
'''simple docstring'''







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 			lowerCAmelCase__       (  lowerCamelCase_					):






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[Any]													=	pa.array(TypedSequence([1, 2, 3]		)		)
											self.assertEqual(arr.type    ,							pa.intaa()		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											with self.assertRaises(__SCREAMING_SNAKE_CASE		):
																	lowercase_     :					List[str]													=	pa.array(TypedSequence([1, 2, 3]		)    ,							type=pa.intaa()		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											with self.assertRaises(__SCREAMING_SNAKE_CASE		):
																	lowercase_     :					int													=	pa.array(TypedSequence([1, 2, 3]    ,							try_type=Value('''bool'''		)    ,							type=Value('''int64'''		)		)		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					str													=	pa.array(TypedSequence([1, 2, 3]    ,							type=Value('''int32'''		)		)		)
											self.assertEqual(arr.type    ,							pa.intaa()		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											with self.assertRaises((TypeError, pa.lib.ArrowInvalid)		):
																	lowercase_     :					List[Any]													=	pa.array(TypedSequence(['''foo''', '''bar''']    ,							type=Value('''int64'''		)		)		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[Any]													=	pa.array(TypedSequence([1, 2, 3]    ,							try_type=Value('''int32'''		)		)		)
											self.assertEqual(arr.type    ,							pa.intaa()		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[str]													=	pa.array(TypedSequence(['''foo''', '''bar''']    ,							try_type=Value('''int64'''		)		)		)
											self.assertEqual(arr.type    ,							pa.string()		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Tuple													=	pa.array(TypedSequence([[[1, 2, 3]]]    ,							type=ArrayaD((1, 3)    ,							'''int64'''		)		)		)
											self.assertEqual(arr.type    ,							ArrayaDExtensionType((1, 3)    ,							'''int64'''		)		)






					def      _snake_case   (       self		):


											"""simple docstring"""





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






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Any													=	pa.array(TypedSequence([[[1, 2, 3]]]    ,							try_type=ArrayaD((1, 3)    ,							'''int64'''		)		)		)
											self.assertEqual(arr.type    ,							ArrayaDExtensionType((1, 3)    ,							'''int64'''		)		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	pa.array(TypedSequence(['''foo''', '''bar''']    ,							try_type=ArrayaD((1, 3)    ,							'''int64'''		)		)		)
											self.assertEqual(arr.type    ,							pa.string()		)






					@require_pil
					def      _snake_case   (       self		):


											"""simple docstring"""





											import PIL.Image

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







def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Tuple				,				__SCREAMING_SNAKE_CASE :			int							):



						"""simple docstring"""





						lowercase_     :					Tuple													=	pa.BufferReader(__SCREAMING_SNAKE_CASE							) if isinstance(__SCREAMING_SNAKE_CASE				,				pa.Buffer							) else pa.memory_map(__SCREAMING_SNAKE_CASE							)
						lowercase_     :					Optional[Any]													=	pa.ipc.open_stream(__SCREAMING_SNAKE_CASE							)
						lowercase_     :					pa.Table													=	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, 10]							)
@pytest.mark.parametrize(
    '''fields'''				,				[None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Dict				,				__SCREAMING_SNAKE_CASE :			int							):



						"""simple docstring"""





						lowercase_     :					Tuple													=	pa.BufferOutputStream()
						lowercase_     :					Tuple													=	pa.schema(__SCREAMING_SNAKE_CASE							) if fields else None
						with ArrowWriter(stream=__SCREAMING_SNAKE_CASE				,				schema=__SCREAMING_SNAKE_CASE				,				writer_batch_size=__SCREAMING_SNAKE_CASE							) as writer:
												writer.write({'''col_1''': '''foo''', '''col_2''': 1}							)
												writer.write({'''col_1''': '''bar''', '''col_2''': 2}							)
												lowercase_						,			lowercase_     :					Union[str, Any]													=	writer.finalize()
						assert num_examples == 2
						assert num_bytes > 0
						if not fields:
												lowercase_     :					List[str]													=	{'''col_1''': pa.string(), '''col_2''': pa.intaa()}
						assert writer._schema == pa.schema(__SCREAMING_SNAKE_CASE				,				metadata=writer._schema.metadata							)
						_check_output(output.getvalue()				,				expected_num_chunks=num_examples if writer_batch_size == 1 else 1							)
def  snake_case_		(    ):



