


'''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		=logging.get_logger(__name__)


class 						_snake_case   (       a__		):
					lowerCAmelCase :Union[str, Any]						=   ['''input_values''', '''padding_mask''']




					def __init__( self							,							_lowerCamelCase = 1							,							_lowerCamelCase = 2_4000							,							_lowerCamelCase = 0.0							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								super().__init__(feature_size=_lowerCamelCase							,							sampling_rate=_lowerCamelCase							,							padding_value=_lowerCamelCase							,							**_lowerCamelCase)
								UpperCAmelCase__					:   Tuple       				=       chunk_length_s
								UpperCAmelCase__					:   List[Any]       				=       overlap




					@property
					def 			snake_case__  ( self):
								if self.chunk_length_s is None:
											return None
								else:
											return int(self.chunk_length_s * self.sampling_rate)




					@property
					def 			snake_case__  ( self):
								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							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = False							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = 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
											UpperCAmelCase__					:   str       				=       True

								UpperCAmelCase__					:   Optional[Any]       				=       bool(
								    isinstance(_lowerCamelCase							,							(list, tuple)) and (isinstance(raw_audio[0]							,							(np.ndarray, tuple, list))))

								if is_batched:
											UpperCAmelCase__					:   Optional[Any]       				=       [np.asarray(_lowerCamelCase							,							dtype=np.floataa).T for audio in raw_audio]
								elif not is_batched and not isinstance(_lowerCamelCase							,							np.ndarray):
											UpperCAmelCase__					:   Tuple       				=       np.asarray(_lowerCamelCase							,							dtype=np.floataa)
								elif isinstance(_lowerCamelCase							,							np.ndarray) and raw_audio.dtype is np.dtype(np.floataa):
											UpperCAmelCase__					:   Dict       				=       raw_audio.astype(np.floataa)

								# always return batch
								if not is_batched:
											UpperCAmelCase__					:   List[str]       				=       [np.asarray(_lowerCamelCase).T]

								# verify inputs are valid
								for idx, example in enumerate(_lowerCamelCase):
											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__					:   List[str]       				=       None
								UpperCAmelCase__					:   Tuple       				=       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__					:   Union[str, Any]       				=       min(array.shape[0] for array in raw_audio)
														UpperCAmelCase__					:   Any       				=       int(np.floor(max_length / self.chunk_stride))
														UpperCAmelCase__					:   Optional[int]       				=       (nb_step - 1) * self.chunk_stride + self.chunk_length
											elif padding:
														UpperCAmelCase__					:   Any       				=       max(array.shape[0] for array in raw_audio)
														UpperCAmelCase__					:   str       				=       int(np.ceil(max_length / self.chunk_stride))
														UpperCAmelCase__					:   Tuple       				=       (nb_step - 1) * self.chunk_stride + self.chunk_length
														UpperCAmelCase__					:   Dict       				=       """max_length"""
											else:
														UpperCAmelCase__					:   Optional[Any]       				=       input_values

        # normal padding on batch
								if padded_inputs is None:
											UpperCAmelCase__					:   str       				=       self.pad(
											    _lowerCamelCase							,							max_length=_lowerCamelCase							,							truncation=_lowerCamelCase							,							padding=_lowerCamelCase							,							return_attention_mask=_lowerCamelCase							,							)
											if padding:
														UpperCAmelCase__					:   Union[str, Any]       				=       padded_inputs.pop("""attention_mask""")

								UpperCAmelCase__					:   Any       				=       []
								for example in padded_inputs.pop("""input_values"""):
											if self.feature_size == 1:
														UpperCAmelCase__					:   Any       				=       example[..., None]
											input_values.append(example.T)

								UpperCAmelCase__					:   List[Any]       				=       input_values
								if return_tensors is not None:
											UpperCAmelCase__					:   Union[str, Any]       				=       padded_inputs.convert_to_tensors(_lowerCamelCase)

								return padded_inputs



'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A		={
    'configuration_table_transformer': [
        'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TableTransformerConfig',
        'TableTransformerOnnxConfig',
    ]
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A		=[
		    'TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TableTransformerForObjectDetection',
		    'TableTransformerModel',
		    'TableTransformerPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_table_transformer import (
		    TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    TableTransformerConfig,
		    TableTransformerOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_table_transformer import (
				    TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TableTransformerForObjectDetection,
				    TableTransformerModel,
				    TableTransformerPreTrainedModel,
				)

else:
		import sys

		__A		=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)