



'''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


__lowerCAmelCase									=   logging.get_logger(__name__)

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    str   				=					['input_values', 'padding_mask']







							def __init__(							self					:       Optional[int]	,_UpperCAmelCase					:       int = 1	,_UpperCAmelCase					:       int = 24000	,_UpperCAmelCase					:       float = 0.0	,_UpperCAmelCase					:       float = None	,_UpperCAmelCase					:       float = None	,**_UpperCAmelCase					:       str	,):
									super().__init__(feature_size=_UpperCAmelCase	,sampling_rate=_UpperCAmelCase	,padding_value=_UpperCAmelCase	,**_UpperCAmelCase		)
									_a     :      Optional[Any]        =							chunk_length_s
									_a     :      str        =							overlap







							@property
							def 							__lowercase			(							self					:       Union[str, Any]		):
									if self.chunk_length_s is None:
											return None
									else:
											return int(self.chunk_length_s * self.sampling_rate		)







							@property
							def 							__lowercase			(							self					:       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					:       Tuple	,_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	,):
									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
											_a     :      Union[str, Any]        =							True

									_a     :      Optional[int]        =							bool(
									    isinstance(_UpperCAmelCase	,(list, tuple)		) and (isinstance(raw_audio[0]	,(np.ndarray, tuple, list)		))		)

									if is_batched:
											_a     :      Tuple        =							[np.asarray(_UpperCAmelCase	,dtype=np.floataa		).T for audio in raw_audio]
									elif not is_batched and not isinstance(_UpperCAmelCase	,np.ndarray		):
											_a     :      List[str]        =							np.asarray(_UpperCAmelCase	,dtype=np.floataa		)
									elif isinstance(_UpperCAmelCase	,np.ndarray		) and raw_audio.dtype is np.dtype(np.floataa		):
											_a     :      Optional[int]        =							raw_audio.astype(np.floataa		)

									# always return batch
									if not is_batched:
											_a     :      int        =							[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"""		)

									_a     :      Tuple        =							None
									_a     :      str        =							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:
													_a     :      Optional[Any]        =							min(array.shape[0] for array in raw_audio		)
													_a     :      Optional[int]        =							int(np.floor(max_length / self.chunk_stride		)		)
													_a     :      int        =							(nb_step - 1) * self.chunk_stride + self.chunk_length
											elif padding:
													_a     :      str        =							max(array.shape[0] for array in raw_audio		)
													_a     :      Optional[int]        =							int(np.ceil(max_length / self.chunk_stride		)		)
													_a     :      Optional[Any]        =							(nb_step - 1) * self.chunk_stride + self.chunk_length
													_a     :      str        =							'max_length'
											else:
													_a     :      Dict        =							input_values

        # normal padding on batch
									if padded_inputs is None:
											_a     :      Tuple        =							self.pad(
											    _UpperCAmelCase	,max_length=_UpperCAmelCase	,truncation=_UpperCAmelCase	,padding=_UpperCAmelCase	,return_attention_mask=_UpperCAmelCase	,)
											if padding:
													_a     :      Optional[Any]        =							padded_inputs.pop('attention_mask'		)

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

									_a     :      Optional[Any]        =							input_values
									if return_tensors is not None:
											_a     :      str        =							padded_inputs.convert_to_tensors(_UpperCAmelCase		)

									return padded_inputs





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a  :     Any       =  {
    """configuration_bloom""": ["""BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BloomConfig""", """BloomOnnxConfig"""],
}
try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a  :     Optional[int]       =  ["""BloomTokenizerFast"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a  :     str       =  [
		    """BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """BloomForCausalLM""",
		    """BloomModel""",
		    """BloomPreTrainedModel""",
		    """BloomForSequenceClassification""",
		    """BloomForTokenClassification""",
		    """BloomForQuestionAnswering""",
		]

if TYPE_CHECKING:
		from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_bloom_fast import BloomTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_bloom import (
				    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BloomForCausalLM,
				    BloomForQuestionAnswering,
				    BloomForSequenceClassification,
				    BloomForTokenClassification,
				    BloomModel,
				    BloomPreTrainedModel,
				)

else:
		import sys

		__a  :     Dict       =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)