						"""simple docstring"""





						lowercase_     :					Optional[Any]													=	pa.BufferOutputStream()
						lowercase_     :					List[Any]													=	Features({'''labels''': ClassLabel(names=['''neg''', '''pos''']							)}							)
						with ArrowWriter(stream=__SCREAMING_SNAKE_CASE				,				features=__SCREAMING_SNAKE_CASE							) as writer:
												writer.write({'''labels''': 0}							)
												writer.write({'''labels''': 1}							)
												lowercase_						,			lowercase_     :					Tuple													=	writer.finalize()
						assert num_examples == 2
						assert num_bytes > 0
						assert writer._schema == features.arrow_schema
						assert writer._schema.metadata == features.arrow_schema.metadata
						lowercase_     :					Dict													=	pa.BufferReader(output.getvalue()							)
						lowercase_     :					List[str]													=	pa.ipc.open_stream(__SCREAMING_SNAKE_CASE							)
						lowercase_     :					pa.Table													=	f.read_all()
						lowercase_     :					Union[str, Any]													=	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(__SCREAMING_SNAKE_CASE							)
@pytest.mark.parametrize('''writer_batch_size'''				,				[None, 1, 10]							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str							):



						"""simple docstring"""





						lowercase_     :					Any													=	pa.BufferOutputStream()
						with ArrowWriter(
						    stream=__SCREAMING_SNAKE_CASE				,				writer_batch_size=__SCREAMING_SNAKE_CASE				,				hash_salt='''split_name'''				,				check_duplicates=__SCREAMING_SNAKE_CASE				,				) as writer:
												with pytest.raises(__SCREAMING_SNAKE_CASE							):
																		writer.write({'''col_1''': '''foo''', '''col_2''': 1}				,				key=[1, 2]							)
																		lowercase_						,			lowercase_     :					Any													=	writer.finalize()
@pytest.mark.parametrize('''writer_batch_size'''				,				[None, 2, 10]							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			int							):



						"""simple docstring"""





						lowercase_     :					List[str]													=	pa.BufferOutputStream()
						with ArrowWriter(
						    stream=__SCREAMING_SNAKE_CASE				,				writer_batch_size=__SCREAMING_SNAKE_CASE				,				hash_salt='''split_name'''				,				check_duplicates=__SCREAMING_SNAKE_CASE				,				) as writer:
												with pytest.raises(__SCREAMING_SNAKE_CASE							):
																		writer.write({'''col_1''': '''foo''', '''col_2''': 1}				,				key=10							)
																		writer.write({'''col_1''': '''bar''', '''col_2''': 2}				,				key=10							)
																		lowercase_						,			lowercase_     :					Tuple													=	writer.finalize()
@pytest.mark.parametrize('''writer_batch_size'''				,				[None, 2, 10]							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Optional[int]							):



						"""simple docstring"""





						lowercase_     :					Any													=	pa.BufferOutputStream()
						with ArrowWriter(
						    stream=__SCREAMING_SNAKE_CASE				,				writer_batch_size=__SCREAMING_SNAKE_CASE				,				hash_salt='''split_name'''				,				check_duplicates=__SCREAMING_SNAKE_CASE				,				) as writer:
												writer.write({'''col_1''': '''foo''', '''col_2''': 1}				,				key=1							)
												writer.write({'''col_1''': '''bar''', '''col_2''': 2}				,				key=2							)
												lowercase_						,			lowercase_     :					List[Any]													=	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, 10]							)
@pytest.mark.parametrize(
    '''fields'''				,				[None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Tuple				,				__SCREAMING_SNAKE_CASE :			Union[str, Any]							):



