



"""simple docstring"""


from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


__A   :							int										=   logging.get_logger(__name__)
class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	["""input_values""", """padding_mask"""]



			def __init__(		self     :    str				,   __UpperCamelCase     :    int = 1				,   __UpperCamelCase     :    int = 2_4_0_0_0				,   __UpperCamelCase     :    float = 0.0				,   __UpperCamelCase     :    float = None				,   __UpperCamelCase     :    float = None				,   **__UpperCamelCase     :    List[Any]				,   )->Optional[int]:
							super().__init__(feature_size=__UpperCamelCase				,   sampling_rate=__UpperCamelCase				,   padding_value=__UpperCamelCase				,   **__UpperCamelCase   )
							_UpperCAmelCase					=    chunk_length_s
							_UpperCAmelCase					=    overlap



			@property
			def  lowercase__						(		self     :    int   )->Optional[int]:
							if self.chunk_length_s is None:
											return None
							else:
											return int(self.chunk_length_s * self.sampling_rate   )



			@property
			def  lowercase__						(		self     :    Union[str, Any]   )->Optional[int]:
							if self.chunk_length_s is None or self.overlap is None:
											return None
							else:
											return max(1				,   int((1.0 - self.overlap) * self.chunk_length   )   )



			def __call__(		self     :    Optional[Any]				,   __UpperCamelCase     :    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]				,   __UpperCamelCase     :    Optional[Union[bool, str, PaddingStrategy]] = None				,   __UpperCamelCase     :    Optional[bool] = False				,   __UpperCamelCase     :    Optional[int] = None				,   __UpperCamelCase     :    Optional[Union[str, TensorType]] = None				,   __UpperCamelCase     :    Optional[int] = None				,   )->BatchFeature:
							if sampling_rate is not None:
											if sampling_rate != self.sampling_rate:
															raise ValueError(
															    F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
															    F' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
															    F' {self.sampling_rate} and not {sampling_rate}.'   )
							else:
											logger.warning(
											    '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
											    '''Failing to do so can result in silent errors that might be hard to debug.'''   )

							if padding and truncation:
											raise ValueError('''Both padding and truncation were set. Make sure you only set one.'''   )
							elif padding is None:
											# by default let's pad the inputs
											_UpperCAmelCase					=    True

							_UpperCAmelCase					=    bool(
							    isinstance(__UpperCamelCase				,   (list, tuple)   ) and (isinstance(raw_audio[0]				,   (np.ndarray, tuple, list)   ))   )

							if is_batched:
											_UpperCAmelCase					=    [np.asarray(__UpperCamelCase				,   dtype=np.floataa   ).T for audio in raw_audio]
							elif not is_batched and not isinstance(__UpperCamelCase				,   np.ndarray   ):
											_UpperCAmelCase					=    np.asarray(__UpperCamelCase				,   dtype=np.floataa   )
							elif isinstance(__UpperCamelCase				,   np.ndarray   ) and raw_audio.dtype is np.dtype(np.floataa   ):
											_UpperCAmelCase					=    raw_audio.astype(np.floataa   )

							# always return batch
							if not is_batched:
											_UpperCAmelCase					=    [np.asarray(__UpperCamelCase   ).T]

							# verify inputs are valid
							for idx, example in enumerate(__UpperCamelCase   ):
											if example.ndim > 2:
															raise ValueError(F'Expected input shape (channels, length) but got shape {example.shape}'   )
											if self.feature_size == 1 and example.ndim != 1:
															raise ValueError(F'Expected mono audio but example has {example.shape[-1]} channels'   )
											if self.feature_size == 2 and example.shape[-1] != 2:
															raise ValueError(F'Expected stereo audio but example has {example.shape[-1]} channels'   )

							_UpperCAmelCase					=    None
							_UpperCAmelCase					=    BatchFeature({'''input_values''': raw_audio}   )
							if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
											if truncation:
															_UpperCAmelCase					=    min(array.shape[0] for array in raw_audio   )
															_UpperCAmelCase					=    int(np.floor(max_length / self.chunk_stride   )   )
															_UpperCAmelCase					=    (nb_step - 1) * self.chunk_stride + self.chunk_length
											elif padding:
															_UpperCAmelCase					=    max(array.shape[0] for array in raw_audio   )
															_UpperCAmelCase					=    int(np.ceil(max_length / self.chunk_stride   )   )
															_UpperCAmelCase					=    (nb_step - 1) * self.chunk_stride + self.chunk_length
															_UpperCAmelCase					=    '''max_length'''
											else:
															_UpperCAmelCase					=    input_values

