


'''simple docstring'''



import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__A		='▁'

__A		=get_tests_dir('fixtures/test_sentencepiece.model')


@require_sentencepiece
@require_tokenizers
class 						_snake_case   (       a__    ,				unittest.TestCase		):
					lowerCAmelCase :Optional[int]						=   BigBirdTokenizer
					lowerCAmelCase :Any						=   BigBirdTokenizerFast
					lowerCAmelCase :List[str]						=   True
					lowerCAmelCase :Tuple						=   True




					def 			snake_case__  ( self):
								super().setUp()

								UpperCAmelCase__					:   Tuple       				=       self.tokenizer_class(_lowerCamelCase							,							keep_accents=_lowerCamelCase)
								tokenizer.save_pretrained(self.tmpdirname)




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   int       				=       """<s>"""
								UpperCAmelCase__					:   int       				=       1

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




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[Any]       				=       list(self.get_tokenizer().get_vocab().keys())

								self.assertEqual(vocab_keys[0]							,							"""<unk>""")
								self.assertEqual(vocab_keys[1]							,							"""<s>""")
								self.assertEqual(vocab_keys[-1]							,							"""[MASK]""")
								self.assertEqual(len(_lowerCamelCase)							,							1004)




					def 			snake_case__  ( self):
								self.assertEqual(self.get_tokenizer().vocab_size							,							1000)




					def 			snake_case__  ( self):
								if not self.test_rust_tokenizer:
											return

								UpperCAmelCase__					:   Optional[int]       				=       self.get_tokenizer()
								UpperCAmelCase__					:   Union[str, Any]       				=       self.get_rust_tokenizer()

								UpperCAmelCase__					:   Any       				=       """I was born in 92000, and this is falsé."""

								UpperCAmelCase__					:   Tuple       				=       tokenizer.tokenize(_lowerCamelCase)
								UpperCAmelCase__					:   Dict       				=       rust_tokenizer.tokenize(_lowerCamelCase)
								self.assertListEqual(_lowerCamelCase							,							_lowerCamelCase)

								UpperCAmelCase__					:   Optional[Any]       				=       tokenizer.encode(_lowerCamelCase							,							add_special_tokens=_lowerCamelCase)
								UpperCAmelCase__					:   int       				=       rust_tokenizer.encode(_lowerCamelCase							,							add_special_tokens=_lowerCamelCase)
								self.assertListEqual(_lowerCamelCase							,							_lowerCamelCase)

								UpperCAmelCase__					:   Optional[Any]       				=       self.get_rust_tokenizer()
								UpperCAmelCase__					:   str       				=       tokenizer.encode(_lowerCamelCase)
								UpperCAmelCase__					:   Optional[Any]       				=       rust_tokenizer.encode(_lowerCamelCase)
								self.assertListEqual(_lowerCamelCase							,							_lowerCamelCase)




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Dict       				=       BigBirdTokenizer(_lowerCamelCase							,							keep_accents=_lowerCamelCase)

								UpperCAmelCase__					:   Any       				=       tokenizer.tokenize("""This is a test""")
								self.assertListEqual(_lowerCamelCase							,							["""▁This""", """▁is""", """▁a""", """▁t""", """est"""])

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(_lowerCamelCase)							,							[285, 46, 10, 170, 382]							,							)

								UpperCAmelCase__					:   Union[str, Any]       				=       tokenizer.tokenize("""I was born in 92000, and this is falsé.""")
								self.assertListEqual(
								    _lowerCamelCase							,							[
								        SPIECE_UNDERLINE + """I""",
								        SPIECE_UNDERLINE + """was""",
								        SPIECE_UNDERLINE + """b""",
								        """or""",
								        """n""",
								        SPIECE_UNDERLINE + """in""",
								        SPIECE_UNDERLINE + """""",
								        """9""",
								        """2""",
								        """0""",
								        """0""",
								        """0""",
								        """,""",
								        SPIECE_UNDERLINE + """and""",
								        SPIECE_UNDERLINE + """this""",
								        SPIECE_UNDERLINE + """is""",
								        SPIECE_UNDERLINE + """f""",
								        """al""",
								        """s""",
								        """é""",
								        """.""",
								    ]							,							)
								UpperCAmelCase__					:   List[Any]       				=       tokenizer.convert_tokens_to_ids(_lowerCamelCase)
								self.assertListEqual(
								    _lowerCamelCase							,							[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]							,							)