						"""simple docstring"""





						lowercase_     :					Dict													=	pa.BufferOutputStream()
						lowercase_     :					Union[str, Any]													=	pa.schema(__SCREAMING_SNAKE_CASE							) if fields else None
						with ArrowWriter(stream=__SCREAMING_SNAKE_CASE				,				schema=__SCREAMING_SNAKE_CASE				,				writer_batch_size=__SCREAMING_SNAKE_CASE							) as writer:
												writer.write_batch({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]}							)
												writer.write_batch({'''col_1''': [], '''col_2''': []}							)
												lowercase_						,			lowercase_     :					Dict													=	writer.finalize()
						assert num_examples == 2
						assert num_bytes > 0
						if not fields:
												lowercase_     :					List[str]													=	{'''col_1''': pa.string(), '''col_2''': pa.intaa()}
						assert writer._schema == pa.schema(__SCREAMING_SNAKE_CASE				,				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, 10]							)
@pytest.mark.parametrize(
    '''fields'''				,				[None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			int				,				__SCREAMING_SNAKE_CASE :			Tuple							):



						"""simple docstring"""





						lowercase_     :					Tuple													=	pa.BufferOutputStream()
						lowercase_     :					Any													=	pa.schema(__SCREAMING_SNAKE_CASE							) if fields else None
						with ArrowWriter(stream=__SCREAMING_SNAKE_CASE				,				schema=__SCREAMING_SNAKE_CASE				,				writer_batch_size=__SCREAMING_SNAKE_CASE							) as writer:
												writer.write_table(pa.Table.from_pydict({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]}							)							)
												lowercase_						,			lowercase_     :					Optional[Any]													=	writer.finalize()
						assert num_examples == 2
						assert num_bytes > 0
						if not fields:
												lowercase_     :					Tuple													=	{'''col_1''': pa.string(), '''col_2''': pa.intaa()}
						assert writer._schema == pa.schema(__SCREAMING_SNAKE_CASE				,				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, 10]							)
@pytest.mark.parametrize(
    '''fields'''				,				[None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Tuple				,				__SCREAMING_SNAKE_CASE :			List[str]							):



						"""simple docstring"""





						lowercase_     :					Tuple													=	pa.BufferOutputStream()
						lowercase_     :					List[Any]													=	pa.schema(__SCREAMING_SNAKE_CASE							) if fields else None
						with ArrowWriter(stream=__SCREAMING_SNAKE_CASE				,				schema=__SCREAMING_SNAKE_CASE				,				writer_batch_size=__SCREAMING_SNAKE_CASE							) 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]}							)							)
												lowercase_						,			lowercase_     :					List[Any]													=	writer.finalize()
						assert num_examples == 2
						assert num_bytes > 0
						if not fields:
												lowercase_     :					Dict													=	{'''col_1''': pa.string(), '''col_2''': pa.intaa()}
						assert writer._schema == pa.schema(__SCREAMING_SNAKE_CASE				,				metadata=writer._schema.metadata							)
						_check_output(output.getvalue()				,				expected_num_chunks=num_examples if writer_batch_size == 1 else 1							)
def  snake_case_		(    ):



						"""simple docstring"""





						with tempfile.TemporaryDirectory() as tmp_dir:
												lowercase_     :					Any													=	{'''col_1''': pa.string(), '''col_2''': pa.intaa()}
												lowercase_     :					List[str]													=	os.path.join(__SCREAMING_SNAKE_CASE				,				'''test.arrow'''							)
												with ArrowWriter(path=__SCREAMING_SNAKE_CASE				,				schema=pa.schema(__SCREAMING_SNAKE_CASE							)							) as writer:
																		writer.write_batch({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]}							)
																		lowercase_						,			lowercase_     :					Union[str, Any]													=	writer.finalize()
												assert num_examples == 2
												assert num_bytes > 0
												assert writer._schema == pa.schema(__SCREAMING_SNAKE_CASE				,				metadata=writer._schema.metadata							)
												_check_output(__SCREAMING_SNAKE_CASE				,				1							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			List[Any]							):