        # normal padding on batch
							if padded_inputs is None:
											_UpperCAmelCase					=    self.pad(
											    __UpperCamelCase				,   max_length=__UpperCamelCase				,   truncation=__UpperCamelCase				,   padding=__UpperCamelCase				,   return_attention_mask=__UpperCamelCase				,   )
											if padding:
															_UpperCAmelCase					=    padded_inputs.pop('''attention_mask'''   )

							_UpperCAmelCase					=    []
							for example in padded_inputs.pop('''input_values'''   ):
											if self.feature_size == 1:
															_UpperCAmelCase					=    example[..., None]
											input_values.append(example.T   )

							_UpperCAmelCase					=    input_values
							if return_tensors is not None:
											_UpperCAmelCase					=    padded_inputs.convert_to_tensors(__UpperCamelCase   )

							return padded_inputs








"""simple docstring"""


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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
					from .tokenization_fnet import FNetTokenizer
else:
					__A   :							Tuple										=   None

__A   :							Optional[Any]										=   logging.get_logger(__name__)
__A   :							Union[str, Any]										=   {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

__A   :							int										=   {
    "vocab_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json",
    },
}

__A   :							Optional[int]										=   {
    "google/fnet-base": 512,
    "google/fnet-large": 512,
}

__A   :							List[Any]										=   "▁"
class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	VOCAB_FILES_NAMES
			UpperCamelCase__			      =	PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__			      =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__			      =	["""input_ids""", """token_type_ids"""]
			UpperCamelCase__			      =	FNetTokenizer



			def __init__(		self     :    Optional[Any]				,   __UpperCamelCase     :    Tuple=None				,   __UpperCamelCase     :    Dict=None				,   __UpperCamelCase     :    List[str]=False				,   __UpperCamelCase     :    Union[str, Any]=True				,   __UpperCamelCase     :    List[str]=True				,   __UpperCamelCase     :    List[Any]="<unk>"				,   __UpperCamelCase     :    Any="[SEP]"				,   __UpperCamelCase     :    Dict="<pad>"				,   __UpperCamelCase     :    Optional[int]="[CLS]"				,   __UpperCamelCase     :    List[Any]="[MASK]"				,   **__UpperCamelCase     :    Optional[Any]				,   )->Dict:
							# Mask token behave like a normal word, i.e. include the space before it and
							# is included in the raw text, there should be a match in a non-normalized sentence.
							_UpperCAmelCase					=    (
							    AddedToken(__UpperCamelCase				,   lstrip=__UpperCamelCase				,   rstrip=__UpperCamelCase				,   normalized=__UpperCamelCase   )
							    if isinstance(__UpperCamelCase				,   __UpperCamelCase   )
							    else mask_token
							)

							super().__init__(
							    __UpperCamelCase				,   tokenizer_file=__UpperCamelCase				,   do_lower_case=__UpperCamelCase				,   remove_space=__UpperCamelCase				,   keep_accents=__UpperCamelCase				,   unk_token=__UpperCamelCase				,   sep_token=__UpperCamelCase				,   pad_token=__UpperCamelCase				,   cls_token=__UpperCamelCase				,   mask_token=__UpperCamelCase				,   **__UpperCamelCase				,   )

							_UpperCAmelCase					=    do_lower_case
							_UpperCAmelCase					=    remove_space
							_UpperCAmelCase					=    keep_accents
							_UpperCAmelCase					=    vocab_file
							_UpperCAmelCase					=    False if not self.vocab_file else True



			def  lowercase__						(		self     :    int				,   __UpperCamelCase     :    List[int]				,   __UpperCamelCase     :    Optional[List[int]] = None   )->List[int]:
							_UpperCAmelCase					=    [self.sep_token_id]
							_UpperCAmelCase					=    [self.cls_token_id]
							if token_ids_a is None:
											return cls + token_ids_a + sep
							return cls + token_ids_a + sep + token_ids_a + sep



			def  lowercase__						(		self     :    Optional[Any]				,   __UpperCamelCase     :    List[int]				,   __UpperCamelCase     :    Optional[List[int]] = None   )->List[int]:
							_UpperCAmelCase					=    [self.sep_token_id]
							_UpperCAmelCase					=    [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   ) * [0] + len(token_ids_a + sep   ) * [1]



			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    Optional[str] = None   )->Tuple[str]:
							if not os.path.isdir(__UpperCamelCase   ):
											logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
											return
							_UpperCAmelCase					=    os.path.join(
							    __UpperCamelCase				,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

							if os.path.abspath(self.vocab_file   ) != os.path.abspath(__UpperCamelCase   ):
											copyfile(self.vocab_file				,   __UpperCamelCase   )

							return (out_vocab_file,)