								UpperCAmelCase__					:   Optional[int]       				=       tokenizer.convert_ids_to_tokens(_lowerCamelCase)
								self.assertListEqual(
								    _lowerCamelCase							,							[
								        SPIECE_UNDERLINE + """I""",
								        SPIECE_UNDERLINE + """was""",
								        SPIECE_UNDERLINE + """b""",
								        """or""",
								        """n""",
								        SPIECE_UNDERLINE + """in""",
								        SPIECE_UNDERLINE + """""",
								        """<unk>""",
								        """2""",
								        """0""",
								        """0""",
								        """0""",
								        """,""",
								        SPIECE_UNDERLINE + """and""",
								        SPIECE_UNDERLINE + """this""",
								        SPIECE_UNDERLINE + """is""",
								        SPIECE_UNDERLINE + """f""",
								        """al""",
								        """s""",
								        """<unk>""",
								        """.""",
								    ]							,							)




					@cached_property
					def 			snake_case__  ( self):
								return BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base""")




					@slow
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   int       				=       """Hello World!"""
								UpperCAmelCase__					:   int       				=       [65, 1_8536, 2260, 101, 66]

								self.assertListEqual(_lowerCamelCase							,							self.big_tokenizer.encode(_lowerCamelCase))




					@slow
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[Any]       				=       (
								    """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
								    """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
								)
								# fmt: off
								UpperCAmelCase__					:   Union[str, Any]       				=       [65, 871, 419, 358, 946, 991, 2521, 452, 358, 1357, 387, 7751, 3536, 112, 985, 456, 126, 865, 938, 5400, 5734, 458, 1368, 467, 786, 2462, 5246, 1159, 633, 865, 4519, 457, 582, 852, 2557, 427, 916, 508, 405, 3_4324, 497, 391, 408, 1_1342, 1244, 385, 100, 938, 985, 456, 574, 362, 1_2597, 3200, 3129, 1172, 66]  # noqa: E231
								# fmt: on
								self.assertListEqual(_lowerCamelCase							,							self.big_tokenizer.encode(_lowerCamelCase))




					@require_torch
					@slow
					def 			snake_case__  ( self):
								import torch

								from transformers import BigBirdConfig, BigBirdModel

								# Build sequence
								UpperCAmelCase__					:   Union[str, Any]       				=       list(self.big_tokenizer.get_vocab().keys())[:10]
								UpperCAmelCase__					:   str       				=       """ """.join(_lowerCamelCase)
								UpperCAmelCase__					:   int       				=       self.big_tokenizer.encode_plus(_lowerCamelCase							,							return_tensors="""pt"""							,							return_token_type_ids=_lowerCamelCase)
								UpperCAmelCase__					:   Tuple       				=       self.big_tokenizer.batch_encode_plus(
								    [sequence + """ """ + sequence]							,							return_tensors="""pt"""							,							return_token_type_ids=_lowerCamelCase)

								UpperCAmelCase__					:   int       				=       BigBirdConfig(attention_type="""original_full""")
								UpperCAmelCase__					:   List[str]       				=       BigBirdModel(_lowerCamelCase)

								assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

								with torch.no_grad():
											model(**_lowerCamelCase)
											model(**_lowerCamelCase)




					@slow
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   int       				=       BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base""")
								UpperCAmelCase__					:   Any       				=       tokenizer.decode(tokenizer("""Paris is the [MASK].""").input_ids)

								self.assertTrue(decoded_text == """[CLS] Paris is the[MASK].[SEP]""")




					@slow
					def 			snake_case__  ( self):
								# fmt: off
								UpperCAmelCase__					:   int       				=       {"""input_ids""": [[65, 3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114, 66], [65, 448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
								# fmt: on

								self.tokenizer_integration_test_util(
								    expected_encoding=_lowerCamelCase							,							model_name="""google/bigbird-roberta-base"""							,							revision="""215c99f1600e06f83acce68422f2035b2b5c3510"""							,							)