						"""simple docstring"""





						if pa.types.is_list(__SCREAMING_SNAKE_CASE							):
												return get_base_dtype(arr_type.value_type							)
						else:
												return arr_type
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Any				,				__SCREAMING_SNAKE_CASE :			List[Any]							):



						"""simple docstring"""





						if isinstance(lst[0]				,				__SCREAMING_SNAKE_CASE							):
												change_first_primitive_element_in_list(lst[0]				,				__SCREAMING_SNAKE_CASE							)
						else:
												lowercase_     :					Dict													=	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  snake_case_		(    __SCREAMING_SNAKE_CASE :			Union[str, Any]				,				__SCREAMING_SNAKE_CASE :			int				,				__SCREAMING_SNAKE_CASE :			int							):



						"""simple docstring"""





						lowercase_     :					List[str]													=	pa.array(TypedSequence(__SCREAMING_SNAKE_CASE				,				optimized_int_type=__SCREAMING_SNAKE_CASE							)							)
						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  snake_case_		(    __SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			Optional[Any]				,				__SCREAMING_SNAKE_CASE :			str							):



						"""simple docstring"""





						lowercase_     :					Dict													=	pa.array(OptimizedTypedSequence(__SCREAMING_SNAKE_CASE				,				col=__SCREAMING_SNAKE_CASE							)							)
						assert get_base_dtype(arr.type							) == expected_dtype

						# not in range
						if col != "other":
												# avoids errors due to in-place modifications
												lowercase_     :					str													=	copy.deepcopy(__SCREAMING_SNAKE_CASE							)
												lowercase_     :					str													=	np.iinfo(expected_dtype.to_pandas_dtype()							).max + 1
												change_first_primitive_element_in_list(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							)
												lowercase_     :					List[str]													=	pa.array(OptimizedTypedSequence(__SCREAMING_SNAKE_CASE				,				col=__SCREAMING_SNAKE_CASE							)							)
												assert get_base_dtype(arr.type							) == pa.intaa()
@pytest.mark.parametrize('''raise_exception'''				,				[False, True]							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Union[str, Any]				,				__SCREAMING_SNAKE_CASE :			Any							):



						"""simple docstring"""





						lowercase_     :					Tuple													=	str(tmp_path / '''dataset-train.arrow'''							)
						try:
												with ArrowWriter(path=__SCREAMING_SNAKE_CASE							) as writer:
																		if raise_exception:
																								raise pa.lib.ArrowInvalid()
																		else:
																								writer.stream.close()
						except pa.lib.ArrowInvalid:
												pass
						finally:
												assert writer.stream.closed
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			List[Any]							):



						"""simple docstring"""





						lowercase_     :					List[Any]													=	'''mock://dataset-train.arrow'''
						with ArrowWriter(path=__SCREAMING_SNAKE_CASE				,				storage_options=mockfs.storage_options							) as writer:
												assert isinstance(writer._fs				,				type(__SCREAMING_SNAKE_CASE							)							)
												assert writer._fs.storage_options == mockfs.storage_options
												writer.write({'''col_1''': '''foo''', '''col_2''': 1}							)
												writer.write({'''col_1''': '''bar''', '''col_2''': 2}							)
												lowercase_						,			lowercase_     :					str													=	writer.finalize()
						assert num_examples == 2
						assert num_bytes > 0
						assert mockfs.exists(__SCREAMING_SNAKE_CASE							)
def  snake_case_		(    ):



