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


__lowercase			,	__lowercase			,	__lowercase										= False, False, False


@dataclass
class      lowerCamelCase_					:






    '''simple docstring'''






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







    def __call__(     self)	->     List[Any]:
        return self.pa_type







    def       UpperCamelCase__       (     self			,			__lowercase)	->     dict:
        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(__lowercase			,			__lowercase):
            return {"bytes": None, "path": value}
        elif isinstance(__lowercase			,			__lowercase):
            return {"bytes": value, "path": None}
        elif "array" in value:
            # convert the audio array to wav bytes
            __UpperCamelCase					:Any        =		BytesIO()
            sf.write(__lowercase			,			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					:Optional[int]        =		np.frombuffer(value['''bytes''']			,			dtype=np.intaa).astype(np.floataa) / 32_767
                else:
                    __UpperCamelCase					:List[str]        =		np.memmap(value['''path''']			,			dtype='''h'''			,			mode='''r''').astype(np.floataa) / 32_767

                __UpperCamelCase					:Dict        =		BytesIO(bytes())
                sf.write(__lowercase			,			__lowercase			,			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       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None)	->     dict:
        if not self.decode:
            raise RuntimeError('''Decoding is disabled for this feature. Please use Audio(decode=True) instead.''')

        __UpperCamelCase					:Optional[int]        =		(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					:Optional[int]        =		xsplitext(__lowercase)[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					:List[Any]        =		token_per_repo_id or {}
            __UpperCamelCase					:Any        =		path.split('''::''')[-1]
            try:
                __UpperCamelCase					:List[str]        =		string_to_dict(__lowercase			,			config.HUB_DATASETS_URL)['''repo_id''']
                __UpperCamelCase					:Tuple        =		token_per_repo_id[repo_id]
            except (ValueError, KeyError):
                __UpperCamelCase					:Optional[Any]        =		None

            with xopen(__lowercase			,			'''rb'''			,			use_auth_token=__lowercase) as f:
                __UpperCamelCase					:Tuple        =		sf.read(__lowercase)

        else:
            __UpperCamelCase					:Dict        =		sf.read(__lowercase)

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

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







    def       UpperCamelCase__       (     self)	->     Union["FeatureType", Dict[str, "FeatureType"]]:
        from .features import Value

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







    def       UpperCamelCase__       (     self			,			__lowercase)	->     pa.StructArray:
        if pa.types.is_string(storage.type):
            __UpperCamelCase					:Dict        =		pa.array([None] * len(__lowercase)			,			type=pa.binary())
            __UpperCamelCase					:str        =		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(__lowercase)			,			type=pa.string())
            __UpperCamelCase					:Dict        =		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					:Dict        =		pa.array([Audio().encode_example(__lowercase) 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					:Any        =		storage.field('''bytes''')
            else:
                __UpperCamelCase					:str        =		pa.array([None] * len(__lowercase)			,			type=pa.binary())
            if storage.type.get_field_index('''path''') >= 0:
                __UpperCamelCase					:int        =		storage.field('''path''')
            else:
                __UpperCamelCase					:Dict        =		pa.array([None] * len(__lowercase)			,			type=pa.string())
            __UpperCamelCase					:str        =		pa.StructArray.from_arrays([bytes_array, path_array]			,			['''bytes''', '''path''']			,			mask=storage.is_null())
        return array_cast(__lowercase			,			self.pa_type)







    def       UpperCamelCase__       (     self			,			__lowercase)	->     pa.StructArray:

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

        __UpperCamelCase					:int        =		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[int]        =		pa.array(
            [os.path.basename(__lowercase) if path is not None else None for path in storage.field('''path''').to_pylist()]			,			type=pa.string()			,			)
        __UpperCamelCase					:Tuple        =		pa.StructArray.from_arrays([bytes_array, path_array]			,			['''bytes''', '''path''']			,			mask=bytes_array.is_null())
        return array_cast(__lowercase			,			self.pa_type)



import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__lowercase										= '''▁'''

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


@require_sentencepiece
class      lowerCamelCase_					(       UpperCAmelCase_						,     unittest.TestCase			):