'''simple docstring'''



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

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
		from .features import FeatureType


__A		,	__A		,	__A		=False, False, False


@dataclass
class 						_snake_case   :
					lowerCAmelCase :Optional[int]						=   None
					lowerCAmelCase :bool						=   True
					lowerCAmelCase :bool						=   True
					lowerCAmelCase :Optional[str]						=   None
					# Automatically constructed
					lowerCAmelCase :ClassVar[str]						=   "dict"
					lowerCAmelCase :ClassVar[Any]						=   pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()}		)
					lowerCAmelCase :str						=   field(default='''Audio'''    ,				init=a__    ,				repr=a__		)




					def __call__( self):
								return self.pa_type




					def 			snake_case__  ( self							,							_lowerCamelCase):
								try:
											import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
								except ImportError as err:
											raise ImportError("""To support encoding audio data, please install 'soundfile'.""") from err
								if isinstance(_lowerCamelCase							,							_lowerCamelCase):
											return {"bytes": None, "path": value}
								elif isinstance(_lowerCamelCase							,							_lowerCamelCase):
											return {"bytes": value, "path": None}
								elif "array" in value:
											# convert the audio array to wav bytes
											UpperCAmelCase__					:   Optional[int]       				=       BytesIO()
											sf.write(_lowerCamelCase							,							value["""array"""]							,							value["""sampling_rate"""]							,							format="""wav""")
											return {"bytes": buffer.getvalue(), "path": None}
								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
											if value["path"].endswith("""pcm"""):
														# "PCM" only has raw audio bytes
														if value.get("""sampling_rate""") is None:
																	# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
																	raise KeyError("""To use PCM files, please specify a 'sampling_rate' in Audio object""")
														if value.get("""bytes"""):
																	# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
																	UpperCAmelCase__					:   Tuple       				=       np.frombuffer(value["""bytes"""]							,							dtype=np.intaa).astype(np.floataa) / 3_2767
														else:
																	UpperCAmelCase__					:   List[str]       				=       np.memmap(value["""path"""]							,							dtype="""h"""							,							mode="""r""").astype(np.floataa) / 3_2767

														UpperCAmelCase__					:   List[str]       				=       BytesIO(bytes())
														sf.write(_lowerCamelCase							,							_lowerCamelCase							,							value["""sampling_rate"""]							,							format="""wav""")
														return {"bytes": buffer.getvalue(), "path": None}
											else:
														return {"bytes": None, "path": value.get("""path""")}
								elif value.get("""bytes""") is not None or value.get("""path""") is not None:
											# store the audio bytes, and path is used to infer the audio format using the file extension
											return {"bytes": value.get("""bytes"""), "path": value.get("""path""")}
								else:
											raise ValueError(
											    f'''An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.''')




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = None):
								if not self.decode:
											raise RuntimeError("""Decoding is disabled for this feature. Please use Audio(decode=True) instead.""")

								UpperCAmelCase__				,    UpperCAmelCase__					:   Tuple       				=       (value["""path"""], BytesIO(value["""bytes"""])) if value["""bytes"""] is not None else (value["""path"""], None)
								if path is None and file is None:
											raise ValueError(f'''An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.''')

								try:
											import librosa
											import soundfile as sf
								except ImportError as err:
											raise ImportError("""To support decoding audio files, please install 'librosa' and 'soundfile'.""") from err

								UpperCAmelCase__					:   Dict       				=       xsplitext(_lowerCamelCase)[1][1:].lower() if path is not None else None
								if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
											raise RuntimeError(
											    """Decoding 'opus' files requires system library 'libsndfile'>=1.0.31, """
											    """You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. """)
								elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
											raise RuntimeError(
											    """Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, """
											    """You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. """)

								if file is None:
											UpperCAmelCase__					:   int       				=       token_per_repo_id or {}
											UpperCAmelCase__					:   Optional[int]       				=       path.split("""::""")[-1]
											try:
														UpperCAmelCase__					:   Dict       				=       string_to_dict(_lowerCamelCase							,							config.HUB_DATASETS_URL)["""repo_id"""]
														UpperCAmelCase__					:   Dict       				=       token_per_repo_id[repo_id]
											except (ValueError, KeyError):
														UpperCAmelCase__					:   List[Any]       				=       None

											with xopen(_lowerCamelCase							,							"""rb"""							,							use_auth_token=_lowerCamelCase) as f:
														UpperCAmelCase__				,    UpperCAmelCase__					:   List[str]       				=       sf.read(_lowerCamelCase)

								else:
											UpperCAmelCase__				,    UpperCAmelCase__					:   List[Any]       				=       sf.read(_lowerCamelCase)