						"""simple docstring"""





						lowercase_     :					Optional[int]													=	pa.BufferOutputStream()
						with ParquetWriter(stream=__SCREAMING_SNAKE_CASE							) as writer:
												writer.write({'''col_1''': '''foo''', '''col_2''': 1}							)
												writer.write({'''col_1''': '''bar''', '''col_2''': 2}							)
												lowercase_						,			lowercase_     :					Tuple													=	writer.finalize()
						assert num_examples == 2
						assert num_bytes > 0
						lowercase_     :					Tuple													=	pa.BufferReader(output.getvalue()							)
						lowercase_     :					pa.Table													=	pq.read_table(__SCREAMING_SNAKE_CASE							)
						assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
@require_pil
@pytest.mark.parametrize('''embed_local_files'''				,				[False, True]							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Dict				,				__SCREAMING_SNAKE_CASE :			str							):



						"""simple docstring"""





						import PIL.Image

						lowercase_     :					List[str]													=	str(tmp_path / '''test_image_rgb.jpg'''							)
						PIL.Image.fromarray(np.zeros((5, 5)				,				dtype=np.uinta							)							).save(__SCREAMING_SNAKE_CASE				,				format='''png'''							)
						lowercase_     :					str													=	pa.BufferOutputStream()
						with ParquetWriter(
						    stream=__SCREAMING_SNAKE_CASE				,				features=Features({'''image''': Image()}							)				,				embed_local_files=__SCREAMING_SNAKE_CASE							) as writer:
												writer.write({'''image''': image_path}							)
												writer.finalize()
						lowercase_     :					List[Any]													=	pa.BufferReader(output.getvalue()							)
						lowercase_     :					pa.Table													=	pq.read_table(__SCREAMING_SNAKE_CASE							)
						lowercase_     :					Tuple													=	pa_table.to_pydict()
						if embed_local_files:
												assert isinstance(out['''image'''][0]['''path''']				,				__SCREAMING_SNAKE_CASE							)
												with open(__SCREAMING_SNAKE_CASE				,				'''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  snake_case_		(    ):



						"""simple docstring"""





						lowercase_     :					Tuple													=	pa.schema([pa.field('''col_1'''				,				pa.string()				,				nullable=__SCREAMING_SNAKE_CASE							)]							)
						lowercase_     :					Union[str, Any]													=	pa.BufferOutputStream()
						with ArrowWriter(stream=__SCREAMING_SNAKE_CASE							) as writer:
												writer._build_writer(inferred_schema=__SCREAMING_SNAKE_CASE							)
												assert writer._schema == pa.schema([pa.field('''col_1'''				,				pa.string()							)]							)





'''simple docstring'''







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


_lowercase			:  Optional[Any]   							=	logging.get_logger(__name__)

_lowercase			:  str   							=	"▁"

_lowercase			:  Optional[int]   							=	{"vocab_file": "sentencepiece.bpe.model"}

_lowercase			:  Dict   							=	{
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    }
}

_lowercase			:  Optional[Any]   							=	{
    "facebook/xglm-564M": 2_0_4_8,
}






class 			lowerCAmelCase__       (  lowerCamelCase_					):
					lowerCAmelCase_					       =					VOCAB_FILES_NAMES
					lowerCAmelCase_					       =					PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase_					       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase_					       =					['''input_ids''', '''attention_mask''']






					def __init__(       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE="<s>"    ,							__SCREAMING_SNAKE_CASE="</s>"    ,							__SCREAMING_SNAKE_CASE="</s>"    ,							__SCREAMING_SNAKE_CASE="<s>"    ,							__SCREAMING_SNAKE_CASE="<unk>"    ,							__SCREAMING_SNAKE_CASE="<pad>"    ,							__SCREAMING_SNAKE_CASE = None    ,							**__SCREAMING_SNAKE_CASE    ,							):


											"""simple docstring"""





											lowercase_     :					Any													=	{} if sp_model_kwargs is None else sp_model_kwargs

											# Compatibility with the original tokenizer
											lowercase_     :					Optional[Any]													=	7
											lowercase_     :					List[Any]													=	[F'''<madeupword{i}>''' for i in range(self.num_madeup_words		)]

											lowercase_     :					Tuple													=	kwargs.get('''additional_special_tokens'''    ,							[]		)
											kwargs["additional_special_tokens"] += [
											    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
											]

											super().__init__(
											    bos_token=__SCREAMING_SNAKE_CASE    ,							eos_token=__SCREAMING_SNAKE_CASE    ,							unk_token=__SCREAMING_SNAKE_CASE    ,							sep_token=__SCREAMING_SNAKE_CASE    ,							cls_token=__SCREAMING_SNAKE_CASE    ,							pad_token=__SCREAMING_SNAKE_CASE    ,							sp_model_kwargs=self.sp_model_kwargs    ,							**__SCREAMING_SNAKE_CASE    ,							)

											lowercase_     :					int													=	spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(str(__SCREAMING_SNAKE_CASE		)		)
											lowercase_     :					Dict													=	vocab_file

											# Original fairseq vocab and spm vocab must be "aligned":
											# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
											# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
											# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
											# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

											# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
											lowercase_     :					List[Any]													=	1

											# Mimic fairseq token-to-id alignment for the first 4 token
											lowercase_     :					Optional[Any]													=	{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

											lowercase_     :					Dict													=	len(self.sp_model		)
											lowercase_     :					int													=	{F'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words		)}
											self.fairseq_tokens_to_ids.update(__SCREAMING_SNAKE_CASE		)

											lowercase_     :					List[str]													=	{v: k for k, v in self.fairseq_tokens_to_ids.items()}






					def __getstate__(       self		):


											"""simple docstring"""





											lowercase_     :					List[Any]													=	self.__dict__.copy()
											lowercase_     :					Optional[Any]													=	None
											lowercase_     :					List[Any]													=	self.sp_model.serialized_model_proto()
											return state






					def __setstate__(       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					List[Any]													=	d

											# for backward compatibility
											if not hasattr(self    ,							'''sp_model_kwargs'''		):
																	lowercase_     :					Optional[Any]													=	{}

											lowercase_     :					int													=	spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)






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


											"""simple docstring"""





											if token_ids_a is None:
																	return [self.sep_token_id] + token_ids_a
											lowercase_     :					Optional[Any]													=	[self.sep_token_id]
											return sep + token_ids_a + sep + sep + token_ids_a






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = False		):


											"""simple docstring"""





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

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






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


											"""simple docstring"""





											lowercase_     :					List[Any]													=	[self.sep_token_id]

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






					@property
					def      _snake_case   (       self		):


											"""simple docstring"""





											return len(self.sp_model		) + self.fairseq_offset + self.num_madeup_words






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Any													=	{self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE		): i for i in range(self.vocab_size		)}
											vocab.update(self.added_tokens_encoder		)
											return vocab






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return self.sp_model.encode(__SCREAMING_SNAKE_CASE    ,							out_type=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
											lowercase_     :					str													=	self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE		)

											# Need to return unknown token if the SP model returned 0
											return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
											return self.sp_model.IdToPiece(index - self.fairseq_offset		)






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Dict													=	''''''.join(__SCREAMING_SNAKE_CASE		).replace(__SCREAMING_SNAKE_CASE    ,							''' '''		).strip()
											return out_string






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


											"""simple docstring"""





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

											if os.path.abspath(self.vocab_file		) != os.path.abspath(__SCREAMING_SNAKE_CASE		) and os.path.isfile(self.vocab_file		):
																	copyfile(self.vocab_file    ,							__SCREAMING_SNAKE_CASE		)
											elif not os.path.isfile(self.vocab_file		):
																	with open(__SCREAMING_SNAKE_CASE    ,							'''wb'''		) as fi:
																							lowercase_     :					Optional[int]													=	self.sp_model.serialized_model_proto()
																							fi.write(__SCREAMING_SNAKE_CASE		)

											return (out_vocab_file,)