    '''simple docstring'''






    a__     :						int      =				BertGenerationTokenizer
    a__     :						Dict      =				False
    a__     :						str      =				True







    def       UpperCamelCase__       (     self)	->     List[str]:
        super().setUp()

        __UpperCamelCase					:Optional[Any]        =		BertGenerationTokenizer(__lowercase			,			keep_accents=__lowercase)
        tokenizer.save_pretrained(self.tmpdirname)







    def       UpperCamelCase__       (     self)	->     int:
        __UpperCamelCase					:Dict        =		'''<s>'''
        __UpperCamelCase					:Dict        =		1

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







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        __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]			,			'''<pad>''')
        self.assertEqual(len(__lowercase)			,			1_002)







    def       UpperCamelCase__       (     self)	->     Dict:
        self.assertEqual(self.get_tokenizer().vocab_size			,			1_000)







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        __UpperCamelCase					:str        =		BertGenerationTokenizer(__lowercase			,			keep_accents=__lowercase)

        __UpperCamelCase					:Optional[Any]        =		tokenizer.tokenize('''This is a test''')
        self.assertListEqual(__lowercase			,			['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''])

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

        __UpperCamelCase					:List[Any]        =		tokenizer.tokenize('''I was born in 92000, and this is falsé.''')
        self.assertListEqual(
            __lowercase			,			[
                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					:Any        =		tokenizer.convert_tokens_to_ids(__lowercase)
        self.assertListEqual(
            __lowercase			,			[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]			,			)

        __UpperCamelCase					:Tuple        =		tokenizer.convert_ids_to_tokens(__lowercase)
        self.assertListEqual(
            __lowercase			,			[
                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       UpperCamelCase__       (     self)	->     int:
        return BertGenerationTokenizer.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''')







    @slow
    def       UpperCamelCase__       (     self)	->     List[str]:
        __UpperCamelCase					:Optional[Any]        =		'''Hello World!'''
        __UpperCamelCase					:Optional[int]        =		[18_536, 2_260, 101]

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







    @slow
    def       UpperCamelCase__       (     self)	->     List[str]:
        __UpperCamelCase					:Optional[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'''
        )
        __UpperCamelCase					:Union[str, Any]        =		[
            871,
            419,
            358,
            946,
            991,
            2_521,
            452,
            358,
            1_357,
            387,
            7_751,
            3_536,
            112,
            985,
            456,
            126,
            865,
            938,
            5_400,
            5_734,
            458,
            1_368,
            467,
            786,
            2_462,
            5_246,
            1_159,
            633,
            865,
            4_519,
            457,
            582,
            852,
            2_557,
            427,
            916,
            508,
            405,
            34_324,
            497,
            391,
            408,
            11_342,
            1_244,
            385,
            100,
            938,
            985,
            456,
            574,
            362,
            12_597,
            3_200,
            3_129,
            1_172,
        ]

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







    @require_torch
    @slow
    def       UpperCamelCase__       (     self)	->     Optional[int]:
        import torch

        from transformers import BertGenerationConfig, BertGenerationEncoder

        # Build sequence
        __UpperCamelCase					:Optional[Any]        =		list(self.big_tokenizer.get_vocab().keys())[:10]
        __UpperCamelCase					:Optional[int]        =		''' '''.join(__lowercase)
        __UpperCamelCase					:Optional[int]        =		self.big_tokenizer.encode_plus(__lowercase			,			return_tensors='''pt'''			,			return_token_type_ids=__lowercase)
        __UpperCamelCase					:List[str]        =		self.big_tokenizer.batch_encode_plus(
            [sequence + ''' ''' + sequence]			,			return_tensors='''pt'''			,			return_token_type_ids=__lowercase)

        __UpperCamelCase					:List[Any]        =		BertGenerationConfig()
        __UpperCamelCase					:Optional[Any]        =		BertGenerationEncoder(__lowercase)

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

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







    @slow
    def       UpperCamelCase__       (     self)	->     Dict:
        # fmt: off
        __UpperCamelCase					:List[str]        =		{'''input_ids''': [[39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114], [448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 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], [484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 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, 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, 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=__lowercase			,			model_name='''google/bert_for_seq_generation_L-24_bbc_encoder'''			,			revision='''c817d1fd1be2ffa69431227a1fe320544943d4db'''			,			)