								UpperCAmelCase__					:   str       				=       array.T
								if self.mono:
											UpperCAmelCase__					:   List[Any]       				=       librosa.to_mono(_lowerCamelCase)
								if self.sampling_rate and self.sampling_rate != sampling_rate:
											UpperCAmelCase__					:   int       				=       librosa.resample(_lowerCamelCase							,							orig_sr=_lowerCamelCase							,							target_sr=self.sampling_rate)
											UpperCAmelCase__					:   Tuple       				=       self.sampling_rate

								return {"path": path, "array": array, "sampling_rate": sampling_rate}




					def 			snake_case__  ( self):
								from .features import Value

								if self.decode:
											raise ValueError("""Cannot flatten a decoded Audio feature.""")
								return {
								    "bytes": Value("""binary"""),
								    "path": Value("""string"""),
								}




					def 			snake_case__  ( self							,							_lowerCamelCase):
								if pa.types.is_string(storage.type):
											UpperCAmelCase__					:   Dict       				=       pa.array([None] * len(_lowerCamelCase)							,							type=pa.binary())
											UpperCAmelCase__					:   Tuple       				=       pa.StructArray.from_arrays([bytes_array, storage]							,							["""bytes""", """path"""]							,							mask=storage.is_null())
								elif pa.types.is_binary(storage.type):
											UpperCAmelCase__					:   Optional[int]       				=       pa.array([None] * len(_lowerCamelCase)							,							type=pa.string())
											UpperCAmelCase__					:   str       				=       pa.StructArray.from_arrays([storage, path_array]							,							["""bytes""", """path"""]							,							mask=storage.is_null())
								elif pa.types.is_struct(storage.type) and storage.type.get_all_field_indices("""array"""):
											UpperCAmelCase__					:   Optional[Any]       				=       pa.array([Audio().encode_example(_lowerCamelCase) if x is not None else None for x in storage.to_pylist()])
								elif pa.types.is_struct(storage.type):
											if storage.type.get_field_index("""bytes""") >= 0:
														UpperCAmelCase__					:   int       				=       storage.field("""bytes""")
											else:
														UpperCAmelCase__					:   List[str]       				=       pa.array([None] * len(_lowerCamelCase)							,							type=pa.binary())
											if storage.type.get_field_index("""path""") >= 0:
														UpperCAmelCase__					:   List[Any]       				=       storage.field("""path""")
											else:
														UpperCAmelCase__					:   Optional[int]       				=       pa.array([None] * len(_lowerCamelCase)							,							type=pa.string())
											UpperCAmelCase__					:   Optional[int]       				=       pa.StructArray.from_arrays([bytes_array, path_array]							,							["""bytes""", """path"""]							,							mask=storage.is_null())
								return array_cast(_lowerCamelCase							,							self.pa_type)




					def 			snake_case__  ( self							,							_lowerCamelCase):

								@no_op_if_value_is_null
								def path_to_bytes(_lowerCamelCase):
											with xopen(_lowerCamelCase							,							"""rb""") as f:
														UpperCAmelCase__					:   int       				=       f.read()
											return bytes_

								UpperCAmelCase__					:   Optional[Any]       				=       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()							,							)
								UpperCAmelCase__					:   Optional[Any]       				=       pa.array(
								    [os.path.basename(_lowerCamelCase) if path is not None else None for path in storage.field("""path""").to_pylist()]							,							type=pa.string()							,							)
								UpperCAmelCase__					:   List[str]       				=       pa.StructArray.from_arrays([bytes_array, path_array]							,							["""bytes""", """path"""]							,							mask=bytes_array.is_null())
								return array_cast(_lowerCamelCase							,							self.pa_type)