord("""®"""		)	,ord("""ÿ"""		) + 1		)		)
       )
       __snake_case       	=							bs[:]
       __snake_case       	=							0
       for b in range(2**8		):
              if b not in bs:
                     bs.append(__A		)
                     cs.append(2**8 + n		)
                     n += 1
       __snake_case       	=							[chr(__A		) for n in cs]
       return dict(zip(__A	,__A		)		)







def 						lowerCamelCase__      ( __A    :Dict		):


       """simple docstring"""




       __snake_case       	=							set()
       __snake_case       	=							word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)		)
              __snake_case       	=							char
       return pairs




class     __snake_case    (				snake_case__  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							VOCAB_FILES_NAMES
       __SCREAMING_SNAKE_CASE				    =							PRETRAINED_VOCAB_FILES_MAP
       __SCREAMING_SNAKE_CASE				    =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __SCREAMING_SNAKE_CASE				    =							["input_ids", "attention_mask"]


       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase="replace"      , _UpperCamelCase="<s>"      , _UpperCamelCase="</s>"      , _UpperCamelCase="</s>"      , _UpperCamelCase="<s>"      , _UpperCamelCase="<unk>"      , _UpperCamelCase="<pad>"      , _UpperCamelCase="<mask>"      , _UpperCamelCase=False      , **_UpperCamelCase      , )      ->							Optional[int]:






              """simple docstring"""


              __snake_case       	=							AddedToken(_UpperCamelCase      , lstrip=_UpperCamelCase      , rstrip=_UpperCamelCase   ) if isinstance(_UpperCamelCase      , _UpperCamelCase   ) else bos_token
              __snake_case       	=							AddedToken(_UpperCamelCase      , lstrip=_UpperCamelCase      , rstrip=_UpperCamelCase   ) if isinstance(_UpperCamelCase      , _UpperCamelCase   ) else eos_token
              __snake_case       	=							AddedToken(_UpperCamelCase      , lstrip=_UpperCamelCase      , rstrip=_UpperCamelCase   ) if isinstance(_UpperCamelCase      , _UpperCamelCase   ) else sep_token
              __snake_case       	=							AddedToken(_UpperCamelCase      , lstrip=_UpperCamelCase      , rstrip=_UpperCamelCase   ) if isinstance(_UpperCamelCase      , _UpperCamelCase   ) else cls_token
              __snake_case       	=							AddedToken(_UpperCamelCase      , lstrip=_UpperCamelCase      , rstrip=_UpperCamelCase   ) if isinstance(_UpperCamelCase      , _UpperCamelCase   ) else unk_token
              __snake_case       	=							AddedToken(_UpperCamelCase      , lstrip=_UpperCamelCase      , rstrip=_UpperCamelCase   ) if isinstance(_UpperCamelCase      , _UpperCamelCase   ) else pad_token

              # Mask token behave like a normal word, i.e. include the space before it
              __snake_case       	=							AddedToken(_UpperCamelCase      , lstrip=_UpperCamelCase      , rstrip=_UpperCamelCase   ) if isinstance(_UpperCamelCase      , _UpperCamelCase   ) else mask_token

              super().__init__(
                  errors=_UpperCamelCase      , bos_token=_UpperCamelCase      , eos_token=_UpperCamelCase      , unk_token=_UpperCamelCase      , sep_token=_UpperCamelCase      , cls_token=_UpperCamelCase      , pad_token=_UpperCamelCase      , mask_token=_UpperCamelCase      , add_prefix_space=_UpperCamelCase      , **_UpperCamelCase      , )

              with open(_UpperCamelCase      , encoding="""utf-8"""   ) as vocab_handle:
                     __snake_case       	=							json.load(_UpperCamelCase   )
              __snake_case       	=							{v: k for k, v in self.encoder.items()}
              __snake_case       	=							errors  # how to handle errors in decoding
              __snake_case       	=							bytes_to_unicode()
              __snake_case       	=							{v: k for k, v in self.byte_encoder.items()}
              with open(_UpperCamelCase      , encoding="""utf-8"""   ) as merges_handle:
                     __snake_case       	=							merges_handle.read().split("""\n"""   )[1:-1]
              __snake_case       	=							[tuple(merge.split()   ) for merge in bpe_merges]
              __snake_case       	=							dict(zip(_UpperCamelCase      , range(len(_UpperCamelCase   )   )   )   )
              __snake_case       	=							{}
              __snake_case       	=							add_prefix_space

              # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
              __snake_case       	=							re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""   )


       @property
       # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
       def 			a		(		self   )      ->							List[Any]:






              """simple docstring"""


              return len(self.encoder   )


       def 			a		(		self   )      ->							List[str]:






              """simple docstring"""


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


       def 			a		(		self      , _UpperCamelCase   )      ->							str:






              """simple docstring"""


              if token in self.cache:
                     return self.cache[token]
              __snake_case       	=							tuple(_UpperCamelCase   )
              __snake_case       	=							get_pairs(_UpperCamelCase   )

              if not pairs:
                     return token

              while True:
                     __snake_case       	=							min(_UpperCamelCase      , key=lambda _UpperCamelCase   : self.bpe_ranks.get(_UpperCamelCase      , float("""inf"""   )   )   )
                     if bigram not in self.bpe_ranks:
                            break
                     __snake_case	,    __snake_case       	=							bigram
                     __snake_case       	=							[]
                     __snake_case       	=							0
                     while i < len(_UpperCamelCase   ):
                            try:
                                   __snake_case       	=							word.index(_UpperCamelCase      , _UpperCamelCase   )
                            except ValueError:
                                   new_word.extend(word[i:]   )
                                   break
                            else:
                                   new_word.extend(word[i:j]   )
                                   __snake_case       	=							j

                            if word[i] == first and i < len(_UpperCamelCase   ) - 1 and word[i + 1] == second:
                                   new_word.append(first + second   )
                                   i += 2
                            else:
                                   new_word.append(word[i]   )
                                   i += 1
                     __snake_case       	=							tuple(_UpperCamelCase   )
                     __snake_case       	=							new_word
                     if len(_UpperCamelCase   ) == 1:
                            break
                     else:
                            __snake_case       	=							get_pairs(_UpperCamelCase   )
              __snake_case       	=							""" """.join(_UpperCamelCase   )
              __snake_case       	=							word
              return word


       def 			a		(		self      , _UpperCamelCase   )      ->							List[Any]:






              """simple docstring"""


              __snake_case       	=							[]
              for token in re.findall(self.pat      , _UpperCamelCase   ):
                     __snake_case       	=							"""""".join(
                         self.byte_encoder[b] for b in token.encode("""utf-8"""   )   )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
                     bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_UpperCamelCase   ).split(""" """   )   )
              return bpe_tokens


       def 			a		(		self      , _UpperCamelCase   )      ->							Optional[Any]:






              """simple docstring"""


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


       def 			a		(		self      , _UpperCamelCase   )      ->							Any:






              """simple docstring"""


              return self.decoder.get(_UpperCamelCase   )


       def 			a		(		self      , _UpperCamelCase   )      ->							str:






              """simple docstring"""


              __snake_case       	=							"""""".join(_UpperCamelCase   )
              __snake_case       	=							bytearray([self.byte_decoder[c] for c in text]   ).decode("""utf-8"""      , errors=self.errors   )
              return text


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None   )      ->							Tuple[str]:






              """simple docstring"""


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

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

              __snake_case       	=							0
              with open(_UpperCamelCase      , """w"""      , encoding="""utf-8"""   ) as writer:
                     writer.write("""#version: 0.2\n"""   )
                     for bpe_tokens, token_index in sorted(self.bpe_ranks.items()      , key=lambda _UpperCamelCase   : kv[1]   ):
                            if index != token_index:
                                   logger.warning(
                                       F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                                       """ Please check that the tokenizer is not corrupted!"""   )
                                   __snake_case       	=							token_index
                            writer.write(""" """.join(_UpperCamelCase   ) + """\n"""   )
                            index += 1

              return vocab_file, merge_file


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None   )      ->							List[int]:






              """simple docstring"""


              if token_ids_a is None:
                     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
              __snake_case       	=							[self.cls_token_id]
              __snake_case       	=							[self.sep_token_id]
              return cls + token_ids_a + sep + sep + token_ids_a + sep


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None      , _UpperCamelCase = False   )      ->							List[int]:






              """simple docstring"""


              if already_has_special_tokens:
                     return super().get_special_tokens_mask(
                         token_ids_a=_UpperCamelCase      , token_ids_a=_UpperCamelCase      , already_has_special_tokens=_UpperCamelCase   )

              if token_ids_a is None:
                     return [1] + ([0] * len(_UpperCamelCase   )) + [1]
              return [1] + ([0] * len(_UpperCamelCase   )) + [1, 1] + ([0] * len(_UpperCamelCase   )) + [1]


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None   )      ->							List[int]:






              """simple docstring"""


              __snake_case       	=							[self.sep_token_id]
              __snake_case       	=							[self.cls_token_id]

              if token_ids_a is None:
                     return len(cls + token_ids_a + sep   ) * [0]
              return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase=False      , **_UpperCamelCase   )      ->							List[str]:






              """simple docstring"""


              __snake_case       	=							kwargs.pop("""add_prefix_space"""      , self.add_prefix_space   )
              if (is_split_into_words or add_prefix_space) and (len(_UpperCamelCase   ) > 0 and not text[0].isspace()):
                     __snake_case       	=							""" """ + text
              return (text, kwargs)






       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None      , _UpperCamelCase = PaddingStrategy.DO_NOT_PAD      , _UpperCamelCase = None      , _UpperCamelCase = None      , )      ->							dict:






              """simple docstring"""


              __snake_case       	=							super()._pad(
                  encoded_inputs=_UpperCamelCase      , max_length=_UpperCamelCase      , padding_strategy=_UpperCamelCase      , pad_to_multiple_of=_UpperCamelCase      , return_attention_mask=_UpperCamelCase      , )

              # Load from model defaults
              if return_attention_mask is None:
                     __snake_case       	=							"""attention_mask""" in self.model_input_names

              if return_attention_mask and "global_attention_mask" in encoded_inputs:
                     __snake_case       	=							encoded_inputs[self.model_input_names[0]]
                     # `global_attention_mask` need to have the same length as other (sequential) inputs.
                     __snake_case       	=							len(encoded_inputs["""global_attention_mask"""]   ) != len(_UpperCamelCase   )

                     if needs_to_be_padded:
                            __snake_case       	=							len(_UpperCamelCase   ) - len(encoded_inputs["""global_attention_mask"""]   )

                            if self.padding_side == "right":
                                   # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                                   __snake_case       	=							(
                                       encoded_inputs["""global_attention_mask"""] + [-1] * difference
                                   )
                            elif self.padding_side == "left":
                                   __snake_case       	=							[-1] * difference + encoded_inputs[
                                       """global_attention_mask"""
                                   ]
                            else:
                                   raise ValueError("""Invalid padding strategy:""" + str(self.padding_side   )   )

              return encoded_inputs